Handbook of Life Course Health Development

Neal Halfon · Christopher B. Forrest Richard M. Lerner · Elaine M. Faustman Editors Handbook of Life Course Health Development Handbook of Life Course Health Development Neal Halfon • Christopher B. Forrest Richard M. Lerner • Elaine M. Faustman Editors Handbook of Life Course Health Development Editors Neal Halfon Department of Pediatrics David Geffen School of Medicine UCLA, Los Angeles, CA, USA Department of Health Policy and Management Fielding School of Public Health UCLA, Los Angeles, CA, USA Department of Public Policy Luskin School of Public Affairs UCLA, Los Angeles, CA, USA Center for Healthier Children Families, and Communities UCLA, Los Angeles, CA, USA Christopher B. Forrest Applied Clinical Research Center Children’s Hospital of Philadelphia Philadelphia, PA, USA Elaine M. Faustman Institute for Risk Analysis and Risk Communication Department of Environmental and Occupational Health Sciences School of Public Health University of Washington Seattle, Washington, USA Richard M. Lerner Tufts University Medford, MA, USA ISBN 978-3-319-47141-9 ISBN 978-3-319-47143-3 DOI 10.1007/978-3-319-47143-3 (eBook) Library of Congress Control Number: 2017950672 © The Editor(s) (if applicable) and The Author(s) 2018, corrected publication 2018 Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface Maternal and Child Health, Life Course Health Development, and the Life Course Research Network Prior to 1900, the health of mothers and children was considered a domestic concern. Childbirth was often supervised by untrained birth attendants such as family members; basic care of sick children was rudimentary and undeveloped, with the unfortunate but all-too-real expectation that some children would not survive into adulthood (Rosenfeld and Min 2009). With the advent of scientific medicine in the nineteenth century, discoveries in bacteriology, and other sanitary reforms, childbirth came under greater medical scrutiny, and pediatric hospitals were established to care for ailing children. A greater focus on maternal nutrition, the spread of scientifically supported birthing practices, and other newly minted public health practices – along with improved social and living conditions – led to dramatic decreases in infant mortality rates and to improved child survival. In 1912, the Children’s Bureau was established in the United States as a federal agency with responsibility for assuring the health of mothers and children. In 1935, Title V of the Social Security Act established the Maternal and Child Health Bureau (MCHB), which today administers a broad range of programs to address the health needs of the nation’s maternal and child health (MCH) population. For most of the twentieth century, MCH programs and policies continued to focus on two basic areas: (1) promoting healthy births by preventing maternal and infant mortality and, more recently, (2) preventing premature births and providing medical care for children with long-term medical and developmental disorders. Success was marked by decreasing rates of maternal and infant mortality but was challenged by persistent disparities in outcomes, especially differences in infant mortality between White and AfricanAmerican children. Similarly, while great strides were made in reducing child deaths due to infectious disease and improving the effectiveness, availability, and quality of medical interventions for a range of childhood conditions from hemophilia to complex congenital heart diseases, the number of children reported as being disabled due to a chronic health problem rose dramatically from 2% in 1960 to over 8% in 2011 (Halfon et al. 2012). In the late 1980s, a new and rapidly converging set of research findings from the life course health sciences began to recast the importance of early life on v vi lifelong health (Ben-Shlomo and Kuh 2002; Halfon and Hochstein 2002). Research that was particularly relevant to the MCH field revealed how: • Preconception health and perinatal risk can impact birth outcomes and have a sustained and long-term impact on child and adult health several decades later. • Susceptibility and sensitivity of the developing brain to adversity, as well as to supportive and caring relationships, can be measured not only in brain morphology but also using functional measures of cognitive and emotional performance, including school readiness, academic performance, and long-term mental health. • Risky and chaotic family environments, and toxic and unpredictable social environments, are transduced into a child’s biology, manifesting as disease and causing changes in immune, inflammatory, and metabolic function that can be linked with childhood health conditions like obesity and ADHD and adult conditions like diabetes, hypertension, and heart disease, to name a few. These and other research findings also suggested new explanatory mechanisms for seemingly intractable problems such as the persistent racial and ethnic gaps in infant mortality. The dominant biomedical approach to treating infant mortality focused on prenatal care and the prevention of pathological signs and symptoms (e.g., eclampsia), but what the findings from the life course health sciences began to suggest is that women’s preconception reproductive capacity – including neuroendocrine response patterns, vascular health, and stress reactivity – could condition their response to pregnancy, the timing of parturition, and the likelihood of prematurity (Lu and Halfon 2003). This work suggested that in addition to improving technical interventions to pathophysiological responses that emerge during pregnancy by providing access to high-quality prenatal care, more attention should be focused on improving (if not optimizing) health during the preconception and interconception periods. This idea led to a set of new initiatives focused on girls’ and women’s reproductive health trajectories, including public health strategies to improve preconception health and research strategies to better understand how adversity impacts reproductive health across the life course. For the past two decades, there has been a growing recognition across the MCH community that life course health science is building an important evidence base about the central and vital role of health during the prenatal period and the early years on subsequent lifelong health (Halfon and Hochstein 2002; Galobardes et al. 2004 and 2008; Power and Kuh 2013). Research on the changing epidemiology of childhood chronic illness and the growing number of longitudinal studies documenting the legacy of chronic illness in childhood on patterns of adult health, morbidity, and mortality are also connecting the dots between child health and the potential for healthy aging (Halfon 2012; Wise 2004; Wise 2016). As the United States experiences rapidly rising healthcare costs due to rapidly increasing rates of chronic disease and multi-morbidity, life course health science is shining a light on the early part of the life-span when preventable risks are setting in motion the inflammatory, neuroendocrine, Preface Preface vii and metabolic processes that predispose an individual to degenerative chronic disorders manifested decades later. The recent IOM report Shorter Lives, Poorer Health that explores why the United States is the sickest of rich nations also highlights that the health of children in the United States falls far behind the health of children in other nations and that these life course determinants cannot be ignored (Woolf and Aron 2013). Perhaps the most salient and obvious reason for MCH to adopt a life course perspective has come from the epidemic of childhood obesity, which has demonstrated how childhood growth can influence rates of the most common and costly adult health conditions, including diabetes and cardiovascular disease (Gillman 2004). It has also shown how a mother’s prenatal health, along with her preconception weight, influences pregnancy outcomes, the likelihood that an infant will be obese, and the potential for lifelong obesity and resultant comorbidities (Oken and Gillman 2003; Gillman et al. 2008). For at least the past two decades, life course health science research has been reframing our approach to many persistent health and health-care issues, from infant mortality to obesity, and from school readiness to lifelong cognitive potential and reserves. This research has influenced thought leaders, researchers, policymakers, and service providers to consider the importance and essential role of MCH as a vehicle for improving health outcomes for mothers and children and, ultimately, for the population as a whole. In 2010, as MCHB celebrated its 75th birthday, Peter Van Dyck, the Associate Administrator of the Health Resources and Services Administration and Director of MCHB, announced that the Bureau intended to launch a national dialogue about the importance of life course health science in reaching MCH goals. He also highlighted how MCH could use this science to help research, programs, policies, and partnerships coalesce around moving life course theory into life course practice. The transformation would be accomplished by an integrative approach to understanding how health and disease develop. However, although this transformation is aimed at creating a rigorous approach to the study of the development of heath across the life-span, there is no doubt that there are still many outstanding questions about the relationship between early experiences and lifelong health and well-being, and about how existing and emerging knowledge can be applied to the development of evidence-based practice and policy. Unfortunately, the lack of a strong research and data infrastructure, coupled with limits on funding currently available in the United States to support the development of new methodologies and collaborative approaches, has hampered the production of the transformative, transdisciplinary, and translational research that is needed to advance the emerging field we have termed “life course health development” (LCHD). Moreover, the fact that researchers who are interested and engaged in LCHD research continue to work in discipline-specific silos has been a significant impediment to rapid progress. In recognition of and response to these challenges, in 2010, MCHB issued a Request for Proposals to develop a Maternal and Child Health Life Course Research Network (LCRN) that would be charged with providing a virtual platform and undertaking a set of activities that would together serve as a new infrastructure for catalyzing progress and enhancing funding to support basic, viii theoretical, and applied and translational LCHD research of relevance to MCH practice and policy. The UCLA Center for Healthier Children, Families, and Communities – with the support and participation of a diverse array of colleagues from around the United States – submitted a successful application to establish an LCRN with the following goals: 1. Engage a diverse, active, and sustainable community of LCHD stakeholders. 2. Increase capacity for, engagement in, and production of LCHD research. 3. Catalyze the translation and application of LCHD research to practice and policy. To launch the LCRN, the UCLA team initiated a strategic network design process that engaged individuals with substantial expertise in health development, as well as those with deep knowledge of the science of network development and facilitation. This strategic design process included a series of interviews with key informants (see http://www.lcrn.net/tag/expert-interviews), as well as an in-person meeting of the network’s 30-member design team that resulted in the approval of the LCRN charter (see http://www.lcrn. net/wp-content/uploads/2012/07/LCRN-charter-2.pdf), the development of a scope of work comprised of specific activities intended to achieve the network’s aims, a concept for the network’s online presence including a website and social networking platform, and the constitution of an advisory committee that would provide UCLA project staff with guidance for the duration of the project (see http://www.lcrn.net/about). Following the design meeting, project staff undertook a process to develop a series of background papers that would serve as the basis for the MCH Life Course Research Agenda-Setting Meeting that took place in February of 2013 in order to achieve the following aims: 1. Catalyze a paradigm shift in how researchers, practitioners, and policymakers think about, understand, and promote LCHD. 2. Evaluate, refine, and determine the utility of the seven proposed principles of LCHD. 3. Identify the ways in which the topics discussed at the meeting are converging and/or diverging across disciplines. 4. Identify knowledge that is ready for application in order to assist MCH and other practitioners in taking advantage of what we know now and speeding the progression from research to translation. 5. Provide recommendations that will enable the LCRN to develop an MCH Life Course Research Agenda (LCRA) that includes priorities in the areas of basic research, translational research, and methods and data development. 6. Provide background paper authors with input that will advance their papers toward completion and publication. 7. Identify next steps for both the LCRN and the LCHD field as a whole. Preface Preface ix Background paper topics were selected by project staff with the input of the LCRN advisory committee and MCHB staff, and included topics that were selected strategically due to their potential to enhance our understanding of health development and advance the LCHD field, as well as topics that were selected more opportunistically when researchers learned of the project and wanted to ensure that the issues of importance to them had a chance of making it into the preliminary version of the LCRA, version 1.0 (see concluding chapter of this volume). The 2013 agenda-setting meeting brought together 90+ invited stakeholders including researchers, practitioners, policymakers, funders, and other thought leaders from the United States, Canada, and the United Kingdom. Over the 2-day meeting, participants engaged in a highly facilitated process of reviewing the evidence base and providing the background paper authors with the feedback they would need to complete their research and develop a set of recommended research priorities. A highlight of the meeting was to critically examine the seven proposed principles of LCHD (see Halfon and Forrest in this volume) that were intended to provide a more unified theoretical foundation and a more consistent set of terminology for this emerging field. Following the agenda-setting meeting and in response to the enormous amount of momentum and enthusiasm generated among the participants, UCLA staff, again with the guidance of the LCRN advisory committee and representatives from MCHB, began to pursue development and publication of a volume that would contain revised versions of the background papers, as well as several chapters to be commissioned based on gaps identified at the agenda-setting meeting, plus a preliminary version of the LCRA. To this end, a four-member LCRN editorial team was constituted and charged with working closely with the background paper authors to ready their drafts – with a particular focus on trying to align the chapters with regard to the terminology and, more importantly, the conceptual frameworks underlying the writings – for inclusion in the Handbook of Life Course Health Development, and develop additional chapters and material as needed. Concurrent with the preparation of this volume, the LCRN has produced three unique webinar series, organized research nodes focused on particular topic areas, developed strategic partnerships aimed at enabling the translation of LCHD research to practice and policy, and produced several peer-reviewed publications, among other activities. We invite readers to learn more about the LCRN – including how to join – at lcrn.net. References Ben-Shlomo, Y., & Kuh, D. (2002). A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. International journal of epidemiology, 31(2), 285293. Galobardes, B., Lynch, J. W., & Davey Smith, G. (2004). Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation. Epidemiology Reviews, 26, 7–21. Galobardes, B., Lynch, J. W., & Smith, G. D. (2008). Is the association between childhood socioeconomic circumstances and cause-specific mortality established? Update of a systematic review. Epidemiology and Community Health, 6(2), 387–90. x Gillman, M. W. (2004). A life course approach to obesity. A life course approach to chronic disease epidemiology. 1, 473. Gillman, M. W., Rifas-Shiman, S. L., Kleinman, K., Oken, E., Rich-Edwards, J. W., & Taveras, E. M. (2008). Developmental origins of childhood overweight: potential public health impact. Obesity, 16(7), 1651–6. Halfon, N., & Hochstein, M. (2002). Life course health development: an integrated framework for developing health, policy, and research. Milbank Quarterly, 80(3), 433–79. iii. Halfon, N., Houtrow, A., Larson, K., & Newacheck, P. W. (2012). The changing landscape of disability in childhood. The Future of Children, 22(1), 13–42. Lu, M. C., & Halfon, N. Racial and ethnic disparities in birth outcomes: a life-course perspective. Maternal and Child Health, 7(1), 13–30. Oken, E., & Gillman, M. W. (2003). “Fetal origins of obesity.” Obesity research, 11.4, 496–506. Power, C., Kuh, D., & Morton, S. (2013). From developmental origins of adult disease to life course research on adult disease and aging: insights from birth cohort studies. Annual Review of Public Health, 34, 7–28. Rosenfeld, A., & Min, C. J. (2009). A history of international cooperation in maternal and child health. Maternal and child health (pp. 3–17). US: Springer. Wise, P. H. (2004). The transformation of child health in the United States. Health Affairs, 23(5), 9–25. Wise, P. H. (2016). Child poverty and the promise of human capacity: childhood as a foundation for healthy aging. Academic Pediatrics, 16(3), S37–45. Woolf, S. H., & Aron, L, (Eds). (2013) US health in international perspective: Shorter lives, poorer health. National Academies Press. Preface Acknowledgments This project is supported by the Health Resources and Services Administration (HRSA) of the US Department of Health and Human Services (HHS) under grant number UA6MC19803. This information or content and conclusions are those of the author(s) and should not be construed as the official position or policy of, nor should any endorsements be inferred by, HRSA, HHS, or the US Government. xi Contents Introduction to the Handbook of Life Course Health Development............................................................................. Neal Halfon, Christopher B. Forrest, Richard M. Lerner, Elaine M. Faustman, Ericka Tullis, and John Son Part I Emerging Frameworks The Emerging Theoretical Framework of Life Course Health Development............................................................................. Neal Halfon and Christopher B. Forrest Part II 1 19 Life Stages Preconception and Prenatal Factors and Metabolic Risk ................ Guoying Wang, Tami R. Bartell, and Xiaobin Wang 47 Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities ............. W. Thomas Boyce and Clyde Hertzman 61 Middle Childhood: An Evolutionary-Developmental Synthesis................................................................................................ Marco DelGiudice 95 Adolescent Health Development: A Relational Developmental Systems Perspective ................................................... Richard M. Lerner, Claire D. Brindis, Milena Batanova, and Robert Wm. Blum Emerging Adulthood as a Critical Stage in the Life Course ................................................................................. David Wood, Tara Crapnell, Lynette Lau, Ashley Bennett, Debra Lotstein, Maria Ferris, and Alice Kuo Pregnancy Characteristics and Women’s Cardiovascular Health......................................................................... Abigail Fraser, Janet M. Catov, Deborah A. Lawlor, and Janet W. Rich-Edwards 109 123 145 xiii xiv Contents Part III The Life Course Origins and Consequences of Select Major Health Conditions and Issues Early in the Life Course: Time for Obesity Prevention.................... Summer Sherburne Hawkins, Emily Oken, and Matthew W. Gillman Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development Framework ............... Pamela Salsberry, Rika Tanda, Sarah E. Anderson, and Manmohan K. Kamboj Life Course Health Development in Autism Spectrum Disorders ............................................................................. Irene E. Drmic, Peter Szatmari, and Fred Volkmar Self-Regulation ..................................................................................... Megan McClelland, John Geldhof, Fred Morrison, Steinunn Gestsdóttir, Claire Cameron, Ed Bowers, Angela Duckworth, Todd Little, and Jennie Grammer A Life Course Health Development Perspective on Oral Health ...................................................................................... James J. Crall and Christopher B. Forrest Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical, and Translational Research Agenda to Optimize Health, Behavior, and Functioning ..................................................... Michael E. Msall, Sarah A. Sobotka, Amelia Dmowska, Dennis Hogan, and Mary Sullivan A Life Course Approach to Hearing Health ...................................... Shirley A. Russ, Kelly Tremblay, Neal Halfon, and Adrian Davis 169 197 237 275 299 321 349 Chronic Kidney Disease: A Life Course Health Development Perspective ............................................................................................ 375 Patrick D. Brophy, Jennifer R. Charlton, J. Bryan Carmody, Kimberly J. Reidy, Lyndsay Harshman, Jeffrey Segar, David Askenazi, David Shoham, and Susan P. Bagby Part IV Crosscutting Topics in Life Course Health Development Growth and Life Course Health Development .................................. Amanda Mummert, Meriah Schoen, and Michelle Lampl From Epidemiology to Epigenetics: Evidence for the Importance of Nutrition to Optimal Health Development Across the Life Course.................................................. Marion Taylor-Baer and Dena Herman 405 431 Contents xv How Socioeconomic Disadvantages Get Under the Skin and into the Brain to Influence Health Development Across the Lifespan .............................................................................. Pilyoung Kim, Gary W. Evans, Edith Chen, Gregory Miller, and Teresa Seeman 463 Health Disparities: A Life Course Health Development Perspective and Future Research Directions ......................................................... 499 Kandyce Larson, Shirley A. Russ, Robert S. Kahn, Glenn Flores, Elizabeth Goodman, Tina L. Cheng, and Neal Halfon Part V Methodological Approaches Core Principles of Life Course Health Development Methodology and Analytics.......................................... Todd D. Little Epidemiological Study Designs: Traditional and Novel Approaches to Advance Life Course Health Development Research ............................................................ Stephen L. Buka, Samantha R. Rosenthal, and Mary E. Lacy 523 541 Using the National Longitudinal Surveys of Youth (NLSY) to Conduct Life Course Analyses........................................................ Elizabeth C. Cooksey 561 Using the Panel Study of Income Dynamics (PSID) to Conduct Life Course Health Development Analysis .................... Narayan Sastry, Paula Fomby, and Katherine McGonagle 579 Using the Fragile Families and Child Wellbeing Study (FFCWS) in Life Course Health Development Research .................................. Amanda Geller, Kate Jaeger, and Garrett Pace 601 Part VI Future Directions Life Course Research Agenda (LCRA), Version 1.0............................................................................................. Neal Halfon, Christopher B. Forrest, Richard M. Lerner, Elaine M. Faustman, Ericka Tullis, and John Son 623 Errata to: Handbook of Life Course Health Development .............. E1 Index ...................................................................................................... 647 The original version of this book was revised. An erratum to this book can be found at https://doi.org/10.1007/978-3-319-47143-3_27 About the Editors Neal Halfon, MD, MPH is the Director of the UCLA Center for Healthier Children, Families, and Communities. He is also a Professor of pediatrics in the David Geffen School of Medicine at UCLA, of health policy and management in the UCLA Fielding School of Public Health, and of public policy in the UCLA Luskin School of Public Affairs. Dr. Halfon’s research has spanned clinical, health services, epidemiologic, and health policy domains. For more than a decade, he has worked with national, state, and local initiatives aimed at improving early childhood systems. Dr. Halfon has also played a significant role in developing new conceptual frameworks for the study of health and health care, including the Life Course Health Development (LCHD) framework. In 2006, Halfon received the Academic Pediatric Associations Annual Research Award for his lifetime contributions to child health research. He received his MD at the University of California, Davis, and MPH at the University of California, Berkeley. He completed his pediatric residency at the University of California, San Diego, and the University of California, San Francisco. Dr. Halfon was also Robert Wood Johnson Clinical Scholar at the University of California, San Francisco. Christopher B. Forrest, MD, PhD is Professor of Pediatrics and Health Care Management at the Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania. He is the Director of the CHOP Center for Applied Clinical Research, which uses life course health development science to advance clinical and health services research in pediatrics. Dr. Forrest serves as the Principal Investigator of the PEDSnet (pedsnet.org), a national consortium of children’s hospitals (>5 million children) that conducts patient-centered outcomes research among children and youth. He is the Chair of the Research Committee for PCORnet, the national clinical research network funded by PCORI. He also chairs the Steering Committee for the NIH program called PEPR, which is conducting longitudinal studies on person-reported outcome measures in children with chronic conditions. Dr. Forrest received his BA and MD degrees from Boston University and completed his PhD in Health Policy and Management at Johns Hopkins School of Public Health. Richard M. Lerner, PhD is the Bergstrom Chair in Applied Developmental Science and the Director of the Institute for Applied Research in Youth Development at Tufts University. He went from kindergarten through PhD within the New York City public schools, completing his doctorate at the City xvii xviii University of New York in 1971 in developmental psychology. Dr. Lerner has more than 700 scholarly publications, including 80 authored or edited books. He was the Founding Editor of the Journal of Research on Adolescence and Applied Developmental Science, which he continues to edit. He was a 1980– 1981 fellow at the Center for Advanced Study in the Behavioral Sciences and is a fellow of the American Association for the Advancement of Science, the American Psychological Association, and the Association for Psychological Science. Dr. Lerner is known for his theory of relations between life-span human development and social change and for his research about the relations between adolescents and their peers, families, schools, and communities. His work integrates the study of public policies and community-based programs with the promotion of positive youth development and youth contributions to civil society. He is married to Dr. Jacqueline V. Lerner, Professor in the Department of Applied Developmental and Educational Psychology in the Lynch School of Education at Boston College. They live in Wayland, Massachusetts. They have three children: Justin, a Director and Screenwriter living in Los Angeles; Blair, an Advertising Executive at Media Contacts in Boston; and Jarrett, a Novelist and Editor living in Boston. Elaine M. Faustman, PhD, DABT is Professor in the Department of Environmental and Occupational Health and Director of the Institute for Risk Analysis and Risk Communication at the School of Public Health and Community Medicine at the University of Washington. She directs the NIEHS- and EPA-funded Center for Child Environmental Health Risks Research and led the Pacific Northwest Center for the National Children’s Study. She is an elected fellow of both the American Association for the Advancement of Science and the Society for Risk Analysis. Dr. Faustman is an Affiliate Professor in the School of Public Affairs at the University of Washington and has been an Affiliate Professor in the Department of Engineering and Public Policy at Carnegie-Mellon University. She has served on the National Toxicology Program Board of Scientific Counselors, the National Academy of Sciences Committee on Toxicology, the Institute of Medicine Upper Reference Levels Subcommittee of the Food and Nutrition Board, and numerous editorial boards. Dr. Faustman chaired the National Academy of Sciences Committee on Developmental Toxicology. She has or is currently serving on the executive boards of the Society of Toxicology, the Society for Risk Analysis, and NIEHS Council. She is past President of the Teratology Society. Her research interests include understanding mechanisms that put children and the public at risk of environmental agents. Currently she is serving on the Committee on NAS Gulf War and Health, Volume 11: Generational Health Effects of Serving in the Gulf War. In particular, Dr. Faustman is interested in the molecular and cellular mechanisms of developmental and reproductive toxicants, characterizing in vitro techniques for developmental toxicology assessment, and development of biologically based dose-response models for non-cancer risk assessment. Dr. Faustman’s research expertise includes development of decision-analytic tools for communicating and translating new scientific findings into risk assessment and risk management decisions. About the Editors Contributors Sarah E. Anderson, PhD The Ohio State University, College of Public Health, Columbus, OH, USA David Askenazi, MD University of Alabama Children’s Hospital, Pediatric Nephrology, Birmingham, AL, USA Susan P. Bagby, MD Division of Nephrology & Hypertension, Department of Medicine, Moore Institute for Nutrition and Wellness, Oregon Health & Science University, Portland, OR, USA Tami R. Bartell, BA Stanley Manne Children’s Research Institute, Ann & Robert H Lurie Children’s Hospital of Chicago, Chicago, IL, USA Milena Batanova, PhD Institute for Applied Research in Youth Development, Tufts University, Medford, MA, USA Ashley Bennett, MD Department of Pediatrics, USC, Los Angeles, CA, USA Robert Wm. Blum, MD, MPH, PhD Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Ed Bowers Clemson University, Youth Development Leadership, Clemson, SC, USA Claire D. Brindis, DrPH University of California-San Francisco (UCSF), Philip R. Lee Institute for Health Policy Studies and the Adolescent and Young Adult Health National Resource Center, San Francisco, CA, USA Patrick D. Brophy, MD, MHCDS University of Iowa Stead Family Children’s Hospital, Pediatric Nephrology, Iowa City, IA, USA J. Bryan Carmody, MD University of Virginia, Department of Pediatrics, Division of Nephrology, Charlottesville, VA, USA Stephen L. Buka Department of Epidemiology, Brown University, Providence, RI, USA Claire Cameron University at Buffalo, SUNY, Learning and Instruction, Buffalo, NY, USA xix xx Janet M. Catov Department of Obestetrics and Gynecology, University of Pittsburgh, Pittsburgh, PA, USA University of Pittsburgh, Department of Epidemiology, Pittsburgh, PA, USA Magee-Womens Research Institute, Pittsburgh, PA, USA Jennifer R. Charlton, MD, MSc University of Virginia, Department of Pediatrics, Division of Nephrology, Charlottesville, VA, USA Edith Chen Department of Psychology and Institute for Policy Research, Northwestern University, Evanston, IL, USA Tina L. Cheng, MD, MPH Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA Elizabeth C. Cooksey Center for Human Resource Research, The Ohio State University, Columbus, OH, USA James J. Crall Division of Public Health and Community Dentistry, University of California Los Angeles (UCLA) School of Dentistry, Los Angeles, CA, USA Tara Crapnell, OTD, OTR/L UCLA, Department of Pediatrics, Los Angeles, CA, USA Adrian Davis University College London, NHS Newborn Hearing Screening Program, London, UK Amelia Dmowska Section of Developmental and Behavioral Pediatrics, University of Chicago Comer Children’s Hospitals, Chicago, IL, USA Irene E. Drmic Hospital for Sick Children, Toronto, ON, Canada Angela Duckworth University of Pennsylvania, Department of Psychology, Philadelphia, PA, USA Gary W. Evans Department of Design and Environmental Analysis, Department of Human Development, Cornell University, Ithaca, NY, USA Elaine M. Faustman, PhD Institute for Risk Analysis and Risk Communication, Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA Maria Ferris, MD, PhD, MPH UCLA, Department of Pediatrics, Los Angeles, CA, USA Glenn Flores, MD, FAAP Medica Research Institute, Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, USA Paula Fomby Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA Christopher B. Forrest, MD, PhD Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Contributors Contributors xxi Abigail Fraser Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK John Geldhof Oregon State University, Human Development and Family Sciences, Corvallis, OR, USA Amanda Geller New York University, New York, NY, USA Steinunn Gestsdóttir University of Iceland, Department of Psychology, Reykjavik, Iceland Matthew W. Gillman Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA Marco DelGiudice Department of Psychology, University of New Mexico, Albuquerque, NM, USA Elizabeth Goodman, MD Division of General Academic Pediatrics, Mass General Hospital for Children, Department of Pediatrics, Harvard Medical School, Boston, MA, USA Jennie Grammer University of California, Los Angeles, Graduate School of Education and Information Studies, Los Angeles, CA, USA Neal Halfon, MD, MPH Department of Pediatrics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA Department of Health Policy and Management, Fielding School of Public Health, UCLA, Los Angeles, CA, USA Department of Public Policy, Luskin School of Public Affairs, UCLA, Los Angeles, CA, USA Center for Healthier Children, Families, and Communities, UCLA, Los Angeles, CA, USA Lyndsay Harshman, MD University of Iowa Children’s Hospital, Pediatrics, Iowa City, IA, USA Summer Sherburne Hawkins Boston College, Chestnut Hill, MA, USA Dena Herman Department of Family and Consumer Sciences, California State University Northridge, Northridge, CA, USA Clyde Hertzman Human Early Learning Partnership, School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada Dennis Hogan, PhD Sociology and Demography, Population Research and Training Center, Brown University, Providence, RI, USA Kate Jaeger Princeton University, Princeton, NJ, USA Robert S. Kahn, MD, MPH Division of General and Community Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA Manmohan K. Kamboj, MD The Ohio State University, College of Medicine, Endocrinology, Metabolism and Diabetes, Nationwide Children’s Hospital, Columbus, OH, USA xxii Pilyoung Kim Department of Psychology, University of Denver, Denver, CO, USA Alice Kuo, MD, PhD UCLA, Department of Pediatrics, Los Angeles, CA, USA Mary E. Lacy Department of Epidemiology, Brown University, Providence, RI, USA Michelle Lampl, MD, PhD Department of Anthropology, Emory University, Atlanta, GA, USA Center for the Study of Human Health, Emory University, Atlanta, GA, USA Kandyce Larson, PhD Department of Research, American Academy of Pediatrics, Elk Grove Village, IL, USA Lynette Lau, PhD UCLA, Department of Pediatrics, Los Angeles, CA, USA Deborah A. Lawlor Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK Richard M. Lerner, PhD Tufts University, Medford, MA, USA Todd Little Texas Tech University, Department of Educational Psychology and Leadership, Lubbock, TX, USA Debra Lotstein, MD, MPH UCLA, Department of Pediatrics, Los Angeles, CA, USA Megan McClelland Human Development and Family Sciences, 245 Hallie E. Ford Center for Healthy Children and Families, Oregon State University, Corvallis, OR, USA Katherine McGonagle Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA Gregory Miller Department of Psychology and Institute for Policy Research, Northwestern University, Evanston, IL, USA Fred Morrison University of Michigan, Department of Psychology, Ann Arbor, MI, USA Michael E. Msall, MD Developmental and Behavioral Pediatrics University of Chicago, Comer and LaRabida Children’s Hospitals, Chicago, IL, USA JP Kennedy Research Center on Intellectual and Developmental Disabilities, University of Chicago Comer Children’s Hospital, Section of Developmental and Behavioral Pediatrics, Chicago, IL, USA Amanda Mummert, PhD Department of Anthropology, Emory University, Atlanta, GA, USA Center for the Study of Human Health, Emory University, Atlanta, GA, USA Emily Oken Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA Contributors Contributors xxiii Garrett Pace Doctoral Student, School of Social Work, Department of Sociology, University of Michigan, Ann Arbor, MI, USA Kimberly J. Reidy, MD Albert Einstein College of Medicine, Montefiore Medical Center, Pediatric Nephrology, Bronx, NY, USA Janet W. Rich-Edwards Connors Center for Women’s Health and Gender Biology, Brigham and Women’s Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA Harvard School of Public Health, Boston, MA, USA Samantha R. Rosenthal Department of Epidemiology, Brown University, Providence, RI, USA Shirley A. Russ, MD, MPH UCLA Center for Healthier Children, Families and Communities, Department of Pediatrics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA Pamela Salsberry, PhD, RN, FAAN College of Public Health, Division of Health Behavior, Health Promotion, Institute for Population Health, The Ohio State University, Columbus, OH, USA Narayan Sastry Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA Meriah Schoen Center for the Study of Human Health, Emory University, Atlanta, GA, USA Department of Nutrition, Georgia State University, Atlanta, GA, USA Teresa Seeman David Geffen School of Medicine, University of California – Los Angeles, Los Angeles, CA, USA Jeffrey Segar, MD University of Iowa Children’s Hospital, Neonatology, Iowa City, IA, USA David Shoham, PhD, MSPH Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA Sarah A. Sobotka, MD, MS Section of Developmental and Behavioral Pediatrics, University of Chicago Comer Children’s Hospitals, Chicago, IL, USA John Son, MPH Center for Healthier Children, Families and Communities, UCLA, Los Angeles, CA, USA Mary Sullivan, PhD, RN University of Rhode Island, College of Nursing, Women and Infants Hospital, Providence, RI, USA Peter Szatmari Centre for Addiction and Mental Health, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada Rika Tanda, PhD, RN College of Health Science and Professions, Ohio University, Athens, OH, USA xxiv Marion Taylor-Baer Department of Community Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA W. Thomas Boyce Departments of Pediatrics and Psychiatry, University of California San Francisco, San Francisco, CA, USA Kelly Tremblay Speech & Hearing Sciences College of Arts & Sciences, University of Washington, Seattle, WA, USA Ericka Tullis, MPP Center for Healthier Children, Families and Communities, UCLA, Los Angeles, CA, USA Fred Volkmar Child Study Center, Yale University School of Medicine, New Haven, CT, USA Guoying Wang, MD, PhD Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA Xiaobin Wang, MD, MPH, ScD Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA David Wood, MD, MPH Department of Pediatrics, ETSU College of Medicine, Johnson City, TN, USA Contributors Introduction to the Handbook of Life Course Health Development Neal Halfon, Christopher B. Forrest, Richard M. Lerner, Elaine M. Faustman, Ericka Tullis, and John Son 1 Introduction Over the past several decades, countless studies have linked early life events and experiences with adult health conditions, delineating the developmental origins of common chronic health conditions and specifying the processes by which both adversity and opportunity are integrated into developing biobehavioral systems (Baltes et al. 2006; Bronfenbrenner 2005; Elder et al. 2015). As a result, there is a greater understanding of how health and disease develop, which is leading to new kinds of individual- and population-level strategies that have the potential to prevent disease and optimize health by minimizing the impact of adversity, increasing protective factors, and targeting health-promoting interventions to coincide with sensitive periods of health development. Insights and evidence from life course chronic disease epidemiology have converged with research from the fields of developmental biology, neuroscience, and developmental science, with studies of typical and atypical development and with new findings from research examining the developmental origins of chronic disease. This wide-ranging research, all focused on understanding how health and disease develop, has involved researchers from a wide variety of disciplines. Life-span developmental psychologists, N. Halfon, MD, MPH (*) Department of Pediatrics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA Department of Health Policy and Management, Fielding School of Public Health, UCLA, Los Angeles, CA, USA Department of Public Policy, Luskin School of Public Affairs, UCLA, Los Angeles, CA, USA Center for Healthier Children, Families, and Communities, UCLA, Los Angeles, CA, USA e-mail: nhalfon@ucla.edu C.B. Forrest, MD, PhD Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA, USA R.M. Lerner, PhD Tufts University, Medford, MA, USA E.M. Faustman, PhD Institute for Risk Analysis and Risk Communication, Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA 98105, USA E. Tullis, MPP • J. Son, MPH Center for Healthier Children, Families and Communities, UCLA, Los Angeles, CA, USA © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_1 1 N. Halfon et al. 2 life course-focused sociologists, human capitalfocused economists, and political scientists studying the structure of social institutions are not only studying the same developmental processes; they are also working alongside epidemiologists, physicians, and basic scientists to better understand how health develops over the life course and how these health development processes promote human flourishing. In response to this burgeoning knowledge, there is growing momentum among practitioners and policymakers to “connect the dots” between what we know and what we do, that is, between the rapidly expanding evidence base from the emerging field of life course health development (LCHD) and the practices and policies that are implemented within the fields of medicine, public health, nursing, mental health, education, urban planning, community development, social welfare, and others (Halfon et al. 2014; Kuh et al. 2013; Braveman 2014; Gee et al. 2012; Lappé and Landecker 2015). At the same time, there is a strong impetus among researchers to continue to fill the substantial gaps in our knowledge and to ensure that research findings are appropriately synthesized and translated before being applied in clinical, public health, or public policy contexts. Comprised of 26 chapters that grew out of the 2013 Maternal and Child Health (MCH) Life Course Research Agenda-Setting Meeting that was organized by the MCH Life Course Research Network (LCRN) and funded by Health Resources and Services Administration-Maternal and Child Health Bureau (HRSA-MCHB), this volume represents a groundbreaking effort to explore the history of the LCHD field, to take stock of what we know and do not know about how health and disease develop, to provide practitioners and policymakers with guidance regarding the kinds of interventions and efforts that can be beneficial, and to lay the foundation for a research agenda that identifies high-priority areas for basic, clinical, population, and translational investigations in order to strategically target resources and efforts and advance the life course health sciences. Each chapter is written by a team of leading experts that often spans several different disciplines and therefore reflects a wide range of perspectives on how innovative research, practice, and policy can begin to address our most pressing health challenges. Similarly, the volume’s four editors represent different disciplines and perspectives that were brought to bear on the process of selecting topics and authors and on ensuring that each chapter makes a substantial contribution to the field. In this introductory chapter, we begin by providing a rationale for the publication of this volume, including an historical overview that traces the emergence of the LCHD field and provides evidence of a significant, but as yet incomplete, transformation in how we think about and promote health. We go on to describe the purpose, structure, and content of the volume and to examine some of the challenges for further field building in this area. Finally, we provide readers with information about each section and chapter in this volume, including the impressive backgrounds of the various experts who contributed substantial amounts of both time and original thinking in their roles as authors. 2 Rationale 2.1 The Emergence of a New Field The science of health has been guided for well over 150 years by a mechanical model that views the body and its component cells as machines and views disease as a breakdown in organ structure and function. Person-environment relationships as causes of disease are either ignored or relegated to secondary concerns. Even though the oversimplified perspective of the body as a machine has been largely abandoned, reducing physiologic and behavioral phenomena to their smallest observable constituent parts remains a mainstay of the biomedical model that dominates contemporary health sciences. This reductionist approach may tell us how parts of a neuron work, but it does not provide an appropriate model for understanding how the nervous system works, how we think, or what produces consciousness (Capra 1982). Even fields like human genomics are moving away from the simplistic notion of single-gene causation, which has failed to yield substantial insights into disease causation, to research on genetic networks and epigenetics Introduction to the Handbook of Life Course Health Development (Huang 2012; Lappé and Landecker 2015). Complex disorders that manifest as a spectrum of phenotypic variability – including cardiovascular disease, obesity, diabetes, and autism – are increasingly understood as manifestations of relations among networks of genes and complex gene-environment coactions that are mediated by equally complex time signatures and temporal coupling. Moreover, the mechanical model of health cannot account for placebo effects, the mind’s effects on the body, or psychosomatic illness. It presupposes a mind-body dualism and reifies the distinction between physical and mental health, a vestige of the Cartesian mind-body split (Overton 2015). A reductionist approach to understanding health is inadequate for addressing how different molecular, physiologic, social, and environmental networks work together to produce dynamic stability and change, which are the cornerstones of health outcome trajectories. Many fields of science – including physics, biology, and the social sciences (especially developmental science) – have shifted from a CartesianNewtonian mechanistic ontology to a more complex system-oriented ontology (Lerner 2012). The mechanistic view divides the world into separate or split categories (e.g., nature versus nurture) and reduces it into discrete elements (genes, behaviors, molecules) that are combined, added, and assembled to form what we perceive as biological phenotypes, patterns of behavior, and personalities. As Overton (2012) and others have described, this revolutionary shift in the epistemological and ontological foundations of science took place during the twentieth century as Newtonian physics gave way to general relativity theory and as contemporary formulations of knowing the world were shown to lack explanatory power and utility (Aldwin 2014). Thus, the need for new models that explain the complex phenomena of human health development became apparent. The synthesis of human health development as explained by theories associated with relational developmental systems (RDS) metatheory is replacing the now anachronistic mechanical model of health (Lerner and Overton 2008; Lerner 2012; Overton 2012). Overton (2015) explains that compared to earlier formulations of understanding human development, RDS 3 metatheory focuses on process (systematic changes in the developmental system), becoming (moving from potential to actuality; a developmental process as having a past, present, and future; Whitehead 1929/1978), holism (the meanings of entities and events derive from the context in which they are embedded), relational analysis (assessment of the mutually influential relations within the developmental system), and the use of multiple perspectives and explanatory forms. Within RDS metatheory, the organism is seen as inherently active, self-creating (autopoietic), self-organizing, self-regulating (agentic), nonlinear and complex, and adaptive (Overton 2015). The RDS metatheory emphasizes the study and integration of different levels of organization ranging from biology and physiology to culture and history as a means to understand life-span human development (Lerner 2006; Overton 2015). Accordingly, the conceptual emphasis in RDS theories is placed on mutually influential relations between individuals and contexts, represented as individual ⇔ context relations. In a bidirectional relational system, the embeddedness within history (temporality) is of fundamental significance (Elder et al. 2015). The presence of such temporality in the developmental system means that there always exists some potential for systematic change and, thus, for (relative) plasticity in human development. In short, potential plasticity in individual ⇔ context relations derives from the “arrow of time” (Lerner 1984; Lerner and Callina 2014; Overton 2015) running through the integrated (relational) developmental system. Such plasticity also suggests that there are multiple developmental pathways, across the life-span. Similar conceptual advances have also been generated by systems biology, which focuses on the complex interactions of biological systems using a holistic framework and integrative relational strategies rather than traditional reductionist approaches (Kitano 2002; Antony et al. 2012; Schadt and Björkegren 2012, Kandel et al. 2014). This transformation has been catalyzed by a greater appreciation of dynamical system theory and, more specifically, complex adaptive system theory and its application to molecular biology (Huang 2012). Moreover, as 4 our understanding of epigenetics and systems biology has matured, new insights into how complex gene regulatory networks produce multilevel and multidirectional relationships between genotype and phenotype have been elucidated (Foster 2011; Huang 2012; Piro and Di Cunto 2012; Schadt and Björkegren 2012; Greenblum et al. 2012; DavilaVelderrain et al. 2015). This new knowledge would not have been acquired using reductionist statistical models that analyze data by reducing them to their smallest components and estimating marginal effects of linear models. In summary, the study of human development has evolved from a field dominated by split, reductionist (psychogenic or biogenic) approaches to a multidisciplinary field that integrates observations, evidence, and analysis that spans from biological to cultural and historical levels of organization across the life-span (e.g., Elder et al. 2015; Ford and Lerner 1992; Gottlieb 1998; Lerner and Callina 2014). Reductionist accounts have given way to a more integrated framework associated with RDS metatheory (Overton 2015; Lerner 2006). Across the past several decades, several scholars have provided ideas contributing to the evolution of this metatheory (e.g., Baltes et al. 2006; Bronfenbrenner 2005; Elder et al. 2015; Lerner 2006; and, even earlier, see von Bertalanffy 1933). For instance, in psychology, the transition away from what some have characterized as the radical empiricism and atomism of the early behaviorist to ideas akin to those associated with RDS-based theories has followed a similar ontological path (Lerner 2006; Lerner and Overton 2008; Overton 2010, 2012). As Arnold Sameroff explains, psychologists were attempting to find and define the laws that explain behavior and how the mind functions (Sameroff 2010). As it became clear that any particular individual- or population-level signal explained very little of the observed variance in behaviors, developmental scientists began to create new techniques for analyzing intraindividual patterns of change that focus on individuals’ unique person-environmental interactions and that separate the behavioral signal from the noisy complexity of life, especially for long-term predictive purposes (Molenaar and Newell 2010; Sameroff N. Halfon et al. 2010). Rather than reducing cognitive, emotional, or overall mental function to its mechanistic components, this more holistic approach views psychological functioning as the product of a relational nexus that defines an individual in association with multiple contexts that interact dynamically over time. In the same way that biology and psychology have faced the limits of reductionist mechanical models, medicine and health sciences are also experiencing the constraints of the biomedical approach that focuses more on the components of the organism than on the totality of human health. While the biomedical model has been remarkably successful in defining the components of human anatomy, physiology, biochemistry, and metabolism, and has provided useful frameworks for understanding simple mechanics of more linear disease processes (such as infectious diseases), it is increasingly challenged by the complexity of health development and by complex disease pathways that emerge out of multilevel and multiphasic processes that include genetic, biological, behavioral, and wholeorganism processes (Halfon et al. 2014). Similarly, at the clinical and population health levels, simple mechanistic biomedical models, or even more multifaceted biopsychosocial models of health, have difficulty explaining a wide variety of health phenomena, such as how integration of body systems and subsystems results in emergent properties of health at the level of the individual; how evolution constrains the timing and plasticity of human health development; how epigenetic processes result in multiple intermediary endophenotypes that may progress to pathological phenotypes, hover in subclinical states, or resolve; how the adaptive capacities of individuals and populations interact with rapidly changing physical, natural, chemical, social, and nutritional environments to reprogram developing physiology and other regulatory processes through epigenetic modulations of previously selected biological and behavioral scripts; and how integration occurs between biological, behavioral, and environmental systems, organized and driven by adaptive routines structured around different developmentally entrained time Introduction to the Handbook of Life Course Health Development horizons. These conundrums have challenged health researchers to develop new frameworks to explain how each of these complex processes contributes to the development of health over time (i.e., contribute to health development). 2.2 The Maturation of the LCHD Field A vast amount of empirical literature investigating the developmental, genomic, and epigenetic origins of health and disease – as well as on the epidemiology of chronic disease across the life course – has been generated in the past two decades (Halfon and Hochstein 2002; Kuh and Ben-Shlomo 2004; Gluckman and Hanson 2004; Gluckman et al. 2008; Kuh et al. 2013; Berkman et al. 2014; Halfon et al. 2014; the evolution of life course health science is reviewed in Halfon and Forrest 2017). New academic research journals and international research organizations have been spawned that focus on the developmental origins of health and disease. Established professional organizations now include life course and epigenetic and developmental origins of health and disease (DOHaD) tracts at their research meetings, and many major scientific journals have published special issues packaging articles that focus on biological embedding, epigenetics, or other disease-causing mechanisms that are framed from a life course perspective. The US National Academy of Sciences and National Academy of Medicine have both issued several reports on the health, social, and behavioral determinants of health, health measurement, health disparities, and health-care improvement that have incorporated a life course perspective, and the recent framework for the US Healthy People 2020 goals was upgraded to include life course as an organizing principle of the overall framework (Committee on Future Directions for Behavioral and Social Sciences Research 2001; Committee on Evaluation of Children’s Health 2004; Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records 2015). 5 Perhaps the biggest stimulus for thinking differently about origins and development of chronic disease came from a series of provocative studies that were conducted by David Barker and his team. Beginning in the 1980s, Barker’s studies began to describe how the prevalence of heart disease in specific areas of England was related to the distribution of birth weights in those same regions. Barker and others went on to use longitudinal datasets to solidify these observations that birth weight, and the nutritional environment and exposures of the fetus and infant, had a direct influence on the development of heart disease that was often only clinically recognized many decades later (Barker et al. 1989, 1993; Barker 1995). These startling findings challenged conventional models of direct or cumulative risk that posited that heart disease was the result of either contemporaneous or lifelong risks including poor nutrition, lack of exercise, smoking, or other behaviors and suggested that there were important latent effects of early nutrition that were somehow conditioning later pathological response patterns. Barker’s studies brought attention to other research with similar findings that were less dramatic but entirely consistent with the latent lifelong effects that the Barker studies were revealing. As a result, developmental time frames started to become an important consideration, and the timing of exposures and the recognition of sensitive periods of development all took on new salience. As this new perspective on the developmental origins of chronic disease began to unfold, there was also another emerging set of new constructs coming into play in what is now termed the field of population health. Following on in the tradition of the 1974 Lalonde Report (produced under the aegis of Canadian Minister of National Health and Welfare) that challenged the dominance of the biomedical model and proposed that the health field needed to consider biology, environment, lifestyle, and health-care organization, a broad multidisciplinary team of Canadian researchers led by the economist Robert Evans began to ask: why are some people healthy and others not? (Hancock 1986; Evans et al. 1994). This question led not only to a consideration of the crucial influence of upstream social and 6 behavioral determinants on individual and population health but also to a concern about how early social environments can mold lifelong health trajectories. Leading this exploration of the developmental role that upstream social factors on health and development for the Evans-led team was Clyde Hertzman. Hertzman went on to solidify his analysis about the importance of what at the time he termed “biological embedding” through a series of studies, analyses of other studies, and reinterpretations of existing literature through this new life course health development lens. In addition to publishing several important articles of conceptual synthesis, Hertzman and Daniel Keating edited the volume Developmental Health and the Wealth of Nations in which they unpacked the impact of social gradients on health development and began to specify how different time-specific and pathway effects were at play early in development (Hertzman 1999; Keating and Hertzman 1999; Hertzman and Boyce 2010). They synthesized a wealth of evidence on how early experience affects a child’s brain development, social and emotional functioning, and overall health capacities (Hertzman 1994; Keating and Hertzman 1999). The Evans and Hertzman work in Canada emerged about the same time that the Independent Inquiry into Inequalities in Health Report (1998) led by Sir Donald Acheson was released in the UK (Acheson 1998). This review – whose panel of experts included David Barker, Michael Marmot, and Hilary Graham, among others – very clearly identified how many health inequalities have their roots in the conditions and experience of mothers and children, with impacts that feedforward across the life-span. Diana Kuh and Yoav Ben-Shlomo edited a volume entitled A Life Course Approach to Chronic Disease Epidemiology, first published in 1997, which coined the term “life course epidemiology” and presented for the first time a series of articles that integrated the empirical research on several specific types of disease from a life course perspective (Kuh and Ben-Shlomo 1997). This work was followed by a second edition in 2004 that updated each of the chapters on the life course approach to obesity or the life course N. Halfon et al. approach to cardiovascular disease, and that began to provide an overarching framework including chapters on life course pathways to adult health (Kuh and Ben-Shlomo 2004). In that volume, there were chapters on “Life course approaches to differentials in health” (Davey Smith and Lynch 2004), “A life course approach to obesity” (Gillman 2004), “Socioeconomic pathways between childhood and adult health” (Kuh et al. 2004), and “Should we intervene to improve childhood circumstances” (Boyce and Keating 2004). Kuh and Ben-Shlomo have gone on to edit a series of books on life course chronic disease epidemiology that continue to analyze and synthesize the literature on health development from a life course perspective (Lawlor and Mishra 2009; Kuh et al. 2013). In 2000 (Halfon et al. 2000) and 2002 (Halfon and Hochstein 2002), Halfon and colleagues reviewed and synthesized several different life course-focused research streams and suggested that beyond its increasingly well-documented importance for understanding the mechanisms involved with the origins and development of health and disease, this new life course approach was of profound importance for a consideration of how health care should be organized, financed, and delivered. They also suggested that some general principles were emerging and beginning to outline a new model or framework that they termed “life course health development.” At the same time, many other scientists provided their own synthesis of this emerging literature and what they considered to be its implications for health, health-care delivery, and health policy (Ben-Shlomo and Kuh 2002; Halfon and Hochstein 2002; Lu and Halfon 2003; Forrest and Riley 2004; Worthman and Kuzara 2005). These various attempts at synthesizing the evidence from this new field constituted a tipping point, and over the next decade, the number of empirical studies accelerated at a much faster pace as the early objections to the “Barker hypothesis” melted away in the wake of many confirmatory studies, and the explanatory power of this new conceptualization began to take hold. This early LCHD synthesis highlighted the linked importance of biological conditioning; the Introduction to the Handbook of Life Course Health Development role of multiple, ecologically nested risk, protective, and promoting factors in influencing health trajectories; the developmental significance of different time frames; and the evolution of different health development pathways in relationship to particular socially, culturally, and biologically specified transitions and turning points in an individual’s life (Halfon and Hochstein 2002). Over the intervening decade, research has continued to accelerate, advancing in the use of more sophisticated methodologies; employing new and rapidly advancing epigenetic, genomic, and other techniques from systems biology; and, at the same time, supporting and providing more concrete evidence on behalf of these early summative concepts. These threads of scientific inquiry have coalesced to form a network of research that has produced a much more robust and integrated conceptual framework. 3 The Purpose, Structure, and Content of This Volume The absence of an integrative conceptual framework through which scientists could organize and extend the manifold insights about the individual and contextual processes involved in the development of health across the life course was a fundamental challenge constraining the acceptance and understanding of a LCHD perspective (Halfon et al. 2014; Hanson and Gluckman 2014). However, as noted above, empirical and conceptual advances over the last 30 years have crystallized in the generation, and growing acceptance, of just such an integrative perspective. As a result, the life course health sciences are rapidly maturing and progressing, and the life course health development framework is coming into clearer focus. Nevertheless, there remain a number of challenges and growing pains that are evident as scientists, clinicians, and public health professionals from different fields attempt to incorporate LCHD notions into already established areas of scholarship, practice, and policy development. For example, the definitions of terms are not always clear, some terms are being redefined to 7 be integrated into this new approach, and other concepts and constructs are adopted before their meaning and significance are fully vetted and analyzed. Good examples of these challenges include terms like “sensitive and critical periods,” “developmental programming,” “biological embedding,” “trajectories,” and “pathways.” Terms like “programming” have been criticized as being too deterministic given the implication that a certain experience or set of risk factors can program a disease pathway; such a term eschews the nature of developmental plasticity and the phenotypic range that can emerge as the organism or individual encounters other experiences. Hanson and Gluckman have suggested that a term like priming, induction, or conditioning be used to describe the process by which an exposure or experience induces a phenotypic alteration that prepares the organism for a similar environmental challenge later in the life course (Hanson and Gluckman 2014). Throughout this volume, we have sought and encouraged the authors to avoid terms like programming and biological embedding in favor of conditioning or priming. Similarly, notions of critical periods have been part of embryology for over a century, and many biologists will refer to critical periods in biological development as a specific time that usually starts and ends abruptly and during which a given event or its absence has a specific impact on development. The experiments by Hubel and Wiesel to examine the development of the visual cortex seemed to indicate that there were critical periods for specific complex neurons to develop (Hubel and Wiesel 1977). However, most developmental science suggests that because of the inherent plasticity in human development and in many specific regulatory systems, the term “sensitive period” is less deterministic and therefore much more appropriate. In addition to the confusion around terminology, Hanson and Gluckman suggest several other reasons for why the related concept of developmental origins of health and disease has faced challenges, including confusion between factors correlated with disease and those involved in causation, the assumption that the only pathway connecting early exposures and later disease was N. Halfon et al. 8 low birth weight, a lack of plausible biological mechanisms, a failure to recognize developmental origins under normal rather than under extreme conditions, and the lack of evidence of its relative importance in relationship to other risk factors (Hanson and Gluckman 2014). Whereas all of these challenges are important and have begun to be addressed, the relative risk issues have been some of the most daunting since these questions require longitudinal data over very long time horizons to tease out. This volume is designed to address these challenges and bridge the resultant gaps, including the delay in broad acceptance and understanding of how health develops across the life-span (Hanson and Gluckman 2014), as well as in the translation of that perspective to health practice and policy. Each of the six sections comprising this volume was conceptualized during the process of planning the LCRN’s 2013 MCH Life Course Research Agenda-Setting Meeting, with the ultimate goal of informing the development of an MCH Life Course Research Agenda (LCRA) that would provide MCHB, the National Institutes of Health (NIH), and other funding institutions and organizations with guidance regarding priority research questions and topics worthy of investment. With input from the LCRN advisory committee, project staff determined that the LCRA (Sect. 6), which is in many ways the culmination of the LCRN's 6 years of work to date, would have to address theoretical concerns (Sect. I), specific periods of the life course (Sect. II), specific health conditions (Sect. III) – some common and some rare but nevertheless important because of their severity or their representativeness of a set of conditions sharing similar life course origins and/ or implications – cross-cutting topics in LCHD (Sect. IV), and methodological issues (Sect. V) to support researchers in carrying out the kinds of studies the research agenda would call for. Similarly, the majority of the chapter topics contained in this volume were selected early on in the development of the agenda-setting process. However, as noted in the Preface, several chapters were commissioned following the 2013 agenda-setting meeting in response to identified gaps, and others were selected based on author interest in submitting a chapter for inclusion in the agenda-setting process and/or published volume. The editors acknowledge that several important topics have not been included in this edition of the volume. In some cases, this omission was due to the difficulty of identifying a qualified author/author team willing to develop a chapter on a given topic; in other instances, chapters were commissioned but not completed in time for inclusion in this edition. It is the editors’ hope that future editions of this volume will address these worthy topics through new chapters on topics ranging from asthma and ADHD to family experiences, mental health conditions, and LCHD measures and biomarkers, among others. For now, however, it is our hope that readers will appreciate the range of topics included in this edition and the potential for these 26 chapters – all of which synthesize existing LCHD research, identify knowledge gaps, and/or recommend priorities related to future research and efforts to ensure the appropriate and timely translation of that research into practice and policy – to have a significant impact on how LCHD stakeholders think about, study, and work to promote health. Chapters “Middle Childhood: An Evolutionary-Developmental Synthesis” and “Pregnancy Characteristics and Women’s Cardiovascular Health” contain modified versions of previously published reviews and analyses of existing research. Given the relatively recent emergence of the field of LCHD and our goal to further coalesce that field through the publication of this volume, we thought it important to include the content in the handbook so that the important research both chapters contain could be situated firmly within the growing body of LCHD research and have a meaningful influence on the content of the LCRA. 3.1 Section I: Emerging Frameworks This section contains a single chapter by pediatricians and public health experts Neal Halfon, MD, MPH, and Christopher B. Forrest, MD, PhD, entitled The Emerging Theoretical Framework of Introduction to the Handbook of Life Course Health Development Life Course Health Development (Halfon and Forrest 2017). The authors – who also served as two of the editors of the volume – propose a set of principles that together form a more unified theoretical framework for the emerging LCHD field. These principles have the potential to guide future theory building, research, and policy pertaining to optimizing health development in the USA and abroad. Each subsequent chapter refers to the principles when appropriate to demonstrate how they can help to explain empirical findings or set the stage for future inquiry. 3.2 Section II: Life Stages Section II is comprised of six chapters exploring health development from the preconceptional/ prenatal period to early adulthood. Each describes the kinds of experiences and exposures that result in more (or less) optimal outcomes during a given developmental period and the importance of that period for outcomes over the remainder of the life course. As such, this section of the volume grounds the literature reviewed about each life stage firmly within the LCHD framework. Preconception and Prenatal Factors and Metabolic Risk – by pediatrician, MCH researcher, and molecular epidemiologist Xiaobin Wang, MD, MPH, ScD, and colleagues Guoying Wang, MD, PhD, and Tami R. Bartell – examines health during the earliest part of the life course, reviewing what is known about the mechanisms underlying both its sensitivity to alterations in the intrauterine environment. The authors explain the importance of this life period for lifelong and transgenerational health, including links to obesity, diabetes, cardiovascular disease, behavioral and psychiatric disorders, and more (Wang et al. 2017). Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities was authored by social epidemiologist and developmental-behavioral pediatrician W. Thomas Boyce, MD, and the late Clyde Hertzman, MD, MSc, who, as noted in this introduction, played a central role in delineating early childhood development as a determinant of lifelong health. The chapter 9 reviews the literature regarding the susceptibility of young children to social environmental conditions, explains how variation among individuals in terms of both their susceptibility and their exposures helps to explain variation in health development outcomes, and examines the process by which early adversity becomes biologically embedded (Boyce and Hertzman 2017). Marco Del Giudice’s chapter on Middle Childhood: An Evolutionary-Developmental Synthesis demonstrates the unique significance of middle childhood by examining – from an evolutionary perspective – the cognitive, behavioral, and hormonal processes that characterize this life stage, as well as its function as a switch point in the adaptive development of life history strategies and the implications for life course health development (DelGiudice 2017). Adolescent Health Development: A Relational Developmental Systems Perspective is the result of collaboration among experts in developmental psychology, human development, public health, and pediatrics. The authors are Richard M. Lerner, PhD; Claire C. Brindis, DrPH; Milena Batanova, PhD; and Robert Wm. Blum, MD, MPH, PhD. This chapter relates the seven proposed principles of LCHD to the Relational Developmental Systems (RDS) metatheoretical perspective, illustrating their interrelationships and differences. The authors discuss the implications of both conceptual frameworks for studying the life course origins and impact of adolescent health and for promoting thriving during adolescence (Lerner et al. 2017). Early Adulthood as a Critical Stage in the Life Course was produced by a group of authors with expertise in pediatrics, occupational therapy, psychology, and public health. In this chapter, David Wood, MD, MPH; Tara Crapnell, ORD, OTR/L; Lynette Lau, PhD; Ashley Bennett, MD; Debra Lotstein, MD, MPH; Maria Ferris, MD, PhD, MPH; and Alice Kuo, MD, PhD, employ an ecological approach to examine the period of life from 20 to 30 years of age. The authors also discuss the impact of chronic disease and other factors that affect the transition to adulthood in the educational, employment, and social arenas (Wood et al. 2017). N. Halfon et al. 10 Epidemiologists Abigail Fraser, MPH, PhD, MRC; Janet Catov, PhD; Debbie Lawlor, MRC; and Janet Rich-Edwards’, ScD, MPH, chapter on Pregnancy Characteristics and Women’s Cardiovascular Health is a unique contribution exploring the link between women’s reproductive outcomes and their risk for cardiovascular disease. The chapter presents the implications for research and policy including the potential to identify high-risk women earlier in the life course (Fraser et al. 2017). 3.3 Section III: The Life Course Origins and Consequences of Specific Diseases and Health Conditions Section III of this volume examines the life course origins and consequences of some of the most common diseases and health conditions facing the US population today, as well as some less common but nevertheless important ones. Each of the eight chapters reviews and synthesizes prior basic and intervention research, points out the gaps in our knowledge, and speaks to the importance of additional research and its application to the policy and practice arenas. Together, these chapters demonstrate the value of the LCHD perspective in understanding and improving outcomes across a variety of populations and challenges. Social epidemiologist Summer Sherburne Hawkins, PhD, MS, collaborated with pediatrician and prenatal nutrition expert Emily Oken, MD, MPH and pediatrician and preventive cardiology expert Matthew W. Gillman, MD, SM, to produce Early in the Life Course: Time for Obesity Prevention. This chapter adopts a multilevel approach in identifying the periods and factors that are the greatest contributors to obesity and explores how innovative research methodologies can be used to demonstrate causality (Hawkins et al. 2017). Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development Framework was written by Pamela Salsberry, PhD, RN, FAAN; Rika Tanda, PhD, RN; Sarah E. Anderson, PhD; and Manmohan K. Kamboj, MD, an author team representing a range of fields and disciplines including nursing, pediatrics, public health, endocrinology, and epidemiology. Their chapter uses an LCHD perspective to promote a better understanding of the development of pediatric T2DM, as well as a more effective approach to prevention and treatment (Salsberry et al. 2017). Clinical psychologist Irene E. Drmic, PhD, CPsych, collaborated with psychiatric and genetic epidemiologist Peter Szatmari, MD, MSc, and Journal of Autism and Developmental Disabilities editor-in-chief Fred Volkmar, MD, MA, to produce Life Course Health Development in Autism Spectrum Disorders. This chapter applies the LCHD framework to autism spectrum disorders (ASD) in order to inform future research and ultimately improve health development for individuals with ASD, as well as their families and communities (Drmic et al. 2017). Self-Regulation was written by a large and diverse group of experts spanning developmental, quantitative, and educational psychology. Together, Megan McClelland, PhD; John Geldof, PhD; Frederick J. Morrison; Steinunn Gestsdóttir, PhD; Claire Cameron, PhD; Ed Bowers, MEd, PhD; Angela Duckworth, PhD; Todd Little, PhD; and Jennie Grammer, PhD, examine the development and importance of self-regulation through an LCHD lens and from the standpoint of the relational developmental systems (RDS) metatheoretical framework (McClelland et al. 2017). A Life Course Health Development Perspective on Oral Health was written by James J. Crall, DDS, ScD, and Christopher Forrest, MD, PhD. By applying the LCHD framework and their expertise in pediatric oral health and public health, the authors advance a more contemporary conceptualization and definition of oral health as a more integral and integrated component of overall health and well-being (Crall and Forrest 2017). Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical, and Translational Research Agenda to Optimize Health, Behavior and Functioning is Introduction to the Handbook of Life Course Health Development the result of a collaboration among pediatrician Michael E. Msall, MD, and a group of colleagues representing nursing, pediatrics, sociology, and demography, including Sarah A. Sobotka, MD, MS; Amelia Dmowska; Dennis Hoga, PhD; and Mary Sullivan, PhD, RN. Together the authors examine the underlying causes of observed disparities in LCHD outcomes among children born prematurely, the value of life course-focused population-level interventions for closing the gap, and the need to improve the availability and systematic provision of such services (Msall et al. 2017). A Life Course Approach to Hearing Health was written by Shirley A. Russ, MB, ChB, MRCP, FRACP, MCH; Kelly Tremblay, PhD, CCC-A; Neal Halfon, MD, MPH; and Adrian Davis, BSC, MSc, PhD, FFPH, FSS, OBE – an author team representing both pediatrics and speech and hearing. The chapter explores the origins of the full spectrum of hearing loss, including genetic, congenital, and environmental causes, and the mechanisms by which they interact and act upon an individual’s hearing health development over the life-span (Russ et al. 2017). Nephrologists Patrick Brophy, MD; Jennifer R. Charlton, MD, MSc; J. Bryan Carmody, MD; Kimberly J. Reidy, MD; David Askenazi, MD; and Susan P. Bagby, MD, teamed up with pediatrician Lindsay Harshman, MD; neonatologist Jeffrey Segar, MD; and public health expert David Shoham, PhD, MSPH, to produce Chronic Kidney Disease: A Life Course Health Development Perspective. In this chapter, the authors find that the LCHD framework is of great value in both elucidating the sequelae of CKD risk and identifying the kinds of early life interventions that have the potential to mitigate it (Brophy et al. 2017). 3.4 Section IV: Cross-Cutting Topics in Life Course Health Development This section of the volume addresses four key issues – growth, nutrition, adversity, and disparities – that are relevant to understanding and 11 addressing a wide range of diseases and health conditions. Growth and Life Course Health Development was written by a team of authors spanning medicine, public health, and anthropology. In their chapter, Amanda Mummert, MA, Meriah Schoen, and Michelle Lampl, MD, PhD, employ a systems biology approach to examine the pathways affecting growth and explore auxology’s role in a variety of health trends (Mummert et al. 2017). From Epidemiology to Epigenetics: Evidence for the Importance of Nutrition to Optimal Health Development Across the Life Course was written by nutrition experts Marion Taylor-Baer, PhD, MSNS, RD, and Dena Herman, MSNS, MPH, RD. Their chapter uses the LCHD framework to examine the crucial role that nutrition plays in the development of health potential over the life-span, including the role of evolutionarily driven adaptive responses during the prenatal and early childhood periods (Taylor-Baer and Herman 2017). In How Socioeconomic Disadvantages Get Under the Skin and into the Brain to Influence Health Development Across the Lifespan, developmental psychologists Pilyoung Kim, PhD; Gary Evans, PhD; Edith Chen, PhD; and Gregory Miller, PhD, collaborated with epidemiologist Teresa Seeman, PhD, to explain the neurobiological mechanisms and processes by which SES-related adversity, including chronic stress, affect health trajectories from early life to old age (Kim et al. 2017). Health sciences researcher Kandyce Larson, PhD, together with pediatricians Shirley A. Russ, MD, MPH; Robert S. Kahn, MD, MPH; Glenn Flores, MD, FAAP; Elizabeth Goodman, MD; Tina L. Cheng, MD, MPH; and Neal Halfon, MD, MPH, produced Health Disparities: A Life Course Health Development Perspective and Future Research Directions. This chapter explores the factors and processes that contribute to health disparities across lifetimes and generations from the perspective of life course health development in order to illuminate potential practice and policy solutions to this persistent problem (Larson et al. 2017). N. Halfon et al. 12 3.5 Section V: Methodological Approaches Section V is comprised of five chapters that will be of practical use to researchers who are engaged or who would like to engage in future research aimed at enhancing our understanding of how health develops over the life course and/or understanding how practice and policy can optimize health development outcomes. The first two chapters in this section review current research methods and study designs that are of particular value for LCHD research. The remaining three chapters describe existing longitudinal datasets that have the potential to be used to answer the kinds of research questions that are described in the Life Course Research Agenda, Version 1.0 (Halfon et al. 2017). In Core Principles of Life Course Health Development Methodology and Analytics, developmental and educational psychologist and statistician-methodologist Todd Little, PhD, acknowledges the inherent complexity of LCHD studies. He suggests that underutilized techniques, such as structural equation modeling, multilevel modeling, and mixture distribution modeling, as well as new and collaborative teambased research practices, have the potential to rapidly advance the field (Little 2017). In Epidemiological Study Designs: Traditional and Novel Approaches to Advance Life Course Health Development Research, epidemiologists Stephen L. Buka, PhD; Samantha R. Rosenthal, PhD; and Mary E. Lacy explore the benefits and limitations of the various approaches that can be used to study the development of health and disease over the life course (Buka et al. 2017). Using the National Longitudinal Surveys of Youth (NLSY) to Conduct Life Course Analyses by sociologist Elizabeth Cooksey, PhD, demonstrates how this long-running three-cohort longitudinal study – which provides a wealth of data on health, education, employment, household information, family background, marital history, child care, income and assets, attitudes, substance use, and criminal activity – can be used to explore various LCHD-related research questions (Cooksey 2017). Sociologist and demographer Narayan Sastry, PhD, worked with research scientists Paula Fomby, PhD, and Katherine McGonagle, PhD, to develop the chapter on Using the Panel Study of Income Dynamics (PSID) to Conduct Life Course Health Development Analysis. This chapter explains how this nationally representative longitudinal study – which is the longestrunning household panel study in the world, covering 47 years of data on a wide range of economic, demographic, social, and health topics – can be used to examine health development over the life course (Sastry et al. 2017). In Using the Fragile Families and Child Wellbeing Study (FFCWS) in Life Course Health Development Research, sociologist Amanda Geller, PhD, collaborates with FFCWS staff members Kate Jaeger and Garrett Pace to describe this nationally representative birth cohort study that contains both biological and social indicators. The authors provide examples of its use for exploring questions about health development in households with unmarried parents (Geller et al. 2017). 3.6 Section VI: Conclusions Section VI of this volume contains a single chapter entitled Life Course Research Agenda (LCRA), Version 1.0 in which editors Neal Halfon, MD, MPH; Christopher B. Forrest, MD, PhD; Richard M. Lerner, PhD; and Elaine Faustman, PhD, together with LCRN staff members Ericka Tullis, MPP, and John Son, MPH, synthesize the recommendations for future research contained in many of the previous chapters and propose a set of priority research types, topics, and questions, as well as a set of activities aimed at improving our ability to carry out this critical research and ensure its timely translation to practice and policy. The authors also recommend strategies that can support the ongoing refinement of the LCHD theoretical framework (Halfon et al. 2017). As this volume goes to press, the LCRN is initiating an inclusive process to review and refine this initial version of the LCRA so that it is sure to guide both researchers and potential funders toward the Introduction to the Handbook of Life Course Health Development studies that will be of greatest benefit in terms of enhancing our rapidly growing but as yet incomplete understanding of life course health development. References Acheson, D. (1998). Independent inquiry into inequalities in health report. Stationery Office. Aldwin, C. M. (2014). Rethinking developmental science. Research in Human Development, 11(4), 247–254. Antony, P. M., Balling, R., & Vlassis, N. (2012). From systems biology to systems biomedicine. Current Opinion in Biotechnology, 23(4), 604–608. Baltes, P. B., Lindenberger, U., & Staudinger, U. M. (2006). Lifespan theory in developmental psychology. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology. Vol. 1: Theoretical models of human development (6th ed., pp. 569–664). New York: Wiley. Barker, D. J. (1995). Fetal origins of coronary heart disease. BMJ, 311(6998), 171. Barker, D. J., Osmond, C., Winter, P. D., Margetts, B., & Simmonds, S. J. (1989). Weight in infancy and death from ischaemic heart disease. The Lancet, 334(8663), 577–580. Barker, D. J., Godfrey, K. M., Gluckman, P. D., Harding, J. E., Owens, J. A., & Robinson, J. S. (1993). Fetal nutrition and cardiovascular disease in adult life. The Lancet, 341(8850), 938–941. Ben-Shlomo, B., & Kuh, D. (2002). A life course approach to chronic disease epidemiology: Conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology, 31(2), 285–293. Berkman, L. F., Kawachi, I., & Glymour, M. M. (Eds.). (2014). Social epidemiology. Oxford: Oxford University Press. Braveman, P. (2014). What is health equity: And how does a life-course approach take us further toward it? Maternal and Child Health Journal, 18(2), 366–372. Bronfenbrenner, U. (2005). Making human beings human. Thousand Oaks: Sage. Boyce, T., & Hertzman, C. (2017). Early childhood and the life course: The state of the science and proposed research priorities. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Boyce, T. W., & Keating, D. P. (2004). Should we intervene to improve childhood circumstances? In D. Kuh & Y. Ben-Shlomo (Eds.), A life course approach to chronic disease epidemiology (Vol. 2). New York: Oxford University Press. Brophy, P. D., Charlton, J. R., Carmody, J. B., Reidy, K. J., Harshman, L., Segar, J., Askenazi, D., & Shoham, D. (2017). Chronic kidney disease: A life course healthdevelopment perspective. In N. Halfon, C. B. Forrest, 13 R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Buka, S. L., Rosenthal, S. R., & Lacy, M. E. (2017). Epidemiological study designs: Traditional and novel approaches to advance life course health-development research. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Capra, F. (1982). The turning point: Science, society, and the rising culture. Toronto: Bantam Books. Committee on Future Directions for Behavioral and Social Sciences Research., Singer B. H., Ryff, C. D (Eds.). (2001). New horizons in health: An integrative approach. Washington, DC: National Academies Press. Committee on Evaluation of Children’s Health; Board on Children, Youth and Families; Division of Behavioral and Social Sciences and Education; National Research Council; Institute of Medicine. (2004). Children’s health, the nation’s wealth: Assessing and improving child health. Washington, DC: National Academies Press. Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records, Board on Population Health and Public Health Practice, Institute of Medicine. (2015). Capturing social and behavioral domains and measures in electronic health records: Phase 2. Washington, DC: National Academies Press (US). Cooksey, E. (2017). Using the National Longitudinal Surveys of Youth (NLSY) to conduct life course analyses. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Crall, J. J., & Forrest, C. B. (2017). A life course healthdevelopment perspective on oral health. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Davey Smith, G., & Lynch, J. (2004). Life course approaches to socioeconomic differentials in health. In D. Kuh & Y. Ben-Shlomo (Eds.), A life course approach to chronic disease epidemiology (Vol. 2). New York: Oxford University Press. Davila-Velderrain, J., Martinez-Garcia, J. C., & AlvarezBuylla, E. R. (2015). Modeling the epigenetic attractors landscape: Toward a post-genomic mechanistic understanding of development. Frontiers in Genetics, 6, 160. DelGiudice, M. (2017). Middle childhood: An evolutionary-developmental synthesis. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Drmic, I. E., Szatmari, P., & Volkmar, F. (2017). Life course health-development in autism spectrum disorders. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. 14 Elder, G. H., Shanahan, M. J., & Jennings, J. A. (2015). Human development in time and place. In M. H. Bornstein& T. Leventhal (Eds.), Handbook of child psychology and developmental science. Vol. 4: Ecological settings and processes in developmental systems (7th ed., pp. 6–54). Editor-in-chief: R.M. Lerner. Hoboken: Wiley. Evans, R. G., Barer, M. L., & Marmor, T. R. (Eds.). (1994). Why are some people healthy and others not?: The determinants of health of populations. New York: Aldine de Gruyter, 1994. Ford, D. L., & Lerner, R. M. (Eds.). (1992). Developmental systems theory: An integrative approach. Newbury Park: Sage. Forrest, C. B., & Riley, A. W. (2004). Childhood origins of adult health: A basis for life-course health policy. Health Affairs (Millwood), 23(5), 155–164. Foster, K. R. (2011). The sociobiology of molecular systems. Nature Reviews Genetics, 12, 193–203. Fraser, A., Catov, J. M., Lawlor, D. A., Rich-Edwards, J. W. (2017). Pregnancy characteristics and women’s cardiovascular health. In N. Halfon, C. B. Forrest, R. M. Lerner, E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Gee, G. C., Walsemann, K. M., & Brondolo, E. (2012). A life course perspective on how racism may be related to health inequities. American Journal of Public Health, 102(5), 967–974. Geller, A., Jaeger, K., & Pace, G. (2017). Using the fragile families and child wellbeing study (FFCWS) in life course health-development research. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Gillman, M. W. (2004). A life course approach to obesity. In D. Kuh & Y. Ben-Shlomo (Eds.), A life course approach to chronic disease epidemiology (Vol. 2). Oxford University Press. Gluckman, P. D., & Hanson, M. A. (2004). Living with the past: Evolution, development, and patterns of disease. Science, 305(5691), 1733–1736. Gluckman, P. D., Hanson, M. A., Cooper, C., & Thornburg, K. L. (2008). Effect of in utero and early-life conditions on adult health and disease. New England Journal of Medicine, 359(1), 61–73. Gottlieb, G. (1998). Normally occurring environmental and behavioral influences on gene activity: From central dogma to probabilistic epigenesist. Psychological Review, 105, 792–802. Greenblum, S., Turnbaugh, P. J., & Borenstein, E. (2012). Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proceedings of the National Academy of Sciences, 109(2), 594–599. Halfon, N., & Forrest, C. B. (2017). The emerging theoretical framework of life course health-development. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. N. Halfon et al. Halfon, N., Forrest, C. B., Lerner, R. M., Faustman, E., Tullis, E., & Son, J. (2017). Life course research agenda (LCRA), version 1.0. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Halfon, N., & Hochstein, M. (2002). Life course health development: An integrated framework for developing health, policy and research. The Milbank Quarterly, 80(3), 433–479. Halfon, N., Inkelas, M., & Hochstein, M. (2000). The health development organization: An organizational approach to achieving child health development. The Milbank Quarterly, 78(3), 447–497. Halfon, N., Larson, K., Lu, M., Tullis, E., & Russ, S. (2014). Lifecourse health development: Past, present and future. Maternal and Child Health Journal, 18(2), 344–365. Hancock, T. (1986). Lalonde and beyond: Looking back at “a new perspective on the health of Canadians”. Health Promotion International, 1(1), 93–100. Hanson, M. A., & Gluckman, P. D. (2014). Early developmental conditioning of later health and disease: Physiology or pathophysiology? Physiological Reviews, 94(4), 1027–1076. Hawkins, S. S., Oken, E., Gillman, M. W. (2017). Early in the life course: Time for obesity prevention. In N. Halfon, C. B. Forrest, R. M. Lerner, E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Hertzman, C. (1994). The lifelong impact of childhood experiences: A population health perspective. Daedalus, 123(4), 167–180. Hertzman, C. (1999). The biological embedding of early experience and its effects on health in adulthood. Annals of the New York Academy of Sciences, 896(1), 85–95. Hertzman, C., & Boyce, T. (2010). How experience gets under the skin to create gradients in developmental health. Annual Review of Public Health, 31, 329–347. Huang, S. (2012). The molecular and mathematical basis of Waddington’s epigenetic landscape: A framework for post-Darwinian biology? BioEssays, 34(2), 149–157. Hubel, D. H., & Wiesel, T. N. (1977). Ferrier lecture: Functional architecture of macaque monkey visual cortex. Proceedings of the Royal Society of London B: Biological Sciences, 198(1130), 1–59. Kandel, E. R., Dudai, Y., & Mayford, M. R. (2014). The molecular and systems biology of memory. Cell, 157(1), 163–186. Keating, D. P., & Hertzman, C. (Eds.). (1999). Developmental health and the wealth of nations: Social, biological, and educational dynamics. New York: Guilford Press. Kim, P., Evans, G. W., Chen, E., Miller, G., & Seeman, T. (2017). How socioeconomic disadvantages get under the skin and into the brain to influence healthdevelopment across the lifespan. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Introduction to the Handbook of Life Course Health Development Handbook of life course health development. New York: Springer. Kitano, H. (2002). Systems biology: A brief overview. Science, 295(5560), 1662–1664. Kuh, D., & Ben-Shlomo, Y. (Eds.). (1997). A life course approach to chronic disease epidemiology (Vol. 1). Oxford: Oxford University Press. Kuh, D., & Ben-Shlomo, Y. (Eds.). (2004). A life course approach to chronic disease epidemiology (Vol. 2). New York: Oxford University Press. Kuh, D., Cooper, R., Hardy, R., Richards, M., & BenShlomo, Y. (Eds.). (2013). A life course approach to healthy ageing. Oxford: Oxford University Press. Kuh, D., Power, C., Blane, D., & Bartley, M. (2004). Socioeconomic pathways between childhood and adult health. In D. Kuh & Y. Ben-Shlomo (Eds.), A life course approach to chronic disease epidemiology (Vol. 2). New York: Oxford University Press. Lappé, M., & Landecker, H. (2015). How the genome got a life span. New Genetics and Society., 34(2), 152–176. Larson, K., Russ, S. A., Kahn, R. S., Flores, G., Goodman, E., Cheng, T. L., & Halfon, N. (2017). Health disparities: A life course health-development perspective and future research directions. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Lawlor, D. A., & Mishra, G. D. (Eds.). (2009). Family matters: Designing, analysing and understanding family based studies in life course epidemiology. Oxford: Oxford University Press. Lerner, R. M. (1984). On the nature of human plasticity. New York: Cambridge University Press. Lerner, R. M. (2006). Developmental science, developmental systems, and contemporary theories. In R. M. Lerner (Ed.), Theoretical models of human development. Volume 1 of Handbook of child psychology (6th ed.). Editors-in-chief: Damon W & Lerner RM. Hoboken: Wiley. Lerner, R. M. (2012). Developmental science: Past, present and future. International Journal of Developmental Science., 6, 29–36. Lerner, R. M., & Callina, K. S. (2014). The study of character development: Towards tests of a relational developmental systems model. Human Development, 57(6), 322–346. Lerner, R. M., & Overton, W. F. (2008). Exemplifying the integrations of the relational developmental system: Synthesizing theory, research, and application to promote positive development and social justice. Journal of Adolescent Research, 23, 245–255. Lerner, R. M., Brindis, C. C., Batanova, M., & Blum, R. W. (2017). Adolescent health: A relational developmental systems perspective. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Little, T. D. (2017). Core principles of life course health-development methodology and analytics. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman 15 (Eds.), Handbook of life course health development. New York: Springer. Lu, M., & Halfon, N. (2003). Racial and ethnic disparities in birth outcomes: A life-course perspective. Maternal and Child Health Journal, 7(1), 13–30. McClelland, M., Morrison, F., Gestsdóttir, S., Cameron, C., Bowers, E., Duckworth, A., Little, T., & Grammer, J. (2017). Self-regulation. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Molenaar, P. C., & Newell, K. M. (Eds.). (2010). Individual pathways of change: Statistical models for analyzing learning and development. Washington, DC: American Psychological Association. Msall, M. E., Sobotka, S. A., Dmowska, A., Hoga, D., & Sullivan, M. (2017). Life-course health-development outcomes after prematurity: Developing a community, clinical, and translational research agenda to optimize health, behavior and functioning. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Mummert, A., Schoen, M., & Lampl, M. (2017). Growth and life course health-development. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. Springer. Overton, W. F. (2010). Life-span development: Concepts and issues. In W. F. Overton (Ed.)., Lerner RM (Editorin-Chief) Handbook of life-span development, Vol. 1: Cognition, biology, and methods across the lifespan (pp. 1–29). Hoboken: Wiley. Overton, W. F. (2012). Evolving scientific paradigms: Retrospective and prospective. In L. L’Abate (Ed.), The role of paradigms in theory construction (pp. 31–65). New York: Springer. Overton, W. F. (2015). Process and relational developmental systems. In W. F. Overton, & P. C. Molenaar (Eds.), Theory and method. Volume 1 of the Handbook of child psychology and developmental science (7th ed., pp. 9–62) Editor-in-chief: R. M. Lerner. Hoboken: Wiley. Piro, R. M., & Di Cunto, F. (2012). Computational approaches to disease-gene prediction: Rationale, classification and successes. The FEBS Journal, 279(5), 678–696. Russ, S. A., Tremblay, K., Halfon, N., & Davis, A. (2017). A lifecourse approach to hearing health. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Salsberry, P., Tanda, R., Anderson, S. E., & Kamboj, M. K. (2017). Pediatric type 2 diabetes: Prevention and treatment through a life course health-development framework. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Sameroff, A. (2010). A unified theory of development: A dialectic integration of nature and nurture. Child Development, 81(1), 6–22. 16 Sastry, N., Fomby, P., & McGonagle, K. (2017). Using the panel study of income dynamics (PSID) to conduct life course health-development analysis. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Schadt, E. E., & Björkegren, J. L. (2012). NEW: Networkenabled wisdom in biology, medicine, and health care. Science Translational Medicine, 4(115), 115rv1. Taylor-Baer, M., & Herman, D. (2017). From epidemiology to epigenetics: Evidence for the importance of nutrition to optimal health-development across the lifecourse. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. von Bertalanffy, L. (1933). Modern theories of development: An introduction to theoretical biology. London: Oxford University Press. N. Halfon et al. Wang, G., Bartell, T. R., & Wang, X. (2017). Preconception and prenatal factors and metabolic risk. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Whitehead, A. N. (1929/1978). Process and reality: An essay in cosmology. New York: The Free Press. Wood, D., Crapnell, T., Lau, L., Bennett, A., Lotstein, D., Ferris, M., & Kuo, A. (2017). Emerging adulthood as a critical state in the life course. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health development. New York: Springer. Worthman, C. M., & Kuzara, J. (2005). Life history and the early origins of health differentials. American Journal of Human Biology, 17(1), 95–112. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Part I Emerging Frameworks The Emerging Theoretical Framework of Life Course Health Development Neal Halfon and Christopher B. Forrest 1 Part 1: Context and Background Over the last century, we have witnessed the power of the biomedical paradigm to treat the sick and prevent diseases from occurring. Conventional health science, as an applied field, has tried to make sense of what constitutes health by exploring the causes of disease in individual patients (medicine) and populations (public health). This approach has created a perspective of health as absence of disease or its risk factors and has been unsuccessful at explaining what it means to be healthy, how health develops over the lifespan, and the impact of health on the lives of individuals. N. Halfon, MD, MPH (*) Department of Pediatrics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA Department of Health Policy and Management, Fielding School of Public Health, UCLA, Los Angeles, CA, USA Department of Public Policy, Luskin School of Public Affairs, UCLA, Los Angeles, CA, USA Center for Healthier Children, Families, and Communities, UCLA, Los Angeles, CA, USA e-mail: nhalfon@ucla.edu C.B. Forrest, MD, PhD Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Concepts of what constitutes health, and theories about how health is produced and optimized, are constantly evolving in response to myriad social and cultural expectations shaped by our contemporary worldview, scientific advances, improvements in health interventions, and the changing capacity of the health system. Stimulated originally by a series of studies demonstrating how growth during early life is related to chronic health conditions that emerge many decades later, an eruption of new research is identifying developmental processes that shape long-term health trajectories (Ben-Shlomo and Kuh 2002; Kuh and Ben-Shlomo 2004; Hanson and Gluckman 2014). This research is demonstrating how complex developmental processes integrate a range of behavioral, social, and environmental influences that modify gene expression, modulate physiologic and behavioral function, and dynamically shape different pathways of health production (Halfon and Hochstein 2002; Kuh and BenShlomo 2004; Halfon et al. 2014). These empirical findings are highlighting the limitations of the more mechanistic biomedical and biopsychosocial models of health, which fail to offer comprehensive explanations about such phenomena as the developmental origins of health, how stress affects current and future health, and the consequences of dynamic interactions between individuals and their environments over time. Informed by new theoretical perspectives emerging from such fields of study as developmental © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_2 19 20 psychology (Lerner 2012), systems biology (Kitano 2002; Antony et al. 2012; Schadt and Bjorkegren 2012), epigenetics (Egger et al. 2004), the developmental origins of chronic disease (Gluckman and Hanson 2006a), and evolutionary developmental biology (West-Eberhard 2003), a transdisciplinary framework (Gatzweller and Baumuller 2013) is emerging which we call Life Course Health Development (LCHD) (Halfon and Hochstein 2002, Halfon et al. 2014). As a framework, LCHD organizes several different theories and conceptual models in order to make sense of the enormously challenging question of how health develops over the lifespan. The LCHD framework addresses the developmental origins of health, the role that biological and behavioral plasticity play in facilitating different levels of adaptation, and how mismatches between biological propensity and environmental context interact to produce breakdowns in health, known as disease. As a framework that organizes numerous theories and concepts related to how health develops, LCHD is bridging what have sometimes been assumed to be polar opposites: nature versus nurture, mind versus body, individual versus population, and short-term versus long-term change. By unifying these dichotomies, LCHD offers a new perspective that will guide future scientific inquiry on health development and facilitate a long overdue and needed synthesis of medicine and public health—a synthesis that links treatment, prevention, and health promotion and catalyzes more integrated and networked strategies for designing, organizing, and implementing multilevel health interventions that transcend individual and population dichotomies. The LCHD framework will be increasingly useful as the human ecological footprint expands and influences the health development of Earth itself, creating new threats to human health via rapid and disruptive changes in physical environments, geographic dispersion of populations, and changes in social development. This emergence of LCHD is reflective of larger scientific trends that are transforming research in the physical, natural, and social sciences. The comfort and certainty of simple, linear, and deterministic causal pathways are giving way to the N. Halfon and C.B. Forrest uncomfortable uncertainty of nonlinear causal clusters that are networked together into complex, multilevel, interactive, and relational systems. LCHD embraces this complexity as the salient target of inquiry and requires research to be conducted with teams that are multidisciplinary, often large, networked, and highly collaborative. These shifts in scientific approach are helping us understand how our modern interdependent world is organized, how it functions, and how it contributes to the production of human health. Given the explosion of life course-focused research in many scientific fields—including chronic disease epidemiology, developmental neuroscience, developmental psychology, evolutionary biology, genetics, epigenetics, environmental health sciences, economics, sociology, and many more—there is a growing need to provide a systematic framework for understanding and organizing this emerging knowledge base (i.e., sense making) so that it can be more effectively applied to solving health problems and guiding new and productive streams of exploration and discovery. Our intent is not to provide a grand theory of Life Course Health Development. Rather, we seek to establish a set of principles that describe the contours of the rapidly emerging health development knowledge base by organizing many theories and conceptual models into a coherent synthesis. We recognize that LCHD is a work in progress. Our aim is to create conceptual coherence by contextualizing the meaning of disparate research findings, identifying gaps and uncertainties—including how concepts are defined, operationalized, and interpreted—and moving inquiry, application, and implementation forward. We hope that the principles of LCHD presented here, coupled with our explanatory narrative, will encourage theory building and testing, inspire innovative transdisciplinary research, and mature the framework into a scientific model with descriptive, explanatory, and predictive utility. Furthermore, we hope that LCHD will shine a light on the conundrum of how little attributable risk is explained in many studies of chronic disease, how early experience conditions future biological response patterns, and how these early The Emerging Theoretical Framework of Life Course Health Development experiences play through complex, environmentally influenced, and developmentally plastic health development pathways (Table 1). In Part 2 of this chapter, we describe the ontological pathways—including theories, empirical findings, and concepts—that led to the LCHD framework, thereby orienting our view of health development from simple, mechanistic, and reductionist models to contemporary models that are holistic, complex, dynamically relational, and adaptive. In Part 3, we summarize the principles of the LCHD framework, grounding them in a set of transdisciplinary theories, models, and perspectives and addressing their implications for future inquiry (Fig. 1). Beginning with the simple, mechanical, and mostly linear biomedical model, we chart how it was transformed into a more hierarchical, Table 1 Principles of Development Framework Principle 1. Health Development 2. Unfolding 3. Complexity 4. Timing 5. Plasticity 6. Thriving 7. Harmony the Life Course Health Brief description Health development integrates the concepts of health and developmental processes into a unified whole Health development unfolds continuously over the lifespan, from conception to death, and is shaped by prior experiences and environmental interactions Health development results from adaptive, multilevel, and reciprocal interactions between individuals and their physical, natural, and social environments Health development is sensitive to the timing and social structuring of environmental exposures and experiences Health development phenotypes are systematically malleable and enabled and constrained by evolution to enhance adaptability to diverse environments Optimal health development promotes survival, enhances well-being, and protects against disease Health development results from the balanced interactions of molecular, physiological, behavioral, cultural, and evolutionary processes 21 dynamic, and multiply determined biopsychosocial model, as the result of scientific breakthroughs in the understanding of the contribution of behavioral, social factors and their influence on individuals during specific life stages. This biopsychosocial model has now evolved into a more complex, relational, adaptive, dynamic, and developmental model of Life Course Health Development (LCHD) as result of the influence of scientific breakthroughs in epigenetics, neurodevelopment, and life course chronic disease epidemiology. 2 Part 2: Emergence of the Life Course Health Development Framework The LCHD framework has emerged from a network of theories, conceptual models, and empirical findings and provides a more comprehensive description of how health develops over the life course than any single component part. In this section, we describe the streams of scientific inquiry, the key theories and models, and the seminal scientific insights that are brought together by the LCHD framework. Figure 1 provides an epistemological schematic, charting the changing paradigms of health and how different streams of research and their findings influenced the flow of conceptual models. A few decades ago, research linked fetal development with degenerative diseases of old age (Ben-Shlomo and Kuh 2002), stimulating new ways of thinking about the mechanisms underlying what was originally termed “fetal programming” and other environmentally induced modifications in gene expression that presumably take place early in life. These insights pointed out the need to better characterize interactions between genes and the environment, to better understand gene regulation that occurs in response to environmental signal transduction, and to better integrate into explanatory models the importance of the timing and phasing of these developmental processes. The prevailing epidemiological framework, with its simple additive, exposure-response models of risk accumulation N. Halfon and C.B. Forrest 22 Simple, Reductionistic, Mechanistic, Linear Biomedical Models Framingham Alameda Social Epidemiology Behavioral Influences Epidemiology Smoking Diet Physical Activity Stress Hierarchical, Dynamic, Systems, Multiple determinants Life Span Human Developmental Psychology Biopsychosocial Models Life Course Sociology Epigenetics Developmental Origins of Adult Disease (DoAD) Complex, Relational, Adaptive, Dynamic, Developmental Neurodevelopment Life Course Chronic Disease Epidemiology Life Course Health Development (LCHD) Synthesis National Birth Cohort Studies Pathway Influence Fig. 1 The evolution of conceptual models of health development as the etiology for chronic disease in adult life, could not satisfactorily explain these more complex time-dependent phenomena. Instead, a theoretical framework was needed to explain the processes of translating an individual’s experiences and exposures into the development of health over the life course. Two converging streams of biological research and conceptual constructs have contributed to the LCHD framework. The first stream represents the basic biology of human development, informed by the neo-Darwinian synthesis that resulted from the convergence of Darwin’s theory of evolution and Mendel’s notion of genes as the building blocks of heredity. Although the central dogma of “genes/DNA → mRNA → proteins” has served as a foundational construct for modern molecular biology, it led to overly deterministic genotype-to-phenotype models (Huang 2012). Recent advances in panomics (e.g., genomics, epigenomics, proteomics, metabolomics) and systems biology are redefining our understanding of how gene networks are regulated and dynamically interact with each other and the environment, resulting in a new synthesis of biological systems development and function- ing (Huang 2012; Forrest 2014; Davila-Velderrain et al. 2015). Breakthroughs in understanding the relationships between evolutionary processes and biological development, and advances in the use of life history theory to explain how mismatches between biological propensities and modern environments influence the onset of disease, have also provided a new way of considering the relationship of an individual’s or a population’s genetic endowment and the phenotypes that emerge (Del Giudice et al. 2015; Green et al. 2015; Hanson and Gluckman 2014; Lieberman 2014; Gluckman and Hanson 2006a). The second stream of inquiry, which interacted with genetic concepts and models, represents the evolution of models of disease causation, informed by contributions from basic, clinical, epidemiologic, social, and psychological research disciplines. In the first era of health science, scientific methods applied to medicine resulted in the development of a biomedical framework in which anatomical-pathological disease models, along with other mechanistic constructs, were used to explain why disease develops. One prototypical theoretical construct was germ theory (i.e., germs as the unique causes of infectious dis- The Emerging Theoretical Framework of Life Course Health Development eases) (Stewart 1968). Others included theories of inheritance that were informed by simple and mechanistic notions of genes as the unique causes of inherited disorders and risk status. Several decades of research on the “upstream” social and behavioral determinants of health were stimulated by epidemiologic studies like the Framingham Study and Alameda County Study that highlighted how cardiovascular and other chronic diseases were not caused by bad germs, bad genes, or bad luck, but were related to behaviors like smoking, diet, exercise, and other social factors as well as the metabolic changes that these social and behavioral risk factors induced (Dawber et al. 1974; Haynes 1980; Berkman and Syme 1979). This led to a more dynamic ecological analysis of the multiple risk factors that lead to disease causation, informing the creation of a multi-causal, biopsychosocial framework of disease (Engel 1977). Over the last 20 years, this biopsychosocial model of health has continued to evolve as a result of the integration of concepts from life course research in sociology (Elder 1995; Elder and Shanahan 2007), lifespan developmental research in psychology (Lerner 2012), systems biology (Schadt and Bjorkegren 2012), and longitudinal studies on the origins of chronic disease (Gluckman and Hanson 2004b). The biopsychosocial model undergirds much of the current focus on the social determinants of health and the important role that contextual factors play in shaping health outcomes (McMichael 1999; Krieger 2001). Midway through the twentieth century, social scientists started examining how the rapidly changing social circumstances of the second industrial revolution were transforming the developmental pathways of different generations. Separate yet related streams of research emerged, converging around notions of the life course, the lifespan, and the human life cycle development. Two lines of investigation in particular have informed recent notions of health development: life course sociology and lifespan human developmental psychology (Diewald and Mayer 2009). Life course theories emerged in sociology research in the 1960s, championed by Elder, Clausen, and others. These theories distinguished 23 how social institutions and history shape the roles, personal events, transitions, and trajectories of individuals who follow different developmental pathways (Clausen 1986; Elder et al. 2003). Macro-level social processes and social relationships influence interweaving trajectories at different ages, stages, and transitions of development (Elder 1995). Untangling age, period, and cohort effects and understanding the cumulative impact of experience on socially and institutionally constructed life pathways form the basis of life course sociology. For example, the experience of low socioeconomic status, discrimination, and racial segregation may have different effects on health for different cohorts (i.e., groups born at different times), based on prevailing (period effects), compensatory, and mediating factors such as the availability of healthcare or the impact of different social policies (Chen et al. 2010; Masters et al. 2012). Building on the work of Glen Elder, Duane Alwin (2012) suggested five ways that the term “life course” was used to describe etiologic processes in social and behavioral sciences: (1) lifespan development, humans develop over the life course; (2) agency, individuals construct their lives through choices and actions they take within social structures that provide opportunities and impose constraints; (3) cohort and geographic variation, lives of individuals are embedded and shaped by historical time and place where they live; (4) timing, impact of events, experiences, and transitions are conditional on their timing in a person’s life; and, (5) linked lives, people’s lives are lived interdependently (e.g., husband and wife, siblings). Lifespan human developmental psychologists attempt to explain how individual differences emerge at different ages and stages (Lerner 1984; Lerner 2012). These differences are, in part, determined by endogenous characteristics (i.e., each individual’s personal adaptability, plasticity, resilience, and reactivity) interacting with exogenous factors (i.e., external physical, social, and psychological environments that promote adaptation). These interactions cause human behavior to continuously change from conception to death (Lerner 1984, 2012). By focusing on the individ- 24 ual’s capacity to adapt to events and experiences (Dannefer 1984; Alwin 2012), developmental psychologists have suggested that lifespan human development research concentrates on the plasticity associated with individual development (ontogenesis), whereas life course social sciences researchers emphasize “sociogenesis” or how life pathways are informed and structured by socially constructed developmental scaffolding and constraints. In short, psychologists have tended to focus on how endogenous or constitutional ontogenetic processes influence lifelong developmental trajectories, while sociologists have been more concerned with contextual or exogenous factors. Over the past 30 years, there has growing convergence between life course sociology and lifespan human developmental psychology. Research on “linked lives,” where the common and differential impact of shared exposures is experienced by individuals whose lives are linked geographically or socially (e.g., spouses, workers in a town, friendship networks) and work on transitions and turning points that are biologically (menarche, menopause) or socially determined (e.g., transitions from preschool to kindergarten, school to work, work to retirement), have each benefited from consideration of endogenous and exogenous factors. As the sociological approaches to life course and psychological approaches to lifespan research converge into a more integrated discipline of developmental science (Cairns et al. 1996; Bornstein and Lamb 2005; Diewald and Mayer 2009), ongoing conceptual and empirical integration is increasingly influenced by the study of nonlinear dynamic systems, including complex adaptive systems theory (Greenberg and Partridge 2010). Many researchers and thought leaders have contributed to the conceptual evolution and empirical evidence supporting a more integrated developmental systems theory (Sameroff 1975; Bronfenbrenner 1976; Baltes et al. 1980; Lerner 1984; Cicchetti and Cohen 1995; Magnusson 1995; Cairns et al. 1996; Bronfenbrenner and Morris 2006; Sameroff 2010) which built upon earlier behavioral and biological theories (Greenberg and Partridge 2010; Marshall 2014). N. Halfon and C.B. Forrest Overton and Lerner have proposed a theoretical construct that they call “relational developmental systems theory (RDST)” (Lerner 2006; Lerner and Overton 2008). Rejecting what they consider a false dichotomy between individual and context, they suggest that a person’s development is embedded in, organized by, and co-regulated by his or her surrounding environments. Developmental regulatory functions are best understood as mutually influential, bidirectional, person-context interactions. RDST sees individuals as active co-developers of their own developmental pathways, adaptively responding to different biological, social, cultural, and physical environmental contexts that they influence and are also influenced by. RDST has been used as a theoretical foundation for research on selfregulation and positive youth development and has added a stronger relational dimension to life course thinking. Like the converging influences of life course sociology and lifespan human developmental psychology, many fields of the life sciences have also informed this transition toward a life course developmental view of health. Embryologists and teratologists in the first part of the twentieth century understood that environmental insults could disrupt the normal processes of development leading to malformation and other “genetic” abnormalities, and some scientists began to consider how childhood conditions might directly influence adult mortality (Kuh and Davey Smith 2004). But it was not until the 1970s—when Forsdahl suggested a relationship between childhood socioeconomic status and later cardiovascular disease, Barker studied the relationship between birth weight and cardiovascular disease, and Wadsworth observed that other early childhood factors influenced a range of adult health outcomes—that a focus on what is called the developmental origins of adult health and disease (DOHaD) began to emerge (Forsdahl 1977; Arnesen and Forsdahl 1985; Barker et al. 1989a,b; Kuh and Wadsworth 1993). The receptivity to this new perspective was heightened by a growing number of challenges to the biomedical model of causation. Echoing George Engel and others, social epidemiologists The Emerging Theoretical Framework of Life Course Health Development like John Cassel, Leonard Syme, Lisa Berkman, and Michael Marmot and health service researchers like Barbara Starfield began to adopt a more complex, multidimensional “web of causation” construct to explain the origins of disease (Cassel 1964; Starfield 1973; Starfield et al. 1984; Marmot and Syme 1976; Syme and Berkman 1976; Marmot et al. 1978a, b). At the same time, a growing body of new research in psychoneuroimmunology described the “embodiment of disease risk” by demonstrating how different social, cultural, and psychological exposures quite literally “get under the skin” and are encoded or embedded into developing biobehavioral systems (Sapolsky et al. 1985; Maier et al. 1994; Cohen and Herbert 1996; McEwan 1998; Repetti et al. 2002). Over the past two decades, the Barker hypothesis, as it was commonly referred to, was further elaborated by a series of studies examining the impact of birth weight, fetal growth, placental size, and weight gain in the first year of life on metabolic regulation and cardiovascular disease (Barker et al. 1989a,b, 1993; McMillen and Robinson 2005; Barker et al. 2010). An entire field of life course epidemiology was spawned that has not only confirmed Barker’s findings in several other cohorts and settings but vastly expanded the empirical base linking fetal and early childhood growth and nutrition to a growing array of adult health conditions. This work has also gone beyond examining fetal and early childhood origins to explore the developmental origins of health and disease more broadly and has generated various conceptual models to analyze and synthesize results (Schlotz and Phillips 2009; Gluckman et al. 2010; Entringer et al. 2012). A new generation of recent epigenetic studies have begun to provide a stronger biological and theoretical basis for understanding how developmental plasticity is manifested, how gene expression may be modified in response to environmental cues, and how biological and behavioral traits can be perpetuated across multiple generations (Hochberg et al. 2010; Gluckman 2014; Thayer and Kuzawa 2011; Davey Smith 2012; Lillycrop and Burdge 2012; Relton and Davey Smith 2012; Gilbert et al. 2015; Cunliffe 2015). 25 Complementary studies of the developing brain demonstrated how stress and social adversity influence the biology of human development during sensitive periods (Hertzman 1999; Boyce et al. 2012; Hertzman 2012). Building on earlier studies of experience-dependent and experienceexpectant1 neuronal development, neurodevelopmental research demonstrated how development is guided by the combined and interactive influences of genes and experience (Boyce et al. 2012). Animal models of experience-modified neural development demonstrated how early behavioral experiences of adversity or comfort can lead to different DNA methylation patterns, which are believed to affect gene regulation and result in different functional levels of neurotransmission capacity (Meaney 2001; Szyf et al. 2005; Meaney et al. 2007). Similar methylation alterations have been demonstrated in children who have experienced adversity associated with maternal stress in the early years (Essex et al. 2011). Research on the neurobiology of stress and on the role that cumulative physiologic stress can have on the function of neuroendocrine and neuroimmunologic pathways has provided direct evidence for how exposure to risk and/or highly adverse environments is embedded in lifelong biobehavioral function (Seeman 1997; McEwan 1998; Seeman et al. 2001; Repetti et al. 2011; McEwen 2012). This research on neural development, stress, and biological priming provides an important empirical and conceptual bridge between observed social gradients in health and the experience-dependent conditioning of biobe- 1 Experience-dependent neuronal development refers to the role that experience plays in fortifying neuronal connections (e.g., a violin player who shows increased synaptic density in the area of the brain corresponding to the motor cortex controlling the fingers, or the hypertrophy of hippocampus in London cab drivers that is associated with improved spatial navigation and spatial memory). Experience-expectant neuronal development refers to brain development that is contingent on experiences that are expected to occur as part of normal development. For example, typical development of the visual cortex occurs in response to visual stimuli that are available in everyday life. If vision is obstructed and the brain does not receive these expected stimuli, the relevant synapses will either not form or will atrophy. N. Halfon and C.B. Forrest 26 havioral systems that occurs during the process of human development (Hertzman 1999; Keating and Hertzman 1999; Halfon and Hochstein 2002). In several ways, the converging relationship between life course chronic disease epidemiology, neurodevelopmental, and DOHaD research is analogous to the converging relationship between life course sociology and lifespan human developmental psychology. DOHaD and neurodevelopmental research have focused more on the individual differences in developmental plasticity from early development through old age (ontogenesis), leading to a growing understanding that epigenetic factors influence nongermline heredity. For example, the exposures grandparents experience can influence adaptive responses two generations later. In contrast, life course chronic disease epidemiology has focused more on social class, social gradients, and the social scaffolding of exposures (sociogenesis). New longitudinal cohort studies integrate both perspectives, including not only measures of phenotype but also genetic, epigenetic, and other biobehavioral adaptations (Alfred et al. 2012; Borghol et al. 2012; Elks et al. 2012). 3 Part 3: Principles of the Life Course Health Development Framework In this section, we present the seven principles of the Life Course Health Development framework that emerged from our analysis of the network of theories, concepts, models, and research findings related to how health develops over the life course. Each principle is described, connected to relevant theories, models, and perspectives, and discussed in terms of its implications for future research. Together, the principles constitute the LCHD framework, which is proposed as a way of systematically organizing the breadth of theories and conceptual models that help to explain and predict empirical findings on the production of health and disease causation over the life course. 4 Principle 1: Health Development 4.1 What We Mean by “Health Development” Health development integrates the concepts of health and developmental processes into a unified whole. We use the term “health development” to signify the framework’s central focus, which is the linkage of health and development into a single construct. Health is often understood as a set of instrumental attributes that are employed when individuals pursue goal-directed behavior (Richman 2004; Forrest 2014). These attributes can be thought of as “assets” that are desirable, acquired, optimized, and maintained during the life course, enabling growth of an individual, survival, and adaptation to manifold environments. Examples of health assets that emerge at the level of an individual include motor function (capacity for movement), emotional regulation (capacity to manage emotions during challenges or stressful events), and cognitive function (capacity to perceive, process, and act on information leading to the acquisition of knowledge). Development, in this context, refers to the processes by which health attributes change (i.e., mature, weather, degrade) during the lifespan. If health is a set of attributes that emerge at the level of the whole individual, development refers to the evolutionarily informed processes by which these attributes enable adaptation to changing social-environmental conditions. Health is the “what” (i.e., what changes) and development is the “how” (i.e., how health attributes change over time) of health development. As an expression of an organism’s livingness and essential adaptive nature, health development is an emergent property of a living system (Forrest 2014). Importantly, because this principle combines both health and development, it blends a temporal dimension into our conceptualization of human health. Health development has time-dependent and transactional connotations and is therefore dynamic. The Emerging Theoretical Framework of Life Course Health Development The health development of an individual at the level of “self” cannot be understood by isolating the biological function (or dysfunction) of an organ system or a particular behavior of an individual—although of course these subsystems have their own unique health development trajectories. The health development of an individual is comprised of an integrated set of capacities that dynamically mature and are involved in managing energy flows; processing and acting on information; recovering from, adapting to, and growing with environmental challenges; learning and forming capabilities; and producing offspring (Forrest 2014). Health development is a life course-informed phenomenon that results from transactions between the organism and its internal (i.e., gene, panomic, organ system, and physiologic networks) and external environments (i.e., family, social, cultural, and physical networks and environments). 4.2 Theories and Frameworks Relevant to Health Development This principle combines a rich set of theories and conceptual frameworks related to health and development. Conceptualizing health development as an emergent property of an organism differs from earlier linear and reductionistic biomedical models of health and even from multilevel biopsychosocial models. It also sets the stage for considering health development as complex adaptive processes that emerge from living systems interacting with their environments. Its relational ontology implies that other principles contribute to the understanding of this complex emergent process. The optimization of health development is codependent on several contributing developmental processes and resulting propensities that are highlighted in the other LCHD principles and drawn from relational developmental systems theory (Lerner 2006; Overton 2007), developmental systems theory (Oyama 1985), dynamic 27 systems theory (Spencer et al. 2009), and the unified theory of development (Sameroff 2010). 4.3 Implications of the Health Development Principle The health development principle signals the importance of context and our inability to reduce health to its component parts divorced from the contexts within which they develop. This type of developmental systems thinking requires new typologies to describe health development phenotypes. In effect, a new set of concepts is needed to convey a language of health development as observed and experienced at the level of whole persons in dynamic interaction with their environments. As our understanding of the interrelationships between health development and a range of influential environmental variables matures, health development typologies can become fullfledged ontologies that help explain and predict which relational influences are important and have measurable consequences on health development. The creation of “whole person” health development metrics that operationalize health development concepts is necessary to capture developmentally influenced continuity, consistency, and variability. To distinguish “health development” from other fields in the developmental sciences, we will need to specify the unique concepts that constitute it and the measures that assess health development’s multidimensional functionality (adaptation, energy management, reproduction, information processing, capacity to execute tasks in response, and restoration and their integration) as well as its multilevel (from the molecular to the individual to the environmental) nature. Measures of health development will also need to be informed and reflective of the other LCHD principles outlined below. Such measures will be particularly important in enabling and measuring the contribution of health-producing social systems to the optimization of health development. N. Halfon and C.B. Forrest 28 5 Principle 2: Unfolding 5.1 What We Mean by “Unfolding” Health development occurs continuously over the lifespan, from conception to death, and is shaped by prior experiences and environmental interactions. The unfolding principle describes the developmental processes by which expression of a few thousand genes—none of which has a blueprint or roadmap for constituting a viable, living human body—can unfold in an ordered, coherent pattern that has been shaped by the adaptive success of what has worked before. The nonlinear, self-organizing process of development that is made possible by molecule-to-molecule, cell-tocell, tissue-to-tissue, and human-to-human sensing and communication processes means that health development is neither linear, passive, nor static; rather, it is adaptive, self-organizing, and autocatalytic (Davies 2014). By “adaptive,” we mean those biological, behavioral, and cultural differences that are privileged, prioritized, or selected for because of the advantage they imbue on reproductive fitness and success. Adaptive change occurs at multiple levels, from the biochemical and cellular to behavioral change at the level of individuals to environmental change. For some biological systems, such as neural networks, adaptation occurs quite rapidly, enabling real-time responses to acute environmental challenges and acquisition of novel information. However, some biological and behavioral subsystems change slowly responding to gradual shifts in the intensity and quality of ecological exposures. Thus, the adaptation that characterizes health development transpires over multiple time scales enabling response to both fast- and slow-changing variables. The principles of complexity and timing will further elaborate on these features of adaptation. By “self-organizing,” we mean the dynamic nonlinear process of self-assembly and selfperpetuation that emerges through multiple relational coactions between the components of a system and its environments. In the case of human health development, it describes how internally determined structures emerge from a genetic code that is regulated by layers of sensing, signaling, and feedback loops that organize the expression of the code based on chemical self-assembly into variable levels and forms of differentiation. Simple differences in external environments (at the cellular, tissue, organ system, organism, or cultural levels) transform the pathways of development from dull uniformity to autocatalytic diversity of forms and function (Davies 2014). By “autocatalytic,” we mean that health development produces the “fuel” that propels it forward (Henrich 2015). Health development dynamically shapes and is shaped by environmental contexts. Today’s health development serves as substrate for the emergence of future health development states. The personenvironmental transactions that unfold during the life course can influence gene regulation of biobehavioral processes through epigenetic changes. Better characterization of this set of mechanisms is helping to explain how physical and social exposures during childhood affect health and disease during adulthood. The adaptive, self-organizing, autocatalytic processes of unfolding can help to explain how genes and culture have coevolved. According to Henrich (2015), as humans evolved, cultural information and practices began to accumulate and produce cultural adaptations. These new cultural adaptations feed forward and produce significant selection pressure on genes to improve psychological capacities to further acquire, store, process, and organize an array of fitness-enhancing skills and practices. These new adaptive capacities in turn become increasingly available to others in the same cultural group. So as genetic evolution improves the ability of our brains to learn from others, cultural evolution can generate adaptations (i.e., religions, markets, science) that both enhance function and increase the selective pressure on our brains to effectively navigate these increasingly complex cultural forms. Life history theory suggests that variation in the process of unfolding result in part from the optimization of fitness that occurs during func- The Emerging Theoretical Framework of Life Course Health Development tionally organized phases of the lifespan. Borrowing from Paul Baltes’ lifespan theory (Baltes, Lindenberger, Staudinger 2006), the unfolding principle offers a conceptualization of human health development as having four major functional phases: (1) Generativity—the preconception, prenatal, and perinatal periods are dedicated to the formation of the organism. (2) Acquisition of capacity—the early years are dedicated to the acquisition, maturation, and optimization of specific health development capacities. (3) Maintenance of capacity—the middle years are dedicated to maintaining health development capacities in the face of accumulating risks and ongoing weathering. (4) Managing decline—the later years are devoted to managing, adjusting, and adapting to functional decline of various body and regulatory systems, even as other aspects of health development such as stress management and positive psychological functioning may improve with age. Each of these phases is conceptually distinct but can contain overlapping elements, as is the case when an older individual who is mostly in the process of managing decline can also be acquiring new capacities (e.g., learning to play piano at 70). This becomes an adaptive process of maintaining optimal function in the face of declining capacities. By reflecting evolutionarily defined developmental processes, levels of plasticity, and variation in expression within and across individuals (and within and across biobehavioral systems in the same individual), these four phases help us to see and understand the patterns and coherence of health development. For example, evolution has ensured that the anatomic and metabolic process of bone development in women produces strong bones that enable additional weight carrying associated with pregnancy, until the age of the fourth and fifth decade when menopause emerges. This is an anticipatory developmental process whereby early anticipatory changes prepare the individual to meet future developmental needs. Optimizing bone metabolism and preventing 29 osteoporosis can take several forms, such as physical activity (particularly on hard surfaces), and include different strategies that can be employed during the phases of acquisition and maintenance of bone metabolism and strength and during the management of decline after menopause. 5.2 Theories and Frameworks Relevant to Unfolding The concept of health development as a continuously unfolding adaptive and self-organizing process comprised of distinct yet overlapping functional phases provides a framework for considering how evolutionarily defined stages from life history theory (Stearns 1992; Worthman and Kuzara 2005; Del Giudice et al. 2015), psychological constructs from lifespan human development theory (Baltes 1983; Featherman 1983; Lerner et al. 2010), and sociological constructs from life course sociology (Elder 2000; Mayer 2009; Alwin 2012) can be aligned, compared, and potentially integrated. It also provides a better way of articulating and assessing the alignment between biologically, psychologically, socially, and culturally determined transitions and turning points and understanding how they impact health development over the lifespan (Davies 2014; Henrich 2015) (see Principle 4--Timing). For example, the alignment among the biological processes of menarche, the behavioral maturation of reproduction behaviors, and the culturally created process of mating and marriage has dramatically changed as the age of menarche has declined, the age of marriage has increased, and the introduction of sexualized behavioral stimuli has increased through a variety of different media and information platforms. 5.3 Implications of the Unfolding Principle The adaptive, self-organizing, and autocatalytic way that health development unfolds via com- 30 plex sensing, communication, and regulatory processes implies that our basic, clinical, and translational research needs to elucidate how these processes influence the adaptive capacity of individuals and populations. A clearer understanding of how similar self-organizing processes unfold from the cellular level to the cultural level could point to new ways of integrating preventive, health promoting and therapeutic interventions designed to optimize health development by embracing a “cells to society” perspective. We have proposed that there are four major functional phases of LCHD: generativity, acquisition of capacity, maintenance of capacity, and managing decline. Do these four phases provide a logical staging for all aspects of health development? Are there subphases that need to be articulated to help us better understand the dynamics of health development? To address these questions, we need better measurement of the flow of health development, both continuities and discontinuities, and its variation across and within individuals. Childhood obesity provides a useful example to illustrate the research implications of the unfolding principle. To evaluate obesity risk, body mass index (BMI) is assessed at a fixed point in time and contrasted with values obtained with population age-sex-specific norms. Newer statistical methods have been developed that enable analysis of intraindividual trajectories to more accurately characterize the pattern of childhood growth and uncover new associations between the functional form of growth trajectories and future obesity and obesity-related comorbidities (Wen et al. 2012). To apply this sort of methodology to health development more broadly requires precise definitions and frequent assessments of health development measures, as well as an understanding of the expected trajectory of health development for the population. These types of assessments are being made available by electronic health records and other digitized health data collected by healthcare organizations, which are a new and ready data source for health development research. The interaction between various forms of adversity and health development provides N. Halfon and C.B. Forrest another example. Understanding the effects of social adversity and other environmental exposures on the unfolding of health development entails not only connecting the specific types of adversity to different outcomes but also developing a better understanding of the adaptive and self-organizing neuronal and behavioral processes, pathways, and mechanisms by which these outcomes are affected. These include assessing the relationships between neurodevelopmental correlates of socioeconomic adversity and differential structural and functional changes in different regions of the brain (as measured using functional MRIs) and understanding how these changes feed forward and potentially compound or dissipate over time (Caspi et al. 2003; Evans and Schamberg 2009; Hackman and Farah 2009; Noble et al. 2012; Power et al. 2005a, b; Evans et al. 2012). The short- and long-term effects of adverse in utero exposures on health development are an area of inquiry that is producing a wealth of information and ripe for expansion (Gluckman et al. 2008). This research includes studies regarding epigenetic programming associated with fetal exposure to chemical compounds, environmental toxicants, and smoking (Skinner et al. 2008; Launay et al. 2009; Perera et al. 2009; Martino and Prescott 2011), as well as studies examining the impact of nutritional stresses on metabolic function and future disease (Li et al. 2010). Research on the epigenetic effects of adversity on neurodevelopment has exploded in recent years. Beginning with Meaney’s pathbreaking work on the impact of maternal behavior on epigenetic mechanisms that influence gene expression and regulation of the endocrine response to stress (including the glucocorticoid receptor and the corticotropin-releasing factor (CRF) systems that regulate the hypothalamic-pituitary-adrenal axis), there have been a large number of studies examining how different experiences, exposures, and influences can lead to epigenetic alterations affecting a wide range of biobehavioral functions (Meaney 2001, 2010; Turecki and Meaney 2016; Lester et al. 2016). One of the most interesting and challenging areas of epigenetic research concerns the trans-generational transmission of The Emerging Theoretical Framework of Life Course Health Development exposures and risk through non-germline alterations of genetic information and the persistence of these influences across subsequent generations (Bale 2015). 6 Principle 3: Complexity 6.1 What We Mean by “Complexity” Health development results from adaptive, multilevel, and reciprocal relations between individuals and their physical, natural, and social environments. This principle indicates that health development occurs within living systems that are not only adaptive, self-organizing, and autocatalytic but also complex and hierarchically arranged. The topologies of health development phenotypes cannot be fully understood using a traditional biomedical reductionist approach that relies on an analysis and assembly of the parts of subunits. Health development emanates from the hierarchical and relational coactions of the biological and behavioral subsystems and their individual and collective relations with each other and various interconnected external suprasystems (i.e., familial, social, cultural, ecological). Health development phenotypes result from the interplay between the individual and multiple physical, biochemical, psychological, social, and cultural networks that dynamically coact. As in many complex adaptive systems, the directionality of these influences is often context dependent, reciprocal, and influenced by feedback and feedforward influences. On the other hand, small changes in particularly vulnerable parts of a biobehavioral system—or at a specific timesensitive junction in a cascading process of developmental change—can have profound nonlinear effects on the emergence of a capability or health asset or on the overall robustness or fragility of the health development process. Transactions between different environments can influence gene expression, and gene expression and resultant phenotype can also influence 31 various environments, which will in turn influence additional gene expression. Processes at the molecular level can dynamically coact with each other, as well as with processes at the social and ecological levels, and everywhere in between. These are not simply hierarchical relationships of dependent parts, but are holarchical in the sense that each level is both a part and a whole, nested and hierarchically aligned in the common purpose. In some cases, common purposes are optimizing health development, and in other circumstances, they are aligned to ensure reproductive fitness at the expense of optimal health development (Günther and Folke 1993). 6.2 Theories and Frameworks Relevant to Complexity The complexity principle adds the systemsoriented concepts of complexity, adaptation, emergence, nonlinear change (i.e., small changes can produce large effects and vice versa), and multilevel person-environmental coactions. Key theories, frameworks, and perspectives that support the conceptualization of this principle include general systems theory (von Bertalanffy 1968), chaos theory (Gleick 1987; Lorenz 1993), living systems theory (Miller 1978), humansystem framework (Brody 1973; Seeman 1989), and complex adaptive systems theory (Holland 1998). The systems orientation to health development suggests a holistic, integrated view that there is a need to understand the interdependence of the parts that constitute the whole, which is embedded in its natural and social environments. 6.3 Implications of the Complexity Principle Progress in genomics and network analysis is enabling researchers to interrogate all known gene-disease associations simultaneously and to create a network view of patterns and principles of human disease that would not be apparent by examining genetic associations’ one disease at a time (Goh et al. 2007). Extending this approach N. Halfon and C.B. Forrest 32 to health development suggests the need to add environmental exposures, or what has been called the “exposome,” to analytic models to engender a better understanding of how networks of genes and networks of environments produce health development phenotypes. The time-honored scientific approach uses the hypothetico-deductive method that derives its cogency from the certainty of deductive inference and the plausibility of abductive inference. Children exposed to the same interacting family, school, and neighborhood environments experience patterns of risk, protective, and healthpromoting influences that emerge out of the complex topography of those personenvironmental interactions. By statistically categorizing and analyzing children by their ethnicity, family income, and family structure, the rich interactions of different environmental factors are often lost (Molenaar et al. 2003). Health development is non-ergodic, meaning that each individual’s experiences, environmental interactions, and health development phenotypes are unique and that within a population there is marked heterogeneity. Averaging effects across groups tells us something about population effects, but little about individual effects (Molenaar and Campbell 2009). Furthermore, even though individuals may have different life course experiences, they can experience the same health development phenotype, a phenomenon called equifinality (Cicchetti and Rogosch 1996). Systems biology and other systems-oriented sciences offer a set of methods that can address the non-ergodic attribute of health development (Huang 2009). Rather than being hypothesis driven, these methods are systems driven and require a research strategy of interrogating the system at the level of the whole (EA Roberts 2015, 2012). This more complex way of experimenting and generating scientifically valid information bears further discussion and explication. New approaches to study design generation and statistical analysis will be needed to understand how patterns of health development are produced by complex coactions of networks over time. Identifying characteristic health development phenotypes will require nonlinear models that recognize and embrace the complexity of health development. The focus should be on measuring patterns of intraindividual health development, which will require study designs that collect detailed and large volumes of health and environmental information on individuals, forming big health development data resources. Environments coact with individual constitutional factors to produce health development phenotypes within a person. We have only a vague understanding of the specific environmental variables responsible for these interactions. There is an urgent need to create scientifically useful typologies of environmental variables. This will enable research to better understand how health development signals are transduced from the environment to the individual, altering biobehavioral system configuration and function. Better characterization—and, ultimately, standardization—of environmental variables (the exposome) will accelerate research on how an individual’s contexts affect the epigenetic topography and organize what complex systems science might refer to as health development attractor2 states. Standardization of concepts and measures enables synthesis and meta-analyses across studies. 2 An attractor is the end state of a dynamic system as it moves over time. Once the object or data point goes into the basin of attraction, it does not leave unless a strong force is applied. The set of one or more attractors of a dynamic system can be represented visually or graphically as trajectories in state space, where state space represents the multidimensional, abstract space of all possible system behavior. There are four types of possible attractors: fixed points, limit cycles, toroidal attractors, and chaotic (or strange) attractors. Point attractors are regular, terminating in a single point in state space. Cycle attractors are also regular, sometimes oscillating between two or more fixed points or exhibiting a sinusoidal pattern over time. Toroidal attractors are semi-regular, representing coupled rhythms whose ratio of periodicities terminates in an irrational rather than a rational number and appearing in state space as a donut. Chaotic attractors are fully irregular, represented by an aperiodic trajectory in state space that never repeats or settles to a stable pattern, whose basin of attraction is often fractal in shape; see chaos. Regular point and cycle attractors are characteristics of relatively simple systems. Irregular toroidal and chaotic attractors are more characteristics of complex systems. The Emerging Theoretical Framework of Life Course Health Development We have almost no idea at present how to map or represent multilevel emergence, because the transactional nature of health development is not well specified, measured, analyzed, or interpreted. For example, a child exposed to a stressful stimulus that is repeated in unpredictable and unanticipated ways may experience epigenetic changes in neurotransmitter metabolism that cannot be explained just by measuring the stressful stimulus. The developmental timing of these stressful events, their unpredictable repetition, and the temporal rhythms of restorative processes (e.g., sleep) are also at play, which brings us to the next principle. 7 Principle 4: Timing 7.1 What We Mean by “Timing” Health development is sensitive to the timing and social structuring of environmental exposures and experiences. Health development is not a linear process in which exposures to environmental stimuli or internalized experiences have equal effects, regardless of when in the life course they occur. Instead, health development results from nonlinear interactions that are both time-specific and time-dependent. There are sensitive periods of a child’s life when the impact of certain exposures can be greater than during other periods (Halfon et al. 2014). Time-specific health development pathways refer to biological conditioning that occurs during these sensitive periods, when developing systems are most adaptable and plastic and exogenous and endogenous influences can result in different adaptive responses. In other words, the same exposures can have very different effects depending on when during the life course they occur. Because childhood is a phase of life when biological and behavioral systems are shaped by environmental exposures and social experiences, the timing principle emphasizes the importance of nurturing children when they are most sensitive to these influences (Conti and Heckman 2013). For example, exposure to a 33 rich set of words during the early years of life can greatly improve a child’s subsequent language development, with cascading effects on subsequent school performance, health behaviors, and future health status (Hart and Risley 2003). Time-specific transitions and turning points in health development also result from socially structured pathways that link experiences and exposures in time-influenced ways that create recursive and mutually reinforcing patterns of risk, protection, and promotion. Socially structured pathways have both period-specific and time-dependent (cumulative) characteristics. By arraying risk, protective, and promoting factors into socially constructed and institutionally reinforced pathways that interact with sensitive periods of health development, societies can either support the emergence of positive health development phenotypes or reinforce negative ones. The role, relative dose, duration, and coaction of risk, protective, and promoting factors during formative, maintenance, and declining phases of the life course all influence the slope, shape, and contours of health development trajectories. Thus, the timing principle summarizes a set of models and constructs that elaborate the importance of the time dimension on health development. There are time-specific pathways that refer to sensitive periods when environmental exposures and experiences can influence health development, and there are time-dependent pathways that refer to the accumulation of repeated exposures to the same environmental stimuli that can result in a weathering process that accelerates aging (Geronimus 2013). 7.2 Theories and Frameworks Relevant to Timing The unfolding principle introduced the concept of functional phases or epochs that organize the historical foundations of health development. The timing principle adds the concepts of timedependent and time-sensitive health development pathways that create periods of vulnerability and robustness, as well as social structuring of environmental exposures and experiences. These con- N. Halfon and C.B. Forrest 34 cepts are drawn from several theories, frameworks, and perspectives including developmental origins of health and disease (Gluckman and Hanson 2006a), life course perspective (Elder 2000; Mayer 2009; Alwin 2012), biological embedding (Hertzman and Boyce 2010), chronobiology (Kreitzman and Foster 2004), developmental time (Kuzawa and Thayer 2011), and adaptive developmental plasticity (Gluckman et al. 2009). 7.3 Implications of the Timing Principle Scientists have accumulated tantalizing but as yet limited evidence for time-sensitive health development (Hanson and Gluckman 2014; Hertzman 2012; Boyce and Kobor 2015). Progress in this area has been slow in part because of a lack of data systems that integrate large volumes of biological (especially patterns of gene response and epigenetic changes), clinical (such as electronic health records and biosensors), behavioral (self-report questionnaires), and environmental data. Each of these data sources exists in isolation. What is needed is a new field of health development informatics that is devoted to assembling large, integrated, longitudinal data resources and mining them for novel associations between time, environment, and health development outcomes. Another challenge is the lack of research that establishes the specific time-dependent pathways by which human health development phenotypes are altered or protected by various internal and external factors. A variety of studies suggest that physical and social environments can alter a person’s biology via epigenetic pathways that influence regulation of genetic pathways (Hertzman and Boyce 2010). This “embedding” of experience seems to have its largest impact during specific sensitive periods of development. Why the same experience engenders different outcomes among individuals is one of the great mysteries of health development. More work is needed to elucidate these iterative and dynamic pathways that connect environment to gene regulation to physiological states to environmental impact. 8 Principle 5: Plasticity 8.1 What We Mean by “Plasticity” Health development phenotypes are systematically malleable and are enabled and constrained by evolution to enhance adaptability to diverse environments. The relative plasticity of these phenotypes is responsive to transactions between evolutionarily selected biological and behavioral conditioning and supportive, challenging, and constraining environments. These phenotypes have evolved to provide adaptive capacity, plasticity (i.e., ability of the organism to systematically alter its phenotype in response to environmental challenges, opportunities, barriers, and constraints), and growth potential, which in aggregate refer to the robustness of an individual’s health development. Heredity transmits these evolutionary signals through genetic, epigenetic, behavioral, and cultural dimensions (Jablonka and Lamb 2006), which establish the set of health development phenotypes that, depending on environmental circumstances, may or may not be selected and optimized to produce desirable outcomes. At the microlevel, there are a range of strategies to introduce variable types and levels of plasticity to optimize adaptability from the molecular to the behavioral level. At a macro-level, there are social and cultural strategies that organize the phases and life stages of health development into functionally productive entities. Because developmental plasticity enables the genome to produce a repertoire of possible phenotypes based on environmental cues, an individual begins their life with the capacity to develop in different ways. Different exposures and experiences select and instruct a developmental pathway to respond based on these evolutionary determined strategies. Underlying many forms of plasticity are epigenetic process and resulting cascades of secondary and tertiary responses. Because plasticity can manifest at different levels, behavioral plasticity may be influenced by neural plasticity, and neural plasticity in turn may be influenced by molecular plasticity influenced by epigenetic mechanisms (Bateson and Gluckman 2011). The Emerging Theoretical Framework of Life Course Health Development 8.2 Theories and Frameworks Relevant to Plasticity Evolution both enables and constrains the portfolio of adaptive plastic responses that an individual may experience in response to environmental interactions. Therefore, plasticity is relative, not absolute. Although Darwin’s theory of evolution (Darwin 1859; Huxley 1942) laid the foundation for understanding the principle of plasticity, more recent syntheses have expanded our understanding of heredity as including not just genetic change but also epigenetic, behavioral, and cultural phenomena that are transmitted across generations (Waddington 1942; West-Eberhard 2003; Richardson and Boyd 2005; Jablonka and Lamb 2006; Konner 2011; Henrich 2015). These evolutionary forces act at the individual and group level, a perspective known as multilevel selection theory (Okasha 2006). In some cases, health development outcomes result from the developing individual “predicting” likely future environmental stimuli based on the cues received during sensitive periods of health development. This has been called “predictive adaptive responses” (Gluckman and Hanson 2004). If the developing organism predicts incorrectly—that is, if the environment experienced in the future is not compatible with the cues received during periods of developmental plasticity—health development “mismatches” will occur. This phenomenon can be observed among individuals exposed in utero to maternal malnutrition who later become obese and glucose intolerant, a result of being born into an energyrich environment (Hales and Barker 1992). 8.3 Implications of the Plasticity Principle Evolution has acted on body systems in different ways to encode various types and levels of health development plasticity. The formation of some biological subsystems is tightly controlled by time and gene regulation (e.g., cardiovascular), whereas others seem to have a range of phenotypes that can emerge as a result of interactions 35 with the environment (e.g., stress response, executive function). New models are needed to explain the deep archeology of evolution as it relates to the emergence of health development. Fields like comparative biology can test some of these hypotheses by examining the degree to which specific processes and pathways of health development vary or are preserved across species. For example, patterns of sleep have been selected for and preserved across species in ways that affect how sleep is regulated (Tamaki et al. 2016). Moreover, the success of human civilization has removed much of the selection pressure exerted by mortality, so optimization of specific pathways may be more strongly influenced by culture, behavioral, and epigenetic heredity rather than genetic forces (Enriquez and Gullans 2015). This hypothesis should be tested. The predictive adaptive response hypothesis has accumulated a substantial amount of animal and human evidence for energy regulation (Gluckman and Hanson 2004a,b). This work should be extended to other domains—for example, behavioral health. Just as childhood obesity may result from mismatches between children’s energy regulation and exposure to energy-dense environments, it is possible that the proliferation of childhood disorders like attention deficit hyperactivity disorder, anxiety, and learning disabilities may be a consequence of mismatches between predictive adaptive behavioral responses and the demands children face in terms of executive functioning, emotional functioning, and learning in their home, school, and other environments. 9 Principle 6: Thriving 9.1 What We Mean by “Thriving” Optimal health development promotes survival, enhances well-being, and protects against disease. Health development bestows upon the individual resources that have instrumental value, enabling an individual to pursue goals and thrive (Seedhouse 2001; Blaxter 2004; Richman 2004; Forrest 2014). N. Halfon and C.B. Forrest 36 It provides assets that individuals employ to pursue the beings and doings (Sen 1999) that characterize each person’s lived experiences. Thus, health development phenotypes are instrumental resources that enable individuals to pursue desired goals and live long, flourishing lives. Health development phenotypes can be considered optimal according to the degree to which they improve the chances of survival of individuals and groups of individuals, the degree to which they support transmission of heritable information to successor generations, and the degree to which they support physical robustness and psychological flourishing (what we term thriving) across time and within the contexts of its environments. On the other hand, the pathways by which health development phenotypes are formed can be perturbed to create suboptimal states that are precursors to fully formed disease phenotypes. These so-called endophenotypes represent intermediate, subclinical-phased transitions toward a fully manifest phenotypic expression of a disease or disorder (John and Lewis 1966; Gottesman and Gould 2003). For example, the exposure to unpredictable and uncontrollable stressors during sensitive periods of neural development can influence midbrain development and the functional development of attachment relationships, the prefrontal cortex and the functional development of executive function, and the hypothalamic pituitary axis and the regulation of stress responses (Castellanos and Tannock 2002; Boyce 2016). Endophenotypes characterized by anxious attachment, poor impulse control, and hyperactive stress response can impact health behaviors and mental health and contribute to the development of many different chronic diseases including obesity, diabetes, and cardiovascular disease (Duric et al. 2016). 9.2 Theories and Frameworks Relevant to Thriving The principle of health development articulated the singularity of the concepts of health and development. The principle of thriving further clarifies the nature of health development by explicitly characterizing its instrumental nature. That is, health development provides a set of resources that organisms draw on in order to pursue goals, such as surviving, achieving a state of physical robustness and resilience, and psychological flourishing (Seedhouse 2001; Blaxter 2004; Committee on Evaluation of Children’s Health 2004; Richman 2004; Forrest 2014). Health development therefore enables the attainment of various beings (states of happiness, life satisfaction, and meaning and purpose) and doings (desired activities that an individual pursues) as individuals pursue their goals over the life course (Sen 1999). 9.3 Implications of the Thriving Principle LCHD recognizes that phenotype is produced by the continuous coactions of at least five factors: genome, epigenome, environment, developmental time, and life course stage. These coactions do not merely produce single outcomes; instead they produce landscapes of possibilities with peaks and valleys shaped by an individual’s life history, evolutionary determined possibilities and constraints, and the five-way interaction. Which “attractor” state (i.e., health development phenotype) an individual settles in is the result of this complex, nonlinear process. We know very little about which attractor states are most likely to produce desirable outcomes (i.e., thriving) for which individuals under which circumstances. As we learn more about the interrelationships among these variables, we will begin to forge an ontology that specifies how health development variables interrelate with one another, their subsystems and suprasystem environmental influences, and their consequences. Research is needed that links health development phenotypes, singularly and collectively, that enable individuals with varying personal characteristics and environmental exposures to lead long lives, avoid debilitating disease, and achieve desirable goals and an optimal lived experience. The Emerging Theoretical Framework of Life Course Health Development 10 Principle 7: Harmony 10.1 What We Mean by “Harmony” Health development results from the balanced and coherent relations among molecular, physiological, behavioral, cultural, and evolutionary processes. Genetic modulations that occur in molecular time frames measured in nanoseconds are linked to biochemical modulation measured in milliseconds, homeostatic mechanisms measured in seconds to days, social norms that evolve or years and decades, cultural processes that change from years to centuries, and ecological processes that until recently took millennia. Harmonious synchronization of these processes produces the rhythms and variability that characterize health development. Loss of coordination of these processes results in less robustness of the human system, with resultant negative consequences. For example, the age of menarche has decreased in response to a variety of environmental changes that have resulted in better health and nutrition. In traditional societies, and until about 100 years ago, menarche coincided with maturation of a repertoire of psychological capabilities. Now, menarche precedes this process of psychological maturation, which has also been altered and extended by other cultural and environmental changes (Gluckman and Hanson 2006b). So the adaptive response of menarche to better nutrition and health has led to a temporal and functional uncoupling of biological and psychological capabilities that had previously been harmonized. This has been associated with a range of maladaptive outcomes (Mendle et al. 2007; Ellis and Essex 2007). 10.2 Theories and Frameworks Relevant to Harmony The timing principle introduced the concepts of time dependence, time sensitivity, and social structuring of exposures. The harmony principle extends these concepts by adding the notions of harmonious and balanced relations of the various 37 biological, behavioral, environmental, and cultural systems that an individual is embedded within. Extension of the classic evolutionary modern synthesis—which assumes that phenotypic transitions occur through a series of small steps that result in gradual evolutionary change— has questioned and advanced core assumption about pace of such change. Gradualism has given way in the extended evolutionary synthesis to the notion that evolution can manifest variable rates of change, especially when mutations occur in major regulatory control genes or when developmental process responds to environmental challenges with change in coordinated suites of traits or via nonlinear threshold effects (Laland et al. 2015). Although key theories, frameworks, and models in support of this principle come from chronobiology (Kreitzman and Foster 2004), developmental time (Kuzawa and Thayer 2011), and adaptive developmental plasticity (Gluckman et al. 2009), there is much more theoretical work needed for this principle. 10.3 Implication of the Harmony Principle According to the principles of timing and plasticity, we know that there are periods of the life course when environmental influences can have particularly large effects on health development plasticity. The tremendous plasticity of humans contributes to the robustness and the ordinary magic of child health development (Masten 2001). We need better descriptions and conceptualizations of developmental time as it affects all levels and dimensions of health development and how different time frames nest together to produce coherent developmental pathways and robustness and variability in phenotypic expression. This includes a better understanding of how molecular, physiologic, developmental, historical, cultural, and evolutionary time frames independently and in harmony influence phenotypic variation, through genetic, epigenetic, and yet to be determined mechanisms and pathways. Because developmental time is uneven in its potency, intensity of N. Halfon and C.B. Forrest 38 change, and accessibility to environmental influences, there is a great need to better characterize and measure these temporal parameters (Boyce et al. 2012). 11 Summary Health and development are unified into a single construct (health development principle) that adaptively unfolds over the life course (unfolding principle) according to the principles of complex adaptive systems (complexity principle). Change in health development results from time-specific processes (timing principle) that influence biobehavioral systems during sensitive periods when they are most susceptible (plasticity principle), and the balanced alignment of molecular, biological, behavioral, cultural, and evolutionary process (harmony principle) can result in developmental coherence. Health development provides instrumental assets that enable individuals and populations to pursue desired lived experiences (thriving principle). The Life Course Health Development framework organizes its seven principles into a coherent whole to enable the emergence of a new field of science. The principles should not be viewed as static, independent statements or claims. Instead, they should be considered a set of nodes within a highly interconnected knowledge producing and testing network. We anticipate that these principles will change and evolve as the many fields subsumed by the health development framework themselves mature. Ultimately, we anticipate that the framework will transform into a fully formed theoretical model that enables explanation and prediction of health development phenomena. References Alfred, T., Ben-Shlomo, Y., et al. (2012). A multi-cohort study of polymorphisms in the GH/IGF Axis and physical capability: The HALCyon Programme. PloS One, 7(1), e29883. Alwin, D. F. (2012). Integrating varieties of life course concepts. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 67(2), 206–220. Antony, P. M., Balling, R., et al. (2012). From systems biology to systems biomedicine. Current Opinion in Biotechnology, 23(4), 604–608. Arnesen, E., & Forsdahl, A. (1985). The Tromsø heart study: Coronary risk factors and their association with living conditions during childhood. Journal of Epidemiology and Community Health, 39(3), 210–214. Bale, T. L. (2015). Epigenetic and transgenerational reprogramming of brain development. Nature Reviews Neuroscience, 16(6), 332–344. Baltes, P. B. (1983). Life-span developmental psychology: Observations on history and theory revisited. In R. M. Lerner (Ed.), Developmental Psychology: Historical and philosophical perspectives (pp. 79–111). Hillsdale: Erlbaum. Baltes, P. B., Lindenberger, U., & Staudinger, U. M. (2006). Life span theory in developmental psychology. New York: John Wiley & Sons, Inc.. Baltes, P. B., Reese, H. W., et al. (1980). Life-span Developmental Psychology. Annual Review of Psychology, 31(1), 65–110. Barker, D. J., Gluckman, P. D., et al. (1993). Fetal nutrition and cardiovascular disease in adult life. Lancet, 341(8850), 938–941. Barker, D. J., Osmond, C., et al. (1989a). Growth in utero, blood pressure in childhood and adult life, and mortality from cardiovascular disease. BMJ, 298(6673), 564–567. Barker, D. J., Osmond, C., et al. (1989b). The intrauterine and early postnatal origins of cardiovascular disease and chronic bronchitis. Journal of Epidemiology and Community Health, 43(3), 237–240. Barker, D. J. P., Thornburg, K. L., et al. (2010). Beyond birthweight: The maternal and placental origins of chronic disease. Journal of Developmental Origins of Health and Disease, 1(6), 360–364. Bateson, P., & Gluckman, P. (2011). Plasticity, robustness, development and evolution. Cambridge: Cambridge University Press. Ben-Shlomo, Y., & Kuh, D. (2002). A life course approach to chronic disease epidemiology: Conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology, 31(2), 285–293. Berkman, L. F., & Syme, S. L. (1979). Social networks, host resistance, and mortality: A nine-year follow-up study of Alameda County residents. American Journal of Epidemiology, 109(2), 186–204. Blaxter, M. (2004). Health. Cambridge: Polity Press. Borghol, N., Suderman, M., et al. (2012). Associations with early-life socio-economic position in adult DNA methylation. International Journal of Epidemiology, 41(1), 62–74. Bornstein, M. H., & Lamb, M. E. (Eds.). (2005). Developmental science: An advanced textbook (5th ed.). New York: Psychology Press. Boyce, W. T., & Kobor, M. S. (2015). Development and the epigenome: the ‘synapse’of gene–environment interplay. Developmental science, 18(1), 1–23. Boyce, W. T. (2016). Differential susceptibility of the developing brain to contextual adversity and stress. The Emerging Theoretical Framework of Life Course Health Development Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 41(1), 142–162. Boyce, W. T., Sokolowski, M. B., et al. (2012). Toward a new biology of social adversity. Proceedings of the National Academy of Sciences of the United States of America, 109(Suppl 2), 17143–17148. Brody, H. (1973). The systems view of man: Implications for medicine, science, and ethics. Perspectives in Biology and Medicine, 17, 71–92. Bronfenbrenner, U. (1976). The ecology of human development: History and perspectives [The ecology of human development: History and perspectives]. Psychologia Wychowawcza 19(5), 537–549. Bronfenbrenner, U., & Morris, P. A. (2006). The Bioecological model of human development. In R. M. Lerner & W. Damon (Eds.), Handbook of child psychology: Theoretical models of human development (Vol. 1, 6th ed., pp. 793–828). Hoboken: John Wiley & Sons, Inc.. Cairns, R. B., Elder, G. H., et al. (1996). Developmental science: Cambridge studies in social and emotional development. New York: Cambridge University Press. Caspi, A., Sugden, K., et al. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301(5631), 386–389. Cassel, J. (1964). Social science theory as a source of hypotheses in epidemiological research. American Journal of Public Health and the Nation's Health, 54(9), 1482–1488. Castellanos, F. X., & Tannock, R. (2002). Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes. Nature Reviews Neuroscience, 3(8), 617–628. Chen, F., Yang, Y., et al. (2010). Social change and socioeconomic disparities in health over the life course in China a cohort analysis. American Sociological Review, 75(1), 126–150. Cicchetti, D., & Cohen, D. J. (1995). Developmental psychopathology: Theory and methods (Vol. 1). Oxford, UK: John Wiley & Sons. Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8(04), 597–600. Clausen, J. A. (1986). The life course: A sociological perspective. Englewood Cliffs: Prentice-Hall. Cohen, S., & Herbert, T. B. (1996). Health psychology: Psychological factors and physical disease from the perspective of human psychoneuroimmunology. Annual Review of Psychology, 47, 113–142. Committee on Evaluation of Children’s Health, N. R. C. (2004). Children’s health, the Nation’s wealth: Assessing and improving child health. Washington, DC: National Academies Press. Conti, G., & Heckman, J. J. (2013). The developmental approach to child and adult health. Pediatrics, 131(Suppl 2), S133–S141. Cunliffe, V. T. (2015). Experience-sensitive epigenetic mechanisms, developmental plasticity, and the bio- 39 logical embedding of chronic disease risk. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 7(2), 53–71. Dannefer, D. (1984). The role of the social in life-span developmental psychology, past and future: Rejoinder to Baltes and Nesselroade. American Sociological Review, 49(6), 847–850. Darwin, C. (1859). On the origin of species by means of natural selection, or the preservation of Favoured races in the struggle for life. London, UK: John Murray. Davey Smith, G. (2012). Epigenesis for epidemiologists: Does evo-devo have implications for population health research and practice? International Journal of Epidemiology, 41(1), 236–247. Davies, J. (2014). Life unfolding: How the human body creates itself. Oxford, UK: Oxford University Press. Davila-Velderrain, J., Martinez-Garcia, J. C., & AlvarezBuylla, E. R. (2015). Modeling the epigenetic attractors landscape: Toward a post-genomic mechanistic understanding of development. Frontiers in Genetics, 6, 160. Dawber, T. R., Kannel, W. B., & Gordon, T. (1974). Coffee and cardiovascular disease: Observations from the Framingham study. New England Journal of Medicine, 291(17), 871–874. Del Giudice, M., Gangestad, S. W., & Kaplan, H. S. (2015). Life history theory and evolutionary psychology. The handbook of evolutionary psychology. John Wiley & Sons, Inc. Diewald, M., & Mayer, K. U. (2009). The sociology of the life course and life span psychology: Integrated paradigm or complementing pathways? Advances in Life Course Research, 14(1), 5–14. Duric, V., Clayton, S., Leong, M. L., & Yuan, L. L. (2016). Comorbidity factors and brain mechanisms linking chronic stress and systemic illness. Neural Plasticity, 8, 2016. Egger, G., Liang, G., et al. (2004). Epigenetics in human disease and prospects for epigenetic therapy. Nature, 429(6990), 457–463. Elder, G. H., Jr. (1995). The life course paradigm: Social change and individual development. In P. Moen, G. H. Elder Jr., & K. Luscher (Eds.), Examining lives in context: Perspectives on the ecology of human development (pp. 101–139). Washington, DC: American Psychological Association. Elder, G. H., Jr. (2000). The life course. In E. F. Borgatta & R. J. V. Montgomery (Eds.), Encyclopedia of sociology (Vol. 3, pp. 1614–1622). New York: Macmillan Reference. Elder, G. H., & Shanahan, M. J. (2007). The life course and human development. In Handbook of child psychology. New York: John Wiley & Sons, Inc. Elder, G. H., Johnson, M. K., & Crosnoe, R. (2003). The emergence and development of life course theory. In Handbook of the life course (pp. 3–19). New York: Springer US. Elks, C. E., Loos, R. J. F., et al. (2012). Adult obesity susceptibility variants are associated with greater childhood weight gain and a faster tempo of growth: The 40 1946 British birth cohort study. American Journal of Clinical Nutrition, 95(5), 1150–1156. Ellis, B. J., & Essex, M. J. (2007). Family environments, adrenarche, and sexual maturation: A longitudinal test of a life history model. Child Development, 78(6), 1799–1817. Engel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. Science, 196(4286), 129–136. Enriquez, J., & Gullans, S. (2015). Evolving ourselves: How unnatural selection and nonrandom mutation are changing life on earth. New York: Penguin. Entringer, S., Buss, C., et al. (2012). Fetal programming of body composition, obesity, and metabolic function: The role of intrauterine stress and stress biology. Journal of Nutrition and Metabolism, 2012, 632548. Essex, M. J., Thomas Boyce, W., et al. (2011). Epigenetic vestiges of early developmental adversity: Childhood stress exposure and DNA methylation in adolescence. Child Development, 84, 58–75. Evans, G. W., & Schamberg, M. A. (2009). Childhood poverty, chronic stress, and adult working memory. Proceedings of the National Academy of Sciences of the United States of America, 106(16), 6545–6549. Evans, G. W., Chen, E., et al. (2012). How poverty gets under the skin: A life course perspective. In V. Maholmes & R. B. King (Eds.), The Oxford handbook of poverty and child development (pp. 13–100). New York: Oxford University Press. Featherman, D. L. (1983). Life span perspectives in social science research. In P. B. Baltes & G. Brim (Eds.), Life-span development and behavior (Vol. 5, pp. 1–57). New York: Academic Press. Forrest, C. B. (2014). A living systems perspective on health. Medical Hypotheses, 82, 209–214. Forsdahl, A. (1977). Are poor living conditions in childhood and adolescence an important risk factor for arteriosclerotic heart disease? British Journal of Preventive & Social Medicine, 31(2), 91–95. Gatzweiler, F. W., & Baumüller, H. (2013). Marginality—A framework for analyzing causal complexities of poverty. In J. Braun von & F. W. Gatzweiler (Eds.), Marginality: Addressing the nexus of poverty, exclusion and ecology (pp. 27–40). Dordrecht: Springer. Geronimus, A. T. (2013). Deep integration: Letting the epigenome out of the bottle without losing sight of the structural origins of population health. American Journal of Public Health, 103(S1), S56–S63. Gilbert, S. F., Bosch, T. C., & Ledón-Rettig, C. (2015 Oct 1). Eco-Evo-Devo: Developmental symbiosis and developmental plasticity as evolutionary agents. Nature Reviews Genetics, 16(10), 611–622. Gleick, J. (1987). Chaos: Making a new science. New York: Penguin Books. Gluckman, P. D., & Hanson, M. A. (2004a). Developmental origins of disease paradigm: A mechanistic and evolutionary perspective. Pediatric Research, 56(3), 311–317. Gluckman, P. D., & Hanson, M. A. (2004b). Living with the past: Evolution, development, and patterns of disease. Science, 305(5691), 1733–1736. N. Halfon and C.B. Forrest Gluckman, P. D., & Hanson, M. A. (2004c). The developmental origins of the metabolic syndrome. Trends in Endocrinology and Metabolism, 15(4), 183–187. Gluckman, P. D., & Hanson, M. A. (2006a). Developmental origins of health and disease. Cambridge, UK: Cambridge University Press. Gluckman, P. D., & Hanson, M. A. (2006b). Evolution, development and timing of puberty. Trends in Endocrinology and Metabolism, 17(1), 7–12. Gluckman, P. D., Hanson, M. A., et al. (2008). Effect of in utero and early-life conditions on adult health and disease. New England Journal of Medicine, 359(1), 61–73. Gluckman, P. D., Hanson, M. A., et al. (2009). Towards a new developmental synthesis: Adaptive developmental plasticity and human disease. The Lancet, 373(9675), 1654–1657. Gluckman, P. D., Hanson, M. A., et al. (2010). A conceptual framework for the developmental origins of health and disease. Journal of Developmental Origins of Health and Disease, 1(1), 6–18. Goh, K. I., Cusick, M. E., et al. (2007). The human disease network. Proceedings of the National Academy of Sciences, 104(21), 8685–8690. Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: Etymology and strategic intentions. The American Journal of Psychiatry, 160(4), 636–645. Green, S., Fagan, M., & Jaeger, J. (2015). Explanatory integration challenges in evolutionary systems biology. Biological Theory, 10(1), 18–35. Greenberg, G., & Partridge, T. (2010). Biology, evolution, and psychological development, In The Handbook of life-span development (pp. 115–148). Hoboken: John Wiley & Sons. Günther, F., & Folke, C. (1993 Sep). Characteristics of nested living systems. Journal of Biological Systems, 1(03), 257–274. Hackman, D. A., & Farah, M. J. (2009). Socioeconomic status and the developing brain. Trends in Cognitive Sciences, 13(2), 65–73. Hales, C. N., & Barker, D. J. P. (1992). Type 2 (noninsulin dependent) diabetes mellitus: The thrifty phenotype hypothesis. Diabetologia, 35, 595–601. Halfon, N., & Hochstein, M. (2002). Life course healthdevelopment: An integrated framework for developing health, policy, and research. The Milbank Quarterly, 80(3), 433–479. iii. Halfon, N., Larson, K., Lu, M., Tullis, E., & Russ, S. (2014). Lifecourse health development: Past, present and future. Maternal and Child Health Journal, 18(2), 344–365. Hanson, M. A., & Gluckman, P. D. (2014). Early developmental conditioning of later health and disease: physiology or pathophysiology? Physiological Reviews, 94(4), 1027–1076. Hart, B., & Risley, T. R. (2003). The early catastrophe: The 30 million word gap by age 3. American Educator, 27(1), 4–9. The Emerging Theoretical Framework of Life Course Health Development Haynes, S., Feinleib, M., & Kannel, W. B. (1980). The relationship of psychosocial factors to coronary heart disease in the Framingham Study. III. Eight-year incidence of coronary heart disease. American Journal of Epidemiology, 111(1), 37–58. Henrich, J. (2015). The secret of our success: how culture is driving human evolution, domesticating our species, and making us smarter. Princeton: Princeton University Press. Hertzman, C. (1999). The biological embedding of early experience and its effects on health in adulthood. Annals of the New York Academy of Sciences, 896, 85–95. Hertzman, C. (2012). Putting the concept of biological embedding in historical perspective. Proceedings of the National Academy of Sciences of the United States of America, 109(Suppl 2), 17160–17167. Hertzman, C., & Boyce, T. (2010). How experience gets under the skin to create gradients in developmental health. Annual Review of Public Health, 31, 329–347. Hochberg, Z. E., Feil, R., Constancia, M., Fraga, M., Junien, C., Carel, J. C., Boileau, P., Le Bouc, Y., Deal, C. L., Lillycrop, K., & Scharfmann, R. (2010). Child health, developmental plasticity, and epigenetic programming. Endocrine Reviews, 32(2), 159–224. Holland, J. H. (1998). Emergence: From chaos to order. Cambridge, MA: Perseus Books. Huang, S. (2009). Non-genetic heterogeneity of cells in development: More than just noise. Development, 136(23), 3853–3862. Huang, S. (2012). The molecular and mathematical basis of Waddington's epigenetic landscape: A framework for post-Darwinian biology? BioEssays: News and Reviews in Molecular, Cellular and Developmental Biology, 34(2), 149–157. Huxley, J. (1942). Evolution: The modern synthesis. London, UK: Allen & Unwin. Jablonka, E., & Lamb, K. J. (2006). Evolution in four dimensions: Genetic, epigenetic, behavioral, and symbolic variation in the history of life. Cambridge, MA: MIT Press. John, B., & Lewis, K. R. (1966). Chromosome variability and geographic distribution in insects. Science, 152(3723), 711–721. Keating, D. P., & Hertzman, C. (Eds.). (1999). Developmental health and the wealth of nations: Social, biological, and educational dynamics. New York: Guilford Publications. Kitano, H. (2002). Systems biology: A brief overview. Science, 295(5560), 1662–1664. Konner, M. (2011). The evolution of childhood: Relationships, emotion, mind. Cambridge, MA: Belknap Press of Harvard University Press. Kreitzman, L., & Foster, R. G. (2004). Rhythms of life: The biological clocks that control the daily lives of every living thing. New Haven: Yale University Press. Krieger, N. (2001). Theories for social epidemiology in the 21st century: An ecosocial perspective. International Journal of Epidemiology, 30(4), 668–677. Kuh, D., & Ben-Shlomo, Y. (2004). Introduction. In D. Kuh & Y. Ben-Shlomo (Eds.), A life course 41 approach to chronic disease epidemiology (pp. 3–14). New York: Oxford University Press. Kuh, D., & Davey Smith, G. (2004). The life course and adult chronic disease: An historical perspective with particular reference to coronary heart disease. In Life course approach to chronic disease epidemiology (pp. 15–37). Oxford University Press. Kuh, D. J. L., & Wadsworth, M. E. J. (1993). Physical health status at 36 years in a British national birth cohort. Social Science & Medicine, 37(7), 905–916. Kuzawa, C. W., & Thayer, Z. M. (2011). Timescales of human adaptation: The role of epigenetic processes. Epigenomics, 3(2), 221–234. Laland, K. N., Uller, T., Feldman, M. W., Sterelny, K., Müller, G. B., Moczek, A., Jablonka, E., & Odling-Smee, J. (2015). The extended evolutionary synthesis: Its structure, assumptions and predictions. Proceedings. Royal Society B, 282(1813), 20151019. Launay, J. M., Del Pino, M., et al. (2009). Smoking induces long-lasting effects through a monoamineoxidase epigenetic regulation. PloS One, 4(11), e7959. Lerner, D. R. M. (1984). On the nature of human plasticity. Cambridge: Cambridge University Press. Lerner, R. M. (2006). Developmental science, developmental systems, and contemporary theories of human development. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology, Vol. 1: Theoretical models of human development (pp. 1–17). Hoboken: Wiley. Lerner, R. M. (2012). Developmental science: Past, present, and future. International Journal of Developmental Science, 6(1), 29–36. Lerner, R. M., & Overton, W. F. (2008). Exemplifying the integrations of the relational developmental system synthesizing theory, research, and application to promote positive development and social justice. Journal of Adolescent Research, 23(3), 245–255. Lerner, R. M., Overton, W., et al. (2010). Life-span development: Concepts and issues. In The Handbook of lifespan development (pp. 1–29). Hoboken: Wiley. Lester, B. M., Conradt, E., & Marsit, C. (2016). Introduction to the special section on epigenetics. Child Development, 87(1), 29–37. Li, C. C., Maloney, C. A., et al. (2010). Epigenetic programming by maternal nutrition: Shaping future generations. Epigenomics, 2(4), 539–549. Lillycrop, K. A., & Burdge, G. C. (2012). Epigenetic mechanisms linking early nutrition to long term health. Best Practice & Research Clinical Endocrinology & Metabolism, 26(5), 667–676. Lieberman, D. (2014). The story of the human body: evolution, health, and disease. New York: Vintage Books. Lorenz, E. (1993). The essence of chaos. Seattle: University of Washington Press. Magnusson, D. (1995). Individual development: A holistic, integrated model. In A. Holistic, P. Moen, G. H. Elder Jr., & K. Luscher (Eds.), Examining lives in context: Perspectives on the ecology of human development (pp. 19–60). Washington, DC: American Psychological Association. 42 Maier, S. F., Watkins, L. R., et al. (1994). Psychoneuroimmunology - the Interface between behavior, brain, and immunity. American Psychologist, 49(12), 1004–1017. Marshall, P. J. (2014). Beyond different levels: embodiment and the developmental system. Frontiers in Psychology, 5, 929. Marmot, M. G., & Syme, S. L. (1976). Acculturation and coronary heart disease in Japanese- Americans. American Journal of Epidemiology, 104(3), 225–247. Marmot, M. G., Adelstein, A. M., et al. (1978a). Changing social-class distribution of heart disease. British Medical Journal, 2(6145), 1109–1112. Marmot, M. G., Rose, G., et al. (1978b). Employment grade and coronary heart disease in British civil servants. Journal of Epidemiology and Community Health, 32(4), 244–249. Martino, D., & Prescott, S. (2011). Epigenetics and prenatal influences on asthma and allergic airways disease. Chest, 139(3), 640–647. Masten, A. S. (2001). Ordinary magic. Resilience processes in development. The American Psychologist, 56(3), 227–238. Masters, R. K., Hummer, R. A., et al. (2012). Educational differences in U.S. adult mortality a cohort perspective. American Sociological Review, 77(4), 548–572. Mayer, K. U. (2009). New Directions in life course research. Annual Review of Sociology, 35, 413–433. McEwan, B. S. (1998). Protective and damaging effects of stress mediators. New England Journal of Medicine, 338(3), 171–179. McEwen, B. S. (2012). Brain on stress: How the social environment gets under the skin. Proceedings of the National Academy of Sciences of the United States of America, 109(Suppl 2), 17180–17185. McMichael, A. J. (1999). Prisoners of the proximate: Loosening the constraints on epidemiology in an age of change. American Journal of Epidemiology, 149(10), 887–897. McMillen, I. C., & Robinson, J. S. (2005). Developmental origins of the metabolic syndrome: Prediction, plasticity, and programming. Physiological Reviews, 85(2), 571–633. Meaney, M. J. (2001). Maternal care, gene expression, and the transmission of individual differences in stress reactivity across generations. Annual Review of Neuroscience, 24, 1161–1192. Meaney, M. J. (2010). Epigenetics and the biological definition of gene x environment interactions. Child Development, 81(1), 41–79. Meaney, M. J., Szyf, M., et al. (2007). Epigenetic mechanisms of perinatal programming of hypothalamicpituitary-adrenal function and health. Trends in Molecular Medicine, 13(7), 269–277. Mendle, J., Turkheimer, E., & Emery, R. E. (2007). Detrimental psychological outcomes associated with early pubertal timing in adolescent girls. Developmental Review, 27(2), 151–171. Miller, J. G. (1978). Living systems. New York: McGraw-Hill. N. Halfon and C.B. Forrest Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18(2), 112–117. Molenaar, P. C. M., Huizenga, H. M., & Nesselroade, J. R. (2003). The relationship between the structure of interindividual and intraindividual variability: A theoretical and empirical vindication of developmental systems theory. In Understanding Human Development (pp. 339–360). New York: Springer. Mortimer, J. T., & Shanahan, M. J. (2007). Handbook of the life course New York: Springer Science & Business Media. Noble, K. G., Houston, S. M., et al. (2012). Neural correlates of socioeconomic status in the developing human brain. Developmental Science, 15(4), 516–527. Okasha, S. (2006). Evolution and the levels of selection. New York: NY, Oxford University Press. Overton, W. F. (2007). A coherent Metatheory for dynamic systems: Relational Organicism- Contextualism. Human Development, 50(2–3), 154–159. Oyama, S. (1985). The ontogeny of information: Developmental systems and evolution. Cambridge, UK: Cambridge University Press. Perera, F., Tang, W. Y., et al. (2009). Relation of DNA methylation of 5′-CpG island of ACSL3 to transplacental exposure to airborne polycyclic aromatic hydrocarbons and childhood asthma. PloS One, 4(2), e4488. Power, C., Graham, H., et al. (2005a). The contribution of childhood and adult socioeconomic position to adult obesity and smoking behaviour: An international comparison. International Journal of Epidemiology, 34(2), 335–344. Power, C., Hypponen, E., et al. (2005b). Socioeconomic position in childhood and early adult life and risk of mortality: A prospective study of the mothers of the 1958 British birth cohort. American Journal of Public Health, 95(8), 1396–1402. Relton, C. L., & Davey Smith, G. (2012). Is epidemiology ready for epigenetics? International Journal of Epidemiology, 41(1), 5–9. Roberts, E. A. (2012). Using metalloproteomics to investigate the cellular physiology of copper in hepatocytes. Metallomics, 4(7), 633–640. Roberts, E. (2015). System-driven research: Legitimate experimental design for biological/biomedical research. Accessed 2017 https://dalspace.library.dal. ca/bitstream/handle/10222/56275/Roberts-Eve-PhDPHIL-May-2015.pdf?sequence=1&isAllowed=y Relton, C. L., & Davey Smith, G. (2012). Is epidemiology ready for epigenetics? International Journal of Epidemiology, 41(1), 5–9. Repetti, R. L., Robles, T. F., et al. (2011). Allostatic processes in the family. Development and Psychopathology, 23(3), 921–938. Repetti, R. L., Taylor, S. E., et al. (2002). Risky families: Family social environments and the mental and physical health of offspring. Psychological Bulletin, 128(2), 330–366. Richman, K. A. (2004). Ethics and the metaphysics of medicine. Cambridge, MA: The MIT Press. The Emerging Theoretical Framework of Life Course Health Development Richardson, P. J., & Boyd, R. (2005). Not by genes alone: How culture transformed human evolution. University of Chicago Press. Sameroff, A. (1975). Transactional models in early social relations. Human Development, 18(1–2), 65–79. Sameroff, A. (2010). A unified theory of development: A dialectic integration of nature and nurture. Child Development, 81(1), 6–22. Sapolsky, R. M., Krey, L. C., & McEWEN, B. R. U. C. E. S. (1985). Prolonged glucocorticoid exposure reduces hippocampal neuron number: Implications for aging. The Journal of Neuroscience, 5(5), 1222–1227. Schadt, E. E., & Bjorkegren, J. L. (2012). NEW: Networkenabled wisdom in biology, medicine, and health care. Science Translational Medicine, 4(115), 115rv111. Schlotz, W., & Phillips, D. I. (2009). Fetal origins of mental health: Evidence and mechanisms. Brain, Behavior, and Immunity, 23(7), 905–916. Seedhouse, D. D. (2001). Health: The foundations for achievement. Hoboken: Wiley. Seeman, J. (1989). Toward a model of positive health. The American Psychologist, 44(8), 1099–1109. Seeman, T. E. (1997). Price of adaptation--allostatic load and its health consequences. MacArthur studies of successful aging. Archives of Internal Medicine, 157(19), 2259–2268. Seeman, T. E., McEwen, B. S., et al. (2001). Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. Proceedings of the National Academy of Sciences, 98(8), 4770–4775. Sen, A. (1999). Commodities and capabilities. Oxford: Oxford University Press. Skinner, M. K., Anway, M. D., et al. (2008). Transgenerational epigenetic programming of the brain transcriptome and anxiety behavior. PloS One, 3(11), e3745. Spencer, J. P., Thomas, M. S. C., et al. (Eds.). (2009). Toward a unified theory of development: Connectionism and dynamic systems theory re-considered. New York: Oxford University Press. Starfield, B. (1973). Health services research: A working model. The New England Journal of Medicine, 289(3), 132–136. 43 Starfield, B., Katz, H., Gabriel, A., et al. (1984). Morbidity in childhood - a longitudinal view. The New England Journal of Medicine, 310(13), 824–829. Stearns, S. (1992). The evolution of life histories. New York: Oxford. Stewart, G. T. (1968). Limitations of the germ theory. The Lancet, 291(7551), 1077–1081. Syme, S. L., & Berkman, L. F. (1976). Social class, susceptibility and sickness. American Journal of Epidemiology, 104(1), 1–8. Szyf, M., Weaver, I. C. G., et al. (2005). Maternal programming of steroid receptor expression and phenotype through DNA methylation in the rat. Frontiers in Neuroendocrinology, 26(3–4), 139–162. Tamaki, M., Bang, J. W., Watanabe, T., & Sasaki, Y. (2016). Night watch in one brain hemisphere during sleep associated with the first-night effect in humans. Current Biology, 26(9), 1190–1194. Thayer, Z. M., & Kuzawa, C. W. (2011). Biological memories of past environments: Epigenetic pathways to health disparities. Epigenetics: Official Journal of the DNA Methylation Society, 6(7), 798–803. Turecki, G., & Meaney, M. J. (2016). Effects of the social environment and stress on glucocorticoid receptor gene methylation: A systematic review. Biological Psychiatry, 79(2), 87–96. von Bertalanffy, L. (1968). General system theory: Foundations, development, application. New York, NY: George Braziller. Waddington, C. H. (1942). The Epigenotype. Endeavour, 1, 18–20. Wen, X., Kleinman, K., et al. (2012). Childhood body mass index trajectories: Modeling, characterizing, pairwise correlations and socio-demographic predictors of trajectory characteristics. BMC Medical Research Methodology, 12, 38. West-Eberhard, M. J. (2003). Developmental plasticity and evolution. New York: Oxford University Press. Worthman, C. M., & Kuzara, J. (2005). Life history and the early origins of health differentials. American Journal of Human Biology: The Official Journal of the Human Biology Council, 17(1), 95–112. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Part II Life Stages Preconception and Prenatal Factors and Metabolic Risk Guoying Wang, Tami R. Bartell, and Xiaobin Wang 1 Introduction There is growing evidence that early life may have a profound impact on health and disease in later life (Gluckman et al. 2008; Hales et al. 1991). Because this growing evidence about the prenatal and preconception origins of health and disease is vast, this chapter will primarily focused on early life origins of metabolic risk for obesity and type 2 diabetes (T2DM). Obesity and type 2 diabetes affect all age groups including mothers and young children (International Diabetes Federation 2013), especially in poor minority populations (Al-Rubeaan 2015). Increasing evidence points to a profound impact of early life factors (e.g., maternal obesity, G. Wang, MD, PhD Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA T.R. Bartell, BA Stanley Manne Children’s Research Institute, Ann & Robert H Lurie Children’s Hospital of Chicago, Chicago, IL, USA X. Wang, MD, MPH, ScD (*) Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA e-mail: xwang82@jhu.edu diabetes, and unhealthy diet) on offspring metabolic risk (Barbour 2014a), leading to a transgenerational amplification of obesity and diabetes. The period from conception to birth is a time of very rapid growth, cellular differentiation, and functional maturation of organ systems. This early life period is particularly sensitive to alterations of the intrauterine environment including the metabolic milieu. Epigenomic variations (regulation of gene expression) are largely established in utero (Bogdarina et al. 2004) and are particularly sensitive to prenatal environmental factors that may have a lifelong impact on health and disease. Moreover, babies that are large at birth are more likely to be overweight or obese in childhood, with these conditions persisting into adulthood (Knittle et al. 1979; Rolland-Cachera et al. 2006). Recent data suggest that elevated insulin levels may also originate in utero and persist into early childhood (Wang et al. 2014a). Taken together, the prenatal period is a critical developmental stage for obesity and metabolic outcomes (Wang et al. 2014b; Dietz 2004). In light of the global obesity and T2DM epidemic and growing evidence of early life origins of obesity and diabetes, early identification of individuals at high risk and early prevention of obesity and metabolic syndrome are a key to achieve primary prevention and reverse the trends of the obesity and T2DM epidemics. As illustrated in Fig. 1, human health is interconnected from conception to fetal life to childhood and on into adulthood and influenced by multilevel factors from gene to society. This © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_3 47 G. Wang et al. 48 Environmental Exposure Across Life Span (physical, chemical, behavioral, social) G×E G×E Parental Genome Epigenome Fetal Genome Epigenome Long-term metabolic risk G×E In uterine environment Fetal development programming Birth outcomes Clinical outcomes: Overweight or obesity Insulin resistance Dyslipidemia Hypertension Subclinical outcomes: Inflammatory Adipokines Metabolites profile Placental structure function Fig. 1 Conceptual framework for preconceptional and prenatal factors and metabolic risk chapter will discuss the impact of important preconceptional and prenatal factors, including maternal obesity and/or diabetes, gestational weight gain, and maternal micronutrient status, on in utero and lifelong metabolic outcomes and the possible gene–environment interactions and epigenetic mechanisms underlying early life origins of metabolic risk. Finally, it will provide perspectives on current knowledge gaps and recommendations to advance the field. 2 The Effects of Maternal Preconceptional Obesity and/or Diabetes and Micronutrient Status on Offspring Metabolic Outcomes Evidence is growing about the associations between early life exposures and later health. Major exposures encompass multilevel and multifactorial influences from the societal level through to individual lifestyle and biological factors. While a discussion of all of the possible risks or protective factors is beyond the scope of this chapter, below we highlight some important preconceptional and prenatal factors, including prepregnancy obesity, excessive weight gain during pregnancy, preexistent or gestational diabetes, and maternal micronutrient status. 2.1 Maternal Prepregnancy Obesity and Gestational Weight Gain In parallel with the global obesity epidemic, the prevalence of obesity among women of childbearing age has also increased. In 2009–2010, the National Health and Nutrition Examination Survey (NHANES) found that 56% of US women aged 20–39 were overweight or obese (body mass index (BMI) ≥ 25 kg/m2), and in particular, 32% were obese (BMI ≥ 30.0 kg/m2) (Flegal et al. 2012). Thus, more than half of women starting their pregnancy are already overweight or obese, and most of them remain overweight or obese during their entire pregnancy. To further complicate things, women who are overweight or obese going into pregnancy are at an increased risk for developing metabolic disorders, such as gestational diabetes mellitus (GDM) (Torloni et al. 2009), hypertensive disorders of pregnancy (Bautista-Castano et al. 2013), and excessive gestational weight gain (GWG) (Chu et al. Preconception and Prenatal Factors and Metabolic Risk 2009). More important, maternal obesity and its relevant metabolic disorders may impact offspring metabolic risk in later life. Excessive maternal prepregnancy weight and GWG are consistent risk factors for offspring obesity and cardiometabolic risk (Lawlor 2013; Hochner et al. 2012). In the Jerusalem Perinatal Family Follow-Up Study, greater maternal prepregnancy BMI, independent of GWG and confounders, was significantly associated with higher offspring blood pressures, serum insulin and triglyceride concentrations, BMI, waist circumference, and lower high-density lipoprotein cholesterol (Hochner et al. 2012). Of note, the associations between maternal BMI and offspring BP, insulin, and lipids appeared to be largely mediated by offspring concurrent body size (both BMI and waist circumference) (Hochner et al. 2012). This finding emphasizes the impact that maternal adiposity may have through offspring adiposity on various predictors of subclinical and clinical disease, including diabetes mellitus and cardiovascular diseases. A large US cohort study reported that excessive maternal GWG was independently associated with a 46% increased risk of overweight or obesity in offspring at 2–5 years of age (Sridhar et al. 2014). In a retrospective cohort study, excessive maternal GWG had an adverse impact on the risk of childhood overweight and abdominal adiposity (Ensenauer et al. 2013). Kaar et al. further reported that maternal prepregnancy BMI was not only associated with increased general adiposity (BMI) and abdominal adiposity (waist circumference) in offspring but visceral adipose tissue at age 10 years (Kaar et al. 2014). A recent study points to an association between maternal excess weight in pregnancy and offspring BMI increase from adolescence to adulthood (Lawrence et al. 2014). Early pregnancy obesity has also been associated with an increased risk of premature death in adult offspring (Reynolds et al. 2013a). To further the negative impact, maternal prepregnancy BMI was also associated with increased offspring insulin resistance at age 10 years (Kaar et al. 2014) and an increased risk of developing T2DM (Dabelea et al. 2008). 49 2.2 Preexistent and Gestational Diabetes In parallel with the obesity epidemic is a diabetes pandemic, which includes an increasing number of women with type 1 diabetes (T1D), T2DM, and GDM (Torloni et al. 2009). A body of studies has established a link between exposure to maternal diabetes in utero and metabolic risk in later life. In a multiethnic population aged 6–13 years, exposure to maternal GDM was associated with higher BMI, waist circumference, and more abdominal fat (Crume et al. 2011). Adjustment for socioeconomic factors, birthweight, gestational age, maternal smoking during pregnancy, diet, and physical activity did not alter the associations; however, adjustment for maternal prepregnancy BMI attenuated all associations (Crume et al. 2011), suggesting that maternal obesity is an important mediator. The studies in Pima Indians of the long-term effects of diabetic pregnancy on offspring revealed that the offspring of women with preexistent diabetes and GDM were more obese and had higher glucose concentrations and more diabetes than the offspring of nondiabetic women or women who developed diabetes after pregnancy (Pettitt et al. 1993). In the Chicago Diabetes in Pregnancy study, offspring of mothers with preexistent diabetes and GDM had significantly higher 2-h blood glucose and insulin levels and rate of impaired glucose tolerance than the control group of nondiabetic mothers (Silverman et al. 1995). In the SEARCH Case–Control Study (Dabelea et al. 2008), maternal diabetes and obesity were associated with 5.7 times and 2.8 times the risk of T2DM in young offspring aged 10–22 years, respectively. Notably, combined prenatal exposure to maternal diabetes and obesity could explain about 47% of the offspring risk of T2DM (Dabelea et al. 2008). Among Pima Indian adults, individuals whose mothers had diabetes during pregnancy had a 40% lower acute insulin response to a 25 g intravenous glucose challenge than those whose mothers developed diabetes at an early age but after the birth of the child (Gautier et al. 2001). One important mediator is maternal blood glucose level during pregnancy. The Hyperglycemia G. Wang et al. 50 and Adverse Pregnancy Outcome (HAPO) study demonstrated that maternal glucose levels below those diagnostic of diabetes were positively associated with increased birthweight and cord blood C-peptide levels (Metzger et al. 2008). Poor glycemic control in women with pregestational diabetes increased the risk of congenital malformations and spontaneous abortions (Kendrick 2004; Miller et al. 1981). Although T1D and T2DM have different pathogeneses, the rates of pregnancy loss were similar (Cundy et al. 2007). Another factor related to diabetes is the comorbidities. Diabetic gastropathy, a form of diabetic neuropathy, not only worsens nausea and vomiting but also can cause difficulty with glucose control during pregnancy (Kitzmiller et al. 2008). Reece et al. reviewed the literature and reported that maternal diabetic nephropathy is complicated by hypertension (60%), preeclampsia (41%), premature delivery (22–30%), and fetal growth restriction (16%) (Reece et al. 1998). Taken together, better control of blood glucose levels and diabetic complications may improve prenatal outcomes. 2.3 Maternal Micronutrient Status Growing evidence suggests that maternal nutrition, through its impact on the fetal intrauterine environment, has a profound and lifelong influence on later health (Barker et al. 1993). Among a number of specific nutrients that have been implicated, folate is particularly important. Folate is an essential vitamin B involved in nucleic acid synthesis, DNA methylation, cellular growth, differentiation, and repair (Crider et al. 2012; Kim 2000). The demand for folate increases during pregnancy due to the need for fetal and placental growth and uterus enlargement (Greenberg et al. 2011). The causal role of folate deficiency in fetal neural tube defects (NTDs) is well established (De Wals et al. 2007). A recent study showed that low maternal folate concentration was associated with higher BMI in offspring at 5–6 years (Krikke et al. 2016). Our study also showed that maternal low folate was not only independently associated with offspring overweight or obesity but also worsened the adverse effects of maternal prepregnancy obesity on offspring metabolic risk (Wang et al. 2016). On the other hand, excess maternal folate status may also link to offspring adverse metabolic outcomes. A study in an Indian population reported a positive association between maternal folate level and offspring homeostatic model assessment-insulin resistance (HOMA-IR) (Krishnaveni et al. 2014). What we gather from the combined results of these studies is that optimizing folate nutrition in women, and especially OWO women, may offer a safe, simple, and effective way to decrease the risk of transgenerational obesity and diabetes. 2.4 Adverse Birth Outcomes Birthweight reflects cumulative growth in utero. A body of studies has suggested that maternal obesity is associated with a risk for large for gestational age (LGA) or macrosomia in the offspring (Bautista-Castano et al. 2013). In addition, Brumbaugh et al. demonstrated that newborns of obese mothers with GDM showed increased intrahepatic fat at birth (Brumbaugh et al. 2013). Prepregnancy obesity also has been shown to increase the risk of Cesarean section (Martin et al. 2015; Dzakpasu et al. 2014) and preterm birth (Cnattingius et al. 2013). In a case–control study of 914 women with pregestational diabetes and 4000 controls, diabetes was associated with an increased risk of preterm birth (Cnattingius et al. 1994). Maternal diabetes has also been linked to macrosomia and Cesarean section (Barbour 2014b; Koyanagi et al. 2013). These adverse birth outcomes are also associated with obesity and metabolic syndrome (including hypertension, dyslipidemia, obesity, and insulin resistance/diabetes) in later life. Hypertension: Previous studies demonstrated that preterm birth was associated with hypertension in childhood (Sipola-Leppanen et al. 2014) and adulthood (Irving et al. 2000; Johansson et al. 2005). In a prospective study, Irving et al. found that preterm birth was associated with the risk of hypertension and hyperglycemia in adult- Preconception and Prenatal Factors and Metabolic Risk 51 hood (Irving et al. 2000). A meta-analysis of 27 studies further confirmed that preterm birth was associated with cardiovascular risk factors, such as higher systolic and diastolic blood pressure and low-density lipoprotein cholesterol in adulthood (Parkinson et al. 2013). Even more interesting is that the difference in blood pressure between those born preterm and those born at term may be greater in women than in men (Parkinson et al. 2013). Dyslipidemia: A large cohort study reported that boys born early preterm had 6.7% higher total cholesterol, 11.7% higher low-density lipoprotein cholesterol (LDLC), and 12.3% higher apolipoprotein B concentrations than their term peers (Sipola-Leppanen et al. 2014). Obesity: As fat deposition occurs largely during the third trimester of pregnancy, preterm babies are born with low levels of body fat (Rigo et al. 1998; Uthaya et al. 2005). Postnatally, preterm babies are more likely to gain excessive weight, a known risk factor of childhood obesity (Ong et al. 2000), and tend to have a higher proportion of central adiposity (Uthaya et al. 2005). Insulin resistance/diabetes: Like children who were born at term and small for gestational age (SGA), children born preterm have lower insulin sensitivity (Wang et al. 2014a; Hofman et al. 2004). Hovi et al. reported that preterm birth itself, independent of birth size, may contribute to insulin resistance, elevated blood glucose, and higher blood pressure (BP) in young adulthood (Hovi et al. 2007). In addition, a Swedish study found that preterm birth was associated with later T2DM: the hazard ratio for T2DM comparing very preterm (≤32 weeks of gestation) with term birth was 1.67 (Kaijser et al. 2009). There is a particular lack of large-scale longitudinal birth cohort studies to examine the effects of preterm birth on metabolic outcomes over critical developmental windows. One prospective birth cohort study found that preterm birth is associated with elevated plasma insulin levels (indirect evidence of insulin resistance) at birth that persist to age 6.5 years (Wang et al. 2014a), suggesting that insulin resistance originates in utero and persists into later life. There is also evidence from a mechanistic study, which revealed that preterm birth increased the risk of T2DM via diminished insulin sensitivity (Pilgaard et al. 2010), and yet another study showed that preterm birth was associated with changes in the cord blood adipokine profile that may contribute to the impairment of glucose metabolism (Bhargava et al. 2004). 3 Mechanisms/Pathways Underlying Early Life Origins of Metabolic Risk Compelling evidence suggests that prenatal experiences influence metabolic alterations in late life via multiple pathways, including genetic, in utero environment, gene–environment interaction, and epigenetic and shared familial socioeconomic and lifestyle factors (Fig. 1). 3.1 Genetics Several lines of evidence support that genetics play a key role in the long-term effects of maternal obesity and diabetes on offspring metabolic risk. First, obesity and diabetes tend to aggregate among families. We also know that GDM is associated with a history of T2DM. One study showed that, compared to women with nondiabetic parents, women with any parental history of T2DM experienced a 2.3-fold increased risk of GDM, suggesting that the risk of GDM was positively associated with parental history of T2DM (Williams et al. 2003). Second, the extensive study of genetic variation in obesity and diabetes has led to the identification of numerous candidate genes. Although currently identified genetic markers only explain a small proportion of metabolic risks, twin studies reveal that BMI, body fat, and insulin sensitivity are all highly heritable (Zhang et al. 2009; Ouyang et al. 2010). Finally, some gene variants related to adult diseases have been linked to offspring outcomes. A epidemiological study showed that a genetic risk score (GRS) comprised of SNPs associated with adult obesity-related traits may provide an approach for G. Wang et al. 52 predicting LGA birth and newborn adiposity beyond established risk factors (Chawla et al. 2014). A genome-wide association study of women of European descent established that common variants near the melanocortin-4 receptor (MC4R) influence fat mass, weight and obesity risk and over transmission of the risk allele to obese offspring (Loos et al. 2008). GDM is not only associated with later risk of diabetes in mothers but also with metabolic changes that may lead to the development of diabetes in their offspring (Silverman et al. 1998). Some genes and their interactions in functional networks between the mother and fetus may also play a role in organ development (Charalambous et al. 2007). Additionally related is the fetal insulin hypothesis proposed by Drs. Hattersley and Tooke, which highlights that fetal genetic factors affect not only insulin-mediated fetal growth by regulating either fetal insulin secretion or insulin sensitivity but also insulin resistance in childhood and adulthood (Hattersley and Tooke 1999). One example is the case in which a mother transmits mutations and pleiotropic effects in the glucokinase gene to her child. The glucokinase gene codes for the glycolytic enzyme glucokinase, which acts as the pancreatic beta-cell glucose sensor (Matschinsky et al. 1993). Such mutations result in mild beta-cell dysfunction with slightly elevated fasting blood glucose concentrations, which is present in early childhood and shows little change with age (Hattersley 1998). 3.2 Intrauterine Environment Based on observational research, Barker et al. proposed the fetal programming hypothesis, which conceptualized that intrauterine experiences modify fetal systems and influence health in later life (Hales and Barker 1992). Fetal development responds to changes in the in utero environment in response to changing metabolism, hormone production, and tissue sensitivity to hormones (Gluckman et al. 2008). These adaptive changes may influence the relative development of various organs, leading to persistent alterations in physiologic and metabolic homeostasis. Maternal pre- pregnancy BMI: In a large cohort of 4,091 mother–father–offspring trios in Britain, researchers found that the association between parental prepregnancy BMI and offspring adiposity at ages 9–11 years was stronger among mother–offspring pairs than among father–offspring pairs (Lawlor et al. 2008). Given that maternal and paternal prepregnancy BMI are markers of genetic, behavioral, and environmental factors but that only the maternal prepregnancy BMI was reflected in the intrauterine environment, this finding provides some support for the role of the in utero environment in lifelong metabolic risk beyond genetic and shared environmental contributions. Pregestational and Gestational Diabetes: A sibling study of Pima Indians showed that the BMI of siblings born after the mother developed T2DM was significantly higher than that of siblings born before their mother developed T2DM (Dabelea et al. 2000). Offspring born to mothers with T2DM or GDM had up to a sixfold higher risk of developing T2DM as young adults compared to offspring born to mothers before they developed T2DM (Dabelea and Crume 2011). Other strong evidence comes from a study in which comparisons were made between sibling groups where one sibling was born before the mother experienced dramatic weight loss after bariatric surgery and the other was born after the surgery (Kral et al. 2006). Siblings born before maternal bariatric surgery were at a much greater risk for obesity than siblings born after the weight loss surgery (Kral et al. 2006). Moreover, infants of mothers who had bariatric surgery before pregnancy were at a reduced risk of being LGA at birth (Johansson et al. 2015) and developing obesity and insulin resistance in childhood and adolescence (Smith et al. 2009). Collectively, these findings support the role of the intrauterine environment in transmitting a risk for obesity beyond that of genetics. 3.3 Gene and Environment Interaction and Epigenetics Epigenetic mechanisms are critical to normal human development and play an important role in complex human diseases (O’Neill et al. 2014; Preconception and Prenatal Factors and Metabolic Risk 53 Reynolds et al. 2013b; Dolinoy et al. 2007). Both animal models and human studies implicate the intrauterine period as a sensitive time for the establishment of epigenetic variability (Heijmans et al. 2008; Cook et al. 2007; Tang et al. 2012; Radford et al. 2014; Janssen et al. 2013), which in turn influences risk for a range of disorders that develop later in life. In the Boston Birth Cohort, we observed a wide range of interindividual variations in genome-wide methylation at birth (Wang et al. 2012). One possible mechanism by which maternal obesity affects offspring OWO is via alterations in fetal DNA methylation induced by maternal obesity. Animal studies have shown that certain transient environmental influences in utero can produce persistent changes in epigenetic marks that have lifelong consequences (Sinclair et al. 2007; Anway et al. 2005). In humans, our previous study showed an association between maternal prepregnancy BMI and alterations in fetal DNA methylation (Liu et al. 2014). Other studies also showed the link between maternal obesity and fetal epigenome alterations (Lesseur et al. 2013; Yang et al. 2013). In addition, the use of methyl donor supplementation has been shown to prevent transgenerational obesity (Waterland et al. 2008). A recent study indicated lower DNA methylation plasticity in skeletal muscle among low birthweight vs. normal birthweight men, which potentially contributes to our understanding of the link between low birthweight and an increased risk of T2DM (Jacobsen et al. 2014). Genes related to metabolic risk are regulated by epigenetic alteration: Evidence suggests that epigenetic mechanisms are involved in the risk of obesity. Current evidence supports the important role of epigenetic regulation in key genes involved in the control of adipogenesis, glucose homeostasis, inflammation, and/or insulin signaling, including leptin (Milagro et al. 2009), peroxisome proliferator-activated receptor gamma (Noer et al. 2007), insulin (Yang et al. 2011), glucose transporter (Yokomori et al. 1999), proliferatoractivated receptor-γ (Fujiki et al. 2009), lipoprotein lipase (Noer et al. 2007), and fatty acid-binding protein 4 (Noer et al. 2007). In addition, greater methylation in the promoter region of the retinoid X receptor-a gene was also related to greater adiposity in childhood (Godfrey et al. 2011). Genes subject to genomic imprinting are predominantly expressed from one of the two parental chromosomes and are often clustered in the genome and epigenetically regulated. The role of imprinted genes in growth control has been apparent since the discovery of imprinting in the early 1980s. A related study found altered methylation at multiple imprint regulatory regions in children born to obese parents compared with children born to nonobese parents (Soubry et al. 2015). 4 Preconception and Prenatal Care Maternal health is not only important for the mother but also for the fetus and neonate, which makes it critical for women to be and stay healthy during their reproductive years. It is well recognized that optimizing a woman’s health and knowledge before planning and conceiving a pregnancy may eliminate or reduce health risks to her and her baby. As emerging clinical and scientific advances come to be realized through a life course health development approach to health optimization, the AAP/ACOG Guidelines for Perinatal Care have shifted from framing preconceptional care as appropriately targeted toward prospective parents who are contemplating pregnancy to an emphasis on the integration of preconception health promotion throughout the lifespan. Taking this approach, all women and men can benefit from preconception health, whether or not they plan to have a baby one day. The implementation of effective interventions that prioritize risk factors and the provision of quality health services during prepregnancy and pregnancy are recommended (Bilano et al. 2014). In a multicenter randomized trial, 1108 overweight (BMI ≥ 25 kg/m2) women were randomized to a comprehensive dietary and lifestyle intervention. Infants born to women who had received lifestyle advice were significantly less likely to have a birthweight above 4000 g; treatment effect (95%CI):0.82, 0.68–0.99; P = 0.04) (Dodd et al. 2014). All women with T2DM should G. Wang et al. 54 be advised regarding safe, effective contraception and the benefits of optimal glycemic control, folic acid supplementation, and avoidance of potentially harmful medications before attempting pregnancy. Adequate glucose control in a woman with diabetes before conception and throughout pregnancy can decrease maternal morbidity, spontaneous abortion, fetal malformation, fetal macrosomia, intrauterine fetal death, and neonatal morbidity (American College of Obstetricians and Gynecologists 2005). 5 Recommendations for Future Study and Perspectives 5.1 Major Themes and Findings Growing evidence indicates that preconception and prenatal risk factors may play an important role both in fetal metabolic reprogramming and long-term metabolic disorders in later life. Although the onset of obesity and diabetes may begin later in childhood or adulthood, programming at the earliest ages may contribute a latent susceptibility. In addition to genetic susceptibility, epigenetic alterations may be important molecular mechanisms underlying gene–environment interactions in early life origins of disease. Early identification and intervention may improve long-term health outcomes and help to reverse the obesity and diabetes epidemics during the most sensitive developmental stages, when interventions are likely to be most cost-efficient. Particular attention is required for US urban lowincome minority populations that have been disproportionally affected by the obesity and diabetes epidemics, and most likely fall into vicious cycle of transgenerational obesity and diabetes. 5.2 Research Priorities 5.2.1 Epidemiologic research Despite the notion of early life origins of obesity and T2DM, there is a particular lack of wellpowered prospective birth cohort studies to exam- ine to what degree and how early life factors affect pregnancy and infant and child health outcomes across multiple developmental windows in a life course framework, particularly in high-risk US urban low-income minority populations. Findings from this line of research will have important research, clinical, and public health implications. 5.2.2 Mechanism research In light of growing recognition that epigenetic mechanisms may play an important role in mediating early life origins of diseases and that epigenome is potentially modifiable and reversible, well-designed large-scale prospective birth cohort studies are needed to trace the pathways from early life factors to adverse pregnancy outcomes to postnatal long-term metabolic outcomes and to better understand how epigenome changes from fetal to childhood to adolescence in response to environmental exposures. The role of social and environmental adversity in obesity and diabetes among urban low-income minorities has taken on new urgency given that these populations are disproportionally affected by the obesity and diabetes epidemics. In addition to genetics and epigenetics, the field may also leverage and benefit from the latest advances in other “omics” such as metabolomics and microbiome as well as system sciences and bioinformatics. 5.2.3 Translational research A particular challenge in preventing childhood obesity is to identify important and modifiable early life risk and protective factors to design safe, effective, and sustainable interventions. Evidence is needed to inform clinical guidelines regarding the optimal age to screen for obesity in children. Conflicting recommendations have been proposed for the starting age: age 6 by the US Preventive Services Task Force (Barton 2010) vs. age 2 by the Expert Committee (Barlow 2007). While low birthweight has been included in current clinical assessment of future metabolic risk, there is a need to consider preterm birth as an important risk factor of future metabolic risk. The American Diabetes Association included histories of SGA and maternal history of diabetes or GDM during the child’s gestation as part of their diabetes risk assessment for children Preconception and Prenatal Factors and Metabolic Risk (American Diabetes Association 2014). To date, preterm birth has not been included in the diabetes risk assessment guidelines in children and adults, an area that requires more research given growing evidence linking preterm with subsequent metabolic risk. 5.3 Data and Methods Development Priorities Advanced analytical methods are needed to comprehensively examine temporal and causal links of multilevel early life factors with metabolic outcomes. Future studies need to fully capture the complex interplay of broad environmental factors, genome, epigenome, metabolome, and microbiome that affect metabolic outcomes across lifespan and generations. While some systems models exist to help characterize particular sub-systems of the complex set of factors that influences children’s bodyweight, none have tried to comprehensively represent the relationship between early life factors and the subsequent development of childhood metabolic risk across critical developmental stages. 6 Conclusions There is growing evidence that preconceptional and prenatal factors play an important role in fetal metabolic programming and metabolic risk in later life. More research is needed to identify important and modifiable early life risk and protective factors and underlying mechanisms, which will pave the road for developing cost-effective early screening, prevention, and treatment strategies to halt and reverse the obesity and T2DM epidemics in the US, in particular among the most vulnerable populations (urban low-income minorities). 7 Source of Funding Drs. Xiaobin Wang and Guoying Wang are supported in part by the National Institutes of Health (NIH) grant R01HD086013 (PI: X Wang). 55 References Al-Rubeaan, K. (2015). National surveillance for type 1, type 2 diabetes and prediabetes among children and adolescents: A population-based study (SAUDI-DM). Journal of Epidemiology and Community Health. doi:10.1136/jech-2015-205710. American College of Obstetricians and Gynecologists. (2005). ACOG Practice Bulletin. Clinical Management Guidelines for Obstetrician-Gynecologists. Number 60, March 2005. Pregestational diabetes mellitus. Obstetrics and Gynecology, 105(3), 675–685. American Diabetes Association. (2014). Diagnosis and classification of diabetes mellitus. Diabetes Care, 37(Suppl 1), S81–S90. Anway, M. D., Cupp, A. S., Uzumcu, M., & Skinner, M. K. (2005). Epigenetic transgenerational actions of endocrine disruptors and male fertility. Science, 308(5727), 1466–1469. Barbour, L. A. (2014a). Changing perspectives in preexisting diabetes and obesity in pregnancy: Maternal and infant short- and long-term outcomes. Current Opinion in Endocrinology, Diabetes, and Obesity, 21(4), 257–263. Barbour, L. A. (2014b). Changing perspectives in preexisting diabetes and obesity in pregnancy: Maternal and infant short- and long-term outcomes. Current Opinion in Endocrinology, Diabetes, and Obesity, 21(4), 257–263. Barker, D. J., Gluckman, P. D., Godfrey, K. M., Harding, J. E., Owens, J. A., & Robinson, J. S. (1993). Fetal nutrition and cardiovascular disease in adult life. Lancet, 341(8850), 938–941. Barlow, S. E. (2007). Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: Summary report. Pediatrics, 120(Suppl 4), S164–S192. Barton, M. (2010). Screening for obesity in children and adolescents: US Preventive Services Task Force recommendation statement. Pediatrics, 125(2), 361–367. Bautista-Castano, I., Henriquez-Sanchez, P., AlemanPerez, N., et al. (2013). Maternal obesity in early pregnancy and risk of adverse outcomes. PLoS One, 8(11), e80410. Bhargava, S. K., Sachdev, H. S., Fall, C. H., et al. (2004). Relation of serial changes in childhood body-mass index to impaired glucose tolerance in young adulthood. The New England Journal of Medicine, 350(9), 865–875. Bilano, V. L., Ota, E., Ganchimeg, T., Mori, R., & Souza, J. P. (2014). Risk factors of pre-eclampsia/eclampsia and its adverse outcomes in low- and middle-income countries: A WHO secondary analysis. PLoS One, 9(3), e91198. Bogdarina, I., Murphy, H. C., Burns, S. P., & Clark, A. J. (2004). Investigation of the role of epigenetic modification of the rat glucokinase gene in fetal programming. Life Sciences, 74(11), 1407–1415. 56 Brumbaugh, D. E., Tearse, P., Cree-Green, M., et al. (2013). Intrahepatic fat is increased in the neonatal offspring of obese women with gestational diabetes. The Journal of Pediatrics, 162(5), 930–936; e931. Charalambous, M., da Rocha, S. T., & Ferguson-Smith, A. C. (2007). Genomic imprinting, growth control and the allocation of nutritional resources: Consequences for postnatal life. Current Opinion in Endocrinology, Diabetes, and Obesity, 14(1), 3–12. Chawla, R., Badon, S. E., Rangarajan, J., et al. (2014). Genetic risk score for prediction of newborn adiposity and large-for-gestational-age birth. The Journal of Clinical Endocrinology and Metabolism, 99(11), E2377–E2386. Chu, S. Y., Callaghan, W. M., Bish, C. L., D’Angelo, D. (2009). Gestational weight gain by body mass index among US women delivering live births, 2004-2005: Fueling future obesity. American Journal of Obstetrics and Gynecology,. 200(3), 271; e271–277. Cnattingius, S., Berne, C., & Nordstrom, M. L. (1994). Pregnancy outcome and infant mortality in diabetic patients in Sweden. Diabetic Medicine, 11(7), 696–700. Cnattingius, S., Villamor, E., Johansson, S., et al. (2013). Maternal obesity and risk of preterm delivery. JAMA, 309(22), 2362–2370. Cook, J. D., Davis, B. J., Goewey, J. A., Berry, T. D., & Walker, C. L. (2007). Identification of a sensitive period for developmental programming that increases risk for uterine leiomyoma in Eker rats. Reproductive Sciences, 14(2), 121–136. Crider, K. S., Yang, T. P., Berry, R. J., & Bailey, L. B. (2012). Folate and DNA methylation: A review of molecular mechanisms and the evidence for folate’s role. Advances in Nutrition, 3(1), 21–38. Crume, T. L., Ogden, L., West, N. A., et al. (2011). Association of exposure to diabetes in utero with adiposity and fat distribution in a multiethnic population of youth: The Exploring Perinatal Outcomes among Children (EPOCH) Study. Diabetologia, 54(1), 87–92. Cundy, T., Gamble, G., Neale, L., et al. (2007). Differing causes of pregnancy loss in type 1 and type 2 diabetes. Diabetes Care, 30(10), 2603–2607. Dabelea, D., & Crume, T. (2011). Maternal environment and the transgenerational cycle of obesity and diabetes. Diabetes, 60(7), 1849–1855. Dabelea, D., Hanson, R. L., Lindsay, R. S., et al. (2000). Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: A study of discordant sibships. Diabetes, 49(12), 2208–2211. Dabelea, D., Mayer-Davis, E. J., Lamichhane, A. P., et al. (2008). Association of intrauterine exposure to maternal diabetes and obesity with type 2 diabetes in youth: The SEARCH Case-Control Study. Diabetes Care, 31(7), 1422–1426. De Wals, P., Tairou, F., Van Allen, M. I., et al. (2007). Reduction in neural-tube defects after folic acid fortification in Canada. The New England Journal of Medicine, 357(2), 135–142. G. Wang et al. Dietz, W. H. (2004). Overweight in childhood and adolescence. The New England Journal of Medicine, 350(9), 855–857. Dodd, J. M., Turnbull, D., McPhee, A. J., et al. (2014). Antenatal lifestyle advice for women who are overweight or obese: LIMIT randomised trial. BMJ, 348, g1285. Dolinoy, D. C., Weidman, J. R., & Jirtle, R. L. (2007). Epigenetic gene regulation: Linking early developmental environment to adult disease. Reproductive Toxicology, 23(3), 297–307. Dzakpasu, S., Fahey, J., Kirby, R. S., et al. (2014). Contribution of prepregnancy body mass index and gestational weight gain to caesarean birth in Canada. BMC Pregnancy and Childbirth, 14, 106. Ensenauer, R., Chmitorz, A., Riedel, C., et al. (2013). Effects of suboptimal or excessive gestational weight gain on childhood overweight and abdominal adiposity: Results from a retrospective cohort study. International Journal of Obesity, 37(4), 505–512. Flegal, K. M., Carroll, M. D., Kit, B. K., & Ogden, C. L. (2012). Prevalence of obesity and trends in the distribution of body mass index among US adults, 19992010. JAMA, 307(5), 491–497. Fujiki, K., Kano, F., Shiota, K., & Murata, M. (2009). Expression of the peroxisome proliferator activated receptor gamma gene is repressed by DNA methylation in visceral adipose tissue of mouse models of diabetes. BMC Biology, 7, 38. Gautier, J. F., Wilson, C., Weyer, C., et al. (2001). Low acute insulin secretory responses in adult offspring of people with early onset type 2 diabetes. Diabetes, 50(8), 1828–1833. Gluckman, P. D., Hanson, M. A., Cooper, C., & Thornburg, K. L. (2008). Effect of in utero and early-life conditions on adult health and disease. The New England Journal of Medicine, 359(1), 61–73. Godfrey, K. M., Sheppard, A., Gluckman, P. D., et al. (2011). Epigenetic gene promoter methylation at birth is associated with child's later adiposity. Diabetes, 60(5), 1528–1534. Greenberg, J. A., Bell, S. J., Guan, Y., & Yu, Y. H. (2011). Folic Acid supplementation and pregnancy: More than just neural tube defect prevention. Reviews in Obstetrics & Gynecology, 4(2), 52–59. Hales, C. N., & Barker, D. J. (1992). Type 2 (non-insulindependent) diabetes mellitus: The thrifty phenotype hypothesis. Diabetologia, 35(7), 595–601. Hales, C. N., Barker, D. J., Clark, P. M., et al. (1991). Fetal and infant growth and impaired glucose tolerance at age 64. BMJ, 303(6809), 1019–1022. Hattersley, A. T. (1998). Maturity-onset diabetes of the young: Clinical heterogeneity explained by genetic heterogeneity. Diabetic Medicine, 15(1), 15–24. Hattersley, A. T., & Tooke, J. E. (1999). The fetal insulin hypothesis: An alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet, 353(9166), 1789–1792. Preconception and Prenatal Factors and Metabolic Risk 57 Heijmans, B. T., Tobi, E. W., Stein, A. D., et al. (2008). Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proceedings of the National Academy of Sciences of the United States of America, 105(44), 17046–17049. Hochner, H., Friedlander, Y., Calderon-Margalit, R., et al. (2012). Associations of maternal prepregnancy body mass index and gestational weight gain with adult offspring cardiometabolic risk factors: The Jerusalem Perinatal Family Follow-up Study. Circulation, 125(11), 1381–1389. Hofman, P. L., Regan, F., Jackson, W. E., et al. (2004). Premature birth and later insulin resistance. The New England Journal of Medicine, 351(21), 2179–2186. Hovi, P., Andersson, S., Eriksson, J. G., et al. (2007). Glucose regulation in young adults with very low birth weight. The New England Journal of Medicine, 356(20), 2053–2063. International Diabetes Federation. IDF Diabetes Atlas, 5th ed. 2013.; http://www.idf.org/diabetesatlas. Accessed July 19, 2013. Irving, R. J., Belton, N. R., Elton, R. A., & Walker, B. R. (2000). Adult cardiovascular risk factors in premature babies. Lancet, 355(9221), 2135–2136. Jacobsen, S. C., Gillberg, L., Bork-Jensen, J., et al. (2014). Young men with low birthweight exhibit decreased plasticity of genome-wide muscle DNA methylation by high-fat overfeeding. Diabetologia, 57(6), 1154–1158. Janssen, B. G., Godderis, L., Pieters, N., et al. (2013). Placental DNA hypomethylation in association with particulate air pollution in early life. Particle and Fibre Toxicology, 10, 22. Johansson, S., Iliadou, A., Bergvall, N., Tuvemo, T., Norman, M., & Cnattingius, S. (2005). Risk of high blood pressure among young men increases with the degree of immaturity at birth. Circulation, 112(22), 3430–3436. Johansson, K., Cnattingius, S., Naslund, I., et al. (2015). Outcomes of pregnancy after bariatric surgery. The New England Journal of Medicine, 372(9), 814–824. Kaar, J. L., Crume, T., Brinton, J. T., Bischoff, K. J., McDuffie, R., & Dabelea, D. (2014). Maternal obesity, gestational weight gain, and offspring adiposity: The exploring perinatal outcomes among children study. The Journal of Pediatrics, 165(3), 509–515. Kaijser, M., Bonamy, A. K., Akre, O., et al. (2009). Perinatal risk factors for diabetes in later life. Diabetes, 58(3), 523–526. Kendrick, J. M. (2004). Preconception care of women with diabetes. The Journal of Perinatal & Neonatal Nursing, 18(1):14–25; quiz 26–17. Kim, Y. I. (2000). Methylenetetrahydrofolate reductase polymorphisms, folate, and cancer risk: A paradigm of gene-nutrient interactions in carcinogenesis. Nutrition Reviews, 58(7), 205–209. Kitzmiller, J. L., Block, J. M., Brown, F. M., et al. (2008). Managing preexisting diabetes for pregnancy: Summary of evidence and consensus recommendations for care. Diabetes Care, 31(5), 1060–1079. Knittle, J. L., Timmers, K., Ginsberg-Fellner, F., Brown, R. E., & Katz, D. P. (1979). The growth of adipose tissue in children and adolescents. Cross-sectional and longitudinal studies of adipose cell number and size. The Journal of Clinical Investigation, 63(2), 239–246. Koyanagi, A., Zhang, J., Dagvadorj, A., et al. (2013). Macrosomia in 23 developing countries: An analysis of a multicountry, facility-based, cross-sectional survey. Lancet, 381(9865), 476–483. Kral, J. G., Biron, S., Simard, S., et al. (2006). Large maternal weight loss from obesity surgery prevents transmission of obesity to children who were followed for 2 to 18 years. Pediatrics, 118(6), e1644–e1649. Krikke, G. G., Grooten, I. J., Vrijkotte, T., van Eijsden, M., Roseboom, T. J., & Painter, R. C. (2016). Vitamin B12 and folate status in early pregnancy and cardiometabolic risk factors in the offspring at age 5-6 years: Findings from the ABCD multi-ethnic birth cohort. BJOG, 123(3), 384–392. Krishnaveni, G. V., Veena, S. R., Karat, S. C., Yajnik, C. S., & Fall, C. H. (2014). Association between maternal folate concentrations during pregnancy and insulin resistance in Indian children. Diabetologia, 57(1), 110–121. Lawlor, D. A. (2013). The Society for Social Medicine John Pemberton Lecture 2011. Developmental overnutrition--an old hypothesis with new importance? International Journal of Epidemiology, 42(1), 7–29. Lawlor, D. A., Timpson, N. J., Harbord, R. M., et al. (2008). Exploring the developmental overnutrition hypothesis using parental-offspring associations and FTO as an instrumental variable. PLoS Medicine, 5(3), e33. Lawrence, G. M., Shulman, S., Friedlander, Y., et al. (2014). Associations of maternal pre-pregnancy and gestational body size with offspring longitudinal change in BMI. Obesity (Silver Spring), 22(4), 1165–1171. Lesseur, C., Armstrong, D. A., Paquette, A. G., Koestler, D. C., Padbury, J. F., & Marsit, C. J. (2013). Tissuespecific Leptin promoter DNA methylation is associated with maternal and infant perinatal factors. Molecular and Cellular Endocrinology, 381(1–2), 160–167. Liu, X., Chen, Q., Tsai, H. J., et al. (2014). Maternal preconception body mass index and offspring cord blood DNA methylation: Exploration of early life origins of disease. Environmental and Molecular Mutagenesis, 55(3), 223–230. Loos, R. J., Lindgren, C. M., Li, S., et al. (2008). Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nature Genetics, 40(6), 768–775. Martin, K. E., Grivell, R. M., Yelland, L. N., & Dodd, J. M. (2015). The influence of maternal BMI and gestational diabetes on pregnancy outcome. Diabetes Research and Clinical Practice, 108(3), 508–513. Matschinsky, F., Liang, Y., Kesavan, P., et al. (1993). Glucokinase as pancreatic beta cell glucose sensor and diabetes gene. The Journal of Clinical Investigation, 92(5), 2092–2098. 58 Metzger, B. E., Lowe, L. P., Dyer, A. R., et al. (2008). Hyperglycemia and adverse pregnancy outcomes. The New England Journal of Medicine, 358(19), 1991–2002. Milagro, F. I., Campion, J., Garcia-Diaz, D. F., Goyenechea, E., Paternain, L., & Martinez, J. A. (2009). High fat diet-induced obesity modifies the methylation pattern of leptin promoter in rats. Journal of Physiology and Biochemistry, 65(1), 1–9. Miller, E., Hare, J. W., Cloherty, J. P., et al. (1981). Elevated maternal hemoglobin A1c in early pregnancy and major congenital anomalies in infants of diabetic mothers. The New England Journal of Medicine, 304(22), 1331–1334. Noer, A., Boquest, A. C., & Collas, P. (2007). Dynamics of adipogenic promoter DNA methylation during clonal culture of human adipose stem cells to senescence. BMC Cell Biology, 8, 18. O’Neill, R. J., Vrana, P. B., & Rosenfeld, C. S. (2014). Maternal methyl supplemented diets and effects on offspring health. Frontiers in Genetics, 5, 289. Ong, K. K. L., Ahmed, M. L., Emmett, P. M., Preece, M. A., Dunger, D. B., & Pregnancy, A. L. S. (2000). Association between postnatal catch-up growth and obesity in childhood: Prospective cohort study. British Medical Journal, 320(7240), 967–971. Ouyang, F., Christoffel, K. K., Brickman, W. J., et al. (2010). Adiposity is inversely related to insulin sensitivity in relatively lean Chinese adolescents: A population-based twin study. The American Journal of Clinical Nutrition, 91(3), 662–671. Parkinson, J. R., Hyde, M. J., Gale, C., Santhakumaran, S., & Modi, N. (2013). Preterm birth and the metabolic syndrome in adult life: A systematic review and meta-analysis. Pediatrics, 131(4), e1240–e1263. Pettitt, D. J., Nelson, R. G., Saad, M. F., Bennett, P. H., & Knowler, W. C. (1993). Diabetes and obesity in the offspring of Pima Indian women with diabetes during pregnancy. Diabetes Care, 16(1), 310–314. Pilgaard, K., Faerch, K., Carstensen, B., et al. (2010). Low birthweight and premature birth are both associated with type 2 diabetes in a random sample of middleaged Danes. Diabetologia, 53(12), 2526–2530. Radford, E. J., Ito, M., Shi, H., et al. (2014). In utero effects. In utero undernourishment perturbs the adult sperm methylome and intergenerational metabolism. Science, 345(6198), 1255903. Reece, E. A., Leguizamon, G., & Homko, C. (1998). Pregnancy performance and outcomes associated with diabetic nephropathy. American Journal of Perinatology, 15(7), 413–421. Reynolds, R. M., Allan, K. M., Raja, E. A., et al. (2013a). Maternal obesity during pregnancy and premature mortality from cardiovascular event in adult offspring: Follow-up of 1 323 275 person years. BMJ, 347, f4539. Reynolds, R. M., Jacobsen, G. H., & Drake, A. J. (2013b). What is the evidence in humans that DNA methyla- G. Wang et al. tion changes link events in utero and later life disease? Clinical Endocrinology, 78(6), 814–822. Rigo, J., Nyamugabo, K., Picaud, J. C., Gerard, P., Pieltain, C., & De Curtis, M. (1998). Reference values of body composition obtained by dual energy X-ray absorptiometry in preterm and term neonates. Journal of Pediatric Gastroenterology and Nutrition, 27(2), 184–190. Rolland-Cachera, M. F., Deheeger, M., Maillot, M., & Bellisle, F. (2006). Early adiposity rebound: Causes and consequences for obesity in children and adults. International Journal of Obesity, 30(Suppl 4), S11–S17. Silverman, B. L., Metzger, B. E., Cho, N. H., & Loeb, C. A. (1995). Impaired glucose tolerance in adolescent offspring of diabetic mothers. Relationship to fetal hyperinsulinism. Diabetes Care, 18(5), 611–617. Silverman, B. L., Rizzo, T. A., Cho, N. H., & Metzger, B. E. (1998). Long-term effects of the intrauterine environment. The Northwestern University Diabetes in Pregnancy Center. Diabetes Care, 21(Suppl 2), B142–B149. Sinclair, K. D., Allegrucci, C., Singh, R., et al. (2007). DNA methylation, insulin resistance, and blood pressure in offspring determined by maternal periconceptional B vitamin and methionine status. Proceedings of the National Academy of Sciences of the United States of America, 104(49), 19351–19356. Sipola-Leppanen, M., Vaarasmaki, M., Tikanmaki, M., et al. (2014). Cardiovascular risk factors in adolescents born preterm. Pediatrics, 134(4), e1072–e1081. Smith, J., Cianflone, K., Biron, S., et al. (2009). Effects of maternal surgical weight loss in mothers on intergenerational transmission of obesity. The Journal of Clinical Endocrinology and Metabolism, 94(11), 4275–4283. Soubry, A., Murphy, S. K., Wang, F., et al. (2015). Newborns of obese parents have altered DNA methylation patterns at imprinted genes. International Journal of Obesity, 39(4), 650–657. Sridhar, S. B., Darbinian, J., Ehrlich, S. F., et al. (2014). Maternal gestational weight gain and offspring risk for childhood overweight or obesity. American Journal of Obstetrics and Gynecology, 211(3):259; e251–258. Tang, W. Y., Morey, L. M., Cheung, Y. Y., Birch, L., Prins, G. S., & Ho, S. M. (2012). Neonatal exposure to estradiol/bisphenol A alters promoter methylation and expression of Nsbp1 and Hpcal1 genes and transcriptional programs of Dnmt3a/b and Mbd2/4 in the rat prostate gland throughout life. Endocrinology, 153(1), 42–55. Torloni, M. R., Betran, A. P., Horta, B. L., et al. (2009). Prepregnancy BMI and the risk of gestational diabetes: A systematic review of the literature with metaanalysis. Obesity Reviews, 10(2), 194–203. Uthaya, S., Thomas, E. L., Hamilton, G., Dore, C. J., Bell, J., & Modi, N. (2005). Altered adiposity after extremely preterm birth. Pediatric Research, 57(2), 211–215. Preconception and Prenatal Factors and Metabolic Risk 59 Wang, D., Liu, X., Zhou, Y., et al. (2012). Individual variation and longitudinal pattern of genome-wide DNA methylation from birth to the first two years of life. Epigenetics: Official Journal of the DNA Methylation Society, 7(6), 594–605. Wang, G., Divall, S., Radovick, S., et al. (2014a). Preterm birth and random plasma insulin levels at birth and in early childhood. JAMA, 311(6), 587–596. Wang, G., Chen, Z., Bartell, T., & Wang, X. (2014b). Early Life Origins of Metabolic Syndrome: The Role of Environmental Toxicants. Current Environmental Health Reports. doi:10.1007/s40572-013-0004-6. Wang, G., Hu, F., Mistry, K., et al. (2016). Maternal Prepregnancy BMI, Plasma Folate Level and Child Metabolic Risk. JAMA Pediatrics. 170(8), e160845. Waterland, R. A., Travisano, M., Tahiliani, K. G., Rached, M. T., & Mirza, S. (2008). Methyl donor supplementation prevents transgenerational amplification of obesity. International Journal of Obesity, 32(9), 1373–1379. Williams, M. A., Qiu, C., Dempsey, J. C., & Luthy, D. A. (2003). Familial aggregation of type 2 diabetes and chronic hypertension in women with gestational diabetes mellitus. The Journal of Reproductive Medicine, 48(12), 955–962. Yang, B. T., Dayeh, T. A., Kirkpatrick, C. L., et al. (2011). Insulin promoter DNA methylation correlates negatively with insulin gene expression and positively with HbA(1c) levels in human pancreatic islets. Diabetologia, 54(2), 360–367. Yang, Q. Y., Liang, J. F., Rogers, C. J., Zhao, J. X., Zhu, M. J., & Du, M. (2013). Maternal obesity induces epigenetic modifications to facilitate Zfp423 expression and enhance adipogenic differentiation in fetal mice. Diabetes, 62(11), 3727–3735. Yokomori, N., Tawata, M., & Onaya, T. (1999). DNA demethylation during the differentiation of 3T3-L1 cells affects the expression of the mouse GLUT4 gene. Diabetes, 48(4), 685–690. Zhang, S., Liu, X., Brickman, W. J., et al. (2009). Association of plasma leptin concentrations with adiposity measurements in rural Chinese adolescents. The Journal of Clinical Endocrinology and Metabolism, 94(9), 3497–3504. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities A Background Paper for the MCH Life Course Research Network W. Thomas Boyce and Clyde Hertzman But Laura lay awake a little while, listening to Pa’s fiddle softly playing and to the lonely sound of the wind in the Big Woods. She looked at Pa sitting on the bench by the hearth, the firelight gleaming on his brown hair and beard and glistening on the honey-brown fiddle. She looked at Ma, gently rocking and knitting. She thought to herself, ‘This is now.’ She was glad that the cozy house, and Pa and Ma and the firelight and the music, were now. They could not be forgotten, she thought, because now is now. It can never be a long time ago. Little House in the Big Woods Laura Ingalls Wilder (1932) 1 Introduction: What’s So Special About the Early Years? In these lines from the Little House book series recalling her early years in a nineteenth-century pioneer family, Laura Ingalls Wilder evoked what we deem a timeless and evocative, if modestly sentimental, image of childhood: children’s dependency upon the reliable and protective presence of parents, children’s uniquely vulnera- W. Thomas Boyce (*) Departments of Pediatrics and Psychiatry, University of California San Francisco, San Francisco, CA, USA e-mail: istom.boyce@ucsf.edu C. Hertzman Human Early Learning Partnership, School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada ble and consequential sensibilities, their openness to the character and “feel” of the physical and social worlds, and their singular immersion in the present, the moment at hand. The overarching goals of this chapter are to survey extant literature examining evidence for these special susceptibilities of children to social-environmental conditions, to show how variation in life course health development is attributable to interactive differences in constitutional susceptibility and contextual exposure, and to summarize lacunae in our collective vision of how early adversity becomes biologically embedded in the course of individual health development. Wilder’s portrait of early childhood is the same image promulgated in the eighteenth century writings of Jean Jacque Rousseau, who affirmed children’s inherent goodness and receptivity to the social world and educational institutions’ responsibility never to impede children’s natural independence and curiosity (see Émile, or Treatise © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_4 61 62 on Education). Such an image of childhood is also wholly commensurate with twenty-first century visions of youth as a season of special vulnerability within the human life course health development—a developmental discontinuity from the less susceptible, more defended reaches of adulthood and life in an adult society. Some historians of childhood have claimed that these visions of children’s special sentience and fragility are social constructions—products of the temporal and cultural epoch in which we now live. Philippe Ariès in his Centuries of Childhood (Ariès 1962) and Edward Shorter in The Making of the Modern Family (Shorter 1975) argued, for example, that prior to the seventeenth and eighteenth centuries, fewer distinctions were made between children and their adult counterparts in their needs, capacities, and proclivities. They further held that, as a consequence of the extraordinary rates of infant mortality at the time (25–50%; King 2007), parents were less likely to make strong emotional investments in children. Other scholars have judged such claims hyperbolic, however, discerning evidence for qualitative differentiation between children and adults in ancient Egyptian songs (“Hast thou come to kiss the child?/ I will not let thee kiss him!/ Hast thou come to silence him?/ I will not let thee set silence over him!” (Hrdy 1999)), in the writings of Renaissance Italy and Reformation Germany (King 2007) and in the plays of Shakespeare (“Those that do teach young babes/ Do it with gentle means and easy tasks/ He might ha’ chid me so; for, in good faith,/ I am a child to chiding.” Romeo and Juliet) (Chedgzoy et al. 2007). Cultural historian Peter Stearns (Stearns 2011) thus maintained that a special affection for and attentiveness to children and children’s vulnerability are no inventions or fabrications of modernity. Nonetheless, ethnogeographer Jared Diamond points to the dramatic variation that exists, even in the contemporary world, among parenting practices emanating from this differentiated and particular view of childhood (Diamond 2012). Both Stearns and Diamond cite, for example, the historical and contemporary practice of swaddling infants as an iconic parenting act that is grounded in deep cultural beliefs about the nature W. Thomas Boyce and C. Hertzman of infancy and childhood, about the relationship of children to parents, and about the fundamental nature of the human child. Hrdy shows how extraordinary differences in mothering behavior have occurred, historically and contemporaneously, as a consequence of sociocultural and economic conditions, despite strong, evolutionarily conserved commonalities in innate maternal responses (Hrdy 1999). Further, Barr et al. (1991) demonstrate that developmental and temporal patterns of infant crying are invariant between North American and !Kung San infants, but parental responses to such crying are highly divergent. Though parenting practices come and go and beliefs regarding the consequences of such practices vary by geography and cultural era, most historical and present human societies have attributed to young children a special susceptibility to the character of the social and physical contexts in which they are reared. There have also been abiding cultural convictions—crossing geography and time—that the special sensibilities of young children, when exposed to psychological and physical adversities, can produce forms of morbidity extending well beyond the exposure itself, into the domain of adult life. We have strongly held beliefs in the continuities—for better and for worse—between childhood and adulthood. John Milton (1608– 1674) wrote in Paradise Regained that “The childhood shews the man/ As morning shews the day” and William Wordsworth (1770–1850) observed, in My Heart Leaps Up When I Behold, that “The Child is father of the Man.” Marcel Proust (1871–1922) described, in Remembrance of Things Past, the sudden, flooding and vivid awareness of a childhood memory in an ordinary moment of adult life: When from a long distant past nothing subsists, after the people are dead, after the things are broken and scattered, taste and smell alone, more fragile but enduring, more unsubstantial, more persistent, more faithful, remain poised a long time, like souls, remembering, waiting, hoping, amid the ruins of all the rest. In the early and mid-twentieth century, these long held beliefs about childhood’s capacity for intrusion into the experiences of adult life began Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities to find their way into the borderlands of emerging science. Freud famously asserted that neurotic behavior and obsessions manifesting in midadulthood were often linked, without conscious awareness, to experiences of trauma sustained decades before, in very early life (Freud 1940). Biologist and scholar René Dubos invoked “biological Freudianism” to argue that the adverse exposures of childhood can produce lasting neurobiological predispositions that persist even when such exposures are later abated or gone (Dubos et al. 1966). Jerome Kagan (e.g., Brim and Kagan 1980) observed the ways in which human health development is characterized by both constancy and sometimes remarkable discontinuity across developmental periods. Though the experiences of childhood may not overdetermine or dominate the strengths and liabilities of adult life, our cultural heritage avows not only a child’s special receptivity to the provisions and predations of its early world but the lifelong echoes of that receptivity, as well. 2 Susceptibility and Continuity: The Echoes of Childhood The now thriving science of how early social experience influences lifelong health and development might be legitimately portrayed as building a scientific foundation for these two longstanding tenets of cultural belief. These are the claims (a) that childhood is imbued with a distinctive susceptibility to the character of the social and physical world and (b) that, partially as a consequence of the former, the exposures of early life—those both damaging and sustaining—continue to exert their effects on health and well-being long beyond the childhood years. John Bowlby thus concluded that the infants of all primate species show an evolutionarily conserved predisposition to form powerful emotional attachments to their mothers, striving to stay close to her side at all times and thereby reducing the risks of abandonment or endangerment (Bowlby 1969). Harlow, Suomi, and col- 63 leagues, in the nonhuman primate equivalent of Bowlby’s work with human children, showed that the drive toward early attachment is so strong that, in the mother’s absence, an infant macaque will physically and emotionally bond to an inanimate surrogate but with subsequent behavioral aberrations that extend through the infant’s lifetime (Harlow et al. 1971). Over the last two decades, Barker and colleagues produced a large body of findings suggesting that chronic, life-threatening cardiovascular disease in mid- to late life is derived from nutritional deficits and impaired growth occurring in very early, even prenatal life (see, e.g., Barker 1990; Barker and Bagby 2005; Bock and Whelan 1991). Kuh and Ben-Shlomo (2004) outlined a “life course approach” to chronic disease epidemiology, assembling convergent evidence for temporally distant relations between markers of childhood deprivation and compromise and indices of chronic morbidity in adult life. The Human Development Program of the Canadian Institute for Advanced Research (CIFAR) showed how population health and developmental science coincide to reveal powerful societal effects on child development and health and how such effects are transcribed into lifetimes of socially partitioned differences in adult disease (Keating and Hertzman 1999). The American Academy of Pediatrics has recently called public attention to the issue of significant, “toxic” stress and adversity in the lives of young children and asserted that many of the chronic disorders of adult life should now be regarded as “developmental disorders,” stemming as they do from adverse childhood experiences and events. Abstracting these and other programs of research, three major reports—in the USA (Shonkoff and Phillips 2000), the UK (Marmot 2010), and Canada (Boivin and Hertzman 2012)—have sought and achieved a multidisciplinary consensus that the experiences of early life, dramatically partitioned by aspects of socioeconomic status (SES) and social position, result in societies with widely divergent developmental and health outcomes. Such societies produce visibly and persistently disadvantaged social groups W. Thomas Boyce and C. Hertzman 64 whose children follow blighted developmental trajectories, acquire chronic disease and disability more frequently and severely, learn and achieve less well, and live substantially shorter, less adaptive lives. 3 Constitution, Context, and the Nonrandom Distribution of Morbidity Although strong, multinational consensus has been reached on the power and pervasiveness of epidemiologic findings in linking early experience to lifelong disparities in health development and health, urgent and challenging new research questions remain. Among these are the following: • What is the source of the extensive variability in associations between early adversity and health? Why are some children profoundly affected by early stressors and disadvantage, while others appear to thrive in the face of great risk? • How does it happen? That is, what are mechanisms and mediators by which early exposures become biologically embedded in development, yielding lifelong differences in health? How does adversity “get into the body,” altering long-term pathogenic processes that predispose to disease and disorder? • What role, if any, do constitutional differences in vulnerability—genetic variation, neurobiological response biases, or enduring temperamental attributes—play in the consequences of early life adversity? • Does the timing, frequency, and persistence of exposure matter? That is, is there unevenness in the effects of adversity by the age of the child or by the intensity and duration of the exposure? • Are there transgenerational effects of early social adversity? Are the “iniquities of the father visited upon their children, to the second and third generation” (Exodus 34:7)? Are the traumas of a single generation transmitted into the lives of the next? Such questions are not just scientifically compelling; they do not simply appeal to our twentyfirst century zeal to understand nuance, contingency, and mechanism. Conceiving and implementing effective societal interventions to mitigate health disparities will be necessarily conditional upon clear and instructive answers. As an organizational approach to these and other life course health development questions, we propose a useful convergence of two fundamental principles emerging from this new science of early childhood experience and development. These are: Principle 1: Most developmental and health endpoints in human populations are derived from causal interactions between contextual and constitutional factors. Principle 2: Disordered development and health are nonrandomly distributed by population, space, and time. Principle 1 affirms a now broadly accepted refutation of earlier claims that maladaptive outcomes are principally genetically or environmentally determined. Incontrovertible evidence now rejects both environmental and genetic determinism and views the genesis of disease as involving a dynamic interplay between environments and genes. Even the simple partitioning of developmental variance into environmental and genetic components falls short of a truly interactive view of the conjoint operation of nurture and nature (Boyce et al. 2012b; Meaney 2010). Importantly, our assertion that causal interactions between contextual and constitutional differences will predominate in the pathogenesis of human morbidities does not entail a claim that no main effects of such differences will be found. Far more research is needed to conclude confidently that gene-environment interplay predominates in the pathogenesis of most human diseases, but such interactive processes appear almost certainly more prevalent and common that was imagined even a decade ago (Dunn et al. 2011; Wright and Christiani 2010). Principle 2 contends, as did Bronfenbrenner’s bioecological model (e.g., Bronfenbrenner and Morris 2006), that disorders of health and development are neither evenly nor stochastically distributed Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities within human populations and that such disorders are instead informatively clustered by population (both within and across populations), space (both geography and jurisdiction), and time (both developmental and historical). The maldistribution of disorder within human populations signals important and reliable differences in susceptibility to various forms of morbidity and their causal agents. The aggregation of morbidities by space and time extends beyond conventional understandings of epidemicity to include geographic and mobility effects on morbidity and critical periodicity in developmental processes. A cross-referencing of these Principles 1 and 2 produces the table below, which divides supportive evidential territory into six domains of research: I. social stratification, poverty, and subordination; II. vulnerability, resilience, and neurobiological susceptibility; III. the topography of vulnerability; IV. marginalization and scapegoating; V. history and affliction; and VI. critical periodicity. These six domains codify the contextual and constitutional sources of disease maldistribution, aggregated by population, space, and time. In the sections that follow, we abstract and review each of these arenas of research, which together serve to summarize current and emerging findings on the lifetime effects of early childhood exposures. The crosscutting, confluent cell below the six domains designates a section on interactions between context and constitution, in the form of gene-environment interplay, and their role in the biological embedding of early social adversities and the emergence of what has been 65 called “the social brain.” Finally, as indicated in table’s bottom cell, we conclude with a specification of missing areas of study, unaddressed questions, and an agenda for future work on the developmental origins of life morbidities (Table 1). 4 Six Domains of Early Developmental Research 4.1 Social Stratification, Poverty, and Subordination Among the most well-replicated, populationlevel findings in all of the child health services research is illustrated in Fig. 1. Within childhood populations from a broad diversity of settings, cultures, and societies, a subgroup of 15–20% sustain over half of the populations’ morbidities, both biomedical and psychiatric, and are responsible for over half of the health-care utilization (Boyce 1985; Diaz et al. 1986). The public health implications of this finding are self-evident: if we could explain and address the disproportionate morbidity carried by this small subgroup of childhood populations, we might prevent over half of the population’s diseases, injuries, and disorders. The search for an explanation for this highly nonrandom distribution of human morbidity begins and ends with the phenomenon of social stratification, that is, the hierarchical social organization that characterizes much of invertebrate to Table 1 Domains of existing, anticipated, and needed research, organized by Principles 1 and 2 Principle 2: Disordered outcomes are nonrandomly distributed by Population Space Time Principle 1: Developmental and health outcomes stem from interactions between Context Constitution I. Social stratification, poverty, II. Vulnerability, resilience, and and subordination neurobiological susceptibility III. The topography of IV. Marginalization and vulnerability scapegoating V. History and affliction VI. Critical periodicity Interactions between context and constitution, gene-environment interplay An Agenda for Future Research 66 W. Thomas Boyce and C. Hertzman Fig. 1 The nonrandom distribution of biomedical and psychiatric morbidities in child populations vertebrate phylogeny and manifests as SES within human societies. SES constitutes the single, most powerful epidemiologic determinant of virtually all forms of morbidity, a predictor so potent that we question the authenticity of other associations failing to adjust for SES (Adler et al. 1993, 1994). Beginning early in life, impoverished children and families sustain higher rates of virtually every form of human malady: from low birth weight (Blumenshine et al. 2010) to traumatic injury (Brown 2010), from infectious disease (Dowd et al. 2009) to dental caries (Boyce et al. 2010), and from developmental disability (Msall et al. 1998) to poorer academic achievement (Kawachi et al. 2010). Socioeconomic stratification of developmental psychopathology, moreover, which often emerges in preclinical form in the middle childhood years, exerts lasting influences on academic achievement, employment success, interpersonal relationships, and lifelong wellbeing (Offord 1995; U.S. Department of Health and Human Services 2011). Figures 2 and 3 show how poor and neglected populations bear disproportionate burdens of disease and disorder throughout the life span (see Marmot 2010)—Fig. 2 showing the graded association between SES and socioemotional adjustment from middle childhood through adolescence and Fig. 3 revealing the same relation with chronic disease at each stage of the life course. Despite this universality and potency of societal disparities in health, it is only recently that SES has become itself a focus of serious scientific study (Syme 2008). New studies of the SES antecedents of population morbidities have recognized the extended influence of childhood social status on adult disease, even after controlling for SES in adulthood (Cohen et al. 2010; Galobardes et al. 2004; Lawlor et al. 2006). Such evidence for lifelong effects of early disadvantage is rendered still more compelling by research documenting the developmental origins of adult health and disease (Gluckman et al. 2005, 2009) and by epidemiologic work revealing systematic differences in nutrition (Khan and Bhutta 2010), access to medical care (Houweling and Kunst 2010), and physical environmental exposures (Gump et al. 2007) among children of differing social classes. Although these and other material factors undoubtedly play roles in the origins of health disparities (Hackman et al. 2010), another prominent and increasingly persuasive explanation for such disparities is the SES-stratified differences in early exposures to family adversities and stressors (Brown et al. 2009; Hillis et al. 2004; Wadsworth et al. 2008). Low-SES children live with significantly more family chaos (Evans et al. 2005), sustain more frequent and severe psychological stressors (Evans 2004; Evans and Kim Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities 67 Percent poor adjustment 25 20 15 10 5 0 I/II Aged 7 IIINM IIIM IV/V Social class at birth Aged 11 Aged 16 Source: 1958 National Child Development Study64 Fig. 2 Rates of poor social/emotional adjustment at ages 7, 11, and 17, by father’s social class at birth (Marmot 2010) 2007), and experience fewer supportive parentchild communications (Hart and Risley 1995), relative to their higher SES peers. Low SES children also show greater neurobiological sensitivity to aversive social contexts, in the form of heightened reactivity within the stress-responsive, neuroendocrine pathways, i.e., the hypothalamicpituitary-adrenocortical (HPA) axis and the autonomic nervous system (ANS) (Boyce et al. 2010; Evans and Kim 2007; Lupien et al. 2001; Steptoe et al. 2002). Further, the exaggerated reactivity of these peripheral stress response systems among disadvantaged youth is subserved by socially partitioned differences in central neural circuits, which also have profound influences on health development, cognitive function, and educational attainment (Curley et al. 2011; Cushing and Kramer 2005; Gianaros and Manuck 2010; Hackman and Farah 2009; MacDonald and Roskams 2009; McEwen and Gianaros 2010). Influences of childhood SES on health and neurobiological endpoints extend beyond concur- rent effects on health and responsivity during childhood to longer-term relations with health status in adult life. Adverse experiences in early childhood, for example, may carry risk effects for coronary artery disease in adult life before the age of 50 years and effects mediated by some form of biological embedding (Kittleson et al. 2006). One important set of candidate mechanisms for such temporally distant effects is the constellation of pro-inflammatory changes that have been shown associated with childhood disadvantage. Programs of work by Miller and Chen (Miller et al. 2009a; Miller et al. 2009b), Cole (Cole 2009; Cole et al. 2007) and others (Danese et al. 2008; McDade 2012), for example, have demonstrated systematic differences in cytokine signaling pathways, transcriptional control, and risk for inflammationand immune-mediated disease. These studies suggest that changes in immune responsivity and the molecular processes involved in inflammation may be one category of mediating events linking childhood social adversity to adult disease. W. Thomas Boyce and C. Hertzman 68 (a) Males Percent 60 Socioeconomic classification (NS-SEC) 50 40 30 20 10 0 0-15 16-44 45-64 65 & over 45-64 65 & over Age (b) Females Percent 60 Socioeconomic classification (NS-SEC) 50 40 30 20 10 0 0-15 16-44 Age Large employers and higher managerial Small employers and own account Higher professional Lower supervisory and technical Lower managerial and professional Semi-routine Intermediate Routine Note: NS-SEC=National Statistics Socioeconomic Classification Source: Office for National Statistics General Household Survey52 Fig. 3 Percentage of males and females with limiting long-term illness, by age and socioeconomic classification (Marmot 2010) Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities Finally, there is emerging evidence that experiences of social subordination per se, themselves prevalent sources of human adversity (Keltner et al. 2003), may be related to disorders of mental and physical health, educational failure, and maladaptive behavior, especially in the young. The works of Pellegrini (Pellegrini et al. 2007, 2010), Hawley (Hawley 1999), and Fehr (Fehr et al. 2008) and their colleagues, describing the developmental emergence, behavioral signs, and socioemotional sequelae of childhood dominance and egalitarianism, have offered important “down payments” on such a research agenda. More recently, we have examined linkages between subordination and maladaptive health outcomes in a socioeconomically and ethnically diverse sample of kindergarten children, assessing associations between experiences of dominancesubordination and patterns of maladaptive behavior (Boyce et al. 2012a). As shown in Fig. 4, we found that indicators of mental health in 5-year-old children were stratified within classroom hierarchies in a manner parallel to that seen in larger, adult societies. Children occupying subordinate positions within their classrooms had more depressive symptoms, more frequent episodes of inattention, fewer positive peer relationships, and less evidence of prosocial behavior. Further, we found that the children most likely to show low or diminishing health status Fig. 4 Depression by social position and learner-centered pedagogical practices (LCPs) at half-SD increments from the mean (−1, −0.5, mean, +0.5, +1 SD) (Boyce et al. 2012a) 69 were those occupying subordinate social positions who also came from low SES families, and those least likely to show a diminution in health came from classrooms with teachers highly invested in learner-centered pedagogical practices (LCPs). Indeed, as shown in Fig. 4, the link between rank and behavior nearly disappeared in classrooms with strong teacher LCPs, suggesting that classrooms with egalitarian, student-centered “cultures” produced diminished mental health effects of hierarchical social ranks. Such findings—that social subordination is associated with decrements in mental health, even within hierarchical, early childhood groups—are commensurate, moreover, with prior findings that subjective estimates of social class may be a stronger predictor of health outcomes than objective indicators, such as job status, income, or wealth (Adler et al. 2000; Goodman et al. 2003; Ostrove et al. 2000), and that dominance status in primate social hierarchies is similarly associated with health, even among captive animals with equal access to food, open environments, and veterinary care (Abbott et al. 2003; Cohen et al. 1997; Kaplan et al. 1982; Sapolsky 2005). A growing body of research thus demonstrates powerful, graded effects of childhood social stratification and SES on development and health, both in children and their adult counter- W. Thomas Boyce and C. Hertzman 70 parts. Such effects may account, at least in part, for the strikingly uneven distribution of morbidities within human populations. Nonetheless, as noted by Kessler, Duncan, and colleagues (e.g., Kessler et al. 2014), results of large-scale human experiments challenge assumptions that simply augmenting financial resources or moving children out of high-poverty neighborhoods will have salutary effects on health development. Childhood SES effects are likely mediated by a broad array of factors that include diet, exposures to toxic environmental agents, differences in parenting, and access to health care, but substantial evidence also now implicates differential exposures to stress and adversity as a major mechanism of SES influence. Acquired differences in neurobiological reactivity to stressors and pro-inflammatory shifts in immune signaling pathways may also play an important mediating role in SES-health relations. Lastly, research has also begun to document effects of social subordination per se on childhood health outcomes, independent of family or neighborhood SES. Thus, relations between social position within classroom peer hierarchies and subsequent health outcomes show the same graded associations with morbidity found in studies of nations and large human populations. 4.2 Vulnerability, Resilience, and Neurobiological Susceptibility The aggregation of ill health within human populations appears also attributable to constitutional differences underlying a remarkable variation in susceptibility to social-environmental influence. There is now growing evidence for a generalized sensitivity to social contexts within a human subgroups, i.e., highly sensitive or environmentally “permeable” individuals showing maladaptive outcomes in conditions of adversity but unusually positive outcomes in settings characterized by support, predictability, and protection (see, e.g., Aron et al. 2012; Belsky et al. 2007; Boyce 2016; Boyce and Ellis 2005; Ellis et al. 2011a). Such individuals thus show either the least or most adaptive outcomes within the population, depending upon the character of the proximal social contexts in which they are reared. Studies demonstrating this greater susceptibility of neurobiologically responsive children to both positive and negative aspects of their environments have implicated a wide variety of: • Stressors and adversities, including paternal depression (Cummings et al. 2007), marital conflict (El-Sheikh 2005; El-Sheikh et al. 2007), parental psychopathology (Shannon et al. 2007), and overall family distress (Obradovic et al. 2010) • Positive environmental features, including parental warmth (Ellis et al. 1999), beneficial experiences and exposures (Pluess and Belsky 2013), and supportive interventions (BakermansKranenburg et al. 2008a) • Defining biological parameters, including physiological reactivity (e.g., Alkon et al. 2006; Boyce et al. 1995), differences in brain circuitry (Whittle et al. 2010), and gene polymorphisms (Bakermans-Kranenburg et al. 2008b; Knafo et al. 2011; Manuck et al. 2011) Most importantly, highly susceptible children (and adults) show bidirectional effects on outcomes in contrasting highly supportive and highly stressful settings—not simply an attenuation of negative effects in low stress circumstances. Such differences in neurobiological sensitivity—likely based, in part, on genetic and epigenetic variation (Boyce and Kobor 2015)— are figuring prominently in the field’s exploration of the biological embedding of early stress. Findings suggesting differential susceptibility have been replicated in many samples of children, a troop of rhesus macaques (Boyce et al. 1998), and in a series of randomized controlled trials of socially supportive interventions (Bakermans-Kranenburg and Van Ijzendoorn 2015; Quas et al. 2004). Neurobiological sensitivity has also been the focus of a ten-paper special section of Development and Psychopathology Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities 71 Fig. 5 Incidence of violent injuries in free ranging macaques by confinement stress and reactivity status (Boyce et al. 1998) Fall Baseline Daily Cortisol Expression Met carrier Val/Val 1.0 0.7 0.4 0.1 -0.2 -0.5 -0.8 - 1 SD +1 SD Family Income Fig. 6 Daily baseline cortisol expression by family SES and BDNF polymorphism (N. Bush, personal communication, unpublished data, 2017) (see Ellis et al. 2011a). As shown in the accompanying Figs. 5 and 6, examples have included the differential rates of violent injuries among high and low reactivity rhesus macaques before and during a prolonged period of confinement stress (Fig. 5) and the level of HPA activation among kindergartners by allelic variants in the BDNF gene and family income (Fig. 6). In both cases, the “risky endophenotype” (i.e., high reactivity status or being a Met carrier in the BDNF polymorphism) showed high levels of maladaptive outcomes (injuries or basal cortisol secretion) under stressful conditions but remarkably low levels (lower even than their low-risk counterparts) in positive, low stress settings. Importantly, strong evolutionary arguments have been made, albeit not without dissension (e.g., Bateson and Gluckman 2011; Overton and Lerner 2014; West-Eberhard 2003) for the conservation of environmental sensitivity within rearing environments of evolutionary adaptedness. Slavich and Cole, for example, have summarized recent evidence on how social-environmental exposures can regulate gene expression, thereby calibrating individual sensitivity to social context (Slavich and Cole 2013). Such sensitivity appears to influence not only the rates and severity of morbidity but also the timing and pace of important developmental transitions, such as puberty (Ellis et al. 2011b). This characteristic, which probably becomes increasingly trait-like over the course of development, appears to emerge as a conditional adaptation, garnering signals from the early social environment about the inherent levels of threat, adversity, support, and nurturance that the growing child is likely to encounter and calibrating stress-responsive biological systems to optimize survival, health, and developmental well-being (Ellis et al. 2006, 2011a; Hane and Fox 2006). Understanding phenotypic variation in environmental susceptibility might logically also play a role in the conceptualization and development of newly individualized approaches to “precision medicine” (Committee on a Framework for Development a New Taxonomy of Disease 2011). W. Thomas Boyce and C. Hertzman 72 4.3 The Topography of Vulnerability In addition to their aggregation within population subgroups, developmental vulnerability and ill health are also clustered by spatial geography. It is well known, for example, that of the more than 7 million of the world’s children less than 5 years of age that die each year, the majority live in the regions of sub-Saharan Africa and South Asia (Liu et al. 2012). Equally familiar is the epidemiologic, small area clustering of neurodevelopmental disorders such as autism spectrum disorder (Mazumdar et al. 2010). Less well known, but equally compelling, are the dramatic differences in developmental vulnerability at primary school entry—differences with major implications for learning and academic success—that are found within major national jurisdictions, such as the Canadian province of British Columbia (see Fig. 7). Between 2000 and 2011, the provincial BC government, with the assistance and oversight of the Human Early Learning Partnership at UBC Vancouver, completed four population-based assessments of developmental health. Assessments were completed during the kindergarten year, and developmental health was measured using the Early Development Instrument (EDI), a kindergarten teacher-completed checklist for each child based on five scaled measures of development: physical well-being, social competence, emotional maturity, language and cognitive development, and communication and general knowledge (Janus and Offord 2000). The EDI yields, for each child and each scale, a score as “vulnerable” or “not vulnerable.” The designation as “vulnerable” is not given directly to the family, however; rather, rates of EDI vulnerability are computed and mapped according to residential neighborhoods where children live. Mapping is done by neighborhoods because local geography defines unique combinations of factors that support or undermine early child development. Across Canadian neighborhoods, vulnerability rates on one or more of the EDI scales range from Fig. 7 Neighborhood variation in developmental vulnerability in British Columbia on the early development instrument (http://earlylearning.ubc.ca/interactive-map/) Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities as low as 4% to as high as 69%, such that there is a more than 17-fold inequality in developmental vulnerability in Canada at the level of the neighborhood. This range is much larger than would be predicted on the basis of random sample surveys of child development, which rarely demonstrate social gradients larger than threefold. Neighborhoods aggregate family environments, reflect broader environments, and have emergent properties, such as safety and social cohesion, that influence developing children. They tend to include families of similar socioeconomic status, unique mixes of relational characteristics (i.e., the factors that shape a family’s social identity), and similar levels of access or barriers to programs and services. For analytical purposes, random sample surveys aggregate children from different geographical locales into statistical (rather than real) neighborhoods according to a small number of grouping factors (e.g., median family income or proportion of adults with high school graduation). Such “neighborhood effect” analyses from random sample surveys do not capture the unique circumstances of real neighborhoods. Thus, the 17-fold variation in EDI-estimated developmental health validates this spatial geographic approach to local differences, since only this tactic captures the circumstances of real neighborhoods (Hertzman 2010; Kershaw et al. 2009). Such an approach has been used, for example, to reveal that optimal, area level EDI scores occur within neighborhoods with mixed, rather than uniformly affluent, SES representation, i.e., those with relatively equal proportions of affluent and disadvantaged families (Carpiano et al. 2009). Adopted on an even larger scale, such methods are capable of offering social ecological perspectives on the physical, socioeconomic, and social political conditions of children on a global scale (Panter-Brick et al. 2012). 4.4 Marginalization and Scapegoating Interpersonal marginalization and scapegoating can also contribute to uneven distributions of morbidity and maladaptive development, often based on targeting by individual, constitutional characteristics. 73 Victimization is a well-represented and elemental human behavior, even within groups of very young children. Described by Lord Byron as “the longest pleasure” (Don Juan, 13th Canto: “Now hatred is by far the longest pleasure; Men love in haste but they detest at leisure.”), hatred and stigmatization have remained, for all of history, a sadly defining feature of human societies. Family systems theory has regarded scapegoating and the designation of an “identified patient” as an effective if personally costly means of securing family solidarity (Vogel and Bell 1961), much as the vilification of Simon in William Golding’s Lord of the Flies served the cohesion of a marooned band of lost school boys. Behavioral vulnerability stemming from physical weakness, anxiety, poor social skills, or chronic illness has been shown to engender victimization in groups of children (Egan and Perry 1998; Sentenac et al. 2012), and within nonhuman primate troops, the active targeting of highly reactive, more sensitive members has been described during periods of intensive group stress (Boyce et al. 1998). Far from restricted to Western, individualist societies, bullying and victimization are also highly prevalent in more collectivist cultures (Kim et al. 2004). Among human children and youth, such victimization can undermine mental health and lead to poor academic performance (Glew et al. 2005), psychosomatic disorders (Arseneault et al. 2006; Gini and Pozzoli 2009), suicidal ideation behavior (Kim et al. 2009; Turner et al. 2012), and later criminality (Sourander et al. 2007). Among the survivors of populationlevel genocide, specific genetic variants have been linked to traumatic memory retention and the level of risk for post-traumatic stress disorder (PTSD) (de Quervain et al. 2012). Abusive experiences in children, whether at the hands of family or peers, become itself a social determinant of adult mental and physical health (Greenfield 2010), resulting in the sequestering of morbidities within both perpetrators and their victims. 4.5 History and Affliction As Kuzawa and Thayer noted (Kuzawa and Thayer 2011), human adaptation to environmental conditions takes place at a variety of timescales, ranging from homeostatic changes that can W. Thomas Boyce and C. Hertzman 74 occur over seconds or minutes to developmental plasticity emerging over months or years to conserved genetic changes that operate on a timescale of millennia. Thus, historical contexts can also concentrate and skew distributions of disease and disorder over epochs of time. For example, prenatal exposures to a natural disaster, such as the 1998 Québec Ice Storm, can contribute to the risk for childhood obesity, even after adjustment for breastfeeding, SES, obstetrical complications, and smoking during pregnancy (Dancause et al. 2012). During historical periods of war, elevated lifetime risks for PTSD are found among children exposed to violent or traumatic events (Javidi and Yadollahie 2012). Researchers have also studied how early life exposures to famine can influence adult health, using historical cohort data from the Finnish crop failure famine of 1866–1868, the Dutch Hunger Winter of 1944–1945, the Siege of Leningrad in 1944, the seasonal famines in the Gambia between 1949 and 1994, and the Chinese Great Leap Forward famine of 1959–1961. Outcomes examined have included adult height and weight, glucose metabolism, blood pressure, lipid profiles, metabolic syndrome, cardiovascular outcomes, self-reported health, mental performance and cognition, mental disorders, and adult mortality (Falconi et al. 2017; Lumey et al. 2011). Exposure episodes related to war, such as the 1944–1945 Dutch Famine of World War II, resulted in elevated odds of developing coronary heart disease in adult life (Painter et al. 2005; Roseboom et al. 2000) and in systematic differences in the epigenetic modification of genes such as insulin-like growth factor (IGF-2) (Heijmans et al. 2008) and aspects of risk that appear capable of crossing into unexposed generations (Painter et al. 2008). Genocide, whether historical in the case of the Holocaust or contemporary, as in the Rwandan atrocities, exerts powerful, lasting damage on children’s prospects for health and wellbeing, both contemporaneous and longitudinal (Hazani and Shasha 2008; Oberg 2008). Each of these examples illustrates how time, in the form of historical period, and context, in the form of broad socioenvironmental perturbations and assaults, converge to influence and bias life course health development. 4.6 Critical Periodicity Finally, events and exposures within developmental time, rather than historical time, also create constraints and opportunities for healthy development and the avoidance of disorder. The function of neuronal circuits in the brain is guided by experiences in postnatal life that regulate the maturation of inhibitory connections and interneurons (Hensch 2005). Critical periods exist, for example, for the acquisition of language and discrimination of speech sounds in human infants (Weikum et al. 2012), and exposure to music can change auditory preferences in young mice through changes in prelimbic and infralimbic medial prefrontal cortex during an early critical period (Yang et al. 2012). Adult brain plasticity appears to become restricted by structural and functional developmental “brakes,” such as perineuronal nets and myelin that inhibit neurite growth and the balance between excitatory and inhibitory circuitries (Bavelier et al. 2010). Genetic, pharmacological, and environmental influences can alter such plasticity, suggesting future opportunities for reopening closed critical periods, such as those for language acquisition or vision, or enhancing recovery from traumatic or cerebrovascular brain injury. 5 Interactions Between Context and Constitution: The Biological Embedding of Early Adversity These arenas of research, defining the contextual and constitutional origins of disease maldistribution by population, space, and time, converge in a new and growing body of work examining the interplay between genes and environments in determining developmental and health endpoints over the life course. Such research comprises studies of (a) true gene-environment interactions, in which allelic variation moderates primary associations between environmental factors and outcomes, and (b) epigenetic processes, in which exposure-related chemical/structural Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities modifications of chromatin up- or downregulate the transcriptional expression of functionally salient genes. Together, such studies and larger territories of research have begun to advance serious answers, at a variety of analytic levels, to the questions surrounding how socially partitioned early experience “gets under the skin,” affecting broad life course trajectories of development and health (Evans et al. 2012; Hertzman and Boyce 2010; Slavich and Cole 2013). 5.1 Chromatin Modification and the Molecular Biology of Epigenesis As shown in Fig. 8, chromatin is the structural packaging of DNA and its associated proteins into nucleosomes: strands of DNA wrapped around histone protein octamers, like thread around a spool. Nucleosomes in turn form a loosely or tightly wound chromatin “necklace,” arrayed like beads on a string. Epigenetics has been defined as a functional modification of DNA that does not involve an alteration of sequence (Meaney 2010). The differentiation of embryonic stem cells into specific tissue lineages, for example, is epigenetically guided by patterns of downregulation and activation of lineage-specific Fig. 8 Chromatin and nucleosome structure (Courtesy of Joseph Roland© 2003 – www. cytographica.com) 75 genes (Wu and Sun 2006). Figure 9 shows how DNA methylation changes with development throughout the sequential ontogeny of germ cell development, fertilization, and embryogenesis. The more general processes of gene-environment interplay involve important functional distinctions between gene sequence, epigenetic modifications, and gene expression—variations in each of which may influence individual responses to environmental exposures. Gene sequence variation, for example, has been linked to vulnerability to a variety of maladaptive or disordered health outcomes (Caspi et al. 2002, 2003). Differences in gene expression have also been associated with disease susceptibility (Liew et al. 2006; Mohr and Liew 2007) and are, in turn, related to a variety of epigenetic, molecular processes, which include DNA methylation, histone modification, production of noncoding microRNAs, presence of various transcription factors, and nucleosome remodeling (Gilbert and Epel 2009). Given the complexities of these chemical mechanisms, however, it is important to understand that (a) DNA methylation is only one form of epigenetic mark, (b) DNA methylation is not the equivalent of gene expression, and (c) it is sometimes unclear whether DNA methylation is a precursor of gene silencing or a marker of such silencing. For purposes of simplicity, the discussion here W. Thomas Boyce and C. Hertzman 76 Fig. 9 Epigenetic methylation events in early human gestation (Russo et al. 1999) focuses primarily upon true gene-environment interactions and a single form of epigenetic modification, the methylation of cytosine-phosphateguanine (CpG) dinucleotide sites in DNA. The relative stability of DNA methylation suggests that, among epigenetic marks, it may be responsible for longer-term, more developmentally important changes in gene expression. As shown in Fig. 10, genes have both promoter and coding regions, with the latter demarcated by start and stop sequences recognized by RNA polymerase. RNA polymerase, in association with facilitating transcription factors (i.e., a “transcription complex”), transcribes the DNA coding region into messenger RNA, which in turn undergoes ribosomal translation into proteins that alter cellular function. Social experiences can result in epigenetic marks, such as CpG methylation, within a gene’s promoter region, and the degree of such methylation regulates gene expression, like a dimmer switch regulates light in a room. Transcription start sites and promoters are known to be enriched with CpG sites (areas called “CpG islands”) (Turner et al. 2008), and experience-induced methylation of these sites is known to tighten chromatin structure, rendering transcription factor binding sites physically less accessible and interfering with RNA polymerase attachment to the coding start sequence. Thus, the experience-induced methylation of a promoter CpG can effectively silence the gene, altering cellular function and changing physiological processes ranging from neurotransmitter reuptake in neuronal synapses to blood glucose regulation and sympathetic nervous system reactivity. 5.2 The Epigenetics of Social Adversity Although the study of epigenetics began in the field of cancer biology, research has more recently begun to explore epigenetic processes by which socially stratified exposures to early adversities may condition long-term risks to development and health (Gluckman et al. 2007; Jirtle and Skinner 2007; Tsankova et al. 2007). There is evidence for DNA methylation-associated regulation of adversity-responsive genes in both Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities 77 Fig. 10 Simplified representation of epigenetic modification and transcription regulation. CH3 methyl group, Tag TF transcription factor, RNA plmrs RNA polymerase, mRNA messenger RNA laboratory animals (McGowan et al. 2008) and human infants (Oberlander et al. 2008) and for stressor-associated differences in mRNA expression in human subjects (Cole et al. 2007; Morita et al. 2005). Further, the biochemical “markings” of genomic DNA and its associated, nucleosomal histone proteins are the epigenetic mechanisms by which the expression of neuroregulatory genes is controlled (Isles and Wilkinson 2008; Meaney and Ferguson-Smith 2010; Mehler 2008). Epigenetic marks are capable, through this governing of neuroregulatory gene expression, of guiding both neurodevelopment and the function of peripheral and central neural pathways (Liu et al. 2008; MacDonald and Roskams 2009). The work of Meaney, Szyf, and colleagues (Szyf et al. 2008; Weaver 2009; Weaver et al. 2004; Zhang and Meaney 2010), for example, has demonstrated in the rat pup how maternal licking and grooming in the first several days of life downregulate pups’ long-term HPA reactivity through increases in hippocampal glucocorticoid receptor (GR, NR3C1) expression and serotonergic tone. Licking and grooming triggers increases in serotonin in the hippocampus, and activation of the 5-HT receptor induces expression of a transcription factor, nerve growth factorinducible protein-A (NGFI-A). Licking and grooming also facilitates NGFI-A’s association with the exon 17 GR gene promoter by demethylating a CpG site located in the NGFI-A binding region of this exon. The resulting increased expression of GR results in a downregulation of corticotropin-releasing hormone (CRH) and diminished activation of the HPA axis. Readers are referred to (Hackman et al. 2010) for more detailed description of these molecular events. Other work in rat models of early infant maltreatment has demonstrated enduring methylation of the gene coding for brain-derived neurotrophic factor (BDNF), reduced BDNF expression, and extension of both epigenetic marks and maltreatment of young into a subsequent generation (Roth et al. 2009). Because BDNF is a key mediator of neural plasticity in brain regions such as the prefrontal cortex and hippocampus, such findings might present a legitimate model of the neurocognitive sequelae of transgenerational 78 neglect and abuse of human children. Champagne and colleagues (Champagne et al. 2006) have also shown, in the same model, that the increased estrogen receptor-α (ER-α) expression that occurs in the medial preoptic area of high licking and grooming mothers is associated with cytosine methylation of the ER-α gene promoter. Nonhuman primate work by Kinnally et al. (Kinnally et al. 2010a, 2010b, 2011) has explored, in infant macaques, interrelations among early stressors (social group vs. nursery rearing and variable foraging vs. control conditions), allelic variation in the 5-HTT serotonin transporter gene promoter, and both genome-wide and 5-HTTspecific differential methylation in peripheral blood mononuclear cells (PBMCs). Among their findings with direct relevance to the research considered here are the following: • Higher 5-HTT CpG methylation, but not sequence variation in the serotonin transporter promoter region, exacerbates the effects of early life stress on behavioral stress reactivity. • Both greater 5-HTT and whole-genome methylation level (i.e., 5-methylcytosine content) is associated with enhanced stress reactivity across the subsequent life span. • When brain tissue is unavailable for methylation analysis, peripheral blood methylation levels may serve as useful surrogates (see also Cupello et al. 2009; Uebelhack et al. 2006). The Meaney-Szyf research group has also recently advanced evidence, in the postmortem hippocampi of human suicide victims, for hypermethylation of the ribosomal RNA gene and NR3C1 gene promoters and epigenetic marks suggesting dysregulation in cellular protein synthesis machinery and HPA responsivity (McGowan et al. 2008, 2009). Further, two prospective human studies have now documented differential patterns of DNA methylation conditional upon various aspects of early childhood adversity. First, genome-wide, blood DNA methylation analysis in low- and high-SES subjects from the 1958 British Birth Cohort revealed a cluster of probes obtained from the 500 most W. Thomas Boyce and C. Hertzman variable promoters that was enriched with highSES individuals, confirming that SES differences contributed to overall epigenetic variation. Methylation levels for 1,252 gene promoters were associated with childhood SES versus 545 promoters for adulthood SES (Borghol et al. 2012). Second, in a subsample of adolescents from the Wisconsin Study of Family and Work birth cohort, Essex et al. (Essex et al. 2013) found differential methylation in 170 CpG sites among participants whose parents reported high levels of stress during their children’s early lives. Maternal stressors in infancy and paternal stressors in the preschool years were most strongly predictive of differential methylation, and the patterning of such epigenetic marks varied by children’s gender. Both birth cohort studies suggest that epigenetic signatures detectable in adolescence to mid-life are related to early experiences with stressful environments. Other human research has documented: • Altered DNA methylation of the insulin-like growth factor gene (IGF2) in individuals exposed prenatally to the Dutch Famine of 1944–1945 (Heijmans et al. 2008) • Increased NR3C1 methylation at an NGFI-A binding site in newborns exposed to mothers’ depressed mood in the third trimester of pregnancy (Oberlander et al. 2008) • Highly variable methylation patterns within the five GR promoters activated in PBMCs, possibly reflecting epigenetic fine-tuning of GR expression by early life experiences (Turner et al. 2008) • Pro-inflammatory shifts in cytokine expression among young adults with low-SES childhood histories, via transcriptional signaling changes in adrenergic and adrenocortical pathways (Chen et al. 2011; Miller et al. 2009a) • Both between-tissue and between-individual discordance in epigenetic marks at the insulinlike growth factor, IGF2/H19, locus in multiple tissues at birth, even among monozygotic twins (Ollikainen et al. 2010) • Differences in risk for psychopathology related to concurrent allelic and epigenetic Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities variation, such that trauma-related 5-HTT promoter methylation operates as a moderator of the association between the 5-HTT polymorphism and psychological problems (van IJzendoorn et al. 2010) Taken together, these observations offer evidence for social-environmental regulation of mammalian and human gene expression, at the levels of both whole-genome and candidate gene methylation (Cole 2009). The findings also suggest that divergences in methylation and gene expression begin early in development, even during intrauterine life, and that socially partitioned stress and adversity may be transmuted into mental health risks through molecular modifications of the epigenome. Differential exposures to environmental events may explain, for example, the diverging trajectories of epigenetic profiles found among monozygotic twins over time (Fraga et al. 2005). More broadly, epigenetic remodeling in response to developmentally potent environmental events may serve as a mechanism for neural plasticity and the conditional production of individual differences in attributes and risks for psychopathology (Bagot and Meaney 2010). 5.3 Gene-Environment Interplay in Brain Development Indeed, there is already emerging evidence that brain processes and neural plasticity may function as key mediators in the linkages between G-E interplay and developmental and health endpoints. Bush et al., for example, have recently shown that the BDNF Val66Met polymorphism confers an increased neuroendocrine sensitivity to socioeconomic context, with Met carriers having the highest and lowest cortisol expression levels, depending on SES (N. Bush, personal communication, unpublished data, 2017). Other findings in the nascent science of “imaging genomics”—a field merging high-throughput genotyping with new brain imaging technologies—are revealing how variations in DNA sequence are associated with structural and functional connectivity in specific brain regions 79 (Thompson et al. 2010). Recent fMRI studies, for example, have demonstrated the heritability of patterns of task-related brain region activation and shown how the COMT val108/158met functional polymorphism is associated with systematic differences in prefrontal cortical physiology and function (Egan et al. 2001). Consistent with the same general hypothesis, Hariri and colleagues (Hariri et al. 2005; Hariri and Weinberger 2003) have found that subjects carrying the less efficient short allele of the 5-HTT gene promoter had an increased amygdalar response to fearful stimuli in comparison with subjects homozygous for the long allele. Even more to the point in regard to the development of brain structures subserving socioemotional development, Alexander et al. (Alexander et al. 2012) recently reported a significant GxE interaction effect on neural response patterns and functional amygdalahypothalamus connectivity—circuitry that appears closely tied to emotional control and the interpretation of social processes (Blakemore 2010; Norman et al. 2012). Such findings begin to reveal possible neural substrates for previously observed, epidemiological observations linking functional polymorphisms, early stressful events, and vulnerability to psychopathology (Caspi et al. 2003; Moffitt et al. 2005). Also coming into view through the work of a range of investigators (Adolphs 2009; Blakemore 2010, 2012; Lesch 2007; Norman et al. 2012; Robinson et al. 2005, 2008) is a new corpus of research describing and exploring the social brain. Such work has begun to address a neurogenomic basis for complex social cognitions, including the capacities for inferences about others’ thoughts and emotions (a cognitive ability referred to in the child development literature as “theory of mind”), recognition of self, processing of facial information, differentiation of social opportunities and challenges, and control of socially evoked emotion. The substrates for these capacities are already known to lie, at the neural circuit level, in functional connectivity between limbic structures, such as the amygdala, hippocampus, and basal ganglia, and prefrontal cortical regions, including the dorsolateral and orbitofrontal cortex, the anterior cingulate and W. Thomas Boyce and C. Hertzman 80 the posterior superior temporal sulcus at the temporoparietal junction. At the molecular level, there is evidence for perturbations in the functionality of such circuits related to allelic and epigenetic variation in genes such as those coding for the serotonin transporter (Lesch 2007), oxytocin (Norman et al. 2012), and endorphins (Keverne et al. 1989). There is to date no single definition of the social brain, but a consensus appears to be forming that a subset of autonomic, neuroendocrine, neural, and genomic processes influence and are influenced by aspects of social cognition and behavior (see Robinson et al. 2005, 2008). Although it will be important to avoid a simplistic attribution of complex cognitive and perceptual events to discrete neural, endocrine, or cellular structures (Norman et al. 2012), there is clearly much to be learned about the nature of sociality, the mechanisms that underpin it, the degree to which these mechanisms are conserved in animal phylogeny, and the implications of perturbations in its development. In sum, a burgeoning research enterprise is producing evidence, in both animal models and human studies, that many if not most human morbidities have their points of origin in early childhood (Shonkoff and Garner 2012), are the products of gene-environment interplay (Rutter 2006), and influence developing neural circuits that are directly linked to long-term trajectories of health, disease, and life achievement. Although the fields of social epigenetics and developmental neuroscience are yet in their relative infancies, promising advances in both suggest a scientific frontier in which functional interactions among social environments, genetic and epigenetic variation, and the functionality of neural circuits will offer compelling new knowledge of how developmental variation emerges. 6 Summary: What We Know and What We Don’t Deep cultural traditions affirming the special sensibilities of children and the echoing of childhood difficulties and stress into the decades of adult life are becoming now powerfully grounded in a new science describing the biological embedding of early social adversity (Boivin and Hertzman 2012; Boyce et al. 2012a; Hertzman and Boyce 2010). No longer relegated to categories of either unsupported belief or speculative hypothesis, the science of early child development has become a multidisciplinary landscape of novel findings documenting prospective, longitudinal associations, powerful and increasingly causal understandings of mechanism and mediation, and new levels of observation made possible by dramatic technological advances. Among the discrete, foundational discoveries that have become the products of this science are the following: 1. Epidemiologic and population-based studies have soundly documented the propensity for morbidities and difficulties of all kinds—biomedical diseases, psychiatric disorders, injuries, and academic, professional, and personal underachievement—to aggregate by population, space, and time, thereby disproportionately affecting small subgroups of individuals, during constrained periods of historical or developmental time. Thus, especially during periods of strife, war, or economic decline, those people whose health and well-being are most harshly and persistently vulnerable are likely to issue from impoverished, alienated, or otherwise marginalized groups. Misfortune is neither stochastically nor evenly distributed within human populations, and the inequalities that ensue from maldistribution are serious, ethical, and public health challenges to society’s commitments to justice and health equity. Our societal aspirations to create a more “empathic civilization” (Rifkin 2009) will demand a more human practice of medicine (Halpern 2001), a broader allegiance to unbiased opportunity (Robert Wood Johnson Foundation Commission to Build a Healthier America 2009), and a deeper commitment to the rich pluripotentiality of young lives (Shonkoff and Phillips 2000). 2. Studies examining the developmental biology of social adversity have positioned this new Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities science on the cusp of deep mechanistic explanations for the important, earlier insights of Waddington (Waddington 1959, 2012), Gottlieb (Gottlieb 1991), and others (Gilbert 2002; Karmiloff-Smith 2007; Meaney 2010)—insight that organismic development is guided by the combined, interactive influences of genes and experiences. We now know that development includes not only gene expression regulation through experiential modification of chromatin structure but also by real GxE interactions and the joint, interactive effects of allelic variation and chromatin modification together (see, e.g., Lam et al. 2012; Pezawas et al. 2005; Rutter 2012). As asserted here and elsewhere, the simple partitioning of developmental variance into genetic and environmentally determined components now falls far short of a truly interactive view of the operation of nature and nurture (Meaney 2010). Coming now steadily into view are the actual molecular mechanisms by which constitution and context conjointly determine differences in health and development, both in childhood and over the lifespan. 3. The origins, dimensions, and consequences of individual differences in phenotype are emerging as essential components in a full understanding of the biology of social adversity. Biology is replete with both between- and within-species variation that bears convincing witness to the evolutionary uses of diversity, and elegant ethological and epigenetic work, such as that by Meaney (Meaney 2010), Szyf (McGowan and Szyf 2010), and Suomi (Suomi 2006), reveals the adaptive benefits of phenotypic diversification. There is the nearly tautological reality that the operation of natural selection demands the genetic diversity achieved through spontaneous mutation, but beyond such self-evident principles, there are clear examples of how species diversification enhances survival and fitness. Thus, maternally and epigenetically regulated differentiation of rat pups’ adrenocortical responsivity produces a range of low to high reactivity phenotypes, each of which may maximize survival and fitness within particular, early and 81 later life environments (Weaver et al. 2004). Similarly, neither the aggressively uninhibited nor the shy, neophobic phenotypes of young rhesus macaques can be warranted as “normal” or optimal; rather, each has adaptive value within specific social and physical contexts (Cirulli et al. 2009; Stevens et al. 2009). What is salient and important about phenotypic variants is their capacity for enhancing fitness within the diversity of species-typical environments encountered. One illustrative variant are those children and young nonhuman primates that evince an exceptional neurobiological susceptibility to aspects of social settings, a phenotype likely representing a conditional adaptation to early environmental signals (Barr et al. 2004; Belsky 2005; Boyce and Ellis 2005; Ellis et al. 2011a). Such individuals show a heightened risk of morbidity under conditions of stress and adversity but exceptionally good health and positive development in settings characterized by support, nurturance, and stability. 4. Increasingly visible within the emerging literature on the neurobiological consequences of early life adversity is a new collection of studies and papers examining the social brain (Adolphs 2009; Blakemore 2010, 2012; Lesch 2007; Norman et al. 2012; Robinson et al. 2005, 2008). The brain has evolved to acquire specific organizational circuits, cortical regions, and subcortical structures dedicated to the detection of survival threats, and social experiences of loneliness, abandonment, or neglect may chronically activate such circuitry, creating long-term perceptual and affective biases and fundamentally altering a child’s view of the social world (Eisenberger and Cole 2012). Such circuitry is also represented in the periphery, with both animal (Insel 2010) and human (Norman et al. 2012) studies documenting effects on social affiliative processes mediated in part by hormonal events such as the expression of oxytocin and vasopressin. 5. As also discussed, a remarkable diversity of early, social environmental dimensions has been linked to important differences in mental W. Thomas Boyce and C. Hertzman 82 and physical health, trajectories of development, and individual differences in behavior. Such dimensions include, but are not limited to, acute and chronic stressors, poverty and subjective social marginalization, and the absence of positive contextual factors, such as good parenting or a child-supportive community. Of apparently particular importance to social epidemiologic perspectives on early development are studies describing and documenting the effects of social hierarchies, structural subordination, bullying, discrimination, and victimization. The hierarchical and networked social structures found across phylogeny—literally from fruit flies (fish and primates) to human kindergartners (see Boyce et al. 2012a; Fernald and Maruska 2012; Schneider et al. 2012)—suggest an evolved predisposition with implications for survival, reproduction, and safety. Although such hierarchies may be a heritable legacy from our evolutionary past, promising new work addresses approaches to minimizing the health effects of subordinate social positions at the levels of societies, communities, and schools. Such work also attests to the individual and societal benefits of fostering the development of empathy, altruism, and sociality. 6. The central role of time—evolutionary, historical, developmental, neurogenomic, and neurophysiological—in determining phenotypic variation is another recurrent if often implicit theme in emerging developmental science. Much of the biological embedding of current social contexts reflects response predispositions established, selectively and epigenetically, through adaptations to the temporally distant environments of early hominids (Dubos 1965; Nesse and Young 2000). Social disparities in health—products, in part, of the social, economic, and health policies of contemporary societies—wax and wane within historical time according to the era’s dominant sociopolitical philosophies (Beckfield and Krieger 2009; Krieger 2001). Further, developmental time is strikingly uneven in its potency, intensity of change, and accessibility to environmental influence. Thus, at quite different levels of temporal resolution, time and timing appear to play crucial, but not yet fully explored, roles in guiding societal, organismic, and neurobiological responses to the conditions of early life. Taken together, these points of emerging empirical evidence form a constellation of discoveries defining a new and lively research field and the social and developmental biology of early adversity and its influences on life course development and health. Although substantial recent progress has been made within a broad assortment of disciplines, much new research, conceptual integration, and thought remains to be accomplished. The promise of this new field, however, lies in a stunning prospect that understanding the social determinants of morbidities in childhood might unlock new approaches to the prevention and treatment of disorders over the entire life course. A final agenda for future research now outlines a set of proposals for new, promising, and possibly heuristic directions of investigation. 7 An Agenda for Future Research 1. As the fields reporting various forms of interplay between genes and environments exponentially grow, what is now needed are programs of research examining the questions of how and by what mechanisms genes and early social contexts co-determine trajectories of behavioral and biological development. With respect to differences in complex behavior and its disorders, a focus on proximal, neurobiological processes must come strongly to the foreground. Although pursuing the epigenetic pathogenesis of early psychiatric and other disorders is a powerful first step (Docherty and Mill 2008; Robison and Nestler 2011), it must be followed by studies examining the brain structures and neural processes that mediate GxE associations with mental and physical health (Ladd-Acosta et al. 2007; Turecki et al. 2012). As new knowledge of the Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities interactive genetic and environmental influences on social brain development becomes available, a major challenge will be the integration of mechanistic observations across levels of analysis and scale (Norman et al. 2012). 2. A systematic and useful biology of social adversity will necessarily involve not only a search for the mechanisms (i.e., mediators) underpinning associations among stress, development, and morbidity but also the effects of modifiers (i.e., moderators) that reveal when, at what ages, or in what subgroups such associations hold (Baron and Kenny 1986; Kraemer et al. 2001, 2008). Understanding the mechanistic processes by which an environmental exposure is linked to disordered development can be a powerful aid to elucidating pathogenesis (e.g., the role of high-density lipoprotein transport of cholesterol as a mediator of the association between dietary fat and coronary heart disease (Lloyd et al. 2012)) and imagining novel interventions (e.g., changing mother-infant relationships as a means of altering the association between poverty and child development (Olds et al. 2010)). On the other hand, grasping mediational linkages may be a necessary but insufficient condition for understanding causation, and the parsing of populations into subgroups of varying exposure susceptibility, through the discovery of moderator variables, can also advance comprehension and the tractability of a given association (e.g., changes in the potency of stress-illness associations by differences in individual sensitivity to social contextual effects (Belsky and Pluess 2009; Boyce and Ellis 2005; Ellis et al. 2011a)). 3. As attention to epigenetic development grows, the capacity to place a finer, more exacting point on the specific kinds of environments that interact with particular allelic and epigenetic variants will be important and likely illuminating. Evolutionary perspectives on social adversity will also be essential to understand how difficult “environments of evolutionary adaptedness” shaped human and infra-human biology (e.g., Boyce and Ellis 2005; Sapolsky 83 2003), how contextual stressors might have generated species diversity (Wolf et al. 2005), and how adversity may have contributed to the emergence of social cooperation (Andras et al. 2007). We need far finer and more precise renditions of the social-environmental dimensions that interact with genes and produce negative and positive outcomes salient to population and public health. An important subtext to such work is the extraordinary value of cross-species, animal-human, comparative studies, which together not only inform the evolutionary biology of social environments and its consequences but also enable experimental studies capable of bolstering the causal inferences disallowed by human research. 4. Successfully pursuing a new developmental science of childhood adversity will surely also involve the perspective of complex adaptive systems. Social causation is nonlinear, rather than Newtonian, in character. Traditional epidemiologic strategies for understanding the health effects of social-environmental factors involve the ascertainment of such factors’ “independent” influences on a health endpoint through the use of multiple hierarchical regression models (Diez Roux 2007). Although such an approach allows estimation of the isolated effects of single independent variables, it belies the reality that most human disorders are etiologically complex, with multiple interacting “causes.” Even detecting GxE interactions almost certainly underserves the true complexity of pathogenic processes, because allelic variation in a single gene likely interacts with polymorphisms in many other genes, and multiple dimensions of the environment may also interactively influence outcomes. In such circumstances—circumstances that may eventually prove to predominate in disease causation—the use of more sophisticated models and analytic tools may be required to understand the multiply interactive networks of risk factors involved in the ontogeny of disordered development and health (Kauffman 1993; Koopman and Lynch 1999). If so, one such approach with increasingly demonstrable efficacy is the use of complex W. Thomas Boyce and C. Hertzman 84 systems analysis, involving descriptive inventories of system components, nonlinear mathematical modeling, and the construction of agent-based models of causal networks (Diez Roux 2007; Galea et al. 2010). 5. Finally, as highlighted by Garner, Shonkoff, and others (Garner et al. 2012; Shonkoff 2012), prevention science is powerfully in need of new ideas for and approaches to the design of interventions based on the emerging science of early development. The NurseFamily Partnership program conceived and elegantly studied by David Olds and colleagues (see http://www.nursefamilypartnership.org), for example, departed dramatically from prior, outmoded early childhood enrichment programs by envisioning the motherinfant dyad as the unit of intervention and by using nurses as home visitors to effectively place the intervention within a medical frame of reference. Nonetheless, even more radical departures are needed from the conventions of traditional early development programs, including careful consideration of how complex dynamic systems thinking might be wed to novel, preventive interventions. Attending closely to discoveries emanating from the disciplines and sciences surveyed here could potentially prompt approaches to targeting and conceiving imaginative interventions with far greater efficacy and whole population effects. However complex and challenging that task might be, all such efforts could be potentially rewarded with a level of health and well-being for our children and grandchildren that is more enduring, robust, and enabling. The new developmental biology of early life effects on health development over the life course is a now flourishing science, nearing readiness to transform conventional understandings of disease ontogeny, to foster clearer and more vivid perspectives on the temporally distant effects of childhood events, and to broaden and transform approaches to ensuring the health of our children and the developmental sturdiness of their futures. Although much careful and innovative thought will be a prerequisite for such a demanding journey, the road ahead looks promising, indeed. References Abbott, D. H., Keverne, E. B., Bercovitch, F. B., Shively, C. A., Mendoza, S. P., Saltzman, W., et al. (2003). Are subordinates always stressed? A comparative analysis of rank differences in cortisol levels among primates. Hormones and Behavior, 43(1), 67–82. Adler, N. E., Boyce, W. T., Chesney, M. A., Folkman, S., & Syme, S. L. (1993). Socioeconomic inequalities in health: No easy solution. JAMA, 269(24), 3140–3145. Adler, N. E., Boyce, W. T., Chesney, M. A., Cohen, S., Folkman, S., Kahn, R. L., & Syme, S. L. (1994). Socioeconomic status and health: The challenge of the gradient. The American Psychologist, 49(1), 15–24. Adler, N. E., Epel, E. S., Castellazzo, G., & Ickovics, J. R. (2000). Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women. Health Psychology, 19(6), 586–592. Adolphs, R. (2009). The social brain: Neural basis of social knowledge. Annual Review of Psychology, 60, 693– 716. doi:10.1146/annurev.psych.60.110707.163514. Alexander, N., Klucken, T., Koppe, G., Osinsky, R., Walter, B., Vaitl, D., et al. (2012). Interaction of the serotonin transporter-linked polymorphic region and environmental adversity: Increased amygdala-hypothalamus connectivity as a potential mechanism linking neural and endocrine hyperreactivity. Biological Psychiatry, 72(1), 49–56. doi:10.1016/j.biopsych.2012.01.030. Alkon, A., Lippert, S., Vujan, N., Rodriquez, M. E., Boyce, W. T., & Eskenazi, B. (2006). The ontogeny of autonomic measures in 6- and 12-month-old infants. Developmental Psychobiology, 48(3), 197–208. doi:10.1002/dev.20129. Andras, P., Lazarus, J., & Roberts, G. (2007). Environmental adversity and uncertainty favour cooperation. BMC Evolutionary Biology, 7, 240. doi:10.1186/1471-2148-7-240. Ariès, P. (1962). Centuries of childhood: A social history of family life. New York: Alfred A. Knopf. Aron, E. N., Aron, A., & Jagiellowicz, J. (2012). Sensory processing sensitivity: A review in the light of the evolution of biological responsivity. Personality and Social Psychology Review, 16(3), 262–282. doi:10.1177/1088868311434213. Arseneault, L., Walsh, E., Trzesniewski, K., Newcombe, R., Caspi, A., & Moffitt, T. E. (2006). Bullying victimization uniquely contributes to adjustment problems in young children: A nationally representative cohort study. Pediatrics, 118(1), 130–138. doi:10.1542/ peds.2005-2388. Bagot, R. C., & Meaney, M. J. (2010). Epigenetics and the biological basis of gene x environment interactions. Journal of the American Academy of Child and Adolescent Psychiatry, 49(8), 752–771. doi:10.1016/j. jaac.2010.06.001. Bakermans-Kranenburg, M. J., & Van Ijzendoorn, M. H. (2015). The hidden efficacy of interventions: Genexenvironment experiments from a differential susceptibility perspective. Annual Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities Review of Psychology, 66, 381–409. doi:10.1146/ annurev-psych-010814-015407. Bakermans-Kranenburg, M. J., Van Ijzendoorn, M. H., Mesman, J., Alink, L. R., & Juffer, F. (2008a). Effects of an attachment-based intervention on daily cortisol moderated by dopamine receptor D4: A randomized control trial on 1- to 3-year-olds screened for externalizing behavior. Development and Psychopathology, 20(3), 805–820. doi:10.1017/S0954579408000382. Bakermans-Kranenburg, M. J., Van Ijzendoorn, M. H., Pijlman, F. T., Mesman, J., & Juffer, F. (2008b). Experimental evidence for differential susceptibility: Dopamine D4 receptor polymorphism (DRD4 VNTR) moderates intervention effects on toddlers' externalizing behavior in a randomized controlled trial. Developmental Psychology, 44(1), 293–300. doi:10.1037/0012-1649.44.1.293. Barker, D. J. (1990). The fetal and infant origins of adult disease. BMJ, 301(6761), 1111. Barker, D. J., & Bagby, S. P. (2005). Developmental antecedents of cardiovascular disease: A historical perspective. Journal of the American Society of Nephrology, 16(9), 2537–2544. doi:10.1681/ASN.2005020160. Baron, R., & Kenny, D. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. Barr, R. G., Konner, M., Bakeman, R., & Adamson, L. (1991). Crying in !Kung San infants: A test of the cultural specificity hypothesis. Developmental Medicine and Child Neurology, 33(7), 601–610. Barr, C. S., Newman, T. K., Lindell, S., Shannon, C., Champoux, M., Lesch, K. P., et al. (2004). Interaction between serotonin transporter gene variation and rearing condition in alcohol preference and consumption in female primates. Archives of General Psychiatry, 61(11), 1146–1152. Bateson, P., & Gluckman, P. (2011). Plasticity, robustness, development and evolution. Cambridge CB2 8RU, UK: Cambridge University Press, The Edinburgh Building. Bavelier, D., Levi, D. M., Li, R. W., Dan, Y., & Hensch, T. K. (2010). Removing brakes on adult brain plasticity: From molecular to behavioral interventions. The Journal of Neuroscience, 30(45), 14964–14971. Beckfield, J., & Krieger, N. (2009). Epi + demos + cracy: Linking political systems and priorities to the magnitude of health inequities--evidence, gaps, and a research agenda. Epidemiologic Reviews, 31, 152– 177. doi:10.1093/epirev/mxp002. Belsky, J. (2005). Differential susceptibility to rearing influence: An evolutionary hypothesis and some evidence. In B. J. Ellis & D. F. Bjorklund (Eds.), Origins of the social mind: Evolutionary psychology and child development (pp. 139–163). New York: Guilford. Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135(6), 885–908. doi:10.1037/ a0017376. 85 Belsky, J., Bakermans Kranenburg, M. J., & Van IJzendoorn, M. H. (2007). For better and for worse: Differential susceptibility to environmental influences. Current Directions in Psychological Science, 16(6), 300–304. Blakemore, S. J. (2010). The developing social brain: Implications for education. Neuron, 65(6), 744–747. doi:10.1016/j.neuron.2010.03.004. Blakemore, S. J. (2012). Development of the social brain in adolescence. Journal of the Royal Society of Medicine, 105(3), 111–116. doi:10.1258/jrsm.2011.110221. Blumenshine, P., Egerter, S., Barclay, C. J., Cubbin, C., & Braveman, P. A. (2010). Socioeconomic disparities in adverse birth outcomes: A systematic review. American Journal of Preventive Medicine, 39(3), 263–272. doi:10.1016/j.amepre.2010.05.012. Bock, G. R., & Whelan, J. (Eds.). (1991). The childhood environment and adult disease. West Sussez: Ciba Foundation. Boivin, M., & Hertzman, C. (2012). Early childhood development: Adverse experiences and developmental health. Ottawa: Royal Society of Canada. Borghol, N., Suderman, M., McArdle, W., Racine, A., Hallett, M., Pembrey, M., et al. (2012). Associations with early life socio-economic position in adult DNA methylation. International Journal of Epidemiology, 41(1), 62–74. Bowlby, J. (1969). Attachment and loss. London: Hogarth. Boyce, W. T. (1985). Stress and child health: An overview. Pediatric Annals, 14(8), 539–542. Boyce, W. T. (2016). Differential susceptibility of the developing brain to contextual adversity and stress. Neuropsychopharmacology Reviews, 41(1), 142–162. Boyce, W. T., & Ellis, B. J. (2005). Biological sensitivity to context: I. An evolutionary-developmental theory of the origins and functions of stress reactivity. Development and Psychopathology, 17(2), 271–301. Boyce, W. T., & Kobor, M. S. (2015). Development and the epigenome: The ‘synapse’ of gene-environment interplay. Developmental Science, 18(1), 1–23. doi:10.1111/desc.12282. Boyce, W. T., Chesney, M., Alkon-Leonard, A., Tschann, J., Adams, S., Chesterman, B., et al. (1995). Psychobiologic reactivity to stress and childhood respiratory illnesses: Results of two prospective studies. Psychosomatic Medicine, 57, 411–422. Boyce, W. T., O'Neill-Wagner, P., Price, C. S., Haines, M., & Suomi, S. J. (1998). Crowding stress and violent injuriesamong behaviorally inhibited rhesus macaques. Health Psychology, 17(3), 285–289. Boyce, W. T., Den Besten, P. K., Stamperdahl, J., Zhan, L., Jiang, Y., Adler, N. E., & Featherstone, J. D. (2010). Social inequalities in childhood dental caries: The convergent roles of stress, bacteria and disadvantage. Social Science & Medicine, 71(9), 1644–1652. doi:10.1016/j.socscimed.2010.07.045. Boyce, W. T., Obradović, J., Bush, N., Stamperdahl, J., Kim, Y. S., & Adler, N. (2012a). Social stratification, classroom 'climate' and the behavioral adaptation of kindergarten children. Proceedings of the National Academy of Sciences, 109(Suppl 2), 17168–17173. 86 Boyce, W. T., Sokolowski, M. B., & Robinson, G. E. (2012b). Toward a new biology of social adversity. Proceedings of the National Academy of Sciences of the United States of America, 109(Suppl 2), 17143– 17148. doi:10.1073/pnas.1121264109. Brim, O. G., & Kagan, J. (Eds.). (1980). Constancy and change in human development. Cambridge, MA: Harvard University Press. Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human development. In R. M. Lerner & W. Damon (Eds.), Handbook of child psychology: Vol. 1. Theoretical models of human development (6th ed., pp. 793–828). Hoboken: John Wiley & Sons, Inc. Brown, R. L. (2010). Epidemiology of injury and the impact of health disparities. Current Opinion in Pediatrics, 22(3), 321–325. doi:10.1097/ MOP.0b013e3283395f13. Brown, D. W., Anda, R. F., Tiemeier, H., Felitti, V. J., Edwards, V. J., Croft, J. B., & Giles, W. H. (2009). Adverse childhood experiences and the risk of premature mortality. American Journal of Preventive Medicine, 37(5), 389–396. doi:10.1016/j. amepre.2009.06.021. Carpiano, R. M., Lloyd, J. E., & Hertzman, C. (2009). Concentrated affluence, concentrated disadvantage, and children’s readiness for school: A populationbased, multi-level investigation. Social Science & Medicine, 69(3), 420–432. doi:10.1016/j. socscimed.2009.05.028. Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., Craig, I. W., et al. (2002). Role of genotype in the cycle of violence in maltreated children. Science, 297(5582), 851–854. Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., et al. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301(5631), 386–389. Champagne, F. A., Weaver, I. C., Diorio, J., Dymov, S., Szyf, M., & Meaney, M. J. (2006). Maternal care associated with methylation of the estrogen receptoralpha1b promoter and estrogen receptor-alpha expression in the medial preoptic area of female offspring. Endocrinology, 147(6), 2909–2915. doi:10.1210/ en.2005-1119. Chedgzoy, K., Greenhalgh, S., & Shaughnessy, R. (2007). Shakespeare and childhood. Cambridge, UK: Cambridge University Press. Chen, E., Miller, G. E., Kobor, M. S., & Cole, S. W. (2011). Maternal warmth buffers the effects of low early-life socioeconomic status on pro-inflammatory signaling in adulthood. Molecular Psychiatry, 16(7), 729–737. doi:10.1038/mp.2010.53. Cirulli, F., Francia, N., Berry, A., Aloe, L., Alleva, E., & Suomi, S. J. (2009). Early life stress as a risk factor for mental health: Role of neurotrophins from rodents to non-human primates. Neuroscience and Biobehavioral Reviews, 33(4), 573–585. doi:10.1016/j. neubiorev.2008.09.001. Cohen, S., Line, S., Manuck, S. B., Rabin, B. S., Heise, E. R., & Kaplan, J. R. (1997). Chronic social stress, social status, and susceptibility to upper respiratory W. Thomas Boyce and C. Hertzman infections in nonhuman primates [see comments]. Psychosomatic Medicine, 59(3), 213–221. Cohen, S., Janicki-Deverts, D., Chen, E., & Matthews, K. A. (2010). Childhood socioeconomic status and adult health. Annals of the New York Academy of Sciences, 1186, 37–55. doi:10.1111/j.1749-6632.2009.05334.x. Cole, S. W. (2009). Social regulation of human gene expression. Current Directions in Psychological Science, 18(3), 132–137. doi:10.1111/j.1467-8721.2009.01623.x. Cole, S. W., Hawkley, L. C., Arevalo, J. M., Sung, C. Y., Rose, R. M., & Cacioppo, J. T. (2007). Social regulation of gene expression in human leukocytes. Genome Biology, 8(9), R189. doi:10.1186/gb-2007-8-9-r189. Committee on a Framework for Development a New Taxonomy of Disease. (2011). Toward precision medicine: Building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: National Academies Press. Cummings, E. M., El-Sheikh, M., Kouros, C. D., & Keller, P. S. (2007). Children's skin conductance reactivity as a mechanism of risk in the context of parental depressive symptoms. Journal of Child Psychology and Psychiatry, 48(5), 436–445. doi:10.1111/j.1469-7610.2006.01713.x. Cupello, A., Albano, C., Gatta, E., Scarrone, S., Villa, E., & Zona, G. (2009). Binding of paroxetine to the serotonin transporter in membranes from different cells, subcellular fractions and species. Neurochemical Research, 34(2), 255–259. doi:10.1007/s11064-008-9764-z. Curley, J. P., Jensen, C. L., Mashoodh, R., & Champagne, F. A. (2011). Social influences on neurobiology and behavior: Epigenetic effects during development. Psychoneuroendocrinology, 36(3), 352–371. doi:10.1016/j.psyneuen.2010.06.005. Cushing, B. S., & Kramer, K. M. (2005). Mechanisms underlying epigenetic effects of early social experience: The role of neuropeptides and steroids. Neuroscience and Biobehavioral Reviews, 29(7), 1089–1105. doi:10.1016/j.neubiorev.2005.04.001. Dancause, K. N., Laplante, D. P., Fraser, S., Brunet, A., Ciampi, A., Schmitz, N., & King, S. (2012). Prenatal exposure to a natural disaster increases risk for obesity in 5(1/2)-year-old children. Pediatric Research, 71(1), 126–131. doi:10.1038/pr.2011.18. Danese, A., Moffitt, T. E., Pariante, C. M., Ambler, A., Poulton, R., & Caspi, A. (2008). Elevated inflammation levels in depressed adults with a history of childhood maltreatment. Archives of General Psychiatry, 65(4), 409–416. de Quervain, D. J., Kolassa, I. T., Ackermann, S., Aerni, A., Boesiger, P., Demougin, P., et al. (2012). PKCalpha is genetically linked to memory capacity in healthy subjects and to risk for posttraumatic stress disorder in genocide survivors. Proceedings of the National Academy of Sciences of the United States of America, 109(22), 8746–8751. doi:10.1073/pnas.1200857109. Diamond, J. (2012). The world until yesterday: What can we learn from traditional societies. New York: Penguin Group. Diaz, C., Starfield, B., Holtzman, N., Mellits, E. D., Hankin, J., Smalky, K., & Benson, P. (1986). Ill health Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities and use of medical care: Community-based assessment of morbidity in children. Medical Care, 24(9), 848–856. Diez Roux, A. V. (2007). Integrating social and biologic factors in health research: A systems view. Annals of Epidemiology, 17(7), 569–574. doi:10.1016/j. annepidem.2007.03.001. Docherty, S., & Mill, J. (2008). Epigenetic mechanisms as mediators of environmental risks for psychiatric disorders. Psychiatry, 7(12), 500–506. Dowd, J. B., Zajacova, A., & Aiello, A. (2009). Early origins of health disparities: Burden of infection, health, and socioeconomic status in U.S. children. Social Science & Medicine, 68(4), 699–707. doi:10.1016/j. socscimed.2008.12.010. Dubos, R. J. (1965). Man adapting. New Haven: Yale University Press. Dubos, R., Savage, D., & Schaedler, R. (1966). Biological freudianism. Lasting effects of early environmental influences. Pediatrics, 38(5), 789–800. Dunn, E. C., Uddin, M., Subramanian, S. V., Smoller, J. W., Galea, S., & Koenen, K. C. (2011). Research review: Gene-environment interaction research in youth depression – a systematic review with recommendations for future research. Journal of Child Psychology and Psychiatry, 52(12), 1223–1238. doi:10.1111/j.1469-7610.2011.02466.x. Egan, S. K., & Perry, D. G. (1998). Does low self-regard invite victimization? Developmental Psychology, 34(2), 299–309. Egan, M. F., Goldberg, T. E., Kolachana, B. S., Callicott, J. H., Mazzanti, C. M., Straub, R. E., et al. (2001). Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proceedings of the National Academy of Sciences of the United States of America, 98(12), 6917–6922. doi:10.1073/ pnas.111134598. Eisenberger, N. I., & Cole, S. W. (2012). Social neuroscience and health: Neurophysiological mechanisms linking social ties with physical health. Nature Neuroscience, 15(5), 669–674. doi:10.1038/nn.3086. Ellis, B. J., McFadyen-Ketchum, S., Dodge, K. A., Pettit, G. S., & Bates, J. E. (1999). Quality of early family relationships and individual differences in the timing of pubertal maturation in girls: A longitudinal test of an evolutionary model. Journal of Personality and Social Psychology, 77(2), 387–401. Ellis, B. J., Jackson, J. J., & Boyce, W. T. (2006). The stress response systems: Universality and adaptive individual differences. Developmental Review, 26(2), 175–212. Ellis, B. J., Boyce, W. T., Belsky, J., BakermansKranenburg, M. J., & van Ijzendoorn, M. H. (2011a). Differential susceptibility to the environment: An evolutionary--neurodevelopmental theory. Development and Psychopathology, 23(1), 7–28. doi:10.1017/ S0954579410000611. Ellis, B. J., Shirtcliff, E. A., Boyce, W. T., Deardorff, J., & Essex, M. J. (2011b). Quality of early family relationships and the timing and tempo of puberty: Effects depend on biological sensitivity to context. 87 Development and Psychopathology, 23(1), 85–99. doi:10.1017/S0954579410000660. El-Sheikh, M. (2005). Stability of respiratory sinus arrhythmia in children and young adolescents: A longitudinal examination. Developmental Psychobiology, 46(1), 66–74. doi:10.1002/dev.20036. El-Sheikh, M., Keller, P. S., & Erath, S. A. (2007). Marital conflict and risk for child maladjustment over time: Skin conductance level reactivity as a vulnerability factor. Journal of Abnormal Child Psychology, 35(5), 715–727. doi:10.1007/s10802-007-9127-2. Essex, M. J., Thomas Boyce, W., Hertzman, C., Lam, L. L., Armstrong, J. M., Neumann, S. M., & Kobor, M. S. (2013). Epigenetic vestiges of early developmental adversity: Childhood stress exposure and DNA methylation in adolescence. Child Development, 84(1), 58–75. doi:10.1111/j.1467-8624.2011.01641.x. Evans, G. W. (2004). The environment of childhood poverty. The American Psychologist, 59(2), 77–92. Evans, G. W., & Kim, P. (2007). Childhood poverty and health: Cumulative risk exposure and stress dysregulation. Psychological Science, 18(11), 953–957. doi:10.1111/j.1467-9280.2007.02008.x. Evans, G. W., Gonnella, C., Marcynyszyn, L. A., Gentile, L., & Salpekar, N. (2005). The role of chaos in poverty and children's socioemotional adjustment. Psychological Science, 16(7), 560–565. Evans, G. W., Chen, E., Miller, G., & Seeman, T. (2012). How poverty gets under the skin: A life-course perspective. In V. Maholmes & R. B. King (Eds.), The Oxford handbook of poverty and child development (pp. 13–36). Oxford: Oxford University Press. Falconi, A. M., Catalano, R., & Boyce, W. T. (2017). Early life predictors of late life health. In W. Satariano & M. Maus (Eds.), Aging, Place, and Health: A Global Perspective. Burlington: Jones & Bartlett Learning. Fehr, E., Bernhard, H., & Rockenbach, B. (2008). Egalitarianism in young children. Nature, 454(7208), 1079–1083. doi:10.1038/nature07155. Fernald, R. D., & Maruska, K. P. (2012). How does social information change the brain? PNAS. Fraga, M. F., Ballestar, E., Paz, M. F., Ropero, S., Setien, F., Ballestar, M. L., et al. (2005). Epigenetic differences arise during the lifetime of monozygotic twins. Proceedings of the National Academy of Sciences of the United States of America, 102(30), 10604–10609. Freud, S. (1940). An outline of psychoanalysis. In J. Strachey (Ed.), The standard edition of the complete psychological works of Sigmund Freud (Vol. 23). London: Hogarth Press. Galea, S., Riddle, M., & Kaplan, G. A. (2010). Causal thinking and complex system approaches in epidemiology. International Journal of Epidemiology, 39(1), 97–106. Galobardes, B., Lynch, J. W., & Davey Smith, G. (2004). Childhood socioeconomic circumstances and causespecific mortality in adulthood: Systematic review and interpretation. Epidemiologic Reviews, 26, 7–21. Garner, A. S., Shonkoff, J. P., Siegel, B. S., Dobbins, M. I., Earls, M. F., Garner, A. S., et al. (2012). Early childhood adversity, toxic stress, and the role of the 88 pediatrician: Translating developmental science into lifelong health. Pediatrics, 129(1), e224–e231. doi:10.1542/peds.2011-2662. Gianaros, P. J., & Manuck, S. B. (2010). Neurobiological pathways linking socioeconomic position and health. Psychosomatic Medicine, 72(5), 450–461. doi:10.1097/PSY.0b013e3181e1a23c. Gilbert, S. F. (2002). The genome in its ecological context: Philosophical perspectives on interspecies epigenesis. Annals of the New York Academy of Sciences, 981, 202–218. Gilbert, S. F., & Epel, D. (2009). Ecological developmental biology: Integrating epigenetics, medicine, and evolution. Sunderland: Sinauer Associates. Gini, G., & Pozzoli, T. (2009). Association between bullying and psychosomatic problems: A metaanalysis. Pediatrics, 123(3), 1059–1065. doi:10.1542/ peds.2008-1215. Glew, G. M., Fan, M. Y., Katon, W., Rivara, F. P., & Kernic, M. A. (2005). Bullying, psychosocial adjustment, and academic performance in elementary school. Archives of Pediatrics & Adolescent Medicine, 159(11), 1026– 1031. doi:10.1001/archpedi.159.11.1026. Gluckman, P. D., Hanson, M. A., & Pinal, C. (2005). The developmental origins of adult disease. Maternal & Child Nutrition, 1(3), 130–141. Gluckman, P. D., Hanson, M. A., & Beedle, A. S. (2007). Non-genomic transgenerational inheritance of disease risk. BioEssays, 29(2), 145–154. Gluckman, P. D., Hanson, M. A., Bateson, P., Beedle, A. S., Law, C. M., Bhutta, Z. A., et al. (2009). Towards a new developmental synthesis: Adaptive developmental plasticity and human disease. Lancet, 373(9675), 1654–1657. doi:10.1016/S0140-6736(09)60234-8. Goodman, E., Adler, N. E., Daniels, S. R., Morrison, J. A., Slap, G. B., & Dolan, L. M. (2003). Impact of objective and subjective social status on obesity in a biracial cohort of adolescents. Obesity Research, 11(8), 1018–1026. Gottlieb, G. (1991). Experiential canalization of behavioral development: Theory. Developmental Psychology, 27(1), 4–13. Greenfield, E. A. (2010). Child abuse as a life-course social determinant of adult health. Maturitas, 66(1), 51–55. doi:10.1016/j.maturitas.2010.02.002. Gump, B. B., Reihman, J., Stewart, P., Lonky, E., Darvill, T., & Matthews, K. A. (2007). Blood lead (Pb) levels: A potential environmental mechanism explaining the relation between socioeconomic status and cardiovascular reactivity in children. Health Psychology, 26(3), 296–304. doi:10.1037/0278-6133.26.3.296. Hackman, D. A., & Farah, M. J. (2009). Socioeconomic status and the developing brain. Trends in Cognitive Sciences, 13(2), 65–73. doi:10.1016/j. tics.2008.11.003. Hackman, D. A., Farah, M. J., & Meaney, M. J. (2010). Socioeconomic status and the brain: Mechanistic insights from human and animal research. Nature Reviews Neuroscience, 11(9), 651–659. doi:10.1038/ nrn2897. W. Thomas Boyce and C. Hertzman Halpern, J. (2001). From detached concern to empathy: Humanizing medical practice. New York: Oxford University Press. Hane, A. A., & Fox, N. A. (2006). Ordinary variations in maternal caregiving influence human infants' stress reactivity. Psychological Science, 17(6), 550–556. doi:10.1111/j.1467-9280.2006.01742.x. Hariri, A. R., & Weinberger, D. R. (2003). Imaging genomics. British Medical Bulletin, 65, 259–270. Hariri, A. R., Drabant, E. M., Munoz, K. E., Kolachana, B. S., Mattay, V. S., Egan, M. F., & Weinberger, D. R. (2005). A susceptibility gene for affective disorders and the response of the human amygdala. Archives of General Psychiatry, 62(2), 146–152. Harlow, H. F., Harlow, M. K., & Suomi, S. J. (1971). From thought to therapy: Lessons from a primate laboratory. American Scientist, 59(5), 538–549. Hart, T., & Risley, T. R. (1995). Meaningful differences in the everyday experience of young American children. Baltimore, MD: Paul H. Brookes. Hawley, P. H. (1999). The ontogenesis of social dominance: A strategy-based evolutionary perspective. Developmental Review, 19, 97–132. Hazani, E., & Shasha, S. M. (2008). Effects of the Holocaust on the physical health of the offspring of survivors. The Israel Medical Association Journal, 10(4), 251–255. Heijmans, B. T., Tobi, E. W., Stein, A. D., Putter, H., Blauw, G. J., Susser, E. S., et al. (2008). Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proceedings of the National Academy of Sciences of the United States of America, 105(44), 17046–17049. doi:10.1073/pnas.0806560105. Hensch, T. K. (2005). Critical period plasticity in local cortical circuits. Nature Reviews. Neuroscience, 6(11), 877–888. doi:10.1038/nrn1787. Hertzman, C. (2010). Social geography of developmental health in the early years. Healthcare Quarterly, 14(1), 32–40. Hertzman, C., & Boyce, W. T. (2010). How experience gets under the skin to create gradients in developmental health. Annual Review of Public Health, 31, 329–347. 323p following 347. doi:10.1146/annurev. publhealth.012809.103538. Hillis, S. D., Anda, R. F., Dube, S. R., Felitti, V. J., Marchbanks, P. A., & Marks, J. S. (2004). The association between adverse childhood experiences and adolescent pregnancy, long-term psychosocial consequences, and fetal death. Pediatrics, 113(2), 320–327. Houweling, T. A., & Kunst, A. E. (2010). Socio-economic inequalities in childhood mortality in low- and middle-income countries: A review of the international evidence. British Medical Bulletin, 93, 7–26. doi:10.1093/bmb/ldp048. Hrdy, S. B. (1999). Mother nature: Maternal instincts and how they shape the human species. New York: Ballantine Books. Insel, T. R. (2010). The challenge of translation in social neuroscience: A review of oxytocin, vasopressin, and affiliative behavior. Neuron, 65(6), 768–779. doi:10.1016/j.neuron.2010.03.005. Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities Isles, A. R., & Wilkinson, L. S. (2008). Epigenetics: What is it and why is it important to mental disease? British Medical Bulletin, 85, 35–45. doi:10.1093/bmb/ldn004. Janus, M., & Offord, D. (2000). Reporting on readiness to learn at school in Canada. Canadian Journal of Policy Research, 1, 71–75. Javidi, H., & Yadollahie, M. (2012). Post-traumatic stress disorder. Journal of Occupational and Environmental Medicine, 3(1), 2–9. Jirtle, R. L., & Skinner, M. K. (2007). Environmental epigenomics and disease susceptibility. Nature Reviews. Genetics, 8(4), 253–262. doi:10.1038/nrg2045. Kaplan, J. R., Manuck, S. B., Clarkson, T. B., Lusso, F. M., & Taub, D. M. (1982). Social status, environment, and athersclerosis in cynomolgus monkeys. Arteriosclerosis, 2, 359–368. Karmiloff-Smith, A. (2007). Atypical epigenesis. Developmental Science, 10(1), 84–88. doi:10.1111/j.1467-7687.2007.00568.x. Kauffman, S. A. (1993). The origins of order: Selforganization and selection in evolution. New York: Oxford University Press. Kawachi, I., Adler, N. E., & Dow, W. H. (2010). Money, schooling, and health: Mechanisms and causal evidence. Annals of the New York Academy of Sciences, 1186, 56–68. doi:10.1111/j.1749-6632.2009.05340.x. Keating, D. P., & Hertzman, C. (1999). Developmental health and the wealth of nations: Social, biological, and educational dynamics. New York: The Guilford Press. Keltner, D., Gruenfeld, D., & Anderson, C. (2003). Power, approach, and inhibition. Psychological Review, 110(2), 265–284. Kershaw, P., Forer, B., Lloyd, J. E. V., Hertzman, C., Boyce, W. T., Zumbo, B. D., et al. (2009). The use of population-level data to advance interdisciplinary methodology: A cell-through-society sampling framework for child development research. International Journal of Social Research Methodology, 12(5), 387–403. Kessler, R. C., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kling, J. R., Sampson, N. A., et al. (2014). Associations of housing mobility interventions for children in high-poverty neighborhoods with subsequent mental disorders during adolescence. JAMA, 311(9), 937–948. doi:10.1001/jama.2014.607. Keverne, E. B., Martensz, N. D., & Tuite, B. (1989). Beta-endorphin concentrations in cerebrospinal fluid of monkeys are influenced by grooming relationships. Psychoneuroendocrinology, 14(1–2), 155–161. Khan, Y., & Bhutta, Z. A. (2010). Nutritional deficiencies in the developing world: Current status and opportunities for intervention. Pediatric Clinics of North America, 57(6), 1409–1441. doi:10.1016/j. pcl.2010.09.016. Kim, Y. S., Koh, Y. J., & Leventhal, B. L. (2004). Prevalence of school bullying in Korean middle school students. Archives of Pediatrics & Adolescent Medicine, 158(8), 737–741. Kim, Y. S., Leventhal, B. L., Koh, Y. J., & Boyce, W. T. (2009). Bullying increased suicide risk: Prospective study of Korean adolescents. 89 Archives of Suicide Research, 13(1), 15–30. doi:10.1080/13811110802572098. King, M. L. (2007). Concepts of childhood: What we know and where we might go. Renaissance Quarterly, 60(2), 371–407. Kinnally, E. L., Capitanio, J. P., Leibel, R., Deng, L., LeDuc, C., Haghighi, F., & Mann, J. J. (2010a). Epigenetic regulation of serotonin transporter expression and behavior in infant rhesus macaques. Genes, Brain, and Behavior, 9(6), 575–582. doi:10.1111/j.1601-183X.2010.00588.x. Kinnally, E. L., Karere, G. M., Lyons, L. A., Mendoza, S. P., Mason, W. A., & Capitanio, J. P. (2010b). Serotonin pathway gene-gene and gene-environment interactions influence behavioral stress response in infant rhesus macaques. Development and Psychopathology, 22(1), 35–44. doi:10.1017/ S0954579409990241. Kinnally, E. L., Feinberg, C., Kim, D., Ferguson, K., Leibel, R., Coplan, J. D., & John Mann, J. (2011). DNA methylation as a risk factor in the effects of early life stress. Brain Behavior and Immunity. doi:10.1016/j.bbi.2011.05.001. Kittleson, M. M., Meoni, L. A., Wang, N. Y., Chu, A. Y., Ford, D. E., & Klag, M. J. (2006). Association of childhood socioeconomic status with subsequent coronary heart disease in physicians. Archives of Internal Medicine, 166(21), 2356–2361. Knafo, A., Israel, S., & Ebstein, R. P. (2011). Heritability of children's prosocial behavior and differential susceptibility to parenting by variation in the dopamine receptor D4 gene. Development and Psychopathology, 23(1), 53–67. doi:10.1017/S0954579410000647. Koopman, J. S., & Lynch, J. W. (1999). Individual causal models and population system models in epidemiology. American Journal of Public Health, 89(8), 1170–1174. Kraemer, H. C., Stice, E., Kazdin, A., Offord, D., & Kupfer, D. (2001). How do risk factors work together? Mediators, moderators, independent, overlapping and proxy-risk factors. The American Journal of Psychiatry, 158, 848–856. Kraemer, H. C., Kiernan, M., Essex, M., & Kupfer, D. J. (2008). How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Health Psychology, 27(2 Suppl), S101–S108. doi:10.1037/0278-6133.27.2(Suppl.).S101. Krieger, N. (2001). Theories for social epidemiology in the 21st century: An ecosocial perspective. International Journal of Epidemiology, 30, 668–677. Kuh, D., & Ben-Shlomo, Y. (2004). A life course approach to chronic disease epidemiology (2nd ed.). Oxford: Oxford University Press. Kuzawa, C. W., & Thayer, Z. M. (2011). Timescales of human adaptation: The role of epigenetic processes. Epigenomics, 3(2), 221–234. doi:10.2217/epi.11.11. Ladd-Acosta, C., Pevsner, J., Sabunciyan, S., Yolken, R. H., Webster, M. J., Dinkins, T., et al. (2007). DNA methylation signatures within the human brain. 90 American Journal of Human Genetics, 81(6), 1304– 1315. doi:10.1086/524110. Lam, L. L., Emberly, E., Fraser, H. B., Neumann, S. M., Chen, E., Miller, G. E., & Kobor, M. S. (2012). Biological and environmental predictors of variable DNA methylation in a human community cohort. PNAS. Lawlor, D. A., Sterne, J. A., Tynelius, P., Davey Smith, G., & Rasmussen, F. (2006). Association of childhood socioeconomic position with cause-specific mortality in a prospective record linkage study of 1,839,384 individuals. American Journal of Epidemiology, 164(9), 907–915. doi:10.1093/aje/kwj319. Lesch, K. P. (2007). Linking emotion to the social brain. The role of the serotonin transporter in human social behaviour. EMBO Reports, 8, S24–S29. doi:10.1038/ sj.embor.7401008. Liew, C. C., Ma, J., Tang, H. C., Zheng, R., & Dempsey, A. A. (2006). The peripheral blood transcriptome dynamically reflects system wide biology: A potential diagnostic tool. The Journal of Laboratory and Clinical Medicine, 147(3), 126–132. doi:10.1016/j. lab.2005.10.005. Liu, L., Li, Y., & Tollefsbol, T. O. (2008). Geneenvironment interactions and epigenetic basis of human diseases. Current Issues in Molecular Biology, 10(1–2), 25–36. Liu, L., Johnson, H. L., Cousens, S., Perin, J., Scott, S., Lawn, J. E., et al. (2012). Global, regional, and national causes of child mortality: An updated systematic analysis for 2010 with time trends since 2000. Lancet, 379(9832), 2151–2161. doi:10.1016/ S0140-6736(12)60560-1. Lloyd, L. J., Langley-Evans, S. C., & McMullen, S. (2012). Childhood obesity and risk of the adult metabolic syndrome: A systematic review. International Journal of Obesity, 36(1), 1–11. doi:10.1038/ijo.2011.186. Lumey, L. H., Stein, A. D., & Susser, E. (2011). Prenatal famine and adult health. Annual Review of Public Health, 32, 237–262. doi:10.1146/ annurev-publhealth-031210-101230. Lupien, S. J., King, S., Meaney, M. J., & McEwen, B. S. (2001). Can poverty get under your skin? Basal cortisol levels and cognitive function in children from low and high socioeconomic status. Development and Psychopathology, 13, 653–676. MacDonald, J. L., & Roskams, A. J. (2009). Epigenetic regulation of nervous system development by DNA methylation and histone deacetylation. Progress in Neurobiology, 88(3), 170–183. Manuck, S. B., Craig, A. E., Flory, J. D., Halder, I., & Ferrell, R. E. (2011). Reported early family environment covaries with menarcheal age as a function of polymorphic variation in estrogen receptor-alpha. Development and Psychopathology, 23(1), 69–83. doi:10.1017/S0954579410000659. Marmot, M. (2010). Fair society, healthy lives. Firenze: L.S. Olschki. Mazumdar, S., King, M., Liu, K. Y., Zerubavel, N., & Bearman, P. (2010). The spatial structure of autism in California, 1993-2001. Health & Place, 16(3), 539– 546. doi:10.1016/j.healthplace.2009.12.014. W. Thomas Boyce and C. Hertzman McDade, T. (2012). Early environments and the ecologics of inflammation. Proceedings of the National Academy of Sciences of the United States of America, 109(Suppl 2), 17281–17288. McEwen, B. S., & Gianaros, P. J. (2010). Central role of the brain in stress and adaptation: Links to socioeconomic status, health, and disease. Annals of the New York Academy of Sciences, 1186, 190–222. doi:10.1111/j.1749-6632.2009.05331.x. McGowan, P. O., & Szyf, M. (2010). The epigenetics of social adversity in early life: Implications for mental health outcomes. Neurobiology of Disease, 39(1), 66–72. doi:10.1016/j.nbd.2009.12.026. McGowan, P. O., Sasaki, A., Huang, T. C., Unterberger, A., Suderman, M., Ernst, C., et al. (2008). Promoterwide hypermethylation of the ribosomal RNA gene promoter in the suicide brain. PloS One, 3(5), e2085. McGowan, P. O., Sasaki, A., D'Alessio, A. C., Dymov, S., Labonte, B., Szyf, M., et al. (2009). Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nature Neuroscience, 12(3), 342–348. doi:10.1038/nn.2270. Meaney, M. J. (2010). Epigenetics and the biological definition of gene x environment interactions. Child Development, 81(1), 41–79. doi:10.1111/j.1467-8624.2009.01381.x. Meaney, M. J., & Ferguson-Smith, A. C. (2010). Epigenetic regulation of the neural transcriptome: The meaning of the marks. Nature Neuroscience, 13(11), 1313–1318. doi:10.1038/nn1110-1313. Mehler, M. F. (2008). Epigenetic principles and mechanisms underlying nervous system functions in health and disease. Progress in Neurobiology, 86(4), 305–341. Miller, G. E., Chen, E., Fok, A. K., Walker, H., Lim, A., Nicholls, E. F., et al. (2009a). Low early-life social class leaves a biological residue manifested by decreased glucocorticoid and increased proinflammatory signaling. Proceedings of the National Academy of Sciences of the United States of America, 106(34), 14716–14721. doi:10.1073/pnas.0902971106. Miller, A. H., Maletic, V., & Raison, C. L. (2009b). Inflammation and its discontents: The role of cytokines in the pathophysiology of major depression. Biological Psychiatry, 65(9), 732–741. doi:10.1016/j. biopsych.2008.11.029. Moffitt, T. E., Caspi, A., & Rutter, M. (2005). Strategy for investigating interactions between measured genes and measured environments. Archives of General Psychiatry, 62(5), 473–481. Mohr, S., & Liew, C. C. (2007). The peripheral-blood transcriptome: New insights into disease and risk assessment. Trends in Molecular Medicine, 13(10), 422–432. doi:10.1016/j.molmed.2007.08.003. Morita, K., Saito, T., Ohta, M., Ohmori, T., Kawai, K., Teshima-Kondo, S., & Rokutan, K. (2005). Expression analysis of psychological stress-associated genes in peripheral blood leukocytes. Neuroscience Letters, 381(1–2), 57–62. Msall, M. E., Bier, J. A., LaGasse, L., Tremont, M., & Lester, B. (1998). The vulnerable preschool child: The Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities impact of biomedical and social risks on neurodevelopmental function. Seminars in Pediatric Neurology, 5(1), 52–61. Nesse, R. M., & Young, E. A. (2000). Evolutionary origins and functions of the stress response. In G. Fink (Ed.), Encyclopedia of stress (Vol. 2, pp. 79–84). San Diego: Academic Press. Norman, G. J., Hawkley, L. C., Cole, S. W., Berntson, G. G., & Cacioppo, J. T. (2012). Social neuroscience: The social brain, oxytocin, and health. Social Neuroscience, 7(1), 18–29. doi:10.1080/17470919.2 011.568702. Oberg, C. (2008). Children of genocide: A legacy of lost dreams. Pediatrics, 121(3), 611–615. doi:10.1542/ peds.2007-2208. Oberlander, T. F., Weinberg, J., Papsdorf, M., Grunau, R., Misri, S., & Devlin, A. M. (2008). Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress response. Epigenetics, 3(2), 97–106. Obradovic, J., Bush, N. R., Stamperdahl, J., Adler, N. E., & Boyce, W. T. (2010). Biological sensitivity to context: The interactive effects of stress reactivity and family adversity on socio-emotional behavior and school readiness. Child Development, 81(1), 270–289. Offord, D. R. (1995). Child psychiatric epidemiology: Current status and future prospects. Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie, 40(6), 284–288. Olds, D. L., Kitzman, H. J., Cole, R. E., Hanks, C. A., Arcoleo, K. J., Anson, E. A., et al. (2010). Enduring effects of prenatal and infancy home visiting by nurses on maternal life course and government spending: Follow-up of a randomized trial among children at age 12 years. Archives of Pediatrics and Adolescent Medicine, 164(5), 419–424. doi:10.1001/ archpediatrics.2010.49. Ollikainen, M., Smith, K. R., Joo, E. J., Ng, H. K., Andronikos, R., Novakovic, B., et al. (2010). DNA methylation analysis of multiple tissues from newborn twins reveals both genetic and intrauterine components to variation in the human neonatal epigenome. Human Molecular Genetics, 19(21), 4176–4188. doi:10.1093/hmg/ddq336. Ostrove, J. M., Adler, N. E., Kuppermann, M., & Washington, A. E. (2000). Objective and subjective assessments of socioeconomic status and their relationship to self-rated health in an ethnically diverse sample of pregnant women. Health Psychology, 19(6), 613–618. Overton, W. F., & Lerner, R. M. (2014). Fundamental concepts and methods in developmental science: A relational perspective. Research in Human Development, 11, 63–73. Painter, R. C., Roseboom, T. J., & Bleker, O. P. (2005). Prenatal exposure to the Dutch famine and disease in later life: An overview. Reproductive Toxicology, 20(3), 345–352. Painter, R., Osmond, C., Gluckman, P., Hanson, M., Phillips, D., & Roseboom, T. (2008). Transgenerational effects of prenatal exposure to the Dutch famine on 91 neonatal adiposity and health in later life. BJOG: An International Journal of Obstetrics & Gynaecology, 115(10), 1243–1249. Panter-Brick, C., Lende, D., & Kohrt, B. (2012). Children in global adversity: Physical health, mental health, behavioral health, and symbolic health. In V. Maholmes & R. B. King (Eds.), The Oxford handbook of poverty and child development (pp. 603–621). Oxford: Oxford University Press. Pellegrini, A. D., Roseth, C. J., Mliner, S., Bohn, C. M., Van Ryzin, M., Vance, N., et al. (2007). Social dominance in preschool classrooms. Journal of Comparative Psychology, 121(1), 54–64. doi:10.1037/0735-7036.121.1.54. Pellegrini, A. D., Van Ryzin, M. J., Roseth, C., BohnGettler, C., Dupuis, D., Hickey, M., & Peshkam, A. (2010). Behavioral and social cognitive processes in preschool children's social dominance. Aggressive Behavior, 37(3), 248–257. doi:10.1002/ab.20385. Pezawas, L., Meyer-Lindenberg, A., Drabant, E. M., Verchinski, B. A., Munoz, K. E., Kolachana, B. S., et al. (2005). 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: A genetic susceptibility mechanism for depression. Nature Neuroscience, 8(6), 828–834. Pluess, M., & Belsky, J. (2013). Vantage sensitivity: Individual differences in response to positive experiences. Psychological Bulletin, 139(4), 901–916. doi:10.1037/a0030196. Quas, J. A., Bauer, A., & Boyce, W. T. (2004). Physiological reactivity, social support, and memory in early childhood. Child Development, 75(3), 797–814. Rifkin, J. (2009). The empathic civilization: The race to global consciousness in a world in crisis. New York: Jeremy P. Tarcher/Penguin. Robert Wood Johnson Foundation Commission to Build a Healthier America. (2009). Beyond Health Care: New Directions to a Healthier America. Retrieved from Princeton, NJ: Robinson, G. E., Grozinger, C. M., & Whitfield, C. W. (2005). Sociogenomics: Social life in molecular terms. Nature Reviews Genetics, 6, 257–270. Robinson, G. E., Fernald, R. D., & Clayton, D. F. (2008). Genes and social behavior. Science, 322(5903), 896– 900. doi:10.1126/science.1159277. Robison, A. J., & Nestler, E. J. (2011). Transcriptional and epigenetic mechanisms of addiction. Nature Reviews. Neuroscience, 12(11), 623–637. doi:10.1038/nrn3111. Roseboom, T. J., van der Meulen, J. H., Osmond, C., Barker, D. J., Ravelli, A. C., Schroeder-Tanka, J. M., et al. (2000). Coronary heart disease after prenatal exposure to the Dutch famine, 1944-45. Heart, 84(6), 595–598. Roth, T. L., Lubin, F. D., Funk, A. J., & Sweatt, J. D. (2009). Lasting epigenetic influence of early-life adversity on the BDNF gene. Biological Psychiatry, 65(9), 760–769. doi:10.1016/j.biopsych.2008.11.028. Russo, V. E. A., Cove, D. J., Edgar, L. G., Jaenisch, R., & Salamini, F. (Eds.). (1999). Development: Genetics, epigenetics and environmental regulation. Berlin: Springer-Verlag. 92 Rutter, M. (2006). Genes and behaviour: Nature/nurture interplay explained. Oxford, UK: Blackwell Publishing. Rutter, M. (2012). Achievements and challenges in the biology of environmental effects. PNAS. Sapolsky, R. M. (2003). Stress and plasticity in the limbic system. Neurochemical Research, 28(11), 1735–1742. Sapolsky, R. M. (2005). The influence of social hierarchy on primate health. Science, 308(5722), 648–652. Schneider, J., Dickinson, M., & Levine, J. (2012). Social structures depend on innate determinants and chemosensory processing in Drosophila. PNAS. Sentenac, M., Arnaud, C., Gavin, A., Molcho, M., Gabhainn, S. N., & Godeau, E. (2012). Peer victimization among school-aged children with chronic conditions. Epidemiologic Reviews, 34(1), 120–128. doi:10.1093/epirev/mxr024. Shannon, K. E., Beauchaine, T. P., Brenner, S. L., Neuhaus, E., & Gatzke-Kopp, L. (2007). Familial and temperamental predictors of resilience in children at risk for conduct disorder and depression. Development and Psychopathology, 19(3), 701–727. doi:10.1017/ S0954579407000351. Shonkoff, J. (2012). Leveraging the biology of adversity and resilience to address the roots of disparities in health and development. Proceedings of the National Academy of Sciences of the United States of America,109(Suppl 2), 17302–17307. Shonkoff, J. P., & Garner, A. S. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129(1), e232–e246. doi:10.1542/ peds.2011-2663. Shonkoff, J. P., & Phillips, D. A. (Eds.). (2000). From neurons to neighborhoods: The science of early child development. Washington, DC: National Academy Press. Shorter, E. (1975). The making of the modern family. New York: Basic Books. Slavich, G. M., & Cole, S. W. (2013). The emerging field of human social genomics. Clinical Psychological Science: A Journal of the Association for Psychological Science, 1(3), 331–348. Sourander, A., Jensen, P., Ronning, J. A., Elonheimo, H., Niemela, S., Helenius, H., et al. (2007). Childhood bullies and victims and their risk of criminality in late adolescence: The Finnish From a Boy to a Man study. Archives of Pediatrics & Adolescent Medicine, 161(6), 546–552. doi:10.1001/archpedi.161.6.546. Stearns, P. N. (2011). Childhood in world history (2nd ed.). New York, NY: Routledge. Steptoe, A., Feldman, P. J., Kunz, S., Owen, N., Willemsen, G., & Marmot, M. (2002). Stress responsivity and socioeconomic status: A mechanism for increased cardiovascular disease risk? European Heart Journal, 23, 1757–1763. Stevens, H. E., Leckman, J. F., Coplan, J. D., & Suomi, S. J. (2009). Risk and resilience: Early manipulation of macaque social experience and persistent behavioral and neurophysiological outcomes. W. Thomas Boyce and C. Hertzman Journal of the American Academy of Child and Adolescent Psychiatry, 48(2), 114–127. doi:10.1097/ CHI.0b013e318193064c. Suomi, S. J. (2006). Risk, resilience, and gene x environment interactions in rhesus monkeys. Annals of the New York Academy of Sciences, 1094, 52–62. doi:10.1196/annals.1376.006. Syme, S. L. (2008). Reducing racial and social-class inequalities in health: The need for a new approach. Health Affairs (Millwood), 27(2), 456–459. doi:10.1377/hlthaff.27.2.456. Szyf, M., McGowan, P., & Meaney, M. J. (2008). The social environment and the epigenome. Environmental and Molecular Mutagenesis, 49(1), 46–60. doi:10.1002/ em.20357. Thompson, P. M., Martin, N. G., & Wright, M. J. (2010). Imaging genomics. Current Opinion in Neurology, 23(4), 368–373. doi:10.1097/ WCO.0b013e32833b764c. Tsankova, N., Renthal, W., Kumar, A., & Nestler, E. J. (2007). Epigenetic regulation in psychiatric disorders. Nature Reviews. Neuroscience, 8(5), 355–367. doi:10.1038/nrn2132. Turecki, G., Ernst, C., Jollant, F., Labonte, B., & Mechawar, N. (2012). The neurodevelopmental origins of suicidal behavior. Trends in Neurosciences, 35(1), 14–23. doi:10.1016/j.tins.2011.11.008. Turner, J. D., Pelascini, L. P., Macedo, J. A., & Muller, C. P. (2008). Highly individual methylation patterns of alternative glucocorticoid receptor promoters suggest individualized epigenetic regulatory mechanisms. Nucleic Acids Research, 36(22), 7207–7218. doi:10.1093/nar/gkn897. Turner, H. A., Finkelhor, D., Shattuck, A., & Hamby, S. (2012). Recent victimization exposure and suicidal ideation in adolescents. Archives of Pediatrics & Adolescent Medicine, 1–6. doi:10.1001/ archpediatrics.2012.1549. U.S. Department of Health and Human Services. (2011). CDC Health Disparities and Inequalities Report— United States, 2011. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. Uebelhack, R., Franke, L., Herold, N., Plotkin, M., Amthauer, H., & Felix, R. (2006). Brain and platelet serotonin transporter in humans-correlation between [123I]-ADAM SPECT and serotonergic measurements in platelets. Neuroscience Letters, 406(3), 153– 158. doi:10.1016/j.neulet.2006.06.004. van IJzendoorn, M. H., Caspers, K., BakermansKranenburg, M. J., Beach, S. R., & Philibert, R. (2010). Methylation matters: Interaction between methylation density and serotonin transporter genotype predicts unresolved loss or trauma. Biological Psychiatry, 68(5), 405–407. doi:10.1016/j.biopsych.2010.05.008. Vogel, E. F., & Bell, N. W. (1961). The emotionally disturbed child as the family scapegoat. In N. W. Bell & E. F. Vogel (Eds.), A Modern introduction to the family. London: Routledge & Kegan Paul. Early Childhood Health and the Life Course: The State of the Science and Proposed Research Priorities Waddington, C. H. (1959). Canalization of development and genetic assimilation of acquired characters. Nature, 183(4676), 1654–1655. Waddington, C. H. (2012). The epigenotype. 1942. International Journal of Epidemiology, 41(1), 10–13. doi:10.1093/ije/dyr184. Wadsworth, M. E., Raviv, T., Reinhard, C., Wolff, B., Santiago, C. D. C., & Einhorn, L. (2008). An indirect effects model of the association between poverty and child functioning: The role of children. Journal of Loss and Trauma, 30. Weaver, I. C. (2009). Epigenetic effects of glucocorticoids. Seminars in Fetal and Neonatal Medicine, 14(3), 143–150. doi:10.1016/j.siny.2008.12.002. Weaver, I. C., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R., et al. (2004). Epigenetic programming by maternal behavior. Nature Neuroscience, 7(8), 847–854. Weikum, W. M., Oberlander, T. F., Hensch, T. K., & Werker, J. F. (2012). Prenatal exposure to antidepressants and depressed maternal mood alter trajectory of infant speech perception. Proceedings of the National Academy of Sciences, 109(2), 17221–17227. doi:10.1073/pnas.1121263109. West-Eberhard, M. J. (2003). Developmental plasticity and evolution. New York: Oxford University Press. 93 Whittle, S., Yap, M. B., Sheeber, L., Dudgeon, P., Yucel, M., Pantelis, C., et al. (2010). Hippocampal volume and sensitivity to maternal aggressive behavior: A prospective study of adolescent depressive symptoms. Development and Psychopathology, 23(1), 115–129. doi:10.1017/S0954579410000684. Wolf, D. M., Vazirani, V. V., & Arkin, A. P. (2005). Diversity in times of adversity: Probabilistic strategies in microbial survival games. Journal of Theoretical Biology, 234(2), 227–253. doi:10.1016/j. jtbi.2004.11.020. Wright, R. O., & Christiani, D. (2010). Gene-environment interaction and children's health and development. Current Opinion in Pediatrics, 22(2), 197–201. doi:10.1097/MOP.0b013e328336ebf9. Wu, H., & Sun, Y. E. (2006). Epigenetic regulation of stem cell differentiation. Pediatric Research, 59(4 Pt 2), 21R–25R. doi:10.1203/01.pdr.0000203565.76028.2a. Yang, E.-J., Lin, E. W., & Hensch, T. K. (2012). A critical period for acoustic preference in mice. Proceedings of the National Academy of Sciences, 109(Suppl 2), 17213–17220. Zhang, T. Y., & Meaney, M. J. (2010). Epigenetics and the environmental regulation of the genome and its function. Annual Review of Psychology, 61, 439–466, C431–433.. doi:10.1146/annurev. psych.60.110707.163625. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Middle Childhood: An Evolutionary-Developmental Synthesis Marco DelGiudice Middle childhood—conventionally going from about 6–11 years of age—is a crucial yet underappreciated phase of human development. On the surface, middle childhood may appear like a slow-motion interlude between the spectacular transformations of infancy and early childhood and those of adolescence. In reality, this life stage is anything but static: the transition from early to middle childhood heralds a global shift in cognition, motivation, and social behavior, with profound and wide-ranging implications for the development of personality, sex differences, and even psychopathology (Table 1). In the last two decades, converging theories and findings from anthropology, primatology, evolutionary psychology, endocrinology, and behavior genetics have revolutionized our understanding of middle childhood. In this chapter, I show how these diverse contributions can be synthesized into an integrated evolutionary-developmental model of middle childhood. I begin by reviewing the main evolved functions of middle childhood and the cognitive, behavioral, and hormonal processes that characterize this life stage. Then, I introduce the idea that the transition to middle childhood works as a switch point in the development of life history strategies (Del Giudice et al. 2009, 2012; Del Giudice and Belsky 2011) and discuss three insights in the nature of middle childhood that arise from an integrated approach. This chapter was originally published as a short paper in the journal Child Development Perspectives (Del Giudice 2014a). It is reprinted here with updated references and a new section on the model’s implications for health development in a life course perspective (LCHD). 1 This chapter contains a modified version of a previously published review and analysis of existing research, written by Marco Del Giudice, that appeared in Child Development Perspectives. Reprinted with permission from: Del Giudice, M. (2014), Middle Childhood: An Evolutionary-Developmental Synthesis. Child Dev Perspect, 8: 193–200. doi:10.1111/cdep.12084. M. DelGiudice (*) Department of Psychology, University of New Mexico, Logan Hall, 2001 Redondo Dr. NE, Albuquerque, NM 87131, USA e-mail: marcodg@unm.edu What Is Middle Childhood? Middle childhood is one of the main stages of human development, marked by the eruption of the first permanent molars around age 6 and the onset of androgen secretion by the adrenal glands at about 6–8 years (Bogin 1997). In middle childhood, body growth slows considerably, usually following a small mid-growth spurt. At the same time, muscularity increases and the body starts accumulating fat (the adiposity rebound; Hochberg 2008), while sex differences in body composition become more pronounced (Del Giudice et al. 2009; Wells © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_5 95 M. DelGiudice 96 Table 1 Development in middle childhood Body growth Brain growth Motor and perceptual skills Cognitive skills Motivation and social behavior Psychopathology Social context Behavior genetics Eruption of permanent molars Mid-growth spurt, followed by decelerating skeletal growth Increased muscle mass Increased adiposity and BMI (adiposity rebound) Initial development of axillary hair and body odor Increased sex differences in adiposity (F > M), bone strength, and muscularity (M > F) Emergence of sex differences in vocal characteristics Approaching peak of overall brain volume Peak of gray matter volume Continuing increase in white matter volume/integrity Increased gross motor skills (e.g., walking) Increased fine motor skills Local-global shift in visual processing preferences Increased reasoning and problem-solving skills (e.g., concrete operations) Increased self-regulation and executive functions (inhibition, attention, planning, etc.) Increased mentalizing skills (multiple perspectives, conflicting goals) Increased navigational skills (working memory, ability to understand maps) Acquisition of cultural norms (e.g., prosociality) Complex moral reasoning (conflicting points of view) Increased pragmatic abilities (gossiping, storytelling, verbal competition, etc.) Consolidation of status/dominance hierarchies Changes in aggression levels (individual trajectories) Development of disgust Changes in food preferences (e.g., spicy foods) Onset of sexual/romantic attraction Increased frequency of sexual play Increased sense of gender identity Peak of sex segregation Peak of sex differences in social play (including play fighting vs. play parenting) Increased sex differences in physical aggression (M > F) Emergence of sex differences in attachment styles Early peak of psychopathology onset (externalizing, anxiety, phobias, ADHD) Peak onset of fetishistic attractions Emergence of sex differences in conduct disorders (M > F) Active involvement in caretaking, foraging, domestic tasks, helping Expectations of responsible behavior Attribution of individuality and personhood (“getting noticed”) Increased heritability of general intelligence and language skills New genetic influences on general intelligence, language, aggression, and prosociality See the main text for supporting references BMI body mass index, M male, F female, ADHD attention deficit hyperactivity disorder 2007). Figure 1 places middle childhood in the broader context of human growth from conception to adolescence. In biological terms, middle childhood corresponds to human juvenility—a stage in which the individual is still sexually immature, but no lon- ger dependent on parents for survival. In social mammals and primates, juvenility is a phase of intense learning—often accomplished through play—in which youngsters practice adult behavioral patterns and acquire essential social and foraging skills. Indeed, the duration of juvenility in Middle Childhood: An Evolutionary-Developmental Synthesis 97 Fig. 1 Developmental trajectories of human growth and sex hormones production, from conception to adolescence. Arrows show the landmark events that characterize middle childhood (Reproduced from Del Giudice 2014a) primates correlates strongly with the size and complexity of social groups, as well as with cortical brain volume (Joffe 1997). Social learning in juvenility can be understood as investment in embodied capital—skills and knowledge that cost time and effort to acquire but increase an individual’s performance and reproductive success (Kaplan et al. 2000). Human children are no exception to this pattern. Social learning is universally recognized as a key evolved function of middle childhood and is enabled by a global reorganization of cognitive functioning known as the five-to-seven shift (Weisner 1996). By age 6, the brain has almost reached its maximum size and receives a decreasing share of the body’s glucose after the consumption peak of early childhood (Fig. 1; Giedd and Rapoport 2010; Kuzawa et al. 2014). However, brain development proceeds at a sustained pace, with intensive synaptogenesis in cortical areas (gray matter) and rapid maturation of axonal connections (white matter; Lebel et al. 2008). The transition to middle childhood is marked by a simultaneous increase in perceptual abilities (including a transition from local to global visual processing), motor control (including the emergence of adult-like walking), and complex reasoning skills (Bjorklund 2011; Poirel et al. 2011; Weisner 1996). The most dramatic changes probably occur in the domain of selfregulation and executive functions: children become much more capable of inhibiting unwanted behavior, maintaining sustained attention, making and following plans, and so forth (Best et al. 2009; Weisner 1996; see also McClelland et al. 2017). Parallel improvements M. DelGiudice 98 take place in mentalizing (the ability to understand and represent mental states) and moral reasoning, as children become able to consider multiple perspectives and conflicting goals (Jambon and Smetana 2014; Lagattuta et al. 2009). In traditional societies, older relatives—especially parents and grandparents—are the main sources of knowledge for juveniles, supplemented by peers and—where available—professional teachers. Storytelling—both fictional and based in real events—is a powerful technology for transmitting knowledge about foraging and social skills, avoidance of dangers, topography, wayfinding, and social roles and norms (Scalise Sugiyama 2011). Storytelling mimics the format of episodic memories, providing the child with a rich source of indirect experience (Scalise Sugiyama 2011). Intriguingly, episodic memory shows dramatic and sustained improvements across middle childhood (Ghetti and Bunge 2012). However, children at this age are not just learning and playing. Cross-culturally, middle childhood is the time when children are expected to start helping with domestic tasks—such as caring for younger siblings, collecting food and water, tending animals, and helping adults prepare food (Bogin 1997; Lancy and Grove 2011; Scalise Sugiyama 2011; Weisner 1996). In favorable ecologies, juveniles can contribute substantially to family subsistence (Kramer 2011). Thanks to marked increases in spatial cognition—reflected in the emerging ability to understand maps—and navigational skills, children become able to memorize complex routes and find their way without adult supervision (Bjorklund 2011; Piccardi et al. 2014). The important role of juveniles in collecting and preparing food may explain why the emotion of disgust does not fully develop until middle childhood (Rozin 1990a). The transition to middle childhood is typically associated with a strong separation in gender roles, even in societies where tasks are not rigidly assigned by sex. Spontaneous sex segregation of boys and girls peaks during these years, as does the frequency of sexually differentiated play (Del Giudice et al. 2009). On a broader social level, cross-cultural evidence shows that juveniles start “getting noticed” by adults—that is, they begin to be viewed fully as people with their own individuality, personality, and social responsibility (Lancy and Grove 2011). In summary, the life stage of juvenility/middle childhood has two major interlocking functions: social learning and social integration in a system of roles, norms, activities, and shared knowledge. While children are still receiving sustained investment from parents and other relatives—in the form of food, protection, knowledge, and so forth—they also start to actively contribute to their family economy. By providing resources and sharing the burden of child care, juveniles can boost their parents’ reproductive potential. The dual nature of juveniles as both receivers and providers explains many psychological features of middle childhood and has likely played a major role in the evolution of human life history (Kramer 2011). 1.1 Adrenarche The transition to middle childhood is coordinated by a remarkable endocrinological event: the awakening of the adrenal glands, or adrenarche (Auchus and Rainey 2004; Hochberg 2008). Starting at about 6–8 years—with much individual variation and only minor differences between males and females—adrenal glands begin to secrete increasing amounts of androgens (see Fig. 1), mainly dehydroepiandrosterone (DHEA) and its sulfate (DHEAS). Adrenal androgens have only minor effects on physical development, but they have powerful effects on brain functioning. DHEA and DHEAS promote neurogenesis and modulate gamma-aminobutyric acid (GABA) and glutamate receptors; moreover, DHEA can act directly on androgen and estrogen receptors. Even more important, adrenal androgens can be converted to estrogen or testosterone in the brain (Campbell 2006; Del Giudice et al. 2009). As sex hormones, adrenal androgens play a twofold role: They activate sexually differentiated brain pathways that had been previously organized by the hormonal surges of prenatal development and Middle Childhood: An Evolutionary-Developmental Synthesis infancy (Fig. 1), and they further organize brain development along sexually differentiated trajectories (Del Giudice et al. 2009). Adrenal androgens likely provide a major impulse for many of the psychological changes of middle childhood (Campbell 2006, 2011; Del Giudice et al. 2009), including the emergence and intensification of sex differences across domains (see Table 1). Since the age of adrenarche correlates strongly with that of gonadarche (the awakening of the testes/ovaries that marks the beginning of puberty; Hochberg 2008), human development shows a peculiar pattern in which sexually differentiated brain pathways are activated several years before the development of secondary sexual characteristics. This developmental pattern (shared by chimpanzees and, to a lesser extent, gorillas; Bernstein et al. 2012) results in a temporary decoupling between physical and behavioral development, consistent with the idea of middle childhood as a sexually differentiated phase of social learning and experimentation (Geary 2010). Moreover, adrenal androgens promote extended brain plasticity through synaptogenesis and may play an important role in shifting the allocation of the body’s energetic resources away from brain development and toward the accumulation of muscle and fat in preparation for puberty (Campbell 2006, 2011; see also Kuzawa et al. 2014). 2 The Transition to Middle Childhood as a Developmental Switch Point The evolutionary model of middle childhood sketched in the previous section can be enriched and extended by considering the role of adrenarche as a developmental switch (Del Giudice et al. 2009). The concept of a developmental switch was introduced by West-Eberhard (2003); a switch is a regulatory mechanism that activates at a specific point in development, collects input from the external environment or the state of the organism, and shifts the individual along alternative pathways—ultimately resulting in the development of 99 alternative phenotypes (morphological, physiological, or behavioral traits of an organism). For example, a switch may regulate the development of aggressive behavior so that safe conditions entrain the development of low levels of aggression, whereas threatening environments trigger high levels of aggression. Developmental switches enable adaptive plasticity—the ability of an organism to adjust its phenotype to match the local environment in a way that promotes biological fitness (West-Eberhard 2003). In other words, plastic organisms track the state of the environment— usually through indirect cues—and use this information to develop alternative phenotypes that tend to promote survival or reproduction under different conditions. Developmental switches work in a modular fashion (see Fig. 2). Activation of a switch leads to the coordinated expression of different genes— both those involved in the regulatory mechanism itself and those involved in the production of the new phenotype. Moreover, alternative phenotypes (A and B in Fig. 2) involve the expression of modular packages of genes specific to each phenotype. Another key aspect of developmental switches is that they integrate variation in the environment with individual differences in the genes that regulate the switch. For example, different individuals may have genetically different thresholds for switching between aggressive and nonaggressive phenotypes. Finally, the embodied effects of past experiences and conditions (e.g., an individual’s previous exposure to stress or nutritional conditions early in life) may also modulate how the switch functions, allowing the organism to integrate information over time and across different life stages (Del Giudice 2014b; Ellis 2013). In many instances, the effects of past experience on developmental switches may be mediated by epigenetic mechanisms (see Meaney 2010). The concept of a developmental switch point resembles that of a sensitive period, in that the organism is maximally responsive to some environmental input. The crucial difference is that, because genetic and environmental inputs converge in the regulatory mechanism, a developmental switch amplifies both environmental and M. DelGiudice 100 Fig. 2 The concept of a developmental switch. A regulatory mechanism, which may operate through hormonal signals, integrates current and past information from the environment with the individual’s genotype. As a result, the individual’s developmental trajectory is shifted along alternative pathways—here, A and B—depending on whether a threshold is reached within the mechanism. The location of the threshold, the intensity of the signal, and the timing of the switch point all depend on the joint action of the current state of the environment, the embodied effect of past environmental conditions, and individual variation in the genes involved in the regulatory mechanism. Each alternative pathway involves the modular expression of a set of specific genes, in addition to the shared genes expressed in the new developmental stage. A developmental switch may integrate many sources of input from the environment or produce graded phenotypes rather than discrete alternatives such as A and B genetic effects on the phenotype (West-Eberhard 2003). Indeed, the activation of a developmental switch exposes many new potential sources of genetic variation, including the genes involved in the regulatory mechanism and in the expression of the new phenotypes (Fig. 2). Del Giudice et al. 2009) encompasses all the major domains of behavior—from learning and self-regulation to attachment and sexuality (see Table 1). My colleagues and I (Del Giudice and Belsky 2011; Del Giudice et al. 2009, 2012) have argued that the transition to middle childhood is a switch point in the development of life history strategies, which are coordinate suites of morphological, physiological, and behavioral traits that determine how organisms allocate their resources to key biological activities such as growth, reproduction, mating, and parenting (for a non-technical overview of life history theory, see Del Giudice et al. 2015). At the level of 2.1 A Switch Point in Life History Development The role of adrenarche as a developmental switch is not limited to a single trait; in fact, the transition to middle childhood (or juvenile transition; Middle Childhood: An Evolutionary-Developmental Synthesis 101 behavior, individual differences in life history strategy are reflected in patterns of self-regulation, aggression, cooperation and prosociality, attachment, sexuality, and so forth (Del Giudice and Belsky 2011; Del Giudice et al. 2009, 2011; Ellis et al. 2009). Although life history strategies are partly heritable, they also show a degree of plasticity in response to the quality of the environment, including the level of danger and unpredictability –embodied in the experience of early stress—and the availability of adequate nutritional resources. In a nutshell, dangerous and unpredictable environments tend to favor fast strategies characterized by early reproduction, sexual promiscuity, unstable relationships, impulsivity, risk taking, aggression, and exploitative tendencies, whereas safe and predictable environments tend to entrain slow strategies characterized by late reproduction, stable relationships, high self-control, aversion to risk, and prosociality. Slow strategies are also favored by nutritional scarcity when danger is low (see Del Giudice et al. 2016; Ellis et al. 2009). Our argument is that adrenarche coordinates the expression of individual differences in life history strategy by integrating individual genetic variation with information about the child’s social and physical environment collected throughout infancy and early childhood (Belsky et al. 1991). The stress response system plays a major role in gathering and storing information about environmental safety, predictability, and availability of resources; adrenarche contributes by translating that information into adaptive, sexually differentiated patterns of behavior (Del Giudice et al. 2011; Ellis and Del Giudice 2014). Consistent with this view, both early relational stress and early nutrition have been found to modulate the timing of adrenarche (Ellis and Essex 2007; Hochberg 2008). It is no coincidence that the first sexual and romantic attractions typically develop in middle childhood, in tandem with the intensification of sexual play (Bancroft 2003; Herdt and McClintock 2000). By interacting with peers and adults, juveniles receive feedback about the effectiveness of their nascent behavioral strategies. The information collected during middle childhood feeds into the next developmental switch point, that of gonadarche (Ellis 2013); the transition to adolescence offers an opportunity for youth to adjust or revise their initial strategy before attaining sexual and reproductive maturity (Del Giudice and Belsky 2011). The role of adrenarche as a switch point in life history development adds another level of complexity to the biological profile of juvenility. Figure 3 outlines an integrated evolutionarydevelopmental model that brings together the various strands of theory and evidence reviewed in this chapter. Fig. 3 An integrated evolutionary-developmental model of middle childhood. Adrenarche is shown as a switch in the development of life history strategies, as well as a key mechanism underlying the normative changes of middle childhood and the emergence and intensification of sex differences. At a broader level, development in middle childhood serves two complementary functions, social integration and social competition M. DelGiudice 102 3 Three Insights in the Nature of Middle Childhood 3.1 Insight 1: Social Integration and Social Competition Are Complementary Functions of Middle Childhood Evolutionary accounts of middle childhood typically focus on learning, helping, and other forms of social integration. A life history approach emphasizes the need to consider social competition as a crucial, complementary function of human juvenility. In the peer group, children compete for vital social resources—status, reputation, allies, and friends. While learning and play are relatively risk-free, they are not without consequences. The social position achieved in middle childhood is a springboard for adolescence and adulthood; popularity and centrality within the peer network put a child at a considerable advantage, with potentially long-term effects on mating and reproductive success (Del Giudice et al. 2009). Physical and relational aggression are obvious tactics for gaining influence, but social competition also occurs through prosocial behaviors such as forming alliances, doing favors, and displaying valuable skills. Indeed, managing the balance between prosocial and coercive tactics is an important part of developing social skills (Hawley 2014). More broadly, competition shapes many aspects of cognitive and behavioral development in middle childhood; for example, increased pragmatic abilities allow children to gossip, joke, tease, and engage in verbal duels—all forms of social competition mediated by language (Locke and Bogin 2006). Intensifying social competition also contributes to explain the early peak of psychopathology onset observed in middle childhood, characterized by increasing rates of externalizing disorders (e.g., conduct disorder), anxiety disorders (including social phobia), and attention deficit hyperactivity disorder (ADHD; Del Giudice et al. 2009). 3.2 Insight 2: Sexual Selection Contributes to the Emergence and Intensification of Sex Differences in Middle Childhood By determining children’s initial place in social networks and hierarchies, competition in middle childhood indirectly affects their ability to attract sexual and romantic partners later. In other words, middle childhood is a likely target for sexual selection—that is, natural selection arising from the processes of choosing mates (mate choice) and competing for mates (mating competition). My colleagues and I (Del Giudice et al. 2009) argued that sexual selection is one reason why sex differences emerge and intensify in middle childhood. In particular, sex differences in physical aggression increase markedly, in tandem with sex differences in muscularity and play fighting. At the same time, attachment styles begin to diverge between males and females, with insecurely attached boys becoming more avoidant and insecure girls becoming more preoccupied/ambivalent (Del Giudice 2009; Del Giudice and Belsky 2010). Different attachment styles are conducive to different social strategies and may be adaptive in regulating children’s nascent relationships with peers. There is initial evidence that attachment styles in middle childhood reflect the effects of prenatal sex hormones, which according to our model are activated by adrenal androgens (Del Giudice and Angeleri 2016). Sexual selection also has indirect implications for the development of psychopathology; for example, marked sex differences in the prevalence of conduct disorders become apparent at the beginning of middle childhood, likely reflecting the stronger role of aggression in boys’ social competition (see Del Giudice et al. 2009; Martel 2013). Middle Childhood: An Evolutionary-Developmental Synthesis 3.3 Insight 3: In Middle Childhood, Heightened Sensitivity to the Environment Goes Hand in Hand with the Expression of New Genetic Factors When an organism goes through a developmental switch point, inputs from the environment combine with the individual’s genotype to determine the resulting phenotype. For example, when adrenal androgens begin to increase during the transition to middle childhood, they activate many hormone-sensitive brain pathways that have been dormant since infancy. In doing so, they release previously hidden genetic variation (Del Giudice et al. 2009). Thus, middle childhood should be characterized by a mixture of heightened sensitivity to the environment—possibly mediated by newly activated epigenetic mechanisms (Meaney 2010) and expression of new genetic factors. Evidence of increased sensitivity to the environment in middle childhood is not hard to find. Two intriguing and little-known examples concern the development of food preferences and erotic fetishes. In cultures where chili pepper is an essential part of the diet, children tend to dislike spicy food until middle childhood and then increase rapidly their preference for the flavor of chili as a result of social learning (Rozin 1990b). Fetishistic attractions also tend to form in middle childhood, with the onset of pleasurable sensations toward the object of the fetish (e.g., rubber, shoes) that later become fully eroticized (Lawrence 2009). The onset of fetishistic attractions is part of a generalized awakening of sexuality in middle childhood (Table 1) and illustrates the potential for rapid plasticity with long-lasting outcomes. Enhanced sensitivity to the environment extends beyond individual learning to acquiring social norms: for example, cross-cultural differences in prosocial behavior are absent in young children but emerge clearly during middle childhood (House et al. 2013). On the genetic side of the equation, general intelligence and language skills increase markedly in heritability from early to middle childhood. In both cases, new genetic factors come into play around age 7 (Davis et al. 2009; Hayiou- 103 Thomas et al. 2012). Studies of prosociality and aggression find the same pattern, with new genetic influences on behavior emerging during the transition to middle childhood (Knafo and Plomin 2006; van Beijsterveldt et al. 2003). These genetic findings dovetail with converging evidence that individual changes in levels of aggression are especially frequent during the transition to juvenility (Del Giudice et al. 2009). 4 Implications for Health Development The main focus of this chapter has been on psychological development, but the implications of the evolutionary-developmental synthesis extend to both mental and physical health. The transition to middle childhood seems to be a switch point for a number of growth and metabolic processes that have long-term impact on health, including the risk for obesity and type 2 diabetes (Hochberg 2008, 2010). These processes become apparent in middle childhood (e.g., anticipated onset of the adiposity rebound, rapid weight gain, onset of insulin resistance), but respond to the accumulated effects of early nutrition and other sources of stress, starting from prenatal life (e.g., intrauterine growth restriction; see Salsberry et al. 2017). From the standpoint of the model presented here, one can predict that metabolic changes in middle childhood will reflect both the “programming” effects of the early environment (Gluckman et al. 2005) and the activation of new genetic factors. Consistent with this view, a recent study has documented significant genetic correlations between puberty timing, insulin levels, type 2 diabetes, and cardiovascular disease (Day et al. 2015). Moreover, some of those factors are likely to be expressed in sexually differentiated ways, and the different patterns of health risk associated with early adrenarche and puberty in boys and girls may be usefully interpreted in light of different constraints on life history trade-offs in the two sexes (see Hochberg 2010). These predictions are consistent with the nonlinear and multilevel nature of developmental processes—one of the guiding principles of LCHD emphasized in this volume. 104 Another intriguing implication of this perspective is that the juvenile transition may be a promising—and still virtually unexplored—developmental window for intervention. While intervening to change early life conditions may be desirable in view of their long-term effects, this approach is not always possible or realistic. In addition, prenatal factors such as fetal nutrition and gestational stress may be especially difficult to target, as they do not simply mirror the mother’s conditions but reflect a complex—and partially conflictual—interplay between fetal and maternal factors (e.g., Del Giudice 2012; Gangestad et al. 2012; Haig 1993). However, the logic of developmental switches (Fig. 2) suggests that the activation of the mechanisms that initiate the switch (e.g., adrenarche) may correspond to a transient phase of instability and openness in the system. If so, it should be possible to exploit that phase to maximize the efficacy of focused interventions—including pharmacological ones. Of course, this would require a better understanding of how different hormonal and neurobiological systems interact during the transition to middle childhood; the existing evidence points to a central role of the hypothalamic-pituitaryadrenal (HPA) axis, the hypothalamic-pituitaryadrenal-thyroid (HPT) axis, and the insulin/ insulin-like growth factor 1 (IGF-1) signaling systems as mediators of life history allocations, not just in humans but in other vertebrates as well (see Del Giudice et al. 2015; Ellis and Del Giudice 2014). Those systems might be used as direct targets for intervention, but also as “endophenotypes” or early indicators of the efficacy of interventions. Importantly, neurobiological systems such as the HPA axis regulate both metabolism and behavior— as components of coordinated life history allocations—so that many of the same processes may be relevant to both physical and mental health. The idea that intervening during a biological transition may afford more leverage to alter developmental trajectories resonates with two key principles of LCHD, that is, the nonlinearity of developmental processes and their time sensitivity. Probably the most important take-home point of an evolutionary-developmental approach is that researchers should be more cautious in M. DelGiudice assuming that undesirable developmental outcomes reflect dysregulation of a biological system (e.g., see Kim et al. 2017) and more open to the possibility that those outcomes may be part of adaptive—or formerly adaptive—strategies for survival and reproduction. As I have argued in detail elsewhere (Del Giudice 2014b, c), a life history framework is especially useful in teasing out the logic of potentially adaptive combinations of traits, highlighting critical factors in the environment, and bridging behavioral development with physical growth trajectories. As an example, consider the association between intrauterine growth restriction and early maturation in children (Hochberg 2008, 2010). This can be interpreted as a manifestation of physiological dysregulation due to prenatal adversity or as an adaptive programming effect on children’s metabolic processes and life history trajectories. This alternative interpretation is supported by the association between low birth weight, anticipated puberty, and early childbearing in women (e.g., Nettle et al. 2013). A third possibility is that low birth weight partly reflects reduced energetic and metabolic investment by the mother during pregnancy, which may be adaptive as a component of a fast life history strategy. If so, the association between reduced fetal growth and earlier sexual maturation may not be fully causal, but rather result—at least in part—from shared genetic, or epigenetic, factors that influence life history strategy in both the mother and the offspring. Clearly, the implications for intervention are going to be quite different depending on which of these scenarios apply. Another recent example is the finding that early adrenarche is associated with reduced white matter volume in the frontal lobe of children (Klauser et al. 2015). Again, the standard interpretation is that early DHEA exposure has a disruptive effect on neurodevelopmental processes; however, it is also possible that different trajectories of brain development—and even associated “symptoms” such as anxiety and aggressive behaviors—may instead reflect alternative strategies on a fast-slow continuum of life history variation. Ellis et al. (2012) present an extended analysis of adolescent risk-taking from this perspective and discuss sev- Middle Childhood: An Evolutionary-Developmental Synthesis eral implications for the design of interventions. The LCHD principle that evolution both enables and constrains plasticity is especially relevant in this regard; evolutionary scenarios are not just interesting explanatory “stories,” but can illuminate limits as well as opportunities for intervention. For example, when considering mother-fetus interactions, the existence of partial conflicts of interest on nutrition, cortisol production, and so on suggests that interventions designed to favor the fetus may sometimes have detrimental side effects for the mother and vice versa (see Del Giudice 2014b; Haig 1993). In considering potential adaptive explanations for apparently pathological outcomes, it is important to remember that biologically adaptive traits may carry substantial costs. Fitness is ultimately about reproductive success; natural selection does not necessarily promote psychological well-being or physical health and may sacrifice survival in exchange for enhanced reproduction. Moreover, even adaptive developmental processes may result in genuinely maladaptive outcomes for some individuals (Frankenhuis and Del Giudice 2012). It follows that the existence of substantial psychological, social, or health costs does not automatically qualify a trait or behavior as biologically maladaptive (see Del Giudice 2014b; Ellis et al. 2012; Ellis and Del Giudice 2014). 5 Conclusions We cannot make sense of human development without understanding middle childhood and its many apparent paradoxes. An evolutionarydevelopmental approach illuminates the complexity of this life stage and shows how different levels of analysis—from genes to society—can be tied together in a coherent synthesis. This emerging view of middle childhood can help developmental scientists appreciate its centrality in the human life history and stimulate ideas for research and intervention. The study of middle childhood may finally be ready to come of age, opening up promising avenues for a better understanding of health development across the life course. 105 References Auchus, R. J., & Rainey, W. E. (2004). Adrenarche– physiology, biochemistry and human disease. Clinical Endocrinology, 60, 288–296. Bancroft, J. (Ed.). (2003). Sexual development in childhood. Bloomington: Indiana University Press. Belsky, J., Steinberg, L., & Draper, P. (1991). Childhood experience, interpersonal development, and reproductive strategy: An evolutionary theory of socialization. Child Development, 62, 647–670. Bernstein, R. M., Sterner, K. N., & Wildman, D. E. (2012). Adrenal androgen production in catarrhine primates and the evolution of adrenarche. American Journal of Physical Anthropology, 147, 389–400. Best, J. R., Miller, P. H., & Jones, L. L. (2009). Executive functions after age 5: Changes and correlates. Developmental Review, 29, 180–200. Bjorklund, D. F. (2011). Children’s thinking: Cognitive development and individual differences (5th ed.). Belmont: Wadsworth. Bogin, B. (1997). Evolutionary hypotheses for human childhood. Yearbook of Physical Anthropology, 40, 63–89. Campbell, B. C. (2006). Adrenarche and the evolution of human life history. American Journal of Human Biology, 18, 569–589. Campbell, B. C. (2011). Adrenarche and middle childhood. Human Nature, 22, 327–349. Davis, O. S. P., Haworth, C. M. A., & Plomin, R. (2009). Dramatic increase in heritability of cognitive development from early to middle childhood: An 8-year longitudinal study of 8,700 pairs of twins. Psychological Science, 20, 1301–1308. Day, F. R., Bulik-Sullivan, B., Hinds, D. A., Finucane, H. K., Murabito, J. M., Tung, J. Y., et al. (2015). Genetic determinants of puberty timing in men and women: Shared genetic aetiology between sexes and with health-related outcomes. Nature Communications, 6, 8842. Del Giudice, M. (2009). Sex, attachment, and the development of reproductive strategies. Behavioral and Brain Sciences, 32, 1–21. Del Giudice, M. (2012). Fetal programming by maternal stress: Insights from a conflict perspective. Psychoneuroendocrinology, 37, 1614–1629. Del Giudice, M. (2014a). Middle childhood: An evolutionary-developmental synthesis. Child Development Perspectives, 8, 193–200. Del Giudice, M. (2014b). Early stress and human behavioral development: Emerging evolutionary perspectives. Journal of Developmental Origins of Health and Disease, 5, 270–280. Del Giudice, M. (2014c). An evolutionary life history framework for psychopathology. Psychological Inquiry, 25, 261–300. Del Giudice, M., & Angeleri, R. (2016). Digit ratio (2D:4D) and attachment styles in middle childhood: Indirect evidence for an organizational effect of sex hormones. Adaptive Human Behavior and Physiology, 2, 1–10. 106 Del Giudice, M., & Belsky, J. (2010). Sex differences in attachment emerge in middle childhood: An evolutionary hypothesis. Child Development Perspectives, 4, 97–105. Del Giudice, M., & Belsky, J. (2011). The development of life history strategies: Toward a multi-stage theory. In D. M. Buss & P. H. Hawley (Eds.), The evolution of personality and individual differences, (pp. 154–176). New York: Oxford University Press. Del Giudice, M., Angeleri, R., & Manera, V. (2009). The juvenile transition: A developmental switch point in human life history. Developmental Review, 29, 1–31. Del Giudice, M., Ellis, B. J., & Shirtcliff, E. A. (2011). The adaptive calibration model of stress responsivity. Neuroscience & Biobehavioral Reviews, 35, 1562–1592. Del Giudice, M., Angeleri, R., & Manera, V. (2012). Juvenility and the juvenile transition. In R. J. R. Levesque (Ed.), Encyclopedia of adolescence, (pp. 1534–1537). New York: Springer. Del Giudice, M., Gangestad, S. W., & Kaplan, H. S. (2015). Life history theory and evolutionary psychology. In D. M. Buss (Ed.), The handbook of evolutionary psychology – Vol 1: Foundations (2nd ed., pp. 88–114). New York: Wiley. Del Giudice, M., & Angeleri, R. (2016). Digit ratio (2D:4D) and attachment styles in middle childhood: Indirect evidence for an organizational effect of sex hormones. Adaptive Human Behavior and Physiology, 2, 1–10. Ellis, B. J. (2013). The hypothalamic-pituitary-gonadal axis: A switch-controlled, condition-sensitive system in the regulation of life history strategies. Hormones and Behavior, 64, 215–225. Ellis, B. J., & Del Giudice, M. (2014). Beyond allostatic load: Rethinking the role of stress in regulating human development. Development and Psychopathology, 26, 1–20. Ellis, B. J., & Essex, M. J. (2007). Family environments, adrenarche and sexual maturation: A longitudinal test of a life history model. Child Development, 78, 1799–1817. Ellis, B. J., Figueredo, A. J., Brumbach, B. H., & Schlomer, G. L. (2009). The impact of harsh versus unpredictable environments on the evolution and development of life history strategies. Human Nature, 20, 204–268. Ellis, B. J., Del Giudice, M., Dishion, T. J., Figueredo, A. J., Gray, P., Griskevicius, V., et al. (2012). The evolutionary basis of risky adolescent behavior: Implications for science, policy, and practice. Developmental Psychology, 48, 598–623. Frankenhuis, W. E., & Del Giudice, M. (2012). When do adaptive developmental mechanisms yield maladaptive outcomes? Developmental Psychology, 48, 628–642. Gangestad, S. W., Caldwell Hooper, A. E., & Eaton, M. A. (2012). On the function of placental corticotropinreleasing hormone: A role in maternal-fetal conflicts over blood glucose concentrations. Biological Reviews, 87, 856–873. M. DelGiudice Geary, D. C. (2010). Male, female: The evolution of human sex differences. Washington, DC: American Psychological Association. Ghetti, S., & Bunge, S. A. (2012). Neural changes underlying the development of episodic memory during middle childhood. Developmental Cognitive Neuroscience, 2, 381–395. Giedd, J. N., & Rapoport, J. L. (2010). Structural MRI of pediatric brain development: What have we learned and where are we going? Neuron, 67, 728–734. Gluckman, P. D., Hanson, M. A., & Spencer, H. G. (2005). Predictive adaptive responses and human evolution. Trends in Ecology & Evolution, 20, 527–533. Haig, D. (1993). Genetic conflicts in human pregnancy. QUARTERLY REVIEW OF BIOLOGY, 68, 495–532. Hawley, P. H. (2014). Ontogeny and social dominance: A developmental view of human power patterns. Evolutionary Psychology, 12, 318–342. Hayiou-Thomas, M. E., Dale, P. S., & Plomin, R. (2012). The etiology of variation in language skills changes with development: A longitudinal twin study of language from 2 to 12 years. Developmental Science, 15, 233–249. Herdt, G., & McClintock, M. (2000). The magical age of 10. Archives of Sexual Behavior, 29, 587–606. Hochberg, Z. (2008). Juvenility in the context of life history theory. Archives of Disease in Childhood, 93, 534–539. Hochberg, Z. (2010). Evo-devo of child growth III: Premature juvenility as an evolutionary trade-off. Hormone Research in Pædiatrics, 73, 430–437. House, B. R., Silk, J. B., Henrich, J., Barrett, H. C., Scelza, B. A., Boyette, A. H., et al. (2013). Ontogeny of prosocial behavior across diverse societies. Proceedings of the National Academy of Sciences USA, 110, 14586–14591. Jambon, M., & Smetana, J. G. (2014). Moral complexity in middle childhood: Children’s evaluations of necessary harm. Developmental Psychology, 50, 22–33. Joffe, T. H. (1997). Social pressures have selected for an extended juvenile period in primates. Journal of Human Evolution, 32, 593–605. Kaplan, H., Hill, K., Lancaster, J., & Hurtado, A. M. (2000). A theory of human life history evolution: Diet, intelligence, and longevity. Evolutionary Anthropology, 9, 156–185. Kim, P., Evans, G. W., Chen, E., Miller, G., & Seeman, T. (2017). How socioeconomic disadvantages get under the skin and into the brain to influence health development across the lifespan. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health-development science. Cham: Springer. Klauser, P., Whittle, S., Simmons, J. G., Byrne, M. L., Mundy, L. K., Patton, G. C., et al. (2015). Reduced frontal white matter volume in children with early onset of adrenarche. Psychoneuroendocrinology, 52, 111–118. Knafo, A., & Plomin, R. (2006). Prosocial behavior from early to middle childhood: Genetic and environmental influences on stability and change. Developmental Psychology, 42, 771–786. Middle Childhood: An Evolutionary-Developmental Synthesis Kramer, K. L. (2011). The evolution of human parental care and recruitment of juvenile help. Trends in Ecology and Evolution, 26, 533–540. Kuzawa, C. W., Chugani, H. T., Grossman, L. I., Lipovich, L., Muzik, O., Hof, P. R., et al. (2014). Metabolic costs and evolutionary implications of human brain development. Proceedings of the National Academy of Sciences of the United States of America, 111, 13010–13015. Lagattuta, K. H., Sayfan, L., & Blattman, A. J. (2009). Forgetting common ground: Six- to seven-yearolds have an overinterpretive theory of mind. Developmental Psychology, 46, 1417–1432. Lancy, D. F., & Grove, M. A. (2011). Getting noticed: Middle childhood in cross-cultural perspective. Human Nature, 22, 281–302. Lawrence, A. A. (2009). Erotic target location errors: An underappreciated paraphilic dimension. Journal of Sex Research, 46, 194–215. Lebel, C., Walker, L., Leemans, A., Phillips, L., & Beaulieu, C. (2008). Microstructural maturation of the human brain from childhood to adulthood. NeuroImage, 40, 1044–1055. Locke, J. L., & Bogin, B. (2006). Language and life history: A new perspective on the development and evolution of human language. Behavioral and Brain Sciences, 29, 259–280. Martel, M. M. (2013). Sexual selection and sex differences in the prevalence of childhood externalizing and adolescent internalizing disorders. Psychological Bulletin, 139, 1221–1259. McClelland, M., Morrison, F., Gestsdóttir, S., Cameron, C., Bowers, E. D., Duckworth, A., Little, T., & Grammer. J. (2017). Self-regulation. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health-development science. Cham: Springer. Meaney, M. J. (2010). Epigenetics and the biological definition of gene x environment interactions. Child Development, 81, 41–79. Nettle, D., Dickins, T. E., Coall, D. A., & de Mornay Davies, P. (2013). Patterns of physical and psychological development in future teenage mothers. Evolution, Medicine, and Public Health, 2013, 187–196. 107 Piccardi, L., Leonzi, M., D’Amico, S., Marano, A., & Guariglia, C. (2014). Development of navigational working memory: Evidence from 6- to 10-yearold children. British Journal of Developmental Psychology, 32, 205–217. Poirel, N., Simon, G., Cassotti, M., Leroux, G., Perchey, G., Lanoë, C., Lubin, A., Turbelin, M. R., Rossi, S., Pineau, A., & Houdé, O. (2011). The shift from local to global visual processing in 6-year-old children is associated with grey matter loss. PloS One, 6, e20879. Rozin, P. (1990a). Development in the food domain. Developmental Psychology, 26, 555–562. Rozin, P. (1990b). Getting to like the burn of chili pepper: Biological, psychological, and cultural perspectives. In B. G. Green, J. R. Mason, & M. R. Kare (Eds.), Chemical senses – Volume 2: Irritation. New York: Dekker. Salsberry, P., Tanda, R., Anderson, S. E., & Kamboj, M. K. (2017). Pediatric type 2 diabetes: Prevention and treatment through a life course health development framework. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course healthdevelopment science. Cham: Springer. Scalise Sugiyama, M. (2011). The forager oral tradition and the evolution of prolonged juvenility. Frontiers in Psychology, 2, 133. van Beijsterveldt, T. C. E. M., Bartels, M., Hudziak, J. J., & Boomsma, D. I. (2003). Causes of stability of aggression from early childhood to adolescence: A longitudinal genetic analysis in Dutch twins. Behavior Genetics, 33, 591–605. Weisner, T. S. (1996). The 5–7 transition as an ecocultural project. In A. Sameroff & M. Haith (Eds.), The five to seven year shift: The age of reason and responsibility (pp. 295–326). Chicago: University of Chicago Press. Wells, J. C. K. (2007). Sexual dimorphism of body composition. Best Practice & Research Clinical Endocrinology & Metabolism, 21, 415–430. West-Eberhard, M. J. (2003). Developmental plasticity and evolution. New York: Oxford University Press. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Adolescent Health Development: A Relational Developmental Systems Perspective Richard M. Lerner, Claire D. Brindis, Milena Batanova, and Robert Wm. Blum Consistent with the United Nations Conventions, adolescence encompasses the second decade of life (Lerner and Steinberg 2009). Since the emergence of the study of this portion of the life course (Hall 1904), adolescence has been regarded as a period characterized by purportedly troublesome transformations (e.g., in bodily attributes associated with puberty; Susman and Dorn 2009, 2013) and allegedly problematic transitions (e.g., in regard to socioemotional functions linked to self-definition or identity, Erikson 1959, or to changes in the focus of social The original version of this chapter was revised. An erratum to this chapter can be found at https://doi.org/10.1007/978-3-319-47143-3_27 The writing of this chapter was supported in part by grants from the John Templeton Foundation. We are grateful to Elizabeth Goodman for her comments R.M. Lerner, PhD (*) Tufts University, Medford, MA, USA e-mail: Richard.lerner@tufts.edu M. Batanova, PhD Institute for Applied Research in Youth Development, Tufts University, Medford, MA, USA C.D. Brindis, DrPH University of California-San Francisco (UCSF), Philip R. Lee Institute for Health Policy Studies and the Adolescent and Young Adult Health National Resource Center, San Francisco, CA, USA R.Wm. Blum, MD, MPH, PhD Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA relationships, from parents to peers, Freud 1969). When viewed from this “deficit” model, adolescents were seen by both scholars and the general public as both dangerous to themselves and to society (Anthony 1969). They were regarded as “problems to be managed” (Roth and BrooksGunn 2003a, b). Certainly, it may be argued that adolescence is the most profound period of change within the life span. As in infancy and early to middle childhood, the individual’s physiological, psychological, behavioral, and social relationship characteristics undergo both quantitative and qualitative changes, that is, transitions and transformations. For instance, changes in the prefrontal cortex, increases in the interconnectivity among brain regions, and increases in dopamine levels provide both vulnerabilities to risk and opportunities for growth in cognitive control (Steinberg 2010). At the same time, most youth in Western societies are experiencing great contextual changes, such as changing schools (e.g., Eccles 2004) and the increased relevance of peer influences on behavior (e.g., Gardner and Steinberg 2005). Moreover, in adolescence, the individual has the cognitive, behavioral, and social relational skills to contribute actively and often effectively to his or her own developmental changes (Lerner 1982; Lerner and BuschRossnagel 1981; Lerner and Walls 1999; Ricco and Overton 2011). In contrast to earlier developmental periods, adolescents have a burgeoning capacity © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_6 109 110 for self-governance, for formulating and taking actions that exert at least some control over their own development. That is, there is uniquely marked development of intentional agency in adolescence. Agency involves self-regulatory skills that include, for instance, the selection of goals or the formulation of purposes that are of importance to adolescents’ developing senses of self and growing attempts to find a means to “matter” in their world (Eccles 2004; Freund and Baltes 2002; Mascolo and Fischer 2015). There is also growth in the use of executive functioning, strategic thinking, and behavioral skills in recruiting goal-related resources that enable actions optimizing the chances of fulfilling their purposes. Furthermore, there is also development of the ability to compensate effectively when goals are blocked and/or to select new goals when initial optimization attempts fail and the chances of attaining an initial goal are lost (Gestsdottir and Lerner 2008; McClelland et al. 2015). In general, scholars who study the adolescent period acknowledge that multiple dimensions of change involving the individual and his or her context characterize this portion of the life span (Lerner et al. 2015; Lerner and Steinberg 2009). However, substantial variation has emerged in scholars’ approach to this individual-context relationship. Analogous to interpreting only main effects of statistical analyses framed by the General Linear Model, some scholars have focused on one domain of change (e.g., adolescent identity development) and have not emphasized other levels of organization within the ecology of human development (e.g., see Marcia 1980). Other scholars have taken a reductionist orientation to the phenomena of adolescence and have sought to use biogenic ideas (e.g., genetic or evolutionary biological ideas; see Bjorkland and Ellis, 2005) to interpret all psychological and social relationship phenomena of the period (see Elder 1980, 1998; Elder et al. 2015, for critiques of the main effect or reductionist approaches). In turn, however, in 2003, Roth and BrooksGunn (2003a, b) reviewed the literature on youth development programs aimed at enhancing health and thriving among adolescents. They concluded that adolescents should be regarded not as problems to be managed but, instead, as resources to R.M. Lerner et al. be developed. Their insight built on at least three sources. First, Larson (2000) provided a compelling vision for research aimed at emphasizing the strengths of adolescence (e.g., involving their burgeoning capacities for intentional selfregulation) and for using these strengths to promote positive youth development (PYD). Instead of health or positive development being conceptualized as the diminution or the absence of disease or of problem behaviors, respectively, the PYD perspective suggested that there were facets of thriving that, when promoted, resulted in healthy and positive development. Second, Eccles and Gootman (2002) edited a field-defining report issued by the National Academy of Sciences about the ways in which community programs for youth development could promote several attributes of psychological and behavioral functioning believed to be indicative of such development. The report included a summary of the attributes of positive development as involving constructs that could be summarized by “Five Cs:” competence, confidence, character, connection, and caring (see too, Lerner et al. 2005). Third, Hamilton (1999) explained that the idea of PYD was being used in the developmental science literature in three ways: (a) as a label for a model of the processes through which health and thriving developed in adolescence, (b) as a philosophy for or an approach to designing community-based programs aimed at promoting thriving, and (c) as instances of such programs. The emphasis on strengths as compared to deficits and on links between the positive development of youth and their engagement with contextual resources or ecological “developmental assets” (Benson et al. 2006), represented by community-based youth development programs, reflected the spirit of the times (the zeitgeist) of developmental science during the latter years of the twentieth century and the first decade of the twenty-first century (Lerner 2012; Lerner et al. 2015). Conceptions of human development, which in earlier periods stressed genetic or maturational determinants of development or approaches to development that reduced human behavior to stimulus-response relations, were being replaced by ideas emphasizing that development involved mutually influential rela- Adolescent Health Development: A Relational Developmental Systems Perspective tions among a person’s biological and psychological characteristics, social relationships, and influences from families, schools, community institutions, out-of-school-time (OST) programs, and both the designed and natural environment. Developmental scientists argued that all of these influences, although always present across life, change in their significance across both life and across historical periods (e.g., Bronfenbrenner and Morris 2006; Lerner 2012; Overton 2013, 2015). Indeed, we explain below that, today, these ideas have a new manifestation quite compatible with the PYD perspective: They are found in the seven principles of life course health development (see Halfon and Forrest 2017) that address the conceptualization of the nature and bases of health and positive behavior development across the life span. Therefore, in the contemporary study of adolescent development, the main effect and reductionist approaches of the past are regarded as anachronistic ideas incapable of producing models that adequately account for the integration of multiple levels of organization within the ecology of human development (Lerner 2015; Lerner and Benson 2013a, b; Lerner et al. 2015). In particular, such traditional approaches overlook the burgeoning information indicating that this integration reflects systemic relations, that is, mutually influential patterns of covariation within and across these levels (e.g., see von Bertalanffy 1933, 1968). At this writing, then, models of adolescent development that are associated with what Overton has explained as a relational developmental systems (RDS) metatheory define the cutting edge of theory-predicated scholarship about adolescent development. This metatheory is derived from a process-relational paradigm (Overton 2015), and the concepts associated with RDS thinking are being used to describe, explain, and optimize the course of development in the second decade of life and, as such, to frame applied research aimed at promoting health and positive development among diverse adolescents (Lerner et al. 2015). It is not coincidental that concepts associated with the RDS metatheory are entirely consistent with the seven life course health development principles proposed by Halfon and Forrest (2017). This congruence of ideas is based on a 111 few common scholarly roots of these two sets of ideas for understanding human development. For example, the life course conceptions of Elder (1998; Elder et al. 2015), the life span developmental ideas of Baltes (e.g., 1997; Baltes et al. 2006), and the constructs associated with Bronfenbrenner’s (1979, 2005; Bronfenbrenner and Morris 2006) bioecological model of human development have shaped both RDS thinking and the life course health development principles. It will be useful, then, to discuss the features of the RDS metatheory and, in this context, point to the compatibility between RDS concepts and the life course health development principles. This discussion will enable us to illustrate how the ideas associated with both approaches to adolescent development can innovatively integrate and extend scholarship about transitions and transformations characterizing the adolescent period. In addition, because of the emphasis in both sets of ideas on the concept of relative plasticity in human development—that is, on the potential for systematic changes in the structure and function of health assets across the life course—we will discuss how these two approaches afford optimism that relational changes linked to positive change in the health developmental system can be identified and used to promote thriving in adolescence. 1 Viewing Adolescent Development Through the Lens of the Relational Developmental Systems Metatheory The study of human development, in general, and adolescent development, more specifically, has evolved from a field dominated by split, reductionist (psychogenic or biogenic) approaches to a multidisciplinary scholarly field (Lerner and Steinberg 2009), one that seeks to integrate variables from biological through cultural and historical levels of organization across the life span into a synthetic, coactional system (e.g., Elder 1998; Ford and Lerner 1992; Gottlieb 1997, 1998; Lerner 2012). Reductionist accounts 112 of development that adhere to a Cartesian dualism pull apart (split) facets of the integrated developmental system (Overton 2015). For instance, reductionist views typically elevate the importance of such split formulations as nature versus nurture, continuity versus discontinuity, stability versus instability, or basic versus applied science (Lerner 2002, 2006). Split approaches are rejected by proponents of theories derived from an RDS metatheory (e.g., Mistry and Wu 2010; Overton 2013; Overton and Lerner 2014), which in turn are derived from a process-relational paradigm (Overton 2015). Across the past four plus decades, several scholars have provided ideas contributing to the evolution of this paradigm (e.g., Baltes 1997; Baltes, et al., 2006; Brandtstädter 1998; Bronfenbrenner 1979, 2005; Bronfenbrenner and Morris 2006; Elder 1998; Elder et al. 2015; Ford and Lerner 1992; Nesselroade 1988; Overton 1973; Overton and Reese 1981; Riegel 1975, 1976; and, even earlier, see von Bertalanffy 1933). Overton (e.g., 2013, 2015) has integrated and extended this scholarship. Overton explains that, compared to a Cartesian worldview, the processrelational paradigm focuses on process (systematic changes in the developmental system), becoming (moving from potential to actuality; a developmental process as having a past, present, and future; Whitehead 1929/1978), holism (the meanings of entities and events derive from the context in which they are embedded), relational analysis (assessment of the mutually influential relations within the developmental system), and the use of multiple perspectives and explanatory forms (employment of ideas from multiple theory-based models of change within and of the developmental system) in understanding human development. Within the process-relational paradigm, the organism is seen as inherently active, self-creating (autopoietic), self-organizing, selfregulating (agentic), nonlinear/complex, and adaptive (Overton 2015; see too Sokol et al. 2015). Similarly, these ideas are echoed in the life course health development principles (specifically, Nos. 1 and 3) that health development emerges as a consequence of complex, nonlinear process that results from person⇔environment R.M. Lerner et al. coactions that are multidimensional, multidirectional, and multilevel. Accordingly, both within the RDS approach to theory and the life course health development principles, split conceptions are eschewed in favor of a metatheory that emphasizes the study and integration of different levels of organization, ranging from biology/physiology to culture and history, as a means to understand life span human development (Lerner 2006; Overton 2013, 2015), the production of health, and the development of disease. Accordingly, conceptual emphasis is placed on mutually influential relations between individuals and contexts, represented as individual ⇔ context relations. This representation of the coactions between person and setting within RDS-based models is not meant to convey a person-context interaction—which is typically represented in the developmental literature as person X context. An interaction connotes that the entities involved in the relation are separate and independent (as in a statistical interaction) and that, as such, their association involves a linear combination of discrete and separate variables. Both before and after the interaction, these entities (variables) are independent and unchanged by each other. The bidirectional arrow used in the RDS illustration of person ⇔ context relations is intended to emphasize that the coaction of individual and context involves the entire developmental system. As such, the relations among levels of the autopoietic system, and not independent linear combinatorial attributes, are the focus in such a model. Indeed, the fusion of individual and context within the developmental system means that across ontogeny (across the life span), any portion of the system is inextricably embedded with—or embodied by, in Overton’s (2013, 2015) conceptualization—all other portions of the developmental system. Embodiment refers to the way individuals behave, experience, and live in the world by being active agents with particular kinds of bodies; the body is integratively understood as a form (a biological referent), as lived experience (a psychological referent), and as an entity in active engagement with the world (a sociocultural referent) (Overton 2015). Adolescent Health Development: A Relational Developmental Systems Perspective 113 Similarly, within the life course health development perspective, health development is also embodied: It is an emergent property of living systems (Principle 1), and it develops continuously across the life span (Principle 2). Within the context of such a bidirectional relational system, the embeddedness within history (temporality) is of fundamental significance (Elder 1998; Elder et al. 2015). We may note that the developmental system is embedded in history (temporality). This embeddedness means that change is constant in the developmental system and that, as such, there may be either stochastic or systematic changes in person⇔context relations across time and place (Elder 1998; Elder et al. 2015; Misteli 2013). Sensitivity of change to time and place is also a key idea within the life course health development Principle 4, which states that health development is highly sensitive to the timing and social structure of environmental exposures. The presence of such temporality in individual ⇔ context relations within the developmental system means that there always exists some potential for systematic change and, thus, for (relative) plasticity in human development. In short, potential plasticity in individual ⇔ context relations derives from the “arrow of time” (Lerner 1984; Lerner and Benson 2013a, b; Overton 2013) running through the integrated (relational) developmental system. focus in developmental analysis. He notes that the process-relational paradigm involves different moments within a research program. One moment involves the idea of the identity of opposites, a second moment involves the opposites of identity, and a third (relationally integrative) moment involves the synthesis of wholes. In discussing these three moments of scientific analysis within RDS approaches to developmental science, we point to the predominant trait model of individuality, the Five-Factor Theory (FFT, involving the purported Big Five “personality traits” of conscientiousness, agreeableness, neuroticism, openness to experience, and extraversion; Costa and McCrae 1980, 2006; McCrae et al. 2000). The FFT example is a means to explain these RDS-based moments and to contrast their use with thinking associated with Cartesian, split, reductionist approaches to the study of the individual development. The first moment recognizes that both individual and context define— and are mutually constituted by—each other in one moment, or point, in a program of developmental inquiry. Overton (2010, p. 14) notes that: 2 The identity of opposites, therefore, emphasizes the fused person ⇔ context relationship as the primary unit of analysis for understanding development. As such, in this moment of research, developmental scientists would reject the idea that there are any aspects of human behavioral development— for instance, entities such as traits—that “are more or less immune to environmental influences” (McCrae et al. 2000, pp. 175); the idea that such entities are indicators of split notions reflecting “nature over nurture” (McCrae et al. 2000, p. 173) would also be rejected by developmental scientists working within this first moment of analysis. The second moment that Overton (2010, 2013, 2015; Overton and Müller 2013) discusses is the opposites of identity. This moment allows Three Moments of Analysis in an RDS Approach to Adolescent Health Development To elucidate the role of time and place in contributing to the bidirectional relations of focal concern within RDS metatheory, developmental scientists may focus on either the role of the individual or the context, in particular instantiations of individual⇔context exchanges. This focus may seem contradictory to the fusion among levels of organization emphasized in this approach. However, as we noted earlier, Overton (2015) embeds the RDS metatheory in the process-relational paradigm (see, as well, Sokol et al. 2015). Overton uses this paradigm to explain the possibility of this changing The principle of the identity of opposites establishes the identity among parts of a whole by casting them not as exclusive contradictions as in the split epistemology, but as differentiated polarities (i.e., coequals) of a unified (i.e., indissociable) inclusive matrix—as a relation. As differentiations, each pole defines and is defined by its opposite. R.M. Lerner et al. 114 one, in effect, to hold the other parts of the integrated system in abeyance and focus on one part of the system; however, the ultimate aim is one of reintegrating the part into the whole at a subsequent moment. Overton (2013, pp. 47–48) explains that: The limitation of the identity moment of analysis is that, in establishing a flow of categories of one into the other, a stable base for inquiry that was provided by bedrock material atoms of the split metatheory is eliminated…Reestablishing a stable base—not an absolute fixity, nor an absolute relativity, but a relative relativity (Latour 1993)— within relational metatheory requires moving to a second moment of analysis. In this moment of opposition, the law of contradiction is reasserted and categories again exclude each other. As a consequence of this exclusion, parts exhibit unique identities that differentiate each from the other. Therefore, when functioning within this second moment of analysis, developmental scientists could focus solely on attributes of individuals, for instance, the purported traits of conscientiousness, agreeableness, neuroticism, openness to experience, and extraversion that comprise the Big Five components of the FFT (Costa and McCrae 1980, 2006; McCrae et al. 2000) and, for instance, study the psychometric properties of these constructs to provide “objective,” or quantitative, indices of these attributes. Indeed, such psychometric work has often been a part of research programs framed by RDS models (e.g., Damon 2008; Lerner et al. 2015). Thus, working within this second moment of analysis, developmental scientists following an RDS-based model, and social or personality researchers using a Cartesian split model, would be engaging in commensurate work. However, the difference between these two groups of scholars is brought to the fore when the third moment of analysis discussed by Overton (2015) is considered. The third moment, the synthesis of wholes, occurs when the first two moments are embedded in a multi-perspective process-relational paradigm and are recognized as mutually necessary in a systematic, integrative program of research, wherein one needs both of the first two moments. That is, “A complete relational program requires principles according to which the individual identity of each concept of a formerly dichotomous pair is maintained while simultaneously it is affirmed that each concept constitutes, and is constituted by the other” (Overton and Müller 2013, p. 35). Accordingly, the developmental scientist working within an RDS model would use an “objective” measure studied within the second moment of analysis within an integrated, relational empirical approach that focused on the individual⇔context relation. Clearly, the trait theorist would not take such a step, given that the work of such scholars is framed by the ideas, noted above, that context is irrelevant to the understanding (i.e., successful prediction) of the life course of the manifestation of traits. Indeed, in their belief “that personality traits are more or less immune to environmental influences” (McCrae et al. 2000, pp. 175), trait theorists maintain that contextual conditions, whether similar or not, are irrelevant to prediction; given the purported biological base of traits, only nature variables have predictive efficacy. However, there is abundant evidence that purported traits are in fact not “trait-like” at all, that is, these attributes reflect relations between individuals and contexts, as they occur at particular times and places (Ardelt 2000; Block 1995, 2010; Elder 1998; Elder et al. 2015; Roberts et al. 2006). Indeed, methodological work framed by RDS concepts (e.g., Molenaar and Nesselroade 2014, 2015; Nesselroade 1988; Nesselroade and Molenaar 2010) indicates that the purported life span stability of traits, as well as the purported immunity to contextual influences, is empirically counterfactual. Moreover, underscoring the importance of this third moment of analysis discussed by Overton (2015), these methodological innovations demonstrate the ability to index with psychometric precision integrative individual ⇔ context (including individual ⇔ individual) units of analysis (e.g., Molenaar 2014; Molenaar et al. 2014; Molenaar and Nesselroade 2015). This innovation in developmental methodology is important for understanding key features of the individual⇔context relational process propelling developmental change in adolescence. Adolescent Health Development: A Relational Developmental Systems Perspective 3 Developmental Regulations, Adaptive Developmental Regulations, and Human Agency in RDS Metatheory Given the analytic moment of the identity of opposites—that each component of the developmental system constitutes, and is constituted by, the other components of the system—RDS metatheory focuses on the “rules” or processes which govern, or regulate, exchanges between individuals and their contexts. Such processes are the function of the developmental system. An RDS program of research might seek to understand the nature of relations between individuals and their contexts, including the dynamics of those relations across the life course. For instance, RDS-based research might ask how specific features of the individual and specific features of the context coalesce to influence the substantive course of individual⇔individual relations. Brandtstädter (1998) termed these bidirectional relations “developmental regulations” and noted that, when developmental regulations involve mutually beneficial individual⇔context relations, then these developmental regulations are adaptive. Developmental regulations are the fundamental feature of human life, that is, all human life exists in a context and involves bidirectional exchanges with it (Darwin 1859; Forrest 2014; Tobach and Schneirla 1968). These exchanges involve physiological systems and functions (e.g., respiration, circulation, digestion, reproduction) and behaviors (e.g., social affiliation and cooperation, or aggression and competition, as might be involved in protection, hunting, and scavenging; Johanson and Edey 1981) and involve both organismic self-regulation (e.g., hypothalamic functioning, circadian rhythms) and intentional self-regulation (e.g., goal selection, resource recruitment, and executive functioning; Gestsdóttir and Lerner 2008). Gestsdóttir et al. (2014) note that self-regulation is a multidimensional construct, involving a range of behaviors, from basic physiological functions to complex intentional cognitive processes (e.g., Bandura 2001; Brandtstädter 1998; McClelland et al. 2015). As such, self-regulation pertains to all 115 aspects of adaption, as people alter their thoughts, attention, emotions, and behaviors to react to contextual events and, as well, to influence selected features of their contexts. Here, culture plays a key moderating role. Trommsdorff (2012) notes that self-regulation “is assumed to develop by organizing inner mental processes and behavior in line with cultural values, social expectations, internalized standards, and one’s self-construal” (p. 19). The developmental course of self-regulation is, in effect, the developmental course of human agency in the context of individual desires, purposes, needs, goals and identity, perceptions of and coactions with other people, the physical ecology, and culture (e.g., Damon 2008; Geldhof et al. 2010; Gestsdóttir and Lerner 2008; Lerner et al. 2001). Agency is a defining feature of the active, self-creating (autopoietic, enactive), and nonlinear adaptive living system (Overton and Lerner 2014; Narvaez 2008; Witherington 2014). Such agency is the individual’s contribution to adaptive developmental regulations (Brandtstädter 1998, 1999). The development of agency begins in early life, primarily with organismic self-regulation processes. However, as we have noted, by the time of adolescence, self-regulation is increasingly intentional and purposeful (Damon 2008) and involves the self-system and the phenomena associated with identity development (Gestsdóttir and Lerner 2008; Lerner et al. 2001). The adolescent instantiation of agency reflects ideas associated with the work of comparative psychologists Tobach and Schneirla (1968), who distinguished between the biosocial functioning of insects (e.g., ants) and the psychosocial functioning of organisms with higher psychological levels, levels that are marked by greater plasticity, rather than stereotypy, in their eventual highest levels of ontogenetic change. Such higher levels provide the physiological base for symbolic functioning. In turn, reflecting the life course health development principles that evolution enables as well as constrains health development pathways and plasticity (Principle No. 5) and that optimal health development promotes survival, enhances thriving, and protects against disease (Principle No. 6), evolutionary biologists Jablonka and Lamb (2005) note that both psychological R.M. Lerner et al. 116 processes and cultural processes are integrated with the genetic and epigenetic processes of evolution to make human adaptiveness and contributions qualitatively different than corresponding instances of adaptiveness of social contributions among other organisms. Together, these comparative psychology and evolutionary biology literatures suggest that, among humans, adaptiveness and positive contributions reflect integrated cognitive, emotional, and behavioral processes that involve abstract, symbolic constructs, such as language or moral reasoning, or character virtues (e.g., Lerner and Callina 2014). of the developmental systems—that is, molecular, physiological, behavioral, cultural, and evolutionary processes (e.g., Cole 2014; Meaney 2014; Slavich and Cole 2013)—and with the Bornstein (2017) “specificity principle,” we suggest, therefore, that addressing a multipart “what” question is the key to conducting programmatic research about the function, structure, and content of health development in adolescence. In other words, to test RDS-based ideas about the ontogenetically changing structure of adolescent development in general and health development more specifically, researchers need to ascertain answers to the following multipart “what” questions: 4 1. What specific structure-content relations emerge; at 2. What specific levels of organization within the relational developmental system that are linked to; 3. What specific antecedent and consequent adaptive developmental regulations (to what trajectory of individual⇔context relations); at 4. What specific points in adolescent development; for 5. What specific groups of youth; living in 6. What specific contexts; at 7. What specific points in time (history)? Implications for Research About Adolescent Health Development Empirically, indexing such facets of adolescent health development, in general, or the complex dimensions of evolutionary change linked to adolescent health development more specifically (e.g., Gissis and Jablonka 2011; Lerner et al. 2015; Slavich and Cole 2013) may involve both point-in-time (cross-sectional) assessments and historical (longitudinal) assessments (Lerner 2004). Scientists must conduct such assessments within the context of recognizing that contexts are complex (e.g., they exist at multiple levels of organization; Bronfenbrenner 1979). Individuals cannot necessarily act in ways that benefit all levels and all components of the context at all times and places (Elder 1998). Thus, adaption is not treated as a categorical concept (as something that either exists or not) but, instead, as a multivariate concept comprised of ordinal or interval dimensions. Researchers studying adaptation would not ask, then, whether it exists or not; rather, the question would be how beneficial is the developmental regulation (the individual⇔context relation) for specific people or specific social institutions of the context, at specific times and in specific places (e.g., see Bornstein 2006, 2017). Consistent both with the seventh principle of life course health development, that is, that the cadence of human health development results from the synchronized timing of variables from all levels Such work may have several benefits. Questions about coherence and reliability or consistency of indicators of health development with respect to variation in context may be especially useful for understanding the developmental course of multidimensional, latent variable conceptions of health among diverse adolescents. For instance, are specific indicators of, say, cardiovascular, respiratory, nutritional, or mental health manifested consistently across time and place? To what extent and for whom and under which conditions can biologic propensities to such health developmental challenges be moderated? Consider nutrition more closely. How do adolescents’ diets influence their cardio-metabolic risk as adolescents and adults? Do adolescents maintain a healthy diet across different settings involving peers and family members? Is there age-associated variation in answers to this question, for instance, variation associated with Adolescent Health Development: A Relational Developmental Systems Perspective pubertal development or pubertal status/stage (e.g., early, on time, or late)? Do answers to these questions vary in relation to gender? As well, do they vary in relation to normative social transitions (e.g., moving from elementary school to middle school)? How do peer social networks influence these behaviors or family mealtime behaviors? Are answers moderated or changed by the socioeconomic, cultural, religious, or national contexts of youth? In turn, do answers here vary in relation to media exposure about eating and about desirable body types that may be prevalent for youth living in particular settings at particular times in history? Given the synchronization issues raised by the seventh life course health development principle, how are all these answers moderated or changed by what may be nonnormative life or historical events in the lives of adolescents (Baltes et al. 2006)? Examples here may be the death of a parent or deployment in the military (Cozza and Lerner 2013), family disruption due to divorce or separation, or family challenges in the face of environmental tragedies such as weather-related calamities or living in settings beset by violence. The life course health development principles remind us that health development is highly sensitive to the timing and social structure of environmental events (Principle 4), and these examples of questions reflecting the specificity of health-related individual⇔context relations that may impinge on the thriving of adolescents underscore the subtle and nuanced nature of the developmental system within which adolescent health development is embodied. The idea of embodiment and of adolescent health development being an emergent property of the living system (Principle 1) underscores that it is insufficient to take a “main effect” approach to the study of even a facet of health development as complex as the effects of nutrition. Scientists and practitioners framing their research and applications, respectively, within RDS-based models and/or life course health development principles must also ask how do answers to all the questions we have posed regarding nutrition covary with comparable questions pertaining to adolescent lifestyle changes (e.g., involving 117 sleep, exercise, or sexual debut), healthy status of bodily systems, and the presence of chronic and acute health challenges, of both more biological (e.g., hormonal changes) and more social origins (e.g., school bullying). 5 Conclusions The interrelated “what” questions we have presented, illustrated by the example of nutrition’s effects on health development, will, then, help developmental and medical science collaborate to understand the assuredly complex questions that need to be addressed in a scholarly agenda that comprehensively describes, explains, and optimizes the course of individual health development across the adolescent period. Developmental scientists know that in some senses each adolescent is like every other adolescent (e.g., there are nomothetic principles that apply to functioning of the physiological and psychological changes prototypic of the period), that each adolescent is like only some other adolescent (e.g., group differential variation exists as, for instance, when both the presence of good health status and healthy disparities vary across gender, race, socioeconomic level, culture, and nation of residence), and that each adolescent is like no other adolescent (e.g., there are idiographic characteristics pertinent to every young person, for instance, resulting from the relations among his/her timing of molecular, physiological, behavioral, cultural, and evolutionary processes brought into high relief by life course health development Principle 7). Therefore, the scholarly agenda brought to the fore by RDS metatheory and by the principles of life course health development will require scholars seeking to understand and to promote adolescent health to accept that their work will be exceedingly more complex than work predicated on main effect or reductionist approaches. However, the relative plasticity in the development of human health and positive development that is found in studies that have been derived from or associated with RDS-based or life course health development-related scholarship, respectively (e.g., Lerner et al. 2015), is a basis 118 for optimism that embracing such complexity is not only the intellectually correct path to take. It is also a feasible approach to adopt. The advances in relational developmental systems-predicated methods that we have discussed (e.g., Molenaar et al. 2014; Molenaar and Nesselroade 2014, 2015; Nesselroade and Molenaar 2010) provide powerful research and data analytic tools enabling such complexity to be integratively assessed. The adolescent decade is “privileged” because of the generally disease-free status of individuals within it, as compared to subsequent decades of life (e.g., Susman and Dorn 2009; Paus 2009). Nevertheless, application of methods linked to the ideas of RDS metatheory and the principles of life course health development hold the promise of narrowing the disparities that exist across time and place in adolescent health development. In addition, therefore, these conceptions have one other vital asset; their use in research and application will enhance the probability that all youth will transition from adolescence into the adult years in manners that will enable their life course trajectories to more prominently be marked by health and thriving. Such a contribution by these conceptual frameworks will make quite vivid the insight of Lewin (1954) that there is nothing more practical than a good theory. Acknowledgement Supported (in full or in part) by Grant # U45MC27709 from the Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Bureau (Title V, Social Security Act), Division of Child, Adolescent and Family Health, Adolescent Health Branch. References Anthony, E. J. (1969). The reactions of adults to adolescents and their behavior. In G. Caplan & S. Lebovici (Eds.), Adolescence: Psychosocial perspectives (p. 77). New York: Basic Books. Ardelt, M. (2000). Still stable after all these years? Personality stability theory revisited. Social Psychology Quarterly, 63(4), 392–405. Baltes, P. B. (1997). On the incomplete architecture of human ontogeny: Selection, optimization, and compensation as foundations of developmental theory. American Psychologist, 52, 366–380. Baltes, P. B., Lindenberger, U., & Staudinger, U. M. (2006). Lifespan theory in developmental psychology. In W. Damon & R. M. Lerner (Eds.), Theoretical mod- R.M. Lerner et al. els of human development. Vol. 1: Handbook of child psychology (6th ed., pp. 569–664). Hoboken: Wiley. Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52 1–26. Palo Alto: Annual Reviews, Inc. Benson, P. L., Scales, P. C., Hamilton, S. F., & Semsa, A., Jr. (2006). Positive youth development: Theory, research, and applications. In R. M. Lerner (Ed.), Theoretical models of human development, Volume 1 of handbook of child psychology (6th ed., pp. 894–941). Editors-in-chief: W. Damon & R. M. Lerner. Hoboken: Wiley. Bjorklund, D. F., & Ellis, B. J. (2005). Evolutionary psychology and child development: An emerging synthesis. In B. J. Ellis & D. F. Bjorklund (Eds.), Origins of the social mind: Evolutionary psychology and child development (pp. 3–18). New York: Guilford. Block, J. (1995). A contrarian view of the five-factor approach to personality description. Psychological Bulletin, 117, 187–215. Block, J. (2010). The five-factor framing of personality and beyond: Some ruminations. Psychological Inquiry, 21(1), 2–25. Bornstein, M. H. (2006). Parenting science and practice. In K. A. Renninger, & I. E. Sigel (Vol. Eds.), Handbook of child psychology, Vol. 4: Child psychology in practice (6th ed., pp. 893–949). Editors-inChief: W. Damon, & R. M. Lerner. Hoboken: Wiley. Bornstein, M. H. (2017). The specificity principle in acculturation science. Perspectives on Psychological Science, 12(1), 3–45. Brandtstädter, J. (1998). Action perspectives on human development. In R. M. Lerner (Ed.), Handbook of child psychology, Vol. 1 (5th ed., pp. 807–866). New York: Wiley. Brandtstädter, J. (1999). The self in action and development: Cultural, biosocial, and ontogenetic bases of intentional self-development. In J. Brandtstädter & R. M. Lerner (Eds.), Action and self-development: Theory and research through the life-span (pp. 37–65). Thousand Oaks: Sage. Bronfenbrenner, U. (1979). The ecology of human development. Cambridge, MA: Harvard University Press. Bronfenbrenner, U. (2005). Making human beings human. Thousand Oaks: Sage Publications. Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human development. In R. M. Lerner (Ed.), Theoretical models of human development. Volume 1 of Handbook of child psychology (6th ed., pp. 795–828). Editors-in-chief: W. Damon & R. M. Lerner. Hoboken: Wiley. Cole, S. W. (2014). Human social genomics. PLoS Genetics, 10(8), 1–7. Costa, P. T., Jr., & McCrae, R. R. (1980). Still stable after all these years: Personality as a key to some issues in adulthood and old age. In P. B. Baltes & O. G. Brim Jr. (Eds.), Life span development and behavior (Vol. 3, pp. 65–102). New York: Academic Press. Costa, P. T., Jr., & McCrae, R. R. (2006). Age changes in personality and their origins: Comment on Roberts, Adolescent Health Development: A Relational Developmental Systems Perspective Walton, and Viechtbauer (2006). Psychological Bulletin, 132, 28–30. Cozza, S. J., & Lerner, R. M. (Eds.). (2013). Military children and families. The Future of Children, 23(2), 3–11. Damon, W. (2008). The path to purpose: Helping our children find their calling in life. New York: Simon and Schuster. Darwin, C. (1859). The origin of species by means of natural selection or the preservation of favoured races in the struggle for life. London: J. Murray. Eccles, J. S. (2004). Schools, academic motivation, and stage-environment fit. In R. M. Lerner & L. Steinberg (Eds.), Handbook of adolescent psychology (2nd ed., pp. 125–153). Hoboken: Wiley. Eccles, J. S., & Gootman, J. A. (Eds.). (2002). Community programs to promote youth development/committee on community-level programs for youth. Washington, DC: National Academy Press. Elder, G. H., Jr. (1980). Adolescence in historical perspective. In J. Adelson (Ed.), Handbook of adolescent psychology (pp. 3–46). New York: Wiley. Elder, G. H., Jr. (1998). The life course and human development. In R. M. Lerner (Ed.), Handbook of child psychology, Volume 1: Theoretical models of human development (5th ed., pp. 939–991). Editor-in-chief: W. Damon. New York: Wiley. Elder, G. H., Jr., Shanahan, M. J., & Jennings, J. A. (2015). Human development in time and place. In M. H. Bornstein and T. Leventhal (Eds.), Handbook of child psychology and developmental science, Volume 4: Ecological settings and processes in developmental systems (7th ed., pp. 6–54). Editor-in-chief: R. M. Lerner. Hoboken: Wiley. Ford, D. L., & Lerner, R. M. (1992). Developmental systems theory: An integrative approach. Newbury Park: Sage. Forrest, C. B. (2014). A living systems perspective on health. Medical Hypotheses, 82, 209–2143. Freud, A. (1969). Adolescence as a developmental disturbance. In G. Caplan & S. Lebovici (Eds.), Adolescence (pp. 5–10). New York: Basic Books. Freund, A. M., & Baltes, P. B. (2002). Life-management strategies of selection, optimization and compensation: Measurement by self-report and construct validity. Journal of Personality and Social Psychology, 82(4), 642–662. Gardner, M., & Steinberg, L. (2005). Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: An experimental study. Developmental Psychology, 41, 625–635. Geldhof, G. J., Little, T. D., & Colombo, J. (2010). Self-regulation across the lifespan. In M. E. Lamb & A. M. Freund (Vol. Eds.), Social and emotional development. Volume 2 of the Handbook of lifespan development. Editor-in-Chief: R. M. Lerner. Hoboken: Wiley. Gestsdóttir, G., & Lerner, R. M. (2008). Positive development in adolescence: The development and role of intentional self regulation. Human Development, 51, 202–224. Gestsdottir, S., Geldhof, G. J., Paus, T., Freund, A. M., Aðalbjarnardóttir, S., Lerner, J. V., & Lerner, R. M. 119 (2014). Self-regulation among youth in four Western cultures: Is there an adolescence-specific structure of the Selection-Optimization-Compensation (SOC) model? International Journal of Behavioral Development, 39(4), 346–358. Gissis, S. B., & Jablonka, E. (Eds.). (2011). Transformations of Lamarchism: From subtle fluids to molecular biology. Cambridge, MA: The MIT Press. Gottlieb, G. (1997). Synthesizing nature-nurture: Prenatal roots of instinctive behavior. Mahwah: Erlbaum. Gottlieb, G. (1998). Normally occurring environmental and behavioral influences on gene activity: From central dogma to probabilistic epigenesis. Psychological Review, 105(4), 792–802. Halfon, N., & Forrest, C. B. (2017). The emerging theoretical framework of life course health development. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health-development science. Cham: Springer. Hall, G. S. (1904). Adolescence: Its psychology and its relations to psychology. anthropology, sociology, sex, crime, religion, and education. New York: Appleton. Hamilton, S. F. (1999). A three-part definition of youth development. Unpublished manuscript. Ithaca: Cornell University College of Human Ecology. Jablonka, E., & Lamb, M. J. (2005). Evolution in four dimensions: Genetic, epigenetic, behavioral, and symbolic variation in the history of life. Cambridge, MA: MIT Press. Johanson, D. C., & Edey, M. A. (1981). Lucy: The beginnings of humankind. New York: Simon & Schuster. Larson, R. W. (2000). Toward a psychology of positive youth development. American Psychologist, 55(1), 170–183. Latour, B. (1993). We have never been modern (trans. Catherine Porter), Cambridge, MA: Harvard University Press. Lerner, R. M., & Busch-Rossnagel, N. A. (Eds.). (1981). Individuals as producers of their development: A lifespan perspective. New York: Academic Press. Lerner, R. M. (1982). Children and adolescents as producers of their own development. Developmental Review, 2, 342–370. Lerner, R. M. (1984). On the nature of human plasticity. New York: Cambridge University Press. Lerner, R. M., & Walls, T. (1999). Revisiting individuals as producers of their development: From dynamic interactionism to developmental systems. In J. Brandtstädter & R. M. Lerner (Eds.), Action and selfdevelopment: Theory and research through the lifespan (pp. 3–36). Thousand Oaks: Sage. Lerner, R. M. (2002). Concepts and theories of human development (3rd ed.). Mahwah: Lawrence Erlbaum Associates. Lerner, R. M. (2004). Liberty: Thriving and civic engagement among America’s youth. Thousand Oaks: Sage Publications. Lerner, R. M., Lerner, J. V., Almerigi, J. B., Theokas, C., Phelps, E., Gestsdottir, S., et al. (2005). Positive Youth Development, Participation in community youth development programs, and community contribu- 120 tions of fifthgrade adolescents: Findings from the first wave of the 4-H study of Positive Youth Development. Journal of Early Adolescence, 25(1), 17–71. Lerner, R. M. (Ed.). (2006). Theoretical models of human development. Volume 1 of Handbook of child psychology (6th ed.). Editors-in-chief: W. Damon & R. M. Lerner. Hoboken: Wiley. Lerner, R. M. (2012). Essay review: Developmental science: Past, present, and future. International Journal of Developmental Science, 6(1–2), 29–36. Lerner, R. M., & Callina, K. S. (2014). The study of character development: Towards tests of a relational developmental systems model. Human Development, 57(6), 322–346. Lerner, R. M. (Editor-in-Chief). (2015). Handbook of child psychology and developmental science (7th ed.). Hoboken: Wiley. Lerner, R. M., & Benson, J. B. (Eds.). (2013a). Embodiment and epigenesis: Theoretical and methodological issues in understanding the role of biology within the relational developmental system. Volume 1: Philosophical, theoretical, and biological dimensions. advances in child development and behavior (Vol. 44). London: Elsevier. Lerner, R. M., & Benson, J. B. (Eds.). (2013b). Embodiment and epigenesis: Theoretical and methodological issues in understanding the role of biology within the relational developmental system. Volume 2: Ontogenetic dimensions. Advances in child development and behavior (Vol. 45). London: Elsevier. Lerner, R. M., & Callina, K. S. (2015). The study of character development: Towards tests of a relational developmental systems model. Human Development, 57(6), 322–346. Lerner, R. M., & Steinberg, L. (2009). The scientific study of adolescent development. In R. M. Lerner & L. Steinberg (Eds.), Handbook of adolescent psychology (3rd ed., pp. 3–14). Hoboken: Wiley. Lerner, R. M., Freund, A. M., De Stefanis, I., & Habermas, T. (2001). Understanding developmental regulation in adolescence: The use of the selection, optimization, and compensation model. Human Development, 44, 29–50. Lerner, R. M., Lerner, J. V., Bowers, E., & Geldhof, G. J. (2015). Positive youth development: A relational developmental systems model. In W. F. Overton & P. C. Molenaar (Eds.), Theory and method. Volume 1 of the Handbook of child psychology and developmental science (7th ed., pp. 607–651). Editor-in-chief: R. M. Lerner. Hoboken: Wiley. Lewin, K. (1954). Behavior and development as a function of the total situation. In L. Carmichael (Ed.), Manual of child psychology (2nd ed.). New York: Wiley. Marcia, J. E. (1980). Identity in adolescence. In J. Adelson (Ed.), Handbook of adolescent psychology (pp. 159– 187). New York: Wiley. Mascalo, M. F., & Fischer, K. W. (2015). The dynamic development of thinking, feeling, and acting: Infancy through adulthood In W. F. Overton & P. C. M. Molenaar (Eds.), Handbook of child psychology and developmental science (7th ed.), Volume 1: Theory and method. Editor-in-chief: R. M. Lerner. Hoboken: Wiley. R.M. Lerner et al. McClelland, M. M., Geldhof, G. J., Cameron, C. E., & Wanless, S. B. (2015). Development and selfregulation. In W. F. Overton & P. C. Molenaar (Eds.), Theory and method. Volume 1 of the Handbook of child psychology and developmental science (7th ed.). Editor-in-chief: R. M. Lerner. Hoboken: Wiley. McCrae, R. R., Costa, P. T., Hrebickova, M., Ostendord, F., Angleitner, A., & Avia, M. D. (2000). Nature over nurture: Temperament, personality, and life span development. Journal of Personality and Social Psychology, 78(1), 173–186. Meaney, M. (2014, October 10). Epigenetics offer hope for disadvantaged children. [Children and family: Blog]. Retrieved from http://childandfamilyblog.com/ epigenetics-offer-hope-disadvantaged-children/ Misteli, T. (2013). The cell biology of genomes: Bringing the double helix to life. Cell, 152(6), 1209–1212. Mistry, J., & Wu, J. (2010). Navigating cultural worlds and negotiating identities: A conceptual model. Human Development, 53(1), 5–25. Molenaar, P. C. (2014). Dynamic models of biological pattern formation have surprising implications for understanding the epigenetics of development. Research in Human Development, 11(1), 50–62. Molenaar, P. C., & Nesselroade, J. R. (2014). New trends in the inductive use of relation developmental systems theory: Ergodicity, nonstationarity, and heterogeneity. In P. C. Molenaar, R. M. Lerner, & K. M. Newell (Eds.), Handbook of developmental systems and methodology (pp. 442–462). New York: Guilford Press. Molenaar, P. C. M., & Nesselroade, J. R. (2015). Systems methods for developmental research. In W. F. Overton & P. C. M. Molenaar (Eds.), Handbook of child psychology and developmental science (7th ed.), Volume 1: Theory and method. Editor-in-chief: R. M. Lerner. Hoboken: Wiley. Molenaar, P. C., Lerner, R. M., & Newell, K. M. (2014). Developmental systems theory and methodology: A review of the issues. In P. C. Molenaar, R. M. Lerner, & K. M. Newell (Eds.), Handbook of developmental systems and methodology (pp. 3–18). New York: Guilford Press. Narvaez, D. (2008). Human flourishing and moral development: Cognitive and neurobiological perspectives of virtue development. In L. Nucci & D. Narvaez (Eds.), Handbook of moral and character education (pp. 310–327). Oxford: Routledge. Nesselroade, J. R. (1988). Some implications of the traitstate distinction for the study of development over the life-span: The case of personality. In P. B. Baltes, D. L. Featherman, & R. M. Lerner (Eds.), Life-span development and behavior (Vol. 8, pp. 163–189). Hillsdale: Erlbaum. Nesselroade, J. R., & Molenaar, P. C. M. (2010). Emphasizing intraindividual variability in the study of development over the life span. In W. F. Overton (Ed.), The handbook of life-span development. Vol. 1: Cognition, biology, methods (pp. 30–54). Editor-inchief: R. M. Lerner. Hoboken: Wiley. Overton, W. F. (1973). On the assumptive base of the nature-nurture controversy: Additive versus interactive conceptions. Human Development, 16, 74–89. Adolescent Health Development: A Relational Developmental Systems Perspective Overton, W. F. (2010). Life-span development: Concepts and issues. In W. F. Overton (Ed), Cognition, biology, and methods across the lifespan. Handbook of life-span development. (Vol. 1, pp. 1–29). Editor-in-chief: R. M. Lerner. Hoboken: Wiley. Overton, W. F. (2013). Relationism and relational developmental systems; A paradigm for developmental science in the post-Cartesian era. In R. M. Lerner and J. B. Benson (Eds.), Advances in child development and behavior, volume 44: Embodiment and epigenesis: Theoretical and methodological issues in understanding the role of biology within the relational developmental system, Part A: Philosophical, theoretical, and biological dimensions (pp. 24–64). London: Elsevier. Development of Character 49. Overton, W. F. (2015). Process and relational developmental systems. In W. F. Overton & P. C. Molenaar (Eds.), Theory and method. Volume 1 of the Handbook of child psychology and developmental science (7th ed., pp. 9–62). Editor-in-chief: R. M. Lerner. Hoboken: Wiley. Overton, W. F., & Lerner, R. M. (2014). Fundamental concepts and methods in developmental science: A relational perspective. Research in Human Development, 11(1), 63–73. Overton, W. F., & Mueller, U. (2013). Meta-theories, theories, and concepts in the study of development. In R. M. Lerner, M A. Easterbrooks, & J. Mistry (Eds.), Comprehensive handbook of psychology: developmental psychology: Vol. 6. (pp. 19–58). Editorin-Chief: Irving B. Weiner. New York: Wiley. Overton, W. F., & Reese, H. W. (1981). Conceptual prerequisites for an understanding of stability–change and continuity–discontinuity. International Journal of Behavioral Development, 4, 99–123. Paus, T. (2009). Brain development. In R. M. Lerner & L. Steinberg (Eds.), Handbook of adolescent psychology (3rd ed., pp. 95–115). Ricco, R., & Overton, W. F. (2011). Dual systems competence ß-à procedural processing: A relational developmental systems approach to reasoning. Developmental Review, 31, 119–150. http://dx.doi.org/10.1016/j. dr.2011.07.005. Riegel, K. F. (1975). Toward a dialectical theory of human development. Human Development, 18, 50–64. Riegel, K. F. (1976). The dialectics of human development. American Psychologist, 31, 689–700. Roberts, B. W., Walton, K. E., & Viechtbauer, W. (2006). Patterns of mean-level change in personality traits across the life course: A meta-analysis of longitudinal studies. Psychological Bulletin, 132(1), 1–25. 121 Roth, J. L., & Brooks-Gunn, J. (2003a). What is a youth development program? Identification and defining principles. In F. Jacobs, D. Wertlieb, & R. M. Lerner (Eds.), Enhancing the life chances of youth and families: Public service systems and public policy perspectives: Vol. 2 handbook of applied developmental science: Promoting positive child, adolescent, and family development through research, policies, and programs (pp. 197–223). Thousand Oaks: Sage. Roth, J. L., & Brooks-Gunn, J. (2003b). What exactly is a youth development program? Answers from research and practice. Applied Developmental Science, 7, 94–111. Slavich, G. M., & Cole, S. W. (2013). The emerging field of human social genomics. Clinical Psychological Science, 1, 331–348. Sokol, B. W., Hammond, S., Kuebli, J., & Sweetman, L. (2015). The development of agency. In W. F. Overton & P. C. Molenaar (Eds.,) Theory and method. Volume 1 of development of the Handbook of child psychology and developmental science (7th ed.). Editor-in-chief: R. M. Lerner. Hoboken: Wiley. Steinberg, L. (2010). A dual systems models of adolescent risk-taking. Developmental Psychobiology, 52, 216–224. Susman, E., & Dorn, L. D. (2009). Puberty: Its role in development. In R. M. Lerner & L. Steinberg (Eds.), Handbook of adolescent psychology (3rd ed., pp. 116– 151). Hoboken: Wiley. Susman, E. J., & Dorn, L. D. (2013). Puberty: Its role in development. In R. M. Lerner, M. A., Easterbrooks, & J. Mistry (Eds.), Handbook of psychology, Volume 6: Developmental psychology (2nd ed., pp. 289–320). Editor-inchief: I. B. Weiner. Hoboken: Wiley. Tobach, E., & Schneirla, T. C. (1968). The biopsychology of social behavior of animals. In R. E. Cooke & S. Levin (Eds.), Biologic basis of pediatric practice (pp. 68–82). New York: McGraw-Hill. Trommsdorff, G. (2012). Development of “agentic” regulation in cultural context: The role of self and world views. Child Development Perspectives, 6(1), 19–26. von Bertalanffy, L. (1933). Modern theories of development. London: Oxford University Press. von Bertalanffy, L. (1968). General systems theory. New York: Braziller. Whitehead, A. N. (1929/1978). Process and reality. Corrected edn. New York: The Free Witherington, D. C. (2014). Self-organization and explanatory pluralism: Avoiding the snares of reductionism in developmental science. Research In Human Development, 11, 22–36. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Emerging Adulthood as a Critical Stage in the Life Course David Wood, Tara Crapnell, Lynette Lau, Ashley Bennett, Debra Lotstein, Maria Ferris, and Alice Kuo 1 Introduction to Emerging Adulthood The path that individuals take from dependency in childhood to independence in adulthood is now a longer and more complicated one than at any other point in history (Arnett 2014). From the mid- to late twentieth century and extending into the twenty-first century, industrial societies have experienced a surge in the concept of individualism and increased salience of self-realization and personal expression (Arnett 1998). Moreover, we have seen a dramatic increase in the number of youth seeking post-high school education, which is required for success as the economy transitions from an industrial to an information-based econThe original version of this chapter was revised. An erratum to this chapter can be found at https://doi.org/10.1007/978-3-319-47143-3_27 D. Wood, MD, MPH (*) Department of Pediatrics, ETSU College of Medicine, PO Box 70578, Johnson City, TN 37614, USA e-mail: wooddl@etsu.edu T. Crapnell, OTD, OTR/L • L. Lau, PhD D. Lotstein, MD, MPH • M. Ferris, MD, PhD, MPH A. Kuo, MD, PhD UCLA, Department of Pediatrics, Los Angeles, CA, USA A. Bennett, MD Department of Pediatrics, USC, Los Angeles, CA, USA omy (Rifkin 2011). However, stagnation of wages for low-skilled workers and the lack of work opportunities for youth and young adults, combined with the increased costs of education and independent living, have made the pathway to independence and adulthood prolonged, complex, and varied, creating a new stage in the life course that has been labeled emerging adulthood (EA). While not all life course or developmental scientists agree that emerging adulthood constitutes a new developmental stage, there is agreement that social and economic forces have prolonged entry into adulthood and with significant role and developmental challenges beyond the traditional adolescent years (Cote 2014). At the beginning of this stage, 17–18 years of age, emerging adults are generally dependent, living with their parents or caretakers, beginning to engage in romantic relationships, and attending high school. At the end of this stage, mid- to late 20s, most emerging adults live independently, are in long-term relationships, and have clear career paths ahead of them. How they traverse this life stage is dependent upon the personal, family, and social resources they possess as they enter this stage of life, dynamic and reciprocal interaction between the emerging adult and their environment, and the supports they receive during this stage. The result is that there are many pathways that youth and young adults pursue through this stage to achieve stable adulthood. For example, 40% do not © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_7 123 D. Wood et al. 124 pursue post-high school education. While 60% entered college immediately after graduating high school, many drop out or interrupt their college education with periods of work. Some 33% in this stage remain unmarried; however, 67% of them achieve stable domestic partners. Importantly, only a minority of emerging adults are employed in fulltime jobs, limiting the economic opportunities they experience (U.S. Census Bureau. American Community Survey 2006). Emerging adulthood is considered to be the volitional years, as it offers the most opportunity for identity exploration in the areas of love, work, and worldviews (Arnett 2000). During this time, individuals begin to develop the characteristic qualities necessary for becoming self-sufficient, engage in mature, committed relationships, assume more adult roles and responsibilities, and obtain a level of education and training that sets the foundation for work during the adult years. Characterizing emerging adulthood as a stage in the life course has proven to be beneficial to explain the social, cognitive, and psychological development that occurs during this stage. Research demonstrating continued brain development into the late 20s provides further justification for viewing emerging adulthood as a stage in the life course (Spear 2000). 2 Conceptual Framework The Life Course Health Development (LCHD) model posits that myriad factors (e.g., biological, psychological, cultural) on multiple levels (e.g., micro, meso, macro) interact simultaneously in a transactional fashion to influence an individual’s LCHD during each stage to determine a “health developmental” trajectory (see also Halfon and Forrest 2017). The Life Course Health Development (LCHD) model further posits that transitions and pivotal points in an individual’s life have the potential to influence and alter an individual’s developmental pathways. EA is a life stage characterized by changes in person-context cognitive, emotional, physical, and social domains, and the ultimate pathway achieved by the emerging adults during this stage is determined by the ongoing, dynamic, and reciprocal interactions between the individ- ual and their environment. The degree of agency and role exploration that characterizes EA results in the potential for growth in intellectual and emotional functioning (Arnett 2000). EA represents a broad and diverse but fundamentally important area of consideration by virtue of the multiple avenues through which an individual’s developmental trajectories and outcomes may be influenced. Important developmental challenges during EA include the continued formation of identity and values, which occur in the midst of frequent changes in personal relationships, living arrangements, vocational and educational pursuits, and social roles (Shanahan 2000). No stage in life, other than perhaps infancy, experiences such dynamic and complex changes on the personal, social, emotional, neuroanatomical, and developmental levels. For the 10 years between 18 and 28, the vast majority of emerging adults change living situation, change their primary relationships, complete education or vocational preparation, get married, have children, and transition from adolescent/dependent roles to adult/independent roles. This occurs during often volatile emotional, neurodevelopmental, and social development. Increasing agency occurs at the same time as decreasing institutional and family supports. The theoretical framework developed by Learner and others to create the positive youth development theory nicely explains how the developmental trajectories that emerge during the period of EA are dependent upon multiple influential, bidirectional, personcontext coactions. Individuals during emerging adulthood act as co-developers of their own developmental pathways, adaptively responding to different biological, social, cultural, and physical environmental contexts that they influence and are also influenced by (Learner and Overton 2008). Successfully navigating the developmental challenges inherent in EA will likely, in large part, influence the developmental trajectory of adulthood because these challenges ultimately influence the important adult outcomes of independent living, committed intimate relationships, and vocational and educational achievement. Youth and young adults with chronic disease or disabilities face additional challenges (disease management, disease complications, limitations Emerging Adulthood as a Critical Stage in the Life Course in opportunities, etc.) in the context of these multiple domains of growth and development, which impact their pathway through this stage. The continued positive trajectory of the emerging adult’s mental health, identity formation, education achievement, social relationships, and other developmental areas is somewhat dependent on the degree to which there are matches or mismatches between the individual and his/her resources and the environmental challenges and supports. If the transition, such as school to work, provides a reasonable and developmentally appropriate challenge and the emerging adult successfully navigates that challenge, then the developmental trajectory of the emerging adult will be enhanced. In contrast, if the same transition lacks supports or is an inappropriate match for the emerging adult’s abilities (e.g., an inadequate vocational program for an emerging adult with a learning disorder or lack of support for chronic disease management) and the emerging adult experiences failure, then the developmental trajectory may be impaired, resulting in significantly less achievement or developmental progression across the lifespan. A major defining characteristic of the stage of EA is that contexts are changing significantly (family to independence or romantic relationships, school to work or disconnected state, dependent living to independent living arrangements). The changes are so significant that emerging adults need substantial supports to navigate the transition successfully. Emerging adults with disabilities or chronic health conditions require more support to maximize their potential development during EA (Table 1). The above table outlines the seven principles of the Life Course Health Development framework. These principles can be applied to the stage of emerging adulthood in a limited fashion due to the limited research literature focused on this life stage. However, conceptually, using the seven principles to view the stage of emerging adulthood can be instructive and can lead to additional research questions (see end of this article). For example, Principle 5 states that health development expressions are malleable and enable and constrain health development pathways and plasticity. According to Bogin’s reserve capacity hypothesis, prolonged child- 125 Table 1 Principles of the Life Course Health Development framework Principle 1. Health development 2. Unfolding 3. Complexity 4. Timing 5. Plasticity 6. Thriving 7. Harmony Brief description Health development integrates the concepts of health and developmental processes into a unified whole Health development unfolds continuously over the lifespan, from conception to death, and is shaped by prior experiences and environmental interactions Health development results from adaptive, multilevel, and reciprocal interactions between individuals and their physical, natural, and social environments Health development is sensitive to the timing and social structuring of environmental exposures and experiences Health development phenotypes are malleable and enabled and constrained by evolution to enhance adaptability to diverse environments Optimal health development promotes survival, enhances well-being, and protects against disease Health development results from the balanced interactions of molecular, physiological, behavioral, cultural, and evolutionary processes hood into adolescence and perhaps emerging adulthood leads to greater biologic and social resilience in adulthood, which leads to enhanced fertility and greater longevity (Bogin 2013). Principle 4 states that health development is sensitive to life course timing and social structuring of the environment. As we described above, this is particularly true of emerging adulthood, where the interaction between age, personal development, and environment (peers, schools, social institutions, etc.) leads to multiple pathways to identify formation and academic and social achievement during this stage (Benson and Elder 2011; Benson et al. 2012). Multiple factors can influence the life course during EA, including factors at the macro-level (historical and societal influences), meso-level (parent-child relationship, family environment, D. Wood et al. 126 and socioeconomic status), and microlevel (individual cognitive, personality, and emotional development). The timing of these exposures during EA, which we consider a critical or sensitive period in the life course development, can significantly impact adult outcomes. For example, an emerging adult that engages in criminal activity and is convicted of a felony will suffer repercussions that will greatly diminish their chances of achieving success in relationships and work. In the sections that follow, we review the macro-, meso-, and microlevel influences occurring during EA for emerging adults generally. Next we explore the additional challenges faced by those with selected chronic health or developmental conditions including mental illness or substance abuse, diabetes, chronic renal failure, and autism, to serve as case studies of the increased complexity faced by emerging adults with chronic disease. Finally, we finish up with a set of questions and issues that are research priorities for developing an LCHD research agenda on the stage of emerging adulthood. 3 Macro-level Influences on the Trajectory of Emerging Adulthood Emerging adulthood, like many other developmental stages, is a period in the life course that is culturally constructed and not universal (Arnett 1998, 2000). Thus, the very existence and trajectory of EA are dependent on macro-level societal expectations and influences. For many years, it was theorized that an individual transitioned from adolescence (which begins in puberty and ends in the late teens) into young adulthood. Over history and cross-culturally, the length of time during which an individual spends in adolescence has been determined by the age at which the person enters marriage or a committed relationship (Schlegel and Barry III 1991, Gilmore 1990). In fact, human life history posits that the life stage of adolescence came into existence about 75,000 years ago due to changes in human societies and cultures associated with pair bonding and living in large groups (Bogin 2013). So massive culture and reproductive change led to a change in life history with the addition of a new period of development. It is theorized by those promoting emerging adulthood as a new life stage that the same kind of massive cultural and reproductive change is happening once again and new social and cultural demands of adult life necessitate a longer and more complex prologue to adult life. Although the functional outcome of EA, causing a delayed transition to adulthood, appears relatively novel, this practice of prolonging the transition to adulthood may be dated back to early modern English society from the 1500s to 1700s (Ben-Amos 1994). Similar to what generally occurs during EA today, individuals during that era took part in a “life cycle service” between their late teens and 20s, wherein they would engage in developing important vocational skills (e.g., a trade or craft) and marriage was often postponed until the late 20s for both men and women; adult roles and responsibilities for individuals during that era were provided only gradually, as each individual acquired the desired character qualities deemed appropriate by society (Ben-Amos 1994). It was not until industrialization in America began to emerge in the nineteenth century that the concept of individualism developed and strengthened (Rotundo 1994). The twentieth century marked the first time that an individual could obtain and gain control over the resources that would allow them to choose the timing of major life events and personal expression was valued in society (Modell 1991). As we move forward into the twenty-first century, individualism in contemporary postindustrial society continues to strengthen, while the age at which individuals marry continues to increase, and individuals increasingly seek to pursue other life course events (Modell 1991; Alwin 1988; Bellah et al. 2007). As a reflection of these trends, recent evidence indicates that contemporary postindustrial society considers marriage status of low importance in the determination of whether or not an individual has reached adulthood; rather, societies in developed countries appear to now consider the acquisition of certain character qualities as the most important indicator of having attained Emerging Adulthood as a Critical Stage in the Life Course adulthood, the top three character qualities of which include (1) accepting responsibility for oneself, (2) making independent decisions, and (3) assuming financial independence – all of which emphasize an individual’s ability to be self-sufficient (Scheer et al. 1996; Greene et al. 1992; Arnett 1997, 1998). 4 Meso-level Influences of Life Trajectories During Emerging Adulthood Earlier life experiences and family environment can impact one’s life trajectory from childhood through EA and into adulthood. Longitudinal studies that span early childhood through EA indicate that there is both continuity and discontinuity of healthy and unhealthy paths and outcomes. (Masten et al. 2006, 2005). In this section, we will be discussing the impact of the parentchild relationship, family environment, and other sociodemographic factors, such as socioeconomic status (SES) and its influence in EA. 4.1 Earlier Parent-Child Relationships The quality of parent-child relationships during EA is, in large part, a function of the history of early parent-child attachment experiences. Attachment theory suggests that positive bonding with an emotionally available adult during early childhood facilitates the development of the child’s capacity to bond with others and respond to stressful events. A history of positive attachment experiences will ultimately provide a foundation for positive interactions with others and foster secure, lasting relationships in EA which subsequently influence the LCHD (Mikulincer M and Shaver PR 2009), whereas insecure attachments can result in mistrust or lack of security and reduced sense of self-efficacy and selfesteem. Simultaneously, social learning theory suggests that styles of family interactions learned in early and middle childhood are carried by emerging adults into adulthood (Whitbeck et al. 127 1994). Familiar patterns of interaction help individuals deal with new situations and have a significant influence on the emerging adult’s capacity to deal with the multiple changes that occur during EA. Alternatively, inadequate parenting, disrupted family bonds, and poverty are environmental risks for childhood-onset conduct, behavioral and emotional problems, and educational underachievement that can persist into adolescence and emerging adulthood (Moffit and Caspi 2001). Adverse events experienced in childhood such as parental divorce or alcoholism or the experience of abuse are major risk factors for the leading causes of illness and death as well as poor quality of life in adults in the USA. Disruptions in the parent relationship (e.g., divorce) can have strong, negative effects on the parent-child relationship (Aquilino 1994) causing significant emotional impact throughout the life course. Parental divorce may influence the emerging adults’ ability to form stable romantic relationships and their decision to marry early or may undermine the emerging adult’s financial ability to attend college (Jacquet and Surra 2001, Cherlin et al. 1998). Importantly, parental acceptance and support for independence have been linked to higher self-esteem, individualism, and feelings of worthiness among emerging adults (Ryan and Lynch 1989). Critical to the emerging adult’s achievement of independence are feeling connected, secure, understood, and loved in their families and having the willingness to call on parental resources for help in making choices in educational, relationship, living, and other changes that confront the emerging adult. Persistent connectedness to parents facilitates rather than undermines ongoing identity development in emerging adulthood (Grotevant and Cooper 1986; Ryan and Lynch 1989). 4.2 Childhood Socioeconomic Status Socioeconomic status (SES), family supports, and the neighborhood environment all can contribute to the positive or negative life trajectory prior to D. Wood et al. 128 and during EA (Galobardes et al. 2008; Gilman 2012; Gilman and Loucks 2012). Socioeconomic status has been identified as one of the most important health determinants throughout the life course (Miller et al. 2015). Low financial resources and all the other exposures that go along with poverty – poor schools, chaotic families, exposure to violence, and victimization – limit opportunities for growth or acquisition of other resources that enhance quality of life (Wadsworth et al. 2016; Kim et al. 2015). In addition to being an important predictor of disease-specific morbidity and mortality in adulthood, early childhood poverty has been associated with lower adult educational attainment (Duncan and Brooks-Gunn 2000). Educational achievement has a major influence on the life trajectory, including financial stability and health in EA and onward throughout adulthood. Seventy percent of emerging adults who grow up in poverty delay enrollment in postsecondary education, in comparison to 40% who grow up in household of higher SES. Furthermore, research has indicated that individuals who delay enrollment past the age of 22 are less likely to ever enroll in postsecondary education and less likely to complete a degree (Feliciano and Ashtiani 2012). The result is that only 22% of young adults from low-income families earn college degrees, while 48% of young adults from higher-income families earn at least an associate’s degree. Earning a college education results in not only higher lifetime earnings but enhancement of multiple aspects of psychosocial development (Evans and Cassells 2014). However, while SES has been found to be predictive of educational attainment, the impact of poverty can be mitigated by a number of factors such as individual characteristics of self-efficacy and hope. Students from poor families that have high self-efficacy and a similar concept of hope for educational attainment do equally as well in school as their higher SES peers (Osgood et al. 2005). Recent research by Dweck and colleagues indicates that students’ mindsets are critically linked to resilience and achievement and that they can be changed through brief interventions, leading to substantial increases in measures of resilience and achievement (Yeager and Dweck 2012). These studies suggest that in addition to SES, individual characteristics also have a large influence on outcomes of EA and that these characteristics can be influenced through individual interventions. Individual characteristics and development are discussed in the next section. 5 Microlevel Influences on the Trajectories During Emerging Adulthood As discussed in previous sections, individualism and the qualities of character have become increasingly important in the transition to EA and ultimately reaching full adulthood in postindustrial, developed societies. Studies have shown that the ability to accept responsibility for one’s self, make independent decisions, and assume financial independence are the top three most important factors in becoming an adult (Arnett 2014). In order to achieve individualism and obtain these important qualities of character, one needs to reach some degree of cognitive and psychological maturity, as well as possess some level of resilience. These individual factors are discussed in this section. 5.1 Cognitive Development One of the identified qualities of character that is important in EA and reaching full adulthood is independent decision-making. While the ability to make independent decisions is a reflection of one’s psychological and moral identity, it also has to do with cognitive maturity and the ability to weigh a variety of considerations before deciding on a choice. Recent neuroscience research indicates that brain development (particularly in the prefrontal cortex) continues well into the third decade of life, ultimately resulting in the integration and coordination of cognitions, emotion and action, and strategic executive control (Luciana et al. 2005). The continuous unfolding and acquisition of specific neurodevelopmental capacities during adolescences and EA influence Emerging Adulthood as a Critical Stage in the Life Course the acquisition of goal directedness and future orientation that have been observed behaviorally during EA (Dahl 2004; Nelson et al. 2012; Nurmi 1999; Steinberg et al. 2006). As new capacities emerge, they are available to solve problems, delay gratification, and filter unnecessary input. With maturation of these skills, emerging adults are also more capable of reflecting on the influence of their environment and on their internal state, regulate their emotions, and use problemsolving skills to effectively compromise, which is important for the development of meaningful social interactions and personal relationships as well as in the work environment. These skills also support an emerging adult’s capacity for optimally interacting with the health-care system, managing their health-care needs, and making decisions that will influence their long-term health outcomes. However, the preceding paragraph assumes that the individual has experienced optimal neurodevelopment up to the point that they enter the stage of EA. Studies have demonstrated that exposure to chronic stress during childhood (e.g., poverty) or experiencing adverse childhood events (e.g., child maltreatment, neglect, parental divorce, parental substance abuse) may cause detrimental impact to the developing brain. For example, repeated exposure to stressful events has been associated with structural differences in specific brain regions (i.e., amygdala, hippocampus, and prefrontal cortex), which are in turn associated with functional differences in learning, memory, and aspects of executive functioning. Furthermore, preliminary studies go on to suggest that there may be sensitive periods of brain development with increased susceptibility to the effects of stress and adverse events. These environmentally induced modulations in neurodevelopment can impact an individual’s cognitive development and their capacity to develop the necessary skills and relationships that will enable them to thrive. An emerging adult, who has experienced chronic stress or adverse events earlier in childhood and adolescence and is cognitively immature, is more likely, through adverse interaction with their environment, to suffer secondary 129 effects such as school failure, risky/impulsive behaviors, accidental injury, criminal activity, or substance misuse or overuse. These behaviors may in turn interfere with the ongoing development of an optimal pathway to adulthood. Feelings of isolation and rates of substance abuse in individuals often peak during this period as emerging adults are faced with the stresses of having to navigate societal structures that are not adequately informed or equipped to address the needs of an emerging adult population – all while their brains have yet to reach full maturation (Spear 2000). An impulsive emerging adult fueled by alcohol and despair is at high risk for suicide. It is thus apparent that multiple factors related to an individual’s cognitive and emotional development may intersect and interact to multiply the likelihood of adverse or positive health outcomes in EA. It is crucial for service providers to recognize the level of cognitive maturation an emerging adult possesses and tailor their interventions and supports based on this. 5.2 Identity Formation Identity formation is a major developmental activity during EA. Identity development, occurs in a number of dimensions: (1) psychological dimension, or ego identity via a sense of temporalspatial continuity and its concomitants; (2) the personal dimension, or a behavioral and character repertoire that differentiates the individuals; and (3) the social dimension, or recognized roles within a community. These components come together during the stage of identity formation (adolescence and EA) and stabilization (EA and young adulthood), and once the identity is considered stable, this is when a relatively firm sense of ego identity is developed, behavior and character become stabilized, and communitysanctioned roles are acquired (2002). Identity formation during adolescence was thought to be a critical milestone in adolescence; however, it has been recognized that in certain societal contexts, identity formation continues beyond adolescence. In the postindustrial society, with the prolongation of educational and D. Wood et al. 130 vocational attainment, prolongation of identity exploration in the three main areas of love, work, and worldviews into the 20s has become the norm. Identity formation is critical in EA, as it has direct implications on psychological and moral identity and in achieving the three qualities of character deemed as important in the transition to adulthood. 5.3 Resilience in Emerging Adulthood Resilience is an individual’s capacity to adapt to change in healthy and flexible ways during stressful events (Catalano et al. 2004). Resilience can be measured in various ways, including internal adaption (e.g., well-being, happiness, or selfconcept) or external adaption (academic achievement, relationship development) to adverse circumstances, such as how well a person navigates and achieves the developmental tasks presented by the external world (e.g., educational achievement, stable work/career, marriage, etc.). Resiliency in adolescence and young adulthood is higher among youth with higher intellectual resources, optimistic future orientation, the presence of caring relationships with positive adult role models, and opportunities to succeed and serve the community. Emerging adulthood is a particularly important stage in the life course to understand resilience because the important changes in functional capacity, educational achievement, and social roles are large and varied and have a significant influence on life course outcomes. Key individual characteristics that predict resilience during EA are goal-directed motivation and planfulness about the future. In addition, adult support and mentorship are important factors that help promote resilience (Miller GE 2015; Masten et al. 2005; Arnett 2005). Furstenberg, Brooks-Gunn, and others studied young women who became pregnant during adolescence and found that a small proportion with positive adult relationships had dramatic change in trajectory for the better (Furstenberg 2002). Masten, study- ing a cohort of low-income youth (beginning at ages 8–12) over a 20-year period, found that a number of factors predict competence in EA including higher intellectual capacity, higher parenting quality, and higher SES (Masten and Coatsworth 1998). When they examined resilience between the 10- and 20-year follow-up, they found that youth doing well in EA was predicted from doing well 10 years earlier. In the context of high adversity, childhood IQ was particularly an important moderator and predictor of resilience. During EA, individuals who were doing well from high-adversity backgrounds had significant personal resources in intelligence and personal competence and a history of receiving high-quality parenting (Masten et al. 2004). 6 Trajectories During Emerging Adulthood for Emerging Adults with Chronic Health Condition Chronic health conditions can significantly impact the developmental trajectory of emerging adults during this life stage. Many types of chronic conditions, including those that impact physical, intellectual, or emotional functioning, may undermine the assumption of adult roles by the emerging adult, undermine success in school, impair the transition to work, and make living independently more of a challenge. Eighteen percent of youth aged 12–17 in the USA are defined as having a special health-care need, meaning they have a chronic physical, medical, emotional, or developmental condition that requires a more intensive use of health-care and related services. Emerging adults with chronic conditions are at risk for a number of problems during transition to adulthood, including experiencing gaps in needed medical and related services and gaps in health insurance (Lotstein et al. 2008, Reiss et al. 2005). Below we review the impact of several specific chronic conditions on the growth trajectory during emerging adulthood. Emerging Adulthood as a Critical Stage in the Life Course 6.1 Autism Spectrum Disorders (ASDs) For youth with autism spectrum disorders (ASDs) and their families, the transition from adolescence to emerging adulthood is a time of uncertainty and loss of entitlement to many services that were available while in the public school system under the Individuals with Disabilities Education Act (IDEA). In comparison to childhood, young adults with autism often experience a dearth of appropriate supports and opportunities. Leaving high school is associated with deterioration in ASD symptom presentation, increase in maladaptive behaviors, and worsening family functioning (Taylor and Seltzer 2010, 2011a, b). Many persons with ASD are able to work successfully within the community (GarciaVillamisar et al. 2000; Mawhood and Howlin 1999); however, the majority experience difficulty securing meaningful employment (Eaves and Ho 2008; Howlin et al. 2004; Newman et al. 2011). A recent study indicated that only 58% have ever worked for pay. In addition, it was found that only one in five individuals ever lived independently (Roux et al. 2015). Even among those employed, their jobs tended to be low level and low income (Cimera and Cowan 2009; Eaves and Ho 2008; Howlin et al. 2004). Jobs often ended prematurely because of social or behavioral difficulties or other work-related difficulties (Mawhood and Howlin 1999). Young adults on the autism spectrum are reported to experience difficulties in assuming other important adult roles and responsibilities, including attaining postsecondary education and living independently (Roux et al. 2015). A number of studies (Billstedt et al. 2005; GillespieLynch et al. 2012; Howlin et al. 2004) have found that having an IQ of around 70 seemed to be a critical cutoff point for better outcome (e.g., level of independence). A number of comorbid conditions can complicate the trajectory for youth with intellectual disability through adolescence and EA, such as seizure disorder, which substantially increase in prevalence from childhood through adolescence into adulthood. Adults with ASD and related conditions are more vulnerable to 131 anxiety and depression which can require specific treatments (Skokauskas and Gallagher 2010; White et al. 2009). The majority of young adults with ASD remain dependent on parents for support in living, recreation, and occupational situations. The availability of appropriate resources, services, and supports for the individuals with ASD and their families is key for successful transition into adulthood and better outcomes throughout adult life. The following elements are important for high-quality transition services and associated with improved young adult outcomes: (1) individualized services that reflect the strengths of the individual, (2) positive career development and early work experiences, (3) collaboration and interagency involvement, (4) family supports and expectations, (5) fostering self-determination and independence, (6) social and employment-related skill instruction, and (7) establishment of job-related supports. Therefore, outcomes for emerging adults with ASD could be improved if social and institutional supports available through adolescence continued into adulthood. 6.2 Type 1 Diabetes Mellitus (DM) The study of emerging adults with type 1 DM has elucidated the potential challenges facing emerging adults with diabetes and other chronic health conditions that have significant self-management burden. Emerging adults with type 1 diabetes face extensive behavioral demands in order to maintain their health, including the necessity for precisely scheduled daily insulin injections, blood glucose monitoring, dietary monitoring, regular physical exercise, and the management of DM-related complications. In early adulthood, frequent changes in roles, living situations, educational routines, jobs, friendships, and romantic relationships are common and can undermine the routines and the resolve needed to maintain metabolic control (Anderson and Wolpert 2004). The developmental tasks of emerging adulthood may be at odds with maintaining the intensive self-care that is required of those with type 1 diabetes and vice versa. For example, the tasks of D. Wood et al. 132 establishing autonomy and prevailing egocentrism may be associated with the emerging adult not wanting to follow medical advice or advice of his or her family (Masten et al. 1995). Heightened concern for peer acceptance or the establishment of intimate relationships is characteristic of this period of development. Emerging adults may be reluctant to admit to their significant others that they have type 1 diabetes or any other chronic condition that puts them at risk for being rejected (Madsen et al. 2002). Moreover, the major cognitive developmental milestone during the stage of emerging adulthood is the ability to think abstractly (Erikson 1994). Those who have not yet reached this developmental milestone may struggle with taking responsibility to maintain good disease management and understanding the consequences of poor disease management. Lastly, increased rates of drinking, illicit drug use, or other high-risk behaviors may disrupt the emerging adults’ lifestyle and negatively impact self-care management of diabetes, resulting in (Madsen et al. 2002) lack of control of diabetes and the resulting physical and mental consequences of hypo- or hyperglycemia, which undermine emerging adults’ abilities to perform in school, maintain relationships, or keep employment. As has been demonstrated in other chronic childhood conditions, the transition from pediatric- to adult-centered medical care for youth with DM is associated with a decline in DM control (Busse et al. 2007; Oeffinger et al. 2006). Adverse health-related outcomes documented in the young adult population with diabetes include a decline in disease self-care behaviors and an increased risk for diabetes-related complications (Bryden et al. 2001; Fredericks et al. 2010; Yeung et al. 2008). In addition, emerging adults with diabetes are at higher than average risk for psychosocial morbidity including social delays and isolation, impaired social competence, and emotional problems such as depression (Helgeson et al. 2007). As in the case in ASD, additional support for an emerging adult with DM during the transition to adult medical care has been shown to help decrease the adverse outcomes seen with usual practice. For example, DM tran- sition programs that include introductions to new adult care providers or intensive care coordination have been found to be protective (HolmesWalker et al. 2007). 6.3 Chronic Kidney Disease Chronic kidney disease (CKD) is a condition characterized by disease progression, significant cardiovascular morbidity, growth failure, neurocognitive impairment, and impaired quality of life changes (Sarnak 2003; Copelovitch et al. 2011; Gerson et al. 2006; McKenna et al. 2006). CKD is divided into five stages based on glomerular filtration rate, with stage 1 being the mildest form and stage 5 the worst form, also called endstage kidney disease (ESKD ), requiring renal replacement therapy (dialysis or transplant) (Hogg et al. 2003). The prevalence of pediatric CKD is unknown as it may be silent in early stages. The prevalence of pediatric ESKD is 15 cases per million population (Saran et al. 2015). African-Americans and Latinos are disproportionately affected by CKD in part due to a higher incidence of glomerular conditions (Ferris et al. 2006). While the 10-year survival for adolescentonset ESKD is 80–85% (much higher than adultonset ESKD patients), this still represents a 30-fold increase in mortality compared to the general US adolescent population. Survival is better for younger adolescents, males, Caucasians, Asians, and transplant recipients (2006). However, emerging adults with CKD achieve adult milestones (e.g., employment, marriage) less frequently than their healthy peers (Bartosh et al. 2003). The burden of care directly correlates with the stage of CKD. Based on the mean number of unique medications, those with CKD stages 1–4 take 6.2 ± 4 of medications, those with peritoneal dialysis (CKD 5) take 8.5 ± 2 medications, those with kidney transplant (CKD 5) take 9.7 ± 4.2 medications, and those on hemodialysis (CKD 5) take 11.3 ± 2.1 (So et al. 2011). The complexity of care also include procedures such as selfcatheterization several times per day, fluid and dietary restrictions, blood pressure measure- Emerging Adulthood as a Critical Stage in the Life Course ments daily, and injections (erythropoiesisstimulating agents once to thrice weekly, growth hormone daily, or insulin several times per day). The life course of pediatric-onset CKD-ESKD varies by etiology and age at onset of this condition, but once they reach ESKD, most patients share the same comorbidities (hypertension, anemia, acidosis, metabolic bone disease, and growth delay). Patients with greater level of CKD have a decreased sense of self-worth, perceive a poor future, and feel limited in their physical and psychosocial capacities to have the same potential and opportunity as their healthy peers. While most children and adolescent patients with ESKD will receive a kidney transplant, they likely will experience dialysis prior to receiving an organ. The most common cause of kidney transplant rejection in adolescents and emerging adults is treatment nonadherence (Andreoni et al. 2013). Adherence among adolescents is compromised by poor understanding and poor consequence recognition leading to an inconsistent commitment to the treatment regimens. Once the kidney transplant is lost, patients return to dialysis and likely will not receive another kidney transplant for many years. As in adult patients, cardiovascular disease (CVD) accounts for a majority of deaths in patients with pediatric-onset CKD, but unlike adults, pediatric-onset CKD patients rarely demonstrate symptomatic atherosclerosis (Shroff et al. 2011). Lastly, survivors of pediatric-onset CKD are at greater risk for malignancies and posttransplant diabetes mellitus (Koukourgianni et al. 2010). Patients who transfer to adult-focused services without transition preparation and support appear to be at greater risk to lose their kidney transplant (Watson 2000, 2005). The international societies of internal medicine and pediatric nephrology have published position statements and policies to promote health-care transition preparation (Watson 2005; Watson et al. 2011). Strategies to increase patient autonomy, healthcare transition, and self-management are needed to achieve successful outcomes at the time of transfer to adult-focused providers. 133 6.4 Mental Health and Substance Use Emerging adulthood is a time of increased experimentation with tobacco, drugs, and alcohol (Schulenberg et al. 2004; Schulenberg and Maggs 2002), increasing from rates of 12.2% in adolescence to 40.2% in young adulthood for cigarette smoking, from 10.7% to 41.9% for binge drinking, from 11.2% to 20.3% for illicit drug use, and from 2.6% to 14.9% for heavy alcohol use. While cigarette, alcohol, and drug use are normative during adolescence and emerging adulthood, a number of factors predict excessive use rather than experimentation including genetic (e.g., family history of alcohol abuse), biological (e.g., early puberty timing), family (e.g., low parental monitoring), school (e.g., low grade average, school failure), peer (e.g., peer substance use), and youth personality and psychopathology (e.g., depression) factors (Cicchetti 1999). However, the pattern of use/abuse of these substances is influenced by the experience during the stage of EA. For example, increasing responsibility, work, and marriage all are associated with decreased use of these substances (Masten et al. 1999; Bachman et al. 2014). Again, there is great heterogeneity in the trajectories followed by individuals with regard to substance use during this period, all influenced by the multiplicity of factors listed above. Among young adults, 13.7% experience serious mental illness such as major depressive disorders, schizophrenia, and bipolar disorder (AmericanPsychiatricAssociation 2013). The neuropsychological pathology that has onset during EA has many of the predisposing factors related to family adversities, such as social disadvantage, divorce, dysfunctional parenting, multiple family changes/moves, and peer group choices. The expanded independence and selfreliance during EA may be difficult to handle for youth with preexisting emotional or social challenges or poor attachment. The decrease in supports from school or family during EA may be even further destabilizing, leading to increased emotional and interpersonal difficulties. Young D. Wood et al. 134 people with serious mental health conditions have difficulty with impulse control and self-regulation (Walker and Gowen 2011). Their apparent immaturity reflects a delay in social and emotional development and undermines progress toward goals in education, relationships, independence in health, and health-care management. Three-quarters of lifetime cases of mental, emotional, and behavioral disorders begin by age 24 (Kessler et al. 2005). In emerging adults with social, emotional, and behavioral disorders, much higher risk is associated with onset in early adolescence versus onset in late adolescence (Mofitt and Caspi 2001). Dysfunctional family environments and participation in deviant peer group increase the likelihood of early-onset delinquency followed by a long trajectory of social, emotional, and behavioral problems. Youth with later onset of social, emotional, and behavioral problems are more commonly from relatively healthy family backgrounds and more likely to desist from delinquency by age 23 (Roisman et al. 2004). Similar patterns can be seen for major psychiatric disorders such as depression, with early-onset mood disorders being representative of more severe recurrent forms of mood disorders arising from increased severity and complexity of family and biologic risk factors (Fergusson and Woodward 2002) and later-onset disorders reflecting lower levels of risk factors in childhood. 7 Protective and Risk Factors That Impact Emerging Adulthood The supports, opportunities, and experiences that occur during the stage of EA greatly influence the ultimate outcome of this stage and the ensuing life trajectory into young adulthood. As discussed in this chapter, critical influences during EA include mentoring relationships, socioeconomic supports, and educational and vocational opportunities. Emerging adulthood typically is accompanied by an increase in agency/independence and decrease in social and institutional support (e.g., schools and family), such that most indi- viduals have more choice in day-to-day activities and life decisions. This increase in choice may result in greater success which will likely contribute to well-being. However, increased freedom can result in poor choices (e.g., drug use, criminal activity) that have a negative impact on the developmental trajectory as well (Schulenberg and Maggs 2002; Schulenberg et al. 2004). For example, many relationships and other aspects of life are so unstable during EA that important and powerful negative experiences can occur such as date rape, school failure, and substance addiction. These experiences can have a lasting, if not lifetime, detrimental impact on the developmental and mental health trajectory of the emerging adult. Similarly, many potential powerful positive influences can occur during this period in life (e.g., marriage, educational achievement, mentoring) that can enhance the developmental and mental health trajectory. Therefore, experiences both distal (early childhood) and proximal (during emerging adulthood) can influence, either positively or negatively, the developmental and mental health trajectory of the emerging adult (Miller et al. 2015). Practical supports during EA provided by families include financial support for education, transportation, independent living, health insurance, and other necessities. The primary determinant of parental support are the level of parental resources. Emerging adults with wealthier parents and those with fewer siblings receive more financial support for education (Steelman and Powell 1991). Divorced parents provide less financial support than parents with intact marriages (Cooney and Uhlenberg 1992). Support from families by parents and grandparents during EA is also provided in the form of guidance or advice, information, and emotional support. Parents of emerging adults may also help the emerging adult negotiate the health-care system, formulate and carry out educational and vocational goals, and acquire leases for apartments and even provide advice for intimate relationships. In short, continued social and financial connection to parents with adequate emotional and financial resources can be a very significant factor in the positive life trajectory during Emerging Adulthood as a Critical Stage in the Life Course emerging adulthood (Aquilino 2005). Previous studies have found a high correlation between improved EA outcomes and adolescent hope in the future (Bennett 2014). Hope is a form of personal capital that protects youth from social, emotional, and behavioral disorders and correlates with and predicts the rates of many outcomes, including academic achievement, delay of initiation of early sexual activity, and lower likelihood of violent behavior. Hope requires a sense of self-efficacy and knowledge of alternate outcomes. Hope is reflected in adolescent and young adult goals as they reflect and make ongoing assessments of their current and future selves. Individual goals and aspirations relate to the sense of control an individual feels in relation to a particular domain. Positive youth development (PYD) programs are approaches that provide supports to adolescents and emerging adults to achieve social, emotional, behavioral, and moral competence, foster self-efficacy, a clear and positive identity, and belief in the future; and reinforce pro-social norms (Catalano et al. 2004). Positive development approaches involve a paradigm shift from targeting the risk factors to enhancing the assets and protective factors. The emphasis on hope, empowerment, and well-being resonates with youth and mental health professionals. The PYD approach outlined above suggests that effective programs to support EA would include an individualized approach supporting young people to identify and move toward personally meaningful goals. This begins with envisioning a positive future identity (“Who do I want to become?”). Pursuing goals promotes a sense of purpose, and making progress toward those goals contributes to building feelings of efficacy, empowerment, and self-determination, enhancing the individual’s ability to act as the primary causal agent in pursuing personally meaningful goals (Silbereisen and Lerner 2007). A PYD perspective further suggests that youth and emerging adults will benefit from learning specific strategies for increasing and maintaining interpersonal support from positive peers, family, providers, and people in the community (Guerra and Bradshaw 2008). Young people can learn 135 specific steps and skills that can help them increase the quality and the extent of their interpersonal networks, as well as the amount of emotional, instrumental, and informational support available to them. Using a PYD perspective suggests that the development of assets is an important recovery-oriented outcome in and of themselves, as well as a mediator of longer-term outcomes related to education, employment, mental health, and general quality of life. Indeed, a review of the available research on communitybased programs and interventions for EA reveals a common focus on personal asset building (Lerner 2014). Also consistent with the PYD perspective is that many of the programs and interventions include a focus on changing the meso-environment of youth so that it encourages young people to develop or express strengths and assets. 8 Services and Supports As we better understand the limits of and issues related to EA, it is imperative that we review and revise policy and current social and medical services and supports so they optimally address the current and future needs of individuals during this life stage. Current medical, mental health, and social service delivery models are geared toward either child or adult populations. In other words, individuals generally seek child-serving services between the ages of 0 and 18, and they are subsequently transferred directly to adultserving services. However, the various biobehavioral and sociocultural factors discussed above make this direct transition from child to adultcentered care ill-suited for optimally addressing the nuances and challenges that individuals may face during EA. For example, an adult-centered medical doctor may regularly treat patients with fixed habits and lifestyles, who may already suffer from a variety of chronic health conditions. However, many such chronic health conditions in adults represent a culmination of experiences from an individual’s earlier years, which may not yet be apparent during EA. It is thus strongly recommended that EA service providers reorient 136 themselves using a prevention-based paradigm and familiarize themselves with the possible antecedents to social, emotional, and medical conditions so that they may make health-care or social service recommendations to minimize risk factors and to maximize strengths and assets to facilitate development along optimal health trajectories. EA presents a unique opportunity for service providers to intervene and influence an individual’s LCHD before significant social, emotional, and medical pathology fully develops, utilizing existing knowledge of the skills and capacities associated with EA to inform services and interventions. In addition to training service providers to reorient their approach to care and become more sensitive to antecedents of chronic health conditions in adulthood, the multifactorial nature of the LCHD model suggests that it would be important for service providers to consider horizontal (i.e., cross sector) and longitudinal (i.e., across the lifespan) service integration. Specifically, the current model posits that myriad factors (e.g., biological, psychological, cultural) on multiple levels (e.g., micro, meso, macro) interact simultaneously in a transactional fashion to influence an individual’s LCHD and overall health trajectory. However, most current training models which emphasize specialization in their independent fields and opportunities for crossdisciplinary case conceptualization and collaboration with regard to service delivery – particularly with respect to preventive care, which may be of special importance when considering EA as a sensitive period of development – tend to be rare. Such specialization in service delivery may result in service fragmentation. Additionally, adult service providers generally do not work in tandem with child service providers as individuals transition from one care setting to the next, consequently resulting in potential gaps in service delivery and care, which may be exacerbated as individuals work to rebuild rapport with their new service providers. Moreover, health, mental health, social service, and education professionals rarely collaborate to create comprehensive models across the EA stage. Clearly, the current service delivery infrastructure does not ade- D. Wood et al. quately support the needs and challenges unique to emerging adult populations. It is thus important to recognize emerging adults as a unique population requiring additional and specialized skills of service providers and the creation of specific pathways for transition which support continuity of care. The larger sociopolitical and cultural contexts may also facilitate or create challenges which impact service and intervention efforts directed at emerging adults. The majority of individuals in developed nations transitioning through EA, for instance, may be classified as “digital natives,” individuals who have been brought up in environments where exposure to digital technology may be normative and who are therefore familiar with computers and the Internet. As emerging adults increasingly turn to digital and mobile solutions to support and enhance their daily routines, it behooves service providers to creatively explore ways of engaging emerging adult populations using technology. Use of social networks, such as Instagram, Twitter, and Facebook, to advance primary prevention efforts, increase awareness of public health issues, and provide basic health and psychological education affords service providers the opportunity to outreach to unprecedented numbers and populations. Use of text messaging may also be effective as a way of engaging with digital native emerging adult populations and promoting engagement and adherence by emerging adults or to promote youth development programs. Also, as service providers keep pace with technological advancements, they may even begin to explore novel methods of service delivery and intervention using digital or Internetbased platforms, which may increase “buy-in” and potentially preventive service utilization, among digitally native emerging adult populations. For example, service providers might potentially leverage the data that digitally native emerging adults might collect as part of the “quantified self” movement (e.g., pedometer information, weight, blood pressure, etc.) in an effort to inform their ongoing care efforts. On the other hand, due to clinical billing practices and regulations, as well as patient privacy concerns and regulations (e.g., Health Insurance Emerging Adulthood as a Critical Stage in the Life Course Portability and Accountability Act, HIPAA), current service delivery models often tend to operate in a fragmented and siloed fashion, which in turn may limit the ability of service providers to develop a holistic treatment plan to address each individual’s needs. Further complicating this fragmentation is the restricted access to health records once an individual turns 18 years of age, when parents who may have been primarily responsible for managing their child’s healthcare needs no longer are able to access their child’s medical records (unless explicit permission is granted by the child, who is now an emerging adult). Depending on each family’s cultural context and the relationship between the emerging adult and his or her parents, this transition may be a cause for significant stress and/or strife between family members as they work to renegotiate previously familiar boundaries. Mothers of children with diabetes, for example, have indicated feeling increased stress as their children transitioned from pediatric to adult care, particularly if they perceived that their children were not managing their health-care condition as well as they had been when it was previously under greater parent control (Allen et al. 2011). Such stress might result in familial conflict and dysfunction which could subsequently interfere with the emerging adult’s willingness or ability to adequately access services. One potential solution to overcoming the issue of patient privacy may be to implement public health programs and tools to educate and empower individuals with respect to managing and taking an active role in their own care. However, it is important to underscore the need for larger, overall systems to change in order to support service providers in working together with individuals and their families to ensure continuity and comprehensiveness of services and health-related information during the transition. Finally, the implementation of health-care policies may have important implications for service and intervention delivery with an emerging adult population. With the introduction of the Patient Protection and Affordable Care Act of 2010 (ACA), for example, individuals may now remain on their parents’ health insurance plans 137 until they turn 26 years of age. While this theoretically affords emerging adults the opportunity to develop the necessary skills for interacting with the health-care system and managing their own care, research indicates that emerging adults frequently perceived themselves as being at lower risk for health problems and the financial burden of health insurance as lacking in value. Consequently, emerging adults may opt for highdeductible catastrophic insurance which generally costs less compared to other health insurance plans, but translates to fewer opportunities for interaction with the health service sector and fewer opportunities for service providers to intervene early during an individual’s LCHD trajectory prior to the onset of chronic adult disease. Importantly, research indicates that despite preventive maintenance health visits being mandatory under the ACA, emerging adults were less likely to request health maintenance visits. Thus, it may be important for public health efforts to determine the underlying factors associated with this diminished utilization of health services by emerging adults and subsequently develop health literacy programs to promote prevention mindedness and service utilization in emerging adults. 9 Recommendations for Research Priorities The following is a list of issues in life course research that are critical for better understanding the positive supports and challenges that influence the trajectory of development of emerging adults organized along the macro-, meso-, micro-framework. Macro Issues • What community supports in emerging adulthood enhance health development into adulthood and improve outcomes for an emerging adult with specific health, mental health, and developmental conditions? Is there an “early intervention model” to optimize outcomes for EA? What model should be used for an D. Wood et al. 138 • • • • emerging adult with chronic health, mental health, and developmental conditions? What are the historical, economic, and cultural influences on the meaning of EA, adaption, and maladaptation to the stage and expectations for normative transitions? How have these changed over time? How are they different for an emerging adult with social, health, mental health, and developmental problems? How have the delays in marriage/relationship commitment impacted adult life trajectories? What are the racial and ethnic differences for how these factors interact to support development during EA? How has the information economy and the increased demands for education impacted the life course for youth in upper, middle, and lower socioeconomic strata? Are there regional variations in these outcomes and how can they be improved? • • • • • • • Meso Issues • Earlier childhood development and family environment are predictors of adult outcomes, but it Fig. 1 Moving toward resilience: a model of positive change in emerging adulthood is unclear how optimizing health development can be achieved with supports during EA. What is the interaction between health and social system supports, family supports, youth abilities, cognitive and psychological maturity, and EA outcomes for an emerging adult with and without mental or physical disabilities? How can the different support systems be integrated to provide seamless services to youth and emerging adults with significant physical, mental health, or intellectual disabilities? Using the model below from the work of Masten et al. (2005), how do these different resources, supports, and personal characteristics interact to promote optimal development during EA? (Fig. 1) What factors promote resilience for youth during EA who lack family supports? What is the role of extended families in support for EA? How can studies of conditions and processes that influence development during EA inform educational/vocational/EA support policies? How can data be gathered across this stage in the lifespan given that emerging adults leave school and have many changes in living arrangements, education, relationships, and work? RESILIENCE IN EMERGING ADULTHOOD Love Work Opportunities Transformational contexts School Good fortune Mentors Timing Adult support Hope Capacity for Change Future orientation Self-direction Self-efficacy Motivation Aspiration Agency Strategic Executive Control Planfulness Emerging Adulthood as a Critical Stage in the Life Course Micro Issues • To what degree is cognitive, social, physical, moral, and spiritual development mediated during emerging adulthood? How are these developmental pathways impacted by chronic health, mental health, and developmental conditions? • How can we optimize the person-context match to promote development during EA with and without chronic health, mental health, and developmental conditions? • What is the impact of positive turning points in EA compared to early life stages on life trajectories into adulthood? • During EA, exploration of new experiences is normative. Is more exploration better or worse and for which activities is it better or worse? How is this equation different for an emerging adult with chronic health, mental health, and developmental conditions? • Do emerging adults with particular health conditions, such as diabetes, spina bifida, solid organ transplant, or cancer survivorship, experience sharply downward trajectories during EA, and what supports are effective to prevent these sharply downward trajectories? • How does brain development progress during EA? What is the impact of brain development on the factors that influence the developmental trajectory during EA such as executive control (planfulness, future orientation), motivation, self-efficacy, and hope? References Allen, D., Channon, S., Lowes, L., Atwell, C., & Lane, C. (2011). Behind the scenes: The changing roles of parents in the transition from child to adult diabetes service. Diabetic Medicine, 28(8), 994–1000. Alwin, D. F. (1988). From obedience to autonomy changes in traits desired in children, 1924–1978. Public Opinion Quarterly, 52, 33–52. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders. Arlington: American Psychiatric Publishing. Anderson, B. J., & Wolpert, H. A. (2004). A developmental perspective on the challenges of diabetes educa- 139 tion and care during the young adult period. Patient Education and Counseling, 53, 347–352. Andreoni, K. A., Forbes, R., Andreoni, R. M., Phillips, G., Stewart, H., & Ferris, M. (2013). Age-related kidney transplant outcomes: Health disparities amplified in adolescence. JAMA Internal Medicine, 173, 1524–1532. Aquilino, W. S. (1994). Impact of childhood family disruption on young Adults’ relationships with parents. Journal of Marriage and the Family, 56, 295–313. Aquilino, W. S. (2005). Impact of family structure on parental attitudes toward the economic support of adult children over the transition to adulthood. Journal of Family Issues, 26, 143–167. Arnett, J. J. (1997). Young People’s conceptions of the transition to adulthood. Youth & Society, 29, 3–23. Arnett, J. J. (1998). Learning to stand alone: The contemporary American transition to adulthood in cultural and historical context. Human Development, 41, 295–315. Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55, 469. Arnett, J. J. (2005). The developmental context of substance use in emerging adulthood. Journal of Drug Issues, 35, 235–254. Arnett, J. J. (2014). Emerging adulthood: The winding road from the late teens through the twenties. New York: Oxford University Press. Bachman, J. G., O’malley, P. M., Schulenberg, J. E., Johnston, L. D., Bryant, A. L., & Merline, A. C. (2014). The decline of substance use in young adulthood: Changes in social activities, roles, and beliefs. New York: Psychology Press. Bartosh, S. M., Leverson, G., Robillard, D., & Sollinger, H. W. (2003). Long-term outcomes in pediatric renal transplant recipients who survive into adulthood. Transplantation, 76, 1195–1200. Bellah, E. R. N., Bellah, R. N., Tipton, S. M., Sullivan, W. M., Madsen, R., Swidler, A., Sullivan, W. M., & Tipton, S. M. (2007). Habits of the heart: Individualism and commitment in American life. Berkeley: University of California Press. Ben-Amos, I. K. (1994). Adolescence and youth in early modern England. New Haven: Yale University Press. Bennett, A. (2014). Finding hope in hopeless environments. International Journal of Child Health And Human Development, 7, 313. Benson, J. E., & Elder, G. H. (2011). Young adult identities and their pathways: A developmental and life course model. Developmental Psychology, 47, 1646–1657. Benson, J. E., Johnson, M. K., & Elder, G. H. (2012). The implications of adult identity for educational and work attainment in young adulthood. Developmental Psychology, 48(6), 1752. Billstedt, E., Gillberg, C., & Gillberg, C. (2005). Autism after adolescence: Population-based 13-to 22-year follow-up study of 120 individuals with autism diagnosed in childhood. Journal of Autism and Developmental Disorders, 35, 351–360. Bogin, B. (2013). Chapter 2, Childhood, adolescence and longevity: A chapter on human Evolutionary 140 life history. In B. Hewlett (Ed.), Adolescent identify: Evolutionary, cultural and developmental perspectives. New York: Routledge. Brody, G. H., et al. (2013). Is resilience only skin deep? Rural African Americans’ socioeconomic status–related risk and competence in preadolescence and psychological adjustment and Allostatic load at age 19. Psychological Science. doi:10.1177/0956797612471954. Bryden, K. S., Peveler, R. C., Stein, A., Neil, A., Mayou, R. A., & Dunger, D. B. (2001). Clinical and psychological course of diabetes from adolescence to young adulthood a longitudinal cohort study. Diabetes Care, 24, 1536–1540. Busse, F., Hiermann, P., Galler, A., Stumvoll, M., Wiessner, T., Kiess, W., & Kapellen, T. (2007). Evaluation of patients’ opinion and metabolic control after transfer of young adults with type 1 diabetes from a pediatric diabetes clinic to adult care. Hormone Research in Pædiatrics, 67, 132–138. Catalano, R. F., Berglund, M. L., Ryan, J. A., Lonczak, H. S., & Hawkins, J. D. (2004). Positive youth development in the United States: Research findings on evaluations of positive youth development programs. The Annals of the American Academy of Political and Social Science, 591, 98–124. Cherlin, A. J., Chase-Lansdale, P. L., & Mcrae, C. (1998). Effects of parental divorce on mental health throughout the life course. American Sociological Review, 63(2), 239–249. Cicchetti, D., et al. (1999). A developmental psychopathology perspective on drug abuse. Drug abuse: Origins & interventions (pp. 97–117). Washington, DC: American Psychological Association, xxiii, 492 pp. Cimera, R. E., & Cowan, R. J. (2009). The costs of services and employment outcomes achieved by adults with autism in the us. Autism, 13, 285–302. Cooney, T. M., & Uhlenberg, P. (1992). Support from parents over the life course: The adult Child's perspective. Social Forces, 71, 63–84. Copelovitch, L., Warady, B. A., & Furth, S. L. (2011). Insights from the chronic kidney disease in children (Ckid) study. Clinical Journal of the American Society of Nephrology, 6, 2047–2053. Cote, J. E. (2014). The dangerous myth of emerging adulthood: An evidence-based critique of a flawed developmental theory. Applied Developmental Science, 18(4), 177–188. Dahl, R. E. (2004). Adolescent brain development: A period of vulnerabilities and opportunities. Keynote address. Annals of the New York Academy of Sciences, 1021, 1–22. Duncan, G. J., & Brooks-Gunn, J. (2000). Family poverty, welfare reform, and child development. Child Development, 71, 188–196. Eaves, L. C., & Ho, H. H. (2008). Young adult outcome of autism Spectrum disorders. Journal of Autism and Developmental Disorders, 38, 739–747. D. Wood et al. Erikson, E. H. (1994). Identity: Youth and crisis. New York: Ww Norton & Company. Evans, G. W., & Cassells, R. C. (2014). Childhood poverty, cumulative risk exposure, and mental health in emerging adults. Clinical Psychological Science, 2(3), 287–296. Feliciano, C., & Ashtiani, M. (2012). Postsecondary educational pathways of low-income youth: An analysis of add health data. Irvine: Uc/Accord. Fergusson, D. M., & Woodward, L. J. (2002). Mental health, educational, and social role outcomes of adolescents with depression. Archives of General Psychiatry, 59, 225–231. Ferris, M. E., Gipson, D. S., Kimmel, P. L., & Eggers, P. W. (2006). Trends in treatment and outcomes of survival of adolescents initiating end-stage renal disease care in the United States of America. Pediatric Nephrology, 21, 1020–1026. Fredericks, E. M., Dore-Stites, D., Well, A., Magee, J. C., Freed, G. L., Shieck, V., & James Lopez, M. (2010). Assessment of transition readiness skills and adherence in pediatric liver transplant recipients. Pediatric Transplantation, 14, 944–953. Furstenberg, F. F. (2002). How it takes thirty years to do a study. In Looking at lives: American longitudinal studies of the twentieth century (pp. 37–57). Galobardes, B., Lynch, J. W., & Smith, G. D. (2008). Is the Association between childhood socioeconomic circumstances and cause-specific mortality established? Update of a systematic review. Journal of Epidemiology and Community Health, 62, 387–390. Garcia-Villamisar, D., Ross, D., & Wehman, P. (2000). Clinical differential analysis of persons with autism in a work setting: A follow-up study. Journal Of Vocational Rehabilitation, 14, 183–185. Gerson, A. C., Butler, R., Moxey-Mims, M., Wentz, A., Shinnar, S., Lande, M. B., Mendley, S. R., Warady, B. A., Furth, S. L., & Hooper, S. R. (2006). Neurocognitive outcomes in children with chronic kidney disease: Current findings and contemporary endeavors. Mental Retardation and Developmental Disabilities Research Reviews, 12(3), 208–215. Gillespie-Lynch, K., Sepeta, L., Wang, Y., Marshall, S., Gomez, L., Sigman, M., & Hutman, T. (2012). Early childhood predictors of the social competence of adults with autism. Journal of Autism and Developmental Disorders, 42, 161–174. Gilman, S. E. (2012). The successes and challenges of life course Epidemiology: A commentary on Gibb, Fergusson and Horwood (2012). Social Science & Medicine, 75, 2124–2128. Gilman, S. E., & Loucks, E. B. (2012). Invited commentary: Does the childhood environment influence the Association between every X and every Y in adulthood? American Journal of Epidemiology, 176, 684–688. Gilmore, D. D. (1990). Manhood in the making: Cultural concepts of masculinity. New Haven: Yale University Press. Emerging Adulthood as a Critical Stage in the Life Course Greene, A., Wheatley, S. M., & Aldava, J. F. (1992). Stages on Life’s way Adolescents’ implicit theories of the life course. Journal of Adolescent Research, 7, 364–381. Grotevant, H. D., & Cooper, C. R. (1986). Individuation in family relationships. Human Development, 29, 82–100. Guerra, N. G., & Bradshaw, C. P. (2008). Linking the prevention of problem behaviors and positive youth development: Core competencies for positive youth development and risk prevention. New Directions for Child and Adolescent Development, 2008, 1–17. Halfon, N., & Forrest, C. B. (2017). The emerging theoretical framework of life course health development. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health-development science. Cham: Springer. Helgeson, V. S., Snyder, P. R., Escobar, O., Siminerio, L., & Becker, D. (2007). Comparison of adolescents with and without diabetes on indices of psychosocial functioning for three years. Journal of Pediatric Psychology, 32, 794–806. Hogg, R. J., Furth, S., Lemley, K. V., Portman, R., Schwartz, G. J., Coresh, J., Balk, E., Lau, J., Levin, A., & Kausz, A. T. (2003). National Kidney Foundation’s kidney disease outcomes quality initiative clinical practice guidelines for chronic kidney disease in children and adolescents: Evaluation, classification, and stratification. Pediatrics, 111, 1416–1421. Holmes-Walker, D., Llewellyn, A., & Farrell, K. (2007). A transition care Programme which improves diabetes control and reduces hospital admission rates in young adults with type 1 diabetes aged 15–25 years. Diabetic Medicine, 24, 764–769. Howlin, P., Goode, S., Hutton, J., & Rutter, M. (2004). Adult outcome for children with autism. Journal of Child Psychology and Psychiatry, 45, 212–229. Jacquet, S. E., & Surra, C. A. (2001). Parental divorce and premarital couples: Commitment and other relationship characteristics. Journal of Marriage and Family, 63, 627–638. Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., & Walters, E. E. (2005). Lifetime prevalence and age-of-onset distributions of Dsm-iv disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 593–602. Kim, P., Neuendorf, C., Bianco, H., & Evans, G. W. (2015). Exposure to childhood poverty and mental health symptomatology in adolescence: A role of coping strategies. Stress and Health, 32, 494–502. Koukourgianni, F., Harambat, J., Ranchin, B., Euvrard, S., Bouvier, R., Liutkus, A., & Cochat, P. (2010). Malignancy incidence after renal transplantation in children: A 20-year single-Centre experience. Nephrology Dialysis Transplantation, 25, 611–616. Lerner, R. M. (2014). Developmental science, developmental systems, and contemporary theories of human development. In Handbook of child psychology. New York: Wiley and Sons. 141 Lotstein, D. S., Inkelas, M., Hays, R. D., Halfon, N., & Brook, R. (2008). Access to care for youth with special health care needs in the transition to adulthood. Journal of Adolescent Health, 43, 23–29. Luciana, M., Conklin, H. M., Hooper, C. J., & Yarger, R. S. (2005). The development of nonverbal working memory and executive control processes in adolescents. Child Development, 76, 697–712. Madsen, S. D., Roisman, G. I., & Collins, W. A. (2002). The intersection of adolescent development and intensive intervention: Age-related psychosocial correlates of treatment regimens in the diabetes control and complication trial. Journal of Pediatric Psychology, 27, 451–459. Masten, A. S., & Coatsworth, J. D. (1998). The development of competence in favorable and unfavorable environments: Lessons from research on successful children. American Psychologist, 53, 205. Masten, A. S., Coatsworth, J. D., Neemann, J., Gest, S. D., Tellegen, A., & Garmezy, N. (1995). The structure and coherence of competence from childhood through adolescence. Child Development, 66, 1635–1659. Masten, A. S., Hubbard, J. J., Gest, S. D., Tellegen, A., Garmezy, N., & Ramirez, M. (1999). Competence in the context of adversity: Pathways to resilience and maladaptation from childhood to late adolescence. Development and Psychopathology, 11, 143–169. Masten, A. S., Burt, K. B., Roisman, G. I., Obradovic, J., Long, J. D., & Tellegen, A. (2004). Resources and resilience in the transition to adulthood: Continuity and change. Development and Psychopathology, 16, 1071–1094. Masten, A. S., Roisman, G. I., Long, J. D., Burt, K. B., Obradović, J., Riley, J. R., Boelcke-Stennes, K., & Tellegen, A. (2005). Developmental cascades: Linking academic achievement and externalizing and internalizing symptoms over 20 years. Developmental Psychology, 41, 733. Masten, A. S., Burt, K. B., & Coatsworth, J. D. (2006). Competence and psychopathology in development. D. Cicchetti, C. Dante, & J. Donald (Eds). Developmental psychopathology: Risk, disorder, and adaptation (Vol. 3, 2nd ed., pp. 696–738). Hoboken: John Wiley & Sons Inc, xvi 944 pp. Mawhood, L., & Howlin, P. (1999). The outcome of a supported employment scheme for high-functioning adults with autism or Asperger syndrome. Autism, 3, 229–254. Mckenna, A. M., Keating, L. E., Vigneux, A., Stevens, S., Williams, A., & Geary, D. F. (2006). Quality of life in children with chronic kidney disease—Patient and caregiver assessments. Nephrology Dialysis Transplantation, 21, 1899–1905. Mikulincer, M., & Shaver, P. R. (2009). The attachment and behavioral systems perspective on social support. Journal of Social and Personal Relationships, 26(1), 7–19. Miller, G. E., Yu, T., Chen, E., & Brody, G. H. (2015 Aug 18). Self-control forecasts better psychosocial outcomes 142 but faster epigenetic aging in low-Ses youth. Proceedings Of The National Academy Of Sciences., 112(33), 10325–10330. Modell, J. (1991). Into One’s own: From youth to adulthood in the United States, 1920–1975. Berkeley: University of California Press. Moffitt, T. E., & Caspi, A. (2001). Childhood predictors differentiate life-course persistent and adolescencelimited antisocial pathways among males and females. Devcelopment and Psychopathology, 13, 355–375. Nelson, C. A., Thomas, K. M., & De Haan, M. (2012). Neuroscience of cognitive development: The role of experience and the developing brain. Hoboken: John Wiley & Sons. Newman, L., Wagner, M., Knokey, A.-M., Marder, C., Nagle, K., Shaver, D., & Wei, X. (2011). The post-high school outcomes of young adults with disabilities up to 8 years after high school: A Report from the National Longitudinal Transition Study-2 (Nlts2). Ncser 2011– 3005. Washington, DC: National Center For Special Education Research. Nurmi, J.-E. (1999). 15 self-definition and mental health during adolescence and young adulthood. In J. Schulenberg, J. L. Maggs, & K. Hurrelmann (Eds.), Health risks and developmental transitions during adolescence (p. 395). New York: Cambridge University Press. Oeffinger, K. C., Mertens, A. C., Sklar, C. A., Kawashima, T., Hudson, M. M., Meadows, A. T., Friedman, D. L., Marina, N., Hobbie, W., & Kadan-Lottick, N. S. (2006). Chronic health conditions in adult survivors of childhood cancer. New England Journal of Medicine, 355, 1572–1582. Osgood, D. W., Ruth, G., Eccles, J. S., Jacobs, J. E. & Barber, B. L. (2005). Six paths to adulthood: Fast starters, parents without careers, educated partners, educated singles, working singles, and slow starters. na. Reiss, J. G., Gibson, R. W., & Walker, L. R. (2005). Health care transition: Youth, family, and provider perspectives. Pediatrics, 115, 112–120. Rifkin, J. (2011). The Thired industrial revolution. New York: St. Martin’s Press. Roisman, G. I., Aguilar, B., & Egeland, B. (2004). Antisocial behavior in the transition to adulthood: The independent and interactive roles of developmental history and emerging developmental tasks. Development and Psychopathology, 16, 857–871. Rotundo, E. A. (1994). American manhood: Transformations in masculinity from the revolution to the modern era. New York: Basic Books. Roux, A. M., Shattuck, P. T., Rast, J. E., Rava, J. A., & Anderson, K.,. A. (2015). National autism indicators report: Transition into young adulthood. Philadelphia: Life Course Outcomes Research Program/A.J. Drexel Autism Institute/Drexel University. Ryan, R. M., & Lynch, J. H. (1989). Emotional autonomy versus detachment: Revisiting the vicissitudes of adolescence and young adulthood. Child Development, 60(2), 340–356. Saran, R., Li, Y., Robinson, B., Ayanian, J., Balkrishnan, R., Bragg-Gresham, J., Chen, J., Cope, E., Gipson, D. Wood et al. D., & He, K. (2015). US renal data system 2014 annual data Report: Epidemiology of kidney disease in the United States. American Journal of Kidney Diseases: The Official Journal of the National Kidney Foundation, 65, A7. Sarnak, M. J. (2003). Cardiovascular complications in chronic kidney disease. American Journal of Kidney Diseases, 41, 11–17. Scheer, S. D., Unger, D. G., & Brown, M. B. (1996). Adolescents becoming adults: Attributes for adulthood. Adolescence, 31, 127. Schlegel, A., & Barry Iii, H. (1991). Adolescence: An anthropological inquiry. New York: Free Press. Schulenberg, J. E., & Maggs, J. L. (2002). A developmental perspective on alcohol use and heavy drinking during adolescence and the transition to young adulthood. Journal of Studies on Alcohol, Supplement No. 14, 54–70. Schulenberg, J. E., Sameroff, A. J., & Cicchetti, D. (2004). The transition to adulthood as a critical juncture in the course of psychopathology and mental health. Development and Psychopathology, 16, 799–806. Shanahan, M. J. (2000). Pathways to adulthood in changing societies: Variability and mechanisms in life course perspective. Annual Review of Sociology, 26, 667–692. Shroff, R., Weaver, D. J., & Mitsnefes, M. M. (2011). Cardiovascular complications in children with chronic kidney disease. Nature Reviews Nephrology, 7, 642–649. Silbereisen, R. K., & Lerner, R. M. (2007). Approaches to positive youth development. Los Angeles/London: Sage. Skokauskas, N., & Gallagher, L. (2010). Psychosis, affective disorders and anxiety in autistic Spectrum disorder: Prevalence and Nosological considerations. Psychopathology, 43, 8–16. So, T.-Y., Bradley Layton, J., Bozik, K., Farrington, E., Gipson, P. E., Gibson, K., Primack, W., Conley Iii, W., Gipson, D. S., & Ferris, M. (2011). Cognitive pharmacy services at a pediatric nephrology and hypertension clinic. Renal Failure, 33, 19–25. Spear, L. P. (2000). The adolescent brain and agerelated behavioral manifestations. Neuroscience & Biobehavioral Reviews, 24(4), 417–463. Steelman, L. C., & Powell, B. (1991). Sponsoring the next generation: Parental willingness to pay for higher education. American Journal of Sociology, 96(6), 1505–1529. Steinberg, L., Dahl, R., Keating, D., Kupfer, D. J., Masten, A. S., & Pine, D. S. (2006). The study of developmental psychopathology in adolescence: Integrating affective neuroscience with the study of context. In D. Cicchetti & D. Cohen (Eds.), Handbook of developmental psychopathology (Vol. 2, pp. 710–741). New York: Wiley. Taylor, J. L., & Seltzer, M. M. (2010). Changes in the autism behavioral phenotype during the transition to adulthood. Journal of Autism and Developmental Disorders, 40, 1431–1446. Taylor, J. L., & Seltzer, M. M. (2011a). Changes in the mother–child relationship during the transition to Emerging Adulthood as a Critical Stage in the Life Course adulthood for youth with autism Spectrum disorders. Journal of Autism and Developmental Disorders, 41, 1397–1410. Taylor, J. L., & Seltzer, M. M. (2011b). Employment and post-secondary educational activities for young adults with autism Spectrum disorders during the transition to adulthood. Journal of Autism and Developmental Disorders, 41, 566–574. Wadsworth, M. E., et al. (2016). Poverty and the development of psychopathology. In: M. E. Wadsworth, G. W. Evans, K. Grant, J. S. Carter, & S. Duffy (Eds.), Development and psychopathology.Volume Four. Risk, resilience, and intervention, Ch 8 (pp. 215–237). Hoboken: John Wiley & Sons. Walker, J. S., & Gowen, L. K. (2011). Community-based approaches for supporting positive development in youth and young adults with serious mental health conditions. Portland: Research and Training Center for Pathways to Positive Futures, Portland State University. Watson, A. R. (2000). Non-compliance and transfer from Paediatric to adult transplant unit. Pediatric Nephrology, 14, 469–472. Watson, A. R. (2005). Problems and pitfalls of transition from Paediatric to adult renal care. Pediatric Nephrology, 20, 113–117. 143 Watson, A. R., Harden, P., Ferris, M., Kerr, P. G., Mahan, J., & Ramzy, M. F. (2011). Transition from pediatric to adult renal services: A consensus statement by the international society of nephrology (Isn) and the international pediatric nephrology Association (Ipna). Pediatric Nephrology, 26, 1753–1757. Webster, B. H., Jr., & Bishaw, A. (2006). U.S. Census Bureau, American Community Survey Reports, ACS02, Income, Earnings, and Poverty Data From the 2005 American Community Survey. Washington, DC: U.S. Government Printing Office. Whitbeck, L., Hoyt, D. R., & Huck, S. M. (1994). Early family relationships, intergenerational solidarity, and support provided to parents by their adult children. Journal of Gerontology, 49, S85–S94. White, S. W., Oswald, D., Ollendick, T., & Scahill, L. (2009). Anxiety in children and adolescents with autism Spectrum disorders. Clinical Psychology Review, 29, 216–229. Yeager, D. S., & Dweck, C. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist, 47(4), 302–314. Yeung, E., Kay, J., Roosevelt, G. E., Brandon, M., & Yetman, A. T. (2008). Lapse of care as a predictor for morbidity in adults with congenital heart disease. International Journal of Cardiology, 125, 62–65. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Pregnancy Characteristics and Women’s Cardiovascular Health Abigail Fraser, Janet M. Catov, Deborah A. Lawlor, and Janet W. Rich-Edwards 1 Introduction Growing evidence suggests that pregnancy is a “critical period” in a woman’s life when her health development is especially sensitive to certain internal and external stimuli. As a normal response to pregnancy and in order to support the developing fetus, women become more insulin resistant and hyperlipidemic and experience an increase in blood pressure (BP; after an initial drop) and upregulation This chapter contains a modified version of a previously published review and analysis of existing research that appeared in Epidemiologic Reviews. Reprinted with permission from: Janet W. Rich-Edwards, Abigail Fraser, Deborah A. Lawlor, Janet M. Catov; Pregnancy Characteristics and Women’s Future Cardiovascular Health: An Underused Opportunity to Improve Women’s Health?. Epidemiol Rev 2014;36(1):57–70. doi: 10.1093/epirev/mxt006 AF and DAL work in a unit that receives infrastructure funding from the United Kingdom Medical Research Council (MC_UU_12013), and AF is funded by a United Kingdom Medical Research Council fellowship (MR/ M009351/1). A grant to DAL from the Wellcome Trust also supports this collaborative work (WT094529MA). JMC is funded by RO1HL103825 and K12HD43441. JRE is supported by an American Heart Association Founder’s Grant (13GRNT17070022). The authors have no relevant disclosures, financial or otherwise. A. Fraser (*) • D.A. Lawlor Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK BS8 2BN e-mail: abigail.fraser@bristol.ac.uk of coagulation factors and the inflammatory cascade (Sattar 2004). This is perhaps the prime example of health as an emergent property as it is one that enables the bearing of offspring and thus the perpetuation of humanity. While for a majority of women, this adaptation to pregnancy remains “healthy,” in some women it develops into a complication of pregnancy such as gestational diabetes mellitus (GDM), preeclampsia, fetal growth restriction (FGR), and preterm delivery. It has long been understood that pregnancy complications are important for the life course health development of offspring, but much less appreciated that these complications also have key implications for the long-term health development of the mother. An accumulating body of research has shown that these common pregnancy J.M. Catov Department of Obestetrics and Gynecology, University of Pittsburgh, Pittsburgh, PA 15213, USA University of Pittsburgh, Department of Epidemiology, Pittsburgh, PA 15261, USA Magee-Womens Research Institute, Pittsburgh, PA 15213, USA J.W. Rich-Edwards Connors Center for Women’s Health and Gender Biology, Brigham and Women’s Hospital, Boston, MA 02120, USA Harvard Medical School, Boston, MA 02115, USA Harvard School of Public Health, Boston, MA 02120, USA © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_8 145 A. Fraser et al. 146 complications predict the future risk of chronic diseases in women, including cardiovascular disease, diabetes, and breast cancer (Rich-Edwards 2009). In this chapter we use life course health development theoretical principles as a lens for an examination of the implications of pregnancy history for cardiovascular disease (CVD), a leading cause of female mortality (Oblast 1999; Yusuf et al. 2001). Globally, one out of three women dies from CVD (Shah et al. 2009; Mathers et al. 2008). We do a worse job of recognizing and predicting CVD in women than in men, in part because CVD presents itself differently between the sexes (Mosca et al. 2011; Shaw et al. 2006). This has important implications for the prevention of CVD. Primary prevention, if applied to high risk populations early enough to avert the cumulative damage of chronic disease, can reduce CVD incidence (Scarborough and Weissberg 2011; Shay et al. 2012; MMWR 1989). In response to the growing appreciation that many preventive efforts start too late to be effective, there has been a call for “primordial prevention”—the prevention of the major CVD risk factors themselves (Labarthe 1999; Weintraub et al. 2011). In this context, pregnancy complications have the potential to be effective CVD risk “stress tests” to identify women who would most benefit from primordial or primary prevention efforts to reduce CVD risk (Sattar and Greer 2002). The concept of primordial prevention is consistent with the life course health development principle that health development is an emergent phenomenon and the best way to prevent future disease is to build health assets that have long-term salutary benefits. On average, more than 80% of women in highincome countries bear at least one child (Martinez et al. 2012; OECD Family D 2014), as do upward of 90% of women in most lower- and middleincome nations (United Nations DoEaSA, Population Division 2009). A high proportion of women will, in the course of their reproductive career, have a pregnancy complicated by GDM, a hypertensive disorder of pregnancy, FGR macrosomia, or preterm delivery. The prevalence of any one of these conditions in any given pregnancy ranges from 2% to greater than 12%. In one UK study, 36% of singleton pregnancies were compli- cated by at least one of these factors (Fraser et al. 2012). In the U.S. national Nurses’ Health Study 2, we estimated that 29% of parous study participants have had one of these pregnancy complications. As reviewed below, each of these complications has been associated with roughly a twofold increase in the risk of CVD events. If 80% of women are parous and 30% of them have had a pregnancy complication predictive of CVD, then about 25% of women are at heightened risk for future CVD risk. We begin with a review of the evidence for associations of parity and common pregnancy complications (low birth weight, fetal growth restriction, preterm delivery, hypertensive disorders of pregnancy, and GDM) with future CVD risk. We conducted MEDLINE searches for English-language cohort and case-control studies published in the peer-reviewed literature through December 2012, as described in detail elsewhere (Rich-Edwards et al. 2014). Whether pregnancy is a sensitive period in terms of cardiovascular health development across the life course and complications per se contribute to long term CVD risk, whether they simply unmask women with an underlying propensity for CVD, or whether both pathways are in play remains unclear. As suggested by life course health development, longitudinal studies are needed to untangle these temporal effects. In the second part of the chapter, we further explore the physiologic mechanisms that might explain the associations between pregnancy complications and CVD. Finally, we discuss the implications of these findings for future research as well as for health care design and policy. 2 Associations of Parity and Pregnancy Complications with CVD Risk in Mothers 2.1 Parity and CVD Most (Green and Moser 1988; Ness et al. 1993) but not all studies (Steenland et al. 1996) have found a positive association between parity (number of children) and later CVD. In the largest study to date, the association was examined in Pregnancy Characteristics and Women’s Cardiovascular Health 1.3 million with a median follow-up time of 9.5 years (range 0–24) women using Swedish registry data (Parikh et al. 2010). Parity was associated with CVD in a J-shaped fashion, with two births representing the nadir of risk. Compared with women with two births, the multivariableadjusted hazard ratios (95% confidence interval (CI)) for women with 0 and ≥5 births were 1.11 (1.09–1.14) and 1.57 (1.52–1.64), respectively. Desired family size may affect the shape of the parity-CVD risk distribution in different societies and is an example of how societal norms and social structures may affect health development and its determinants. In Sweden, the modal family size (two children) coincides with the nadir of maternal cardiovascular risk (Parikh et al. 2010). This suggests that many women who bore only one child suffered from secondary infertility, first pregnancy complications that precluded further pregnancies, or severe neonatal outcomes that discouraged further childbearing. To the extent that subfertility and severe pregnancy complications predict future CVD risk, they may explain the low-parity “hook” of the J-shaped association of parity and maternal CVD. The increase in CVD risk with increasing parity after two children may be the result of different phenomena. These include rival, but not mutually exclusive, theories that (1) adverse physiologic change accumulates over pregnancies; (2) adverse lifestyle habits accrue with more children; and/or (3) selection bias in which women at higher CVD risk opt for larger families. Thus, it is unclear whether the association of higher parity with CVD risk is causal or correlational. Some insight into the association of parity with maternal CVD risk may be gleaned by examining the association of number of children with paternal CVD risk. Similar associations for mothers and fathers would suggest that the association between parity and maternal CVD is not causal, but is more likely a result of confounding by socioeconomic position and/or behaviors related to child-rearing. Three reports examined associations of number of children with CVD in fathers. In general, men who have fathered the most children appear to have small increased CVD risk, though this association is not always statistically significant and is weaker than the 147 associations observed among mothers (Dekker and Schouten 1993; Lawlor et al. 2003; Ness et al. 1995). Adjustment for lifestyle factors tends to reduce the associations in both mothers and fathers (Lawlor et al. 2003; Catov et al. 2007a). These results suggest that the association between high parity and CVD in later life may be largely the result of socioeconomic position and/or behavioral risk factors associated with childrearing that are shared by both parents. 2.2 Common Pregnancy Complications and CVD in Mothers Offspring birth weight predicts maternal lifespan (Catov et al. 2007a; Davey Smith et al. 1997, 2000a, b, 2007). Figure 1 presents the findings from studies that have examined associations of offspring birth weight or fetal growth—a function of birth weight and gestation length—with maternal CVD risk (Davey Smith et al. 1997, 2007, 2000a, b, 2005; Bellamy et al. 2007, 2011; Friedlander et al. 2007; Lykke et al. 2010a; Mongraw-Chaffin et al. 2010; Smith et al. 2001; Wikström et al. 2005; Fraser et al. 2012; Ness et al. 1993). One meta-analysis has calculated that, for every standard deviation (roughly 500 g) higher birth weight of the firstborn child, maternal CVD mortality is decreased by 25% (Davey Smith et al. 2007). It is unclear whether the inverse association of offspring birth weight with mortality is constant across the entire range of birth weight, as the association of high birth weight with maternal CVD risk varies by study. In some populations, the mothers of the largest infants (>4000 g or >4500 g) have the lowest risks of CVD (Davey Smith et al. 1997, 2000b), while in other populations there is an uptick in CVD risk for the mothers of macrosomic newborns (Davey Smith et al. 2007; Bonamy et al. 2011; Friedlander et al. 2007; Lykke et al. 2012). Given the strong associations of macrosomia with GDM and later type 2 diabetes (Metzger et al. 1993), the presence and magnitude of the association of large birth weight with future CVD risk may depend on the population prevalence of GDM and chronic diabetes during pregnancy (in other words, A. Fraser et al. 148 Relative Risk (95% CI) First Author, Year (Reference No.) Mean or Median Years of Follow-up Caption Notes Birthweight Davey Smith,1997 1.23 (0.97, 1.57) 15 a, h Davey Smith, 2000 1.49 (1.21, 1.84) 10 a, h Davey Smith, 2000 1.30 (1.12, 1.51) 34 a, h Smith, 2001 2.20 (1.80, 2.80) 17 b, i Davey Smith, 2007 1.25 (1.14, 1.37) 45 a, j Friedlander, 2007 2.11 (1.38, 3.21) 34 c, j d, h Fetal Growth Davey Smith, 2005 1.39 (1.23, 1.57) 20 Wikstrom, 2005 1.80 (1.60, 2.30) 19–28 e, i Lykke, 2010 2.56 (2.19, 3.00) 15 e, h Mongraw-Chaffin, 2010 1.44 (1.03, 2.03) 37 f, h Bonamy, 2011 2.13 (1.87, 2.44) 12 g, k 0.5 1 1.0 2.0 3.0 4.0 Relative Risk Fig. 1 Results from studies of offspring birth weight or fetal growth and relative risk of maternal cardiovascular disease (CVD). a Per 1 standard deviation (SD) lower birth weight; b lowest birth weight quintile compared to all others; c <2500 g compared to 1500–3999 g birth weight; d per 1 SD lower birth weight, adjusted for gesta- tional age; e small for gestational age; f intrauterine growth retardation; g ~2 SD below mean birth weight adjusted for gestational age; h CVD mortality; i coronary heart disease (CHD) events ; j CHD mortality; k CVD events (CI confidence interval) the extent to which large infant size is pathological). Indeed, the association of macrosomia with CVD risk is attenuated by adjustment for GDM(36) (Bonamy et al. 2011), indicating that a substantial portion of the association of macrosomia and CVD is explained by metabolic risk. It is abundantly clear, however, that the 8% of deliveries that are low birth weight (<2500 g) are associated with twice the maternal CVD incidence and mortality of other deliveries (Davey Smith et al. 1997, 2000a, b, 2007). Associations of offspring birth weight with maternal CVD are only modestly diminished by adjustment for cigarette smoking and not affected by control for prepregnancy body mass index (Bonamy et al. 2011; Davey Smith et al. 2005). Birth weight is the product of fetal growth rate and gestation length. Fetal growth, represented as birth weight corrected for gestation length, predicts maternal CVD risk (Bonamy et al. 2011; Lykke et al. 2012), as does gestation length (discussed below). In fact, the coincidence of restricted fetal growth and prematurity yields a more than threefold increased CVD risk (Bonamy et al. 2011). The curvilinear association of offspring birth weight with maternal CVD risk observed in many populations may be the product of competing pathological phenomena. At one end of the birth weight spectrum, the association of macrosomia with maternal CVD risk may be explained by underlying metabolic risk; at the other end of the spectrum, the association of low birth weight with maternal CVD risk may be driven by endothelial dysfunction and other pathologies associated with restricted fetal growth and preterm birth. First offspring birth weight also predicts paternal CVD, although the magnitude of the positive association of offspring birth weight with paternal Pregnancy Characteristics and Women’s Cardiovascular Health CVD risk is less than a third of that for the infant’s mother (Davey Smith et al. 2007). The fact that the birth weight of their first child predicts CVD events in both parents suggests that shared lifestyle or environmental factors, such as cigarette smoking, might influence both the growth of the fetus and CVD risk in the parents and/or that pleiotropic genetic variants affect both growth and CVD risk. Birth weight is passed down through maternal and paternal lines (Lie et al. 2006), opening the possibility that paternal CVD/ fetal growth genes could affect both the pregnancy outcome and long-term chronic disease risk in the father (Freathy et al. 2007). However, the stronger association in mothers than in fathers suggests either parent-specific genomic imprinting or—as seems more parsimonious—that maternal health during pregnancy affects fetal growth and is a marker of her future CVD risk. Preterm delivery (<37 weeks’ gestation) accounts for 6–12% of deliveries in the developed world (Beck et al. 2010). The hazard ratios for CVD associated with total preterm delivery are depicted in Fig. 2 and are on the order of 1.3–2.6 for births <37 completed weeks compared with term births (Davey Smith et al. 2000b, 2005; Bonamy et al. 2011; Lykke et al. 2010a, b; Smith et al. 2001; Wikström et al. 2005; Catov et al. 2010a; Irgens et al. 2001; Nardi et al. 2006; Pell et al. 2004; Rich-Edwards et al. 2012). There is a greater range of relative risk when distinct preterm phenotypes are examined separately. While most preterm deliveries follow spontaneous labor or preterm premature rupture of membranes, a significant and growing fraction results from medically induced labor or Caesarean section without labor. The chief reasons for these medically indicated deliveries include preeclampsia and FGR, both of which have been associated with increased maternal CVD risk. In studies that have distinguished them, hypertensive preterm deliveries consistently have a stronger association with maternal CVD outcomes than do normotensive preterm deliveries, though the latter are still associated with a 1.2- to threefold increased risk compared with term deliveries (Catov et al. 2010a; Irgens et al. 2001). In the two studies that have contrasted CVD risk among mothers with 149 spontaneous versus indicated preterm deliveries (Rich-Edwards et al. 2012; Hastie et al. 2011a), indicated delivery was associated with higher risks of CVD mortality than spontaneous preterm delivery. Nevertheless, spontaneous preterm delivery—compared with term delivery—was associated with doubling of CVD risk (RichEdwards et al. 2012; Hastie et al. 2011a). Unlike the associations of parity or birth weight with paternal CVD risk, two studies (Davey Smith et al. 2005; Irgens et al. 2001) have reported that preterm delivery is not associated with paternal risk of CVD, implying that the association of preterm delivery with maternal CVD risk is not the product of a high-CVD risk lifestyle or genetic variants shared between both parents and their offspring. Of relevance, preterm birth risk appears to be passed only through the maternal line (Wilcox et al. 2008). These observations suggest that maternal intrauterine environment and health determine the risk of preterm delivery and explain its association with maternal CVD risk, rather than shared lifestyle or environment of the mother and father. Gestational diabetes mellitus is a common and growing pregnancy complication that affects as many as 5% of pregnancies. It is well established that women with GDM are at increased risk of developing diabetes later in life (Bellamy et al. 2009); between 3% and 70% of women with a history of GDM will develop type 2 diabetes within three decades of the pregnancy (Kim et al. 2002), with a meta-analysis of 675,455 women finding a sevenfold increase in risk of later type 2 diabetes (Bellamy et al. 2009). Type 2 diabetes is an important CVD risk factor, having a markedly higher relative and absolute association with CVD in women than it does in men (Sarwar et al. 2010). Given these associations, it seems self-evident that a history of GDM would be associated with increased CVD risk. However, due largely to the fact that GDM screening during pregnancy was neither routine nor standardized until recent decades, there are few cohorts with long enough follow-up of screened populations to detect CVD incidence or mortality among women with a history of GDM (Shah et al. 2008; Carr et al. 2006). These are displayed in Fig. 3. The only large population-based study of this topic is a A. Fraser et al. 150 Relative Risk (95% CI) Mean or Median Years of Follow-up Caption Notes Davey Smith, 2000 2.06 (1.22, 3.47) 30 a, b Irgens, 2001 2.95 (2.12, 4.81) 13 c, d Smith, 2001 2.10 (1.50, 3.00) 15–19 a, e Davey Smith, 2005 2.45 (2.06, 2.91) 20 a, b Wikstrom, 2005 1.30 (1.10, 1.50) 19–28 a, e Nardi, 2006 2.12 (1.19, 3.78) Unknown a, f Catov, 2010 1.36 (1.31, 1.41) 28 a, g Lykke, 2010 1.49 (1.36, 1.64) 14 h, e Bonamy, 2011 1.68 (1.50, 1.88) 12 h, g Hastie, 2011 1.58 (1.47, 1.71) 22 a, e First Author, Year (Reference No.) 0.5 1.0 2.0 3.0 4.0 Relative Risk Fig. 2 Results from studies of preterm delivery and relative risk of maternal cardiovascular disease (CVD). a <37 weeks’ gestation weeks compared with term; b CVD mortality; c <37 weeks’ gestation length compared with term normotensive pregnancies; d CVD mortality, excluding stroke mortality; e coronary heart disease (CHD) events; f myocardial infarction; g CVD events; h 32–36 weeks’ gestation length compared with term (CI confidence interval) record linkage study conducted in Ontario, Canada, with a median follow-up of 11.5 years (Shah et al. 2008). In that study, a history of GDM was associated with a greater risk of hospital admission for acute myocardial infarction, coronary bypass, coronary angioplasty, stroke, or carotid endarterectomy (hazard ratio (HR) = 1.71; 95% CI, 1.08–2.69). Upon adjustment for diabetes after pregnancy, the association was attenuated toward the null (adjusted HR, 1.13; 0.67–1.89). A smaller, cross-sectional study found that women with a history of GDM had a higher CVD risk than women without a history of GDM (adjusted OR = 1.85, 1.21–2.82) and experienced CVD events 7 years earlier, on average (Carr et al. 2006). Lesser degrees of antepartum hyperglycemia have also been associated with an elevated risk of subsequent diabetes and CVD. In the Ontario study, women with evidence of elevated glycemia short of GDM criteria were at an increased risk of diabetes (HR = 2.56, 2.28–2.87) (Retnakaran and Shah 2009a) and CVD (HR = 1.19, 1.02–1.39) (Retnakaran and Shah 2009b) compared to normoglycemic women. Hypertensive disorders of pregnancy (HDPs) are common pregnancy complications that presage CVD. Preeclampsia, the combination of hypertension and proteinuria, affects approximately 2–5% of pregnancies, with a predominance among first pregnancies (Fraser et al. 2012; Wallis et al. 2008; North et al. 2011). Estimates of the prevalence of gestational hypertension, new-onset hypertension without proteinuria, vary from 3% to 14% (Fraser et al. 2012; Wallis et al. 2008; Roberts et al. 2005). Women with a history of preeclampsia have roughly fourfold higher incidence of hypertension and twofold elevated risks of heart disease, stroke, and venous thromboembolism (Bellamy et al. 2007; McDonald et al. 2008). Two systematic reviews, one of cohort studies (n = 25) and the other of both cohort (n = 10) and case-control (n = 5) studies, Pregnancy Characteristics and Women’s Cardiovascular Health First Author, Year (Reference No.) 151 Relative Risk (95% CI) Mean or Median Years of Caption Follow-up Notes Carr, 2006 1.58 (1.00, 2.49) 30 a Shah, 2008 1.71 (1.08, 2.69) 12 b 0.5 1.0 2.0 3.0 4.0 Relative Risk Fig. 3 Results from studies of gestational diabetes mellitus and relative risk of maternal cardiovascular disease (CVD). a Self-reported coronary artery disease, b CVD events (CI confidence interval) have both reported a doubling of risk for different measures of CVD comparing women with preeclampsia to normotensive women over a median of 10–12-year follow-up (Bellamy et al. 2007; McDonald et al. 2008). Figure 4 depicts the relative risk of coronary heart disease (CHD) and CVD outcomes among mothers with a history of preeclampsia (Mongraw-Chaffin et al. 2010; Smith et al. 2001; Wikström et al. 2005; Irgens et al. 2001; Funai et al. 2005; Hannaford et al. 1997; Jónsdóttir et al. 1995; Kestenbaum et al. 2003; Lin et al. 2011; Lykke et al. 2009; Wilson et al. 2003). Publications from three cohort studies published since those reviews give some insight into the onset and duration of CVD risk following HDP (Mongraw-Chaffin et al. 2010; Smith et al. 2001; Lin et al. 2011; Lykke et al. 2009). In a short-term follow-up of over 1,000,000 pregnancies in Taiwan, women with preeclampsia/ eclampsia were at double the risk of major CVD from the third trimester of pregnancy up to three years postpartum, with particularly high relative risks for stroke (HR = 14.5, 1.3–165.1) and myocardial infarction (HR = 13.0, 4.6–6.3) (Lin et al. 2011). While these results suggest a high relative risk immediately following HDP, the confidence intervals are wide, and the absolute risk of CVD events is very small at this age, so that this immediate risk is unlikely to account for a large number of CVD events. The Child Health and Development Study in California has provided some of the longer follow-up; over 37 years after pregnancy, women with a history of preeclampsia in any pregnancy had double the risk of CVD death (HR = 2.14; 1.29–3.57) (Mongraw-Chaffin et al. 2010). This doubling of risk is consistent with studies with shorter duration of follow-up. Considering the exponential increase in the absolute numbers of CVD events with increasing age, this suggests that the elevated risk of CVD among women with a history of HDP is not limited to the early years postpartum. Thus, studies repeatedly report a doubling of CVD risk among women with a history of preeclampsia and suggest lesser degrees of excess risk among women with a history of gestational hypertension, despite the strong association of gestational hypertension with development of chronic hypertension (Lykke et al. 2009). The combination of preterm delivery and preeclampsia—a likely marker of the severity of preeclampsia—is a particularly potent predictor of CVD risk. Compared to normotensive term pregnancies, women delivering preterm preeclamptic pregnancies have very high relative risks of future A. Fraser et al. 152 Relative Risk (95% CI) First Author, Year (Reference No.) Mean or Median Years of Caption Follow-up Notes Jonsdottir, 1995 1.90 (1.02, 3.52) Unknown a Hannaford, 1997 1.65 (1.26, 2.16) Up to 26 b Irgens, 2001 3.61 (0.76, 17.18) 13 c, e Smith, 2001 2.10 (1.60, 2.60) 15–19 Kestenbaum, 2003 2.55 (1.70, 3.83) Wilson, 2003 1.95 (0.90, 4.22) Funai, 2005 3.01 (2.18, 4.33) 30 d Wikstrom, 2005 2.21 (1.56, 3.31) 19–28 b, e Lykke, 2009 1.82 (1.65, 2.00) 15 b Mongraw-Chaffin, 2010 2.73 (1.78, 4.18) 37 d Skjaerven, 2012 1.90 (1.60, 2.20) 25 d 0.5 1.0 2.0 3.0 4.0 8 Unknown b c, e a 4.5 Relative Risk Fig. 4 Results from studies of hypertensive disorders of pregnancy and relative risk of maternal cardiovascular disease (CVD). a Coronary heart disease (CHD) mortality, b CHD events, c CVD events, d CVD mortality, e composite estimate provided by Bellamy review (18). A 2011 study by Lin (62) reported a relative risk of 23.0 (95% confidence interval (CI), 5.1–103.7) for CVD events (except stroke) during pregnancy and up to three years after delivery. We omitted that study from the figure so that we could keep the relative risk scale consistent across figures CVD ranging from 2.5 to 9.5. (Mongraw-Chaffin et al. 2010; Smith et al. 2001; Lin et al. 2011; Skjaerven et al. 2012) pregnancy has been associated with a modestly increased risk of type 2 diabetes (adjusted HR = 1.16, 1.01–1.34) and each non-GDM pregnancy with a reduced risk of diabetes (HR = 0.34, 0.27–0.41) (Retnakaran et al. 2011). In fact, this highlights an intriguing pattern that is emerging with respect to last births: having preeclampsia (Skjaerven et al. 2012), preterm delivery (RichEdwards et al. 2012), or GDM (Retnakaran et al. 2011) in the last pregnancy appears to be associated with especially high risk of future CVD in mothers. Perhaps reflecting the same phenomenon, women who have one preterm delivery and one term delivery in their first two births appear to be at higher risk of CHD if the preterm delivery was the second birth (Catov et al. 2010a; Lykke et al. 2010b). This suggests that pregnancy complications severe enough to contraindicate or discourage a subsequent pregnancy may be particularly potent predictors of future CVD risk. 2.3 Recurrent Pregnancy Complications, Last Pregnancy Complications, and Maternal CVD Risk Much of the literature is based on first pregnancies, precluding examination of the association of recurring pregnancy complications with CVD risk. There is evidence that recurrent preeclampsia (Lykke et al. 2009) and preterm delivery(Catov et al. 2010a; Lykke et al. 2010b) are associated with a greater risk of CVD than a single complicated pregnancy in multiparous women. Although the association of recurrent GDM with CVD risk has not been studied, after a first GDM pregnancy, each subsequent GDM Pregnancy Characteristics and Women’s Cardiovascular Health 3 Physiological Mechanisms Linking Pregnancy Complications to Maternal CVD Risk Pathways that link pregnancy exposures to later life CVD are not well understood. Considerable evidence supports the existence of common predisposing factors for both pregnancy complications and CVD risk suggesting that pregnancy complications can be thought of as a failed stress test, with pregnancy being the stressor. There have been almost no studies examining the alternative that pregnancy complications might cause increased CVD risk. To address this issue, we summarize evidence that compares CVD risk before, during, and after pregnancies with and without complications. 3.1 Cardiovascular Risk Factors Preceding Pregnancy Complications Higher prepregnancy blood pressure is a risk factor for preeclampsia (Magnussen et al. 2007) and preterm delivery (Catov et al. 2013). Chronic hypertension has a well-established relation to increased risk of preeclampsia—known as “superimposed” preeclampsia. Even within the normotensive range, there is a positive doseresponse association of prepregnancy systolic and/or diastolic blood pressure with preeclampsia, (Magnussen et al. 2007) and women who develop hypertensive disorders of pregnancy have higher blood pressure at 8 weeks’ gestation than normotensive (Macdonald-Wallis et al. 2012). Preexisting hypertension has also been associated with FGR, especially in cases that were also preterm (Catov et al. 2008a). Risks for these complications also rise with increasing maternal age, suggesting that the aging endothelium may less successfully adapt to the profound vascular demands of pregnancy. Prepregnancy lipid concentrations are also associated with pregnancy complications and offspring birth weight; the nature of the associa- 153 tion varies with the pregnancy outcome in question. Lipid profiles consistent with elevated CVD risk, including higher prepregnancy triglyceride levels, total cholesterol, and lower HDL cholesterol, have been associated with preeclampsia and preterm delivery in the study in Norway (Magnussen et al. 2007, 2011). The US Coronary Artery Risk Development in Young Adults Study (CARDIA) found a curvilinear association of prepregnancy cholesterol levels with risk of delivering preterm (Catov et al. 2010b). With respect to fetal growth, women with a more atherogenic lipid profile may bear larger infants (Romundstad et al. 2007); this suggests that the association of low birth weight—at least the fetal growth component of low birth weight—with maternal CVD risk may not operate via dyslipidemia. Prepregnancy adiposity and glucose/insulin dysregulation is strongly implicated in the etiology of GDM, based on the observation that women with GDM tend to have a family history of type 2 diabetes and higher body mass index (BMI) before pregnancy (Solomon et al. 1997), as well as higher levels of glucose and insulin and lower levels of adiponectin before the onset of the midpregnancy hyperglycemia that defines GDM (Nanda et al. 2011; Riskin-Mashiah et al. 2010; Sacks et al. 2003; Williams et al. 2004). Higher BMI and family history of diabetes are also associated with increased risk of preeclampsia (Qiu et al. 2003; O’Brien et al. 2003). The risk of preeclampsia doubles with every 5–7 kg/m2 increase in body mass index before pregnancy (O’Brien et al. 2003). Thus, subclinical elevations in the classic CVD risk factors of blood pressure, lipid levels, elevated BMI, and glucose/insulin dysregulation appear to predate both preeclampsia and GDM. Less clear is the extent to which CVD risk factors precede spontaneous preterm deliveries or FGR in normotensive pregnancies. Furthermore, the roles of prepregnancy inflammatory and coagulation factors with respect to pregnancy complications remain to be elucidated, despite the importance of these systems for both reproduction and CVD risk (Romero et al. 2007). A. Fraser et al. 154 3.2 Cardiovascular Risk Factors During Pregnancy 3.2.1 Cardiovascular Adaptation in Normal Pregnancy In normal gestation, maternal blood volume increases progressively from 6 to 8 weeks’ gestation, peaking at an increase of 45% by 32 weeks (Monga and Creasy 1994). Cardiac output increases by 30–50%, with half of this increase occurring very early in gestation. Pulse rate increases 17%, and there are striking alterations in renal physiology. Although the insulin response to glucose is augmented in early pregnancy, insulin resistance emerges in the second half of pregnancy (Butte 2000). In addition, cholesterol and triglyceride profiles change after gestation week 9 to support steroid synthesis and fetal growth (Butte 2000). In uncomplicated pregnancy, there is a tendency for low-density lipoprotein (LDL) to shift across gestation from large, buoyant particles to smaller, denser, and more atherogenic particles (Hubel et al. 1998a). Fat is accumulated during the second trimester and then mobilized to support the dramatic fetal growth of the third trimester (Herrera 2000). 3.2.2 Cardiovascular Risk Factors During Pregnancy Complications Vascular and endothelial dysfunction is characteristic of pregnancies complicated by preeclampsia or growth restriction. Placental underperfusion is common, and there are elevated markers of endothelial dysfunction in the maternal circulation. Women with hypertensive disorders of pregnancy demonstrate increased resistance in the uterine arteries (Ducey et al. 1987; Campbell and Griffin 1983), vascular stiffness, and impaired endothelial response (Savvidou et al. 2003, 2011). In addition, placental vascular lesions indicative of failed spiral artery remodeling, ischemia, or hemorrhage have also been reported in cases of both medically indicated and spontaneous preterm birth (Kelly et al. 2009). During pregnancy, lipid aberrations accompany several pregnancy complications. Again, the direction of the associations appears to depend on the nature of the pregnancy complication. The dyslipidemias associated with atherosclerosis (hypertriglyceridemia, hypercholesterolemia, elevated free fatty acids, and excess oxidized LDL) are frequently seen during preeclampsia … (Clausen et al. 2001; Hubel et al. 1996, 1998b; Sattar et al. 1997). There is also emerging evidence to suggest that this atherogenic lipid profile is associated with both spontaneous and indicated preterm births (Edison et al. 2007; Catov et al. 2007b). Similarly, women with GDM exhibit elevations in triglycerides and, less consistently, total cholesterol and LDL during pregnancy (Enquobahrie et al. 2005). On the other hand, low maternal total and LDL-cholesterol concentrations appear in the third trimester in pregnancies complicated by FGR (Sattar et al. 1999). Placental studies are conflicting, with some suggesting reduced expression of lipoprotein receptors in placentas from FGR vs. appropriate weight for gestational age births (Wadsack et al. 2007) and others suggesting overexpression of these receptors (Stepan et al. 1999). FGR studies are hampered by nonstandard phenotyping, and thus findings may represent differing levels of severity. Despite these limitations, these data suggest that extremes of lipid concentrations are associated with adverse pregnancy outcomes. Longitudinal studies are needed to better understand how the relative contributions of low or high cholesterol are related to failed or compensatory lipid adaptation required to optimize fetal growth. Metabolic dysregulation in pregnancy defines GDM and is a strong risk factor for preeclampsia; there is considerable overlap of the two conditions, with twice the rate of preeclampsia in diabetic versus nondiabetic pregnancies (Ostlund et al. 2004). However, GDM has only a modest association with spontaneous preterm birth (Hedderson et al. 2003). Higher early-pregnancy BMI is associated with increased risk of HDP and GDM (Solomon et al. 1997; O’Brien et al. 2003; Lawlor et al. 2012), but with reduced risk of SGA and spontaneous preterm birth in most studies (Smith et al. 2007). Systemic inflammation during pregnancy may be important in the pathogenesis of several preg- Pregnancy Characteristics and Women’s Cardiovascular Health nancy complications. Elevated serum levels of C-reactive protein and/or leukocytes have been detected in women who experience GDM, FGR, and both spontaneous and indicated preterm deliveries (Catov et al. 2007c; Ernst et al. 2011; Freeman et al. 2004; Pitiphat et al. 2005; Wolf et al. 2001). However, neither mid-gestation circulating levels of C-reactive protein nor proinflammatory cytokines have proven to have prognostic value for specific pregnancy outcomes (Curry et al. 2007; Gammill et al. 2010). Normal pregnancy is a state of hypercoagulability, and complications such as preeclampsia and preterm birth are characterized by particularly high biomarkers of an activated fibrinolytic cascade, as well as perhaps an impaired ability to mount this response appropriately (Catov et al. 2008b; Hackney et al. 2010; Heilmann et al. 2007). It has been hypothesized that aberrations in the cross talk between inflammation and the coagulation cascades could contribute to the pathophysiology of these pregnancy complications (Girardi 2011). 3.3 3.3.1 Cardiovascular Risk Factors After Pregnancy Enduring Cardiovascular Impact of Normal Pregnancy Most of the cardiovascular adaptations to normal pregnancy resolve in the postpartum period, although there are some detectable and lasting pregnancy effects. Blood pressure is modestly decreased in the postpartum period after a first uncomplicated pregnancy (Hackney et al. 2010). However, other lingering effects are not as salutary. Importantly, women retain, on average, 0.5–5.0 kg of weight following each pregnancy (Heilmann et al. 2007; Girardi 2011). Lactation may help resolve the cardiometabolic adaptations and fat accumulation associated with pregnancy (Agatisa et al. 2004; Berends et al. 2008; Catov et al. 2011). The first birth may be a sentinel marker for complications in later pregnancies and future CVD risk (Lauenborg et al. 2005; Meyers-Seifer and Vohr 1996; Verma et al. 2002). Several fac- 155 tors distinguish first births. First, longitudinal studies suggest that the lasting blood pressure and lipid changes associated with pregnancy occur after first but not subsequent births (Hackney et al. 2010). In addition, first births are at higher risk for the major obstetric complications of preterm delivery, HDP, FGR, and stillbirth. Women with any of these complications are at higher risk in subsequent pregnancies for recurrence of the same complication as well as the onset of other complications. Importantly, complications during a first pregnancy impact the likelihood of having a subsequent pregnancy. As noted above, complications in a last pregnancy appear to be associated with especially high relative risks of CVD events. Thus, health status of the first and last pregnancies may be particularly telling of future maternal health. The cumulative effect of these adaptations and resolutions and risks may contribute to the above-noted J-shaped association between parity and maternal CVD risk, with lowest risk for women who have delivered two infants. It is not clear whether pregnancies exert a cumulative cardiovascular burden with increasing parity, whether higher-order pregnancies at more advanced maternal age exert more cardiovascular risk, or whether women at high cardiovascular risk bear more children. 3.3.2 Cardiovascular Risk After Pregnancy Complications The association of vascular and endothelial dysfunction with pregnancy complications continues after delivery. Women with preeclampsia have impaired endothelial function after pregnancy (Agatisa et al. 2004). This may also be true, although to a lesser extent, of women who deliver small babies due to FGR or preterm delivery. For example, lower offspring birth weight is associated with higher maternal blood pressure in the years after pregnancy (Davey Smith et al. 2005). Some (Catov et al. 2013; Berends et al. 2008) but not all (Catov et al. 2011) studies report higher blood pressure and atherosclerotic carotid vessel remodeling among women who have delivered an FGR neonate. Although studies are not unanimous (Macdonald- 156 Wallis et al. 2012; Lauenborg et al. 2005), women with a history of GDM are more likely to have hypertension (Meyers-Seifer and Vohr 1996; Verma et al. 2002), vascular dysfunction (Heitritter et al. 2005), impaired endotheliumdependent vasodilatation (Anastasiou et al. 1998), and higher carotid artery intima-media thickness (Tarim et al. 2006). These differences are not fully explained by the higher BMI typical of women with a history of GDM. Studies of lipid profiles after pregnancies complicated by preeclampsia are consistent with increased atherogenesis risk, including consistently reported higher total cholesterol, LDL cholesterol, and triglycerides, although these differences are not always statistically significant (Fraser et al. 2012; Manten et al. 2007; Hubel et al. 2008; Laivuori et al. 1996; Magnussen et al. 2009; Romundstad et al. 2010; Sattar et al. 2003; Smith et al. 2009). Associations of reduced HDL cholesterol after preeclampsia have been reported by some (Fraser et al. 2012; Magnussen et al. 2009; Romundstad et al. 2010; Smith et al. 2009) but not all (Manten et al. 2007; Laivuori et al. 1996; Sattar et al. 2003) studies. One study has reported dyslipidemia among women with a history of spontaneous and indicated preterm births (Catov et al. 2011). Some (Lauenborg et al. 2005; Meyers-Seifer and Vohr 1996; Verma et al. 2002; Di Cianni et al. 2007) but not all (Schwarz et al. 2009) have reported elevated total cholesterol, LDL cholesterol, and/or triglycerides in women with a history of GDM. As with the studies of lipid concentrations before and during pregnancy, studies of lipid concentrations in women in the years after FGR are conflicting, with some reporting hyperlipidemia (Kanagalingam et al. 2009) and others reporting no differences compared to women with uncomplicated births (Catov et al. 2011). It is now firmly established that women with a history of GDM have a manifold higher risk of developing type 2 diabetes than women with normoglycemic pregnancies (Bellamy et al. 2009). It is less widely appreciated that women with a history of preeclampsia are also at high risk of type 2 diabetes. After preeclampsia, mothers are three times more likely to develop A. Fraser et al. diabetes within 16 years (Lykke et al. 2009), an observation bolstered by evidence of dysregulated glucose and insulin, as well as insulin resistance as early as 2 years after preeclamptic pregnancy (Fraser et al. 2012; Manten et al. 2007; Laivuori et al. 1996; Magnussen et al. 2009; Smith et al. 2009; Wolf et al. 2004). However, not all pregnancy complications are associated with risk of future metabolic disorder: in the Nurses’ Health Study 2, although 2% of women who delivered a very preterm infant (<32 weeks’ gestation) had a 35% higher risk of developing type 2 diabetes, moderate preterm delivery was not associated with increased diabetes risk (James-Todd et al. 2010). After pregnancy, plasma C-reactive protein is elevated among women with prior eclampsia and indicated preterm births suggesting that systemic low-grade inflammation may link some adverse pregnancy outcomes and later CVD (Hastie et al. 2011b; Hubel et al. 2006). Several studies have documented higher C-reactive protein levels among women with a history of GDM (Heitritter et al. 2005; Di Cianni et al. 2007; Winzer et al. 2004). Although inflammation seems a likely culprit to explain the association of spontaneous preterm delivery with CVD risk, the only study to date that has examined this question has reported no differences in plasma C-reactive protein levels of women with a history of spontaneous preterm delivery compared to term delivery (Hastie et al. 2011b). Women with a history of pregnancies complicated by preeclampsia may maintain a procoagulation state in the years after pregnancy, predisposing them to vascular and thrombotic events (Portelinha et al. 2009), although this pathway is less studied than others linking pregnancy complications to maternal CVD risk. Thus, the associations of pregnancy complications with future CVD events in women are likely explained, at least in part, by their associations with classic CVD risk factors of hypertension, dyslipidemia, type 2 diabetes, and perhaps inflammation and thrombosis, which are evident before, during, and after such complicated pregnancies. Pregnancy provides a challenge to women’s cardiovascular system, and pregnancy Pregnancy Characteristics and Women’s Cardiovascular Health complications may serve as precursors, i.e., early indications that a woman is on a high-CVD risk trajectory, before these classic CVD risk factors are clinically detected. That said, this trajectory is complex and likely to be nonlinear. Given that most pregnancies occur in early adulthood, long before most CVD events occur, there is ample opportunity to influence that trajectory by recognizing that health develops continuously over the lifespan and that health development is a process resulting from the ongoing interactions between person and environment. 4 Recommendations for Future Research 4.1 Major Themes and Findings The associations of pregnancy complications with CVD events are remarkably consistent. Several pregnancy complications are more common among racial minority groups, who are also at higher risk of metabolic and cardiovascular disease. Although untested, the use of pregnancy complication history to screen women for targeted CVD prevention has potential to improve public health, given the magnitude of the associations, the prevalence of the pregnancy complications, and the importance of CVD in women. Pregnancy complications occur early enough in a woman’s life course to offer a significant meaningful “runway” for primordial CVD prevention by lifestyle intervention and primary prevention by statins and antihypertensive drugs. In 2011, both the American Heart Association and the European Society of Cardiology included histories of preeclampsia and (in the case of the American Heart Association) GDM as part of CVD risk assessment that would trigger closer monitoring and control of CVD risk factors (Mosca et al. 2011; Regitz-Zagrosek et al. 2011). Pregnancy appears to be a critical transition period for a woman that stresses her cardiovascular system in ways that may shed light on future disease risk. 4.2 157 Research Priorities (a) Epidemiologic research • Establishing whether pregnancy complications per se contribute to CVD risk We need to establish whether pregnancy complications act as stress tests to unmask women who are already at increased risk of CVD in later life and/or whether (and which) pregnancy complications have a causal, direct contribution to a woman’s CVD risk. If pregnancy complications per se contribute to CVD risk independently of prepregnancy cardiovascular health, prevention strategies and treatment of pregnancy complications could be important not only for women and their offspring during the pregnancy but also later in life. (b) Mechanism research • Identifying mechanistic pathways to pregnancy complications and CVD Should epidemiologic evidence suggest that pregnancy complications are causally associated with greater future CVD risk, it will be important to ascertain the underlying pathways responsible for these effects in order to identify potential treatment pathways. Gaining better understanding of the mechanisms underlying pregnancy complications themselves will also be important in informing prevention strategies. (c) Translational research • Improving risk stratification Irrespective of whether pregnancy complications causally contribute to future CVD risk, a key question is the extent to which pregnancy history can be used to improve CVD risk scoring systems for women, such as the Framingham Risk Score. At present, these scoring systems are of debatable utility for women under age 70 (Greenland et al. 2010), and addition of pregnancy complications to prediction at these relatively younger ages may be particularly important. Several pregnancy A. Fraser et al. 158 complications are more common among racial minority groups, who are also at higher risk of metabolic and cardiovascular disease. Pregnancy history may be of particular importance in identifying risk in these groups. • Establishing strategies to mitigate future CVD risk in women with pregnancy complications If pregnancy complications are useful for early CVD risk prediction, the next question is whether earlier risk identification—as early as at the time of pregnancy—is a cost-effective way of reducing future risk. To do so it is important to examine whether women who experienced pregnancy complications should undergo more intense or earlier screening and monitoring in the postpartum years in order to determine whether they cross thresholds for treatment (e.g., with statins) earlier than women without pregnancy complications. We would also need to test the extent to which different lifestyle or pharmacologic preventions are effective at preventing future CVD in young or middle-aged women with a history of pregnancy complications. Key to this is identifying stages in the lifespan when women are (or are not) receptive to CVD prevention, including the postpartum year. 4.3 Data and Methods Development Priorities (a) Importance of linking research across life stages: need for extended follow-up of women with known reproductive histories The bulk of the research associating pregnancy history to CVD risk is derived from the linkage of large, often national, vital statistics registries for birth, hospitalization, and mortality statistics. These exercises have yielded consistent associations of pregnancy complications with CVD risk. However, as most registries were founded in the 1950s or 1960s, the longest-running has been able to follow women only into the early postmenopausal years. The data on GDM are further limited by the lack of consistent methods for screening and diagnosing GDM. Further follow-up will determine the extent to which the associations of pregnancy complications are maintained into the age range at which CVD events are most common in women. In the meantime, the stratification of risk by time since pregnancy is a helpful way to examine the extent to which risk associated with pregnancy complications changes over time (Hastie et al. 2011a). Although relative risk of CVD events may weaken with time, the absolute risks associated with a history of pregnancy complications is likely to grow with time since pregnancy, as women age. In addition, we should incorporate pregnancy history data into existing CVD cohorts with decades of followup. By illuminating the timing with which particular CVD risk factors emerge in the wake of specific pregnancy complications, we may be able to leverage the information contained by pregnancy history to predict CVD risk earlier than conventional risk screening protocols. (b) Importance of linking research across life stages: need for studies with established CVD risk factors measured before, during, and after pregnancy To understand the trajectory of CVD risk and the role of pregnancy complications in that trajectory, we need more studies to measure CVD risk factors prior to conception, particularly as evidence suggests that changes in blood pressure, augmentation index, and pulse wave velocity occur as early as 6 weeks’ gestation, indicating that maternal adaptations occur very early in gestation (Mahendru et al. 2014). This raises the question of whether “booking” first trimester measures that are available in several birth cohorts is representative of prepregnancy values and emphasizes the importance of ascertaining cardiovascular health trajectories from pre- to postpartum. (c) Need for innovative analytical approaches to improve causal inference Methods for improving our understanding of whether pregnancy complications are causally related to later maternal health need to go beyond Pregnancy Characteristics and Women’s Cardiovascular Health conventional multivariable approaches in prospective cohorts. For example, if it is found that genetic variants associated with high blood pressure and glucose intolerance/type 2 diabetes in general populations of men and nonpregnant women are also associated with HDP and GDM, this would lend some support to the hypothesis of a common etiology and pregnancy unmasking a preexisting (genetic) risk. There is some evidence that several type 2 diabetes mellitus variants from genome-wide association studies show robust associations with GDM (Karlsson et al. 2007; Lauenborg et al. 2009; Cho et al. 2009; Kwak et al. 2012). Although it is not feasible to randomize women to pregnancy complications, long-term follow-up of women who have been in randomized controlled trials that have effectively prevented or treated the pregnancy complication will also address some of the research questions above. Lastly, experimental induction of pregnancy complications in animal models and following the mothers after delivery to examine whether vascular damage was sustained or metabolic risk increased are important for examining the question of a pregnancy causal effect (Bytautiene et al. 2010). However, the generalizability of the animal models depends on the fidelity with which the human pregnancy complications, such as preeclampsia, can be mimicked in other species, where they may not occur naturally. 4.4 Translational Priorities We are just beginning to investigate the clinical implications of this growing body of research. First, we need to establish the role of pregnancy complications in determining maternal chronic disease risk. Independently of that, we also need to determine our ability to change the health trajectories of women with histories of complicated pregnancy. We will then have to consider the many issues of integrating the findings into clinical and public health systems. Some potential clinical implications include the need to link pre- 159 natal with primary care medical records, development of clinical screening strategies, prevention and treatment protocols after pregnancy complications, and increasing awareness among clinicians of these associations that span typical clinical silos between obstetrics and medicine (Rich-Edwards et al. 2010). 5 Conclusions The stress test of pregnancy provides glimpses into the otherwise silent early adult years in which health development and chronic disease trajectories are set. Research to characterize the ways in which pregnancy complications inform us about subclinical and clinical vascular and metabolic risk in the mother is in its infancy. Future research will require large datasets that have prospectively collected accurate data on cardiovascular risk factors before, during, and after pregnancy, into middle age and beyond, when disease begins to emerge; data on pregnancy complications is also required. Only with such detailed information can we determine the extent to which specific pregnancy complications are related to future CVD, over and above prepregnancy risk factors, and whether they add to established risk factor scores calculated in middle age. With large birth cohorts increasingly recognizing the importance of long-term follow-up of the mother as well as their infant, the potential for this research is increasing. Ultimately, randomized controlled trials will be necessary to establish whether pregnancy advice and/or continued monitoring and early treatment of women identified as at risk during pregnancy is a costeffective way of reducing CVD risk in women. Research in this area will require integration across such diverse specialties including obstetrics, primary care, pediatrics, endocrinology, and cardiology. This broader perspective may yield novel insights into the determinants of pregnancy outcomes and health development across the lifespan, perhaps creating a large shift in the ways in which we promote the health of women and children. 160 References Agatisa, P. K., Ness, R. B., Roberts, J. M., Costantino, J. P., Kuller, L. H., & McLaughlin, M. K. (2004). Impairment of endothelial function in women with a history of preeclampsia: An indicator of cardiovascular risk. American Journal of Physiology – Heart and Circulatory Physiology, 286, H1389–H1393. Anastasiou, E., Lekakis, J. P., Alevizaki, M., et al. (1998). Impaired endothelium-dependent vasodilatation in women with previous gestational diabetes. Diabetes Care, 21, 2111–2115. Beck, S., Wojdyla, D., Say, L., et al. (2010). The worldwide incidence of preterm birth: A systematic review of maternal mortality and morbidity. Bulletin of the World Health Organization, 88, 31–38. Bellamy, L., Casas, J. P., Hingorani, A. D., & Williams, D. J. (2007). Pre-eclampsia and risk of cardiovascular disease and cancer in later life: Systematic review and meta-analysis. British Medical Journal, 335, 974. Bellamy, L., Casas, J. P., Hingorani, A. D., & Williams, D. (2009). Type 2 diabetes mellitus after gestational diabetes: A systematic review and meta-analysis. Lancet, 373, 1773–1779. Berends, A. L., de Groot, C. J., Sijbrands, E. J., et al. (2008). Shared constitutional risks for maternal vascular-related pregnancy complications and future cardiovascular disease. Hypertension, 51, 1034–1041. Bonamy, A. K., Parikh, N. I., Cnattingius, S., Ludvigsson, J. F., & Ingelsson, E. (2011). Birth characteristics and subsequent risks of maternal cardiovascular disease: Effects of gestational age and fetal growth. Circulation, 124, 2839–2846. Butte, N. F. (2000). Carbohydrate and lipid metabolism in pregnancy: Normal compared with gestational diabetes mellitus. The American Journal of Clinical Nutrition, 71, 1256S–1261S. Bytautiene, E., Lu, F., Tamayo, E. H., et al. (2010). Longterm maternal cardiovascular function in a mouse model of sFlt-1-induced preeclampsia. American Journal of Physiology. Heart and Circulatory Physiology, 298, H189–H193. Campbell, S., Griffin, D. R., Pearce, J. M., et al. (1983). New doppler technique for assessing uteroplacental blood flow. Lancet, 321, 675–677. Carr, D. B., Utzschneider, K. M., Hull, R. L., et al. (2006). Gestational diabetes mellitus increases the risk of cardiovascular disease in women with a family history of type 2 diabetes. Diabetes Care, 29, 2078–2083. Catov, J., Newman, A., Roberts, J., et al. (2007a). Association between infant birth weight and maternal cardiovascular risk factors in the health, aging, and body composition study. Annals of Epidemiology, 17, 36–43. Catov, J. M., Bodnar, L. M., Kip, K. E., et al. (2007b). Early pregnancy lipid concentrations and spontaneous preterm birth. American Journal of Obstetrics and Gynecology, 197, 610. e611–610. e617. A. Fraser et al. Catov, J. M., Bodnar, L. M., Ness, R. B., Barron, S. J., & Roberts, J. M. (2007c). Inflammation and dyslipidemia related to risk of spontaneous preterm birth. American Journal of Epidemiology, 166, 1312–1319. Catov, J. M., Nohr, E. A., Olsen, J., & Ness, R. B. (2008a). Chronic hypertension related to risk for preterm and term small-for-gestational-age births. Obstetrics and Gynecology, 112, 290. Catov, J., Bodnar, L., Hackney, D., Roberts, J., & Simhan, S. (2008b). Activation of the fibrinolytic cascade early in pregnancy among women with spontaneous preterm birth. Obstetrics and Gynecology, 112, 1116–1122. Catov, J. M., Wu, C. S., Olsen, J., Sutton-Tyrrell, K., Li, J., & Nohr, E. A. (2010a). Early or recurrent preterm birth and maternal cardiovascular disease risk. Annals of Epidemiology, 20, 604–609. Catov, J. M., Ness, R. B., Wellons, M. F., Jacobs, D. R., Roberts, J. M., & Gunderson, E. P. (2010b). Prepregnancy lipids related to preterm birth risk: The coronary artery risk development in young adults study. Journal of Clinical Endocrinology and Metabolism, 95, 2009–2028. Catov, J. M., Dodge, R., Yamal, J. M., Roberts, J. M., Piller, L. B., & Ness, R. B. (2011). Prior preterm or small-for-gestational-age birth related to maternal metabolic syndrome. Obstetrics and Gynecology, 117, 225–232. Catov, J. M., Dodge, R., Barinas-Mitchell, E., et al. (2013). Prior preterm birth and maternal subclinical cardiovascular disease 4 to 12 years after pregnancy. Journal of Women’s Health, 22(2002), 835–843. Cho, Y. M., Kim, T. H., Lim, S., et al. (2009). Type 2 diabetes-associated genetic variants discovered in the recent genome-wide association studies are related to gestational diabetes mellitus in the Korean population. Diabetologia, 52, 253–261. Clausen, T., Djurovic, S., & Henriksen, T. (2001). Dyslipidemia in early second trimester is mainly a feature of women with early onset pre-eclampsia. BJOG, 108, 1081–1087. Curry, A., Vogel, I., Drews, C., et al. (2007). Midpregnancy maternal plasma levels of interleukin 2, 6, and 12, tumor necrosis factor-alpha, interferon-gamma, and granulocyte-macrophage colony-stimulating factor and spontaneous preterm delivery. Acta Obstetricia et Gynecologica Scandinavica, 86, 1103–1110. Davey Smith, G., Hart, C., Ferrell, C., et al. (1997). Birth weight of offspring and mortality in the Renfrew and Paisley study: Prospective observational study. British Medical Journal, 315, 1189–1193. Davey Smith, G., Harding, S., & Rosato, M. (2000a). Relation between infants’ birth weight and mothers’ mortality: Prospective observational study. British Medical Journal, 320, 839–840. Davey Smith, G., Whitley, E., Gissler, M., & Hemminki, E. (2000b). Birth dimensions of offspring, premature birth, and the mortality of mothers. Lancet, 356, 2066–2067. Pregnancy Characteristics and Women’s Cardiovascular Health Davey Smith, G., Sterne, J., Tynelius, P., Lawlor, D. A., & Rasmussen, F. (2005). Birth weight of offspring and subsequent cardiovascular mortality of the parents. Epidemiology, 16, 563–569. Davey Smith, G., Hypponen, E., Power, C., & Lawlor, D. A. (2007). Offspring birth weight and parental mortality: Prospective observational study and metaanalysis. American Journal of Epidemiology, 166, 160–169. Dekker, J. M., & Schouten, E. G. (1993). Number of pregnancies and risk of cardiovascular disease. The New England Journal of Medicine, 329, 1893–1894. author reply 1894–1895. Di Cianni, G., Lencioni, C., Volpe, L., et al. (2007). C-reactive protein and metabolic syndrome in women with previous gestational diabetes. Diabetes/ Metabolism Research and Reviews, 23, 135–140. Ducey, J., Schulman, H., Farmakides, G., et al. (1987). A classification of hypertension in pregnancy based on Doppler velocimetry. American Journal of Obstetrics and Gynecology, 157, 680–685. Edison, R. J., Berg, K., Remaley, A., et al. (2007). Adverse birth outcome among mothers with low serum cholesterol. Pediatrics, 120, 723–733. Enquobahrie, D. A., Williams, M. A., Qiu, C., & Luthy, D. A. (2005). Early pregnancy lipid concentrations and the risk of gestational diabetes mellitus. Diabetes Research and Clinical Practice, 70, 134–142. Ernst, G. D. S., de Jonge, L. L., Hofman, A., et al. (2011). C-reactive protein levels in early pregnancy, fetal growth patterns, and the risk for neonatal complications: The Generation R Study. American Journal of Obstetrics and Gynecology, 205, 132.e131–132. e112. Fraser, A., Nelson, S. M., Macdonald-Wallis, C., et al. (2012). Associations of pregnancy complications with calculated cardiovascular disease risk and cardiovascular risk factors in middle age/clinical perspective. Circulation, 125, 1367–1380. Freathy, R. M., Weedon, M. N., Bennett, A., et al. (2007). Type 2 diabetes TCF7L2 risk genotypes alter birth weight: A study of 24,053 individuals. American Journal of Human Genetics, 80, 1150–1161. Freeman, D. J., McManus, F., Brown, E. A., et al. (2004). Short-and long-term changes in plasma inflammatory markers associated with preeclampsia. Hypertension, 44, 708–714. Friedlander, Y., Paltiel, O., Manor, O., et al. (2007). Birthweight of offspring and mortality of parents: The Jerusalem perinatal study cohort. Annals of Epidemiology, 17, 914–922. Funai, E. F., Friedlander, Y., Paltiel, O., et al. (2005). Longterm mortality after preeclampsia. Epidemiology, 16, 206–215. Gammill, H. S., Powers, R. W., Clifton, R. G., et al. (2010). Does C-reactive protein predict recurrent preeclampsia? Hypertension in Pregnancy, 29, 399–409. Girardi, G. (2011). Role of tissue factor in pregnancy complications: Crosstalk between coagulation and 161 inflammation. Thrombosis Research, 127(Suppl 3), S43–S46. Green, A. B. V., & Moser, K. (1988). Mortality in women in relation to their childbearing history. BMJ (Clinical research ed), 297, 391–395. Greenland, P., Alpert, J. S., Beller, G. A., et al. (2010). 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: A report of the American College of Cardiology Foundation/ American Heart Association Task Force on Practice Guidelines. Circulation, 122, e584–e636. Hackney, D. N., Catov, J. M., & Simhan, H. N. (2010). Low concentrations of thrombin-inhibitor complexes and the risk of preterm delivery. American Journal of Obstetrics and Gynecology, 203, 184.e181–184.e186. Hannaford, P., Ferry, S., & Hirsch, S. (1997). Cardiovascular sequelae of toxaemia of pregnancy. Heart, 77, 154–158. Hastie, C. E., Smith, G. C., MacKay, D. F., & Pell, J. P. (2011a). Maternal risk of ischaemic heart disease following elective and spontaneous pre-term delivery: Retrospective cohort study of 750 350 singleton pregnancies. International Journal of Epidemiology, 40, 914–919. Hastie, C. E., Smith, G. C. S., Mackay, D. F., & Pell, J. P. (2011b). Association between preterm delivery and subsequent C-reactive protein: A retrospective cohort study. American Journal of Obstetrics and Gynecology, 205, 556.e551–556.e554. Hedderson, M. M., Ferrara, A., & Sacks, D. A. (2003). Gestational diabetes mellitus and lesser degrees of pregnancy hyperglycemia: Association with increased risk of spontaneous preterm birth. Obstetrics and Gynecology, 102, 850–856. Heilmann, L., Rath, W., & Pollow, K. (2007). Hemostatic abnormalities in patients with severe preeclampsia. Clinical and Applied Thrombosis/Hemostasis, 13, 285–291. Heitritter, S. M., Solomon, C. G., Mitchell, G. F., Skali-Ounis, N., & Seely, E. W. (2005). Subclinical inflammation and vascular dysfunction in women with previous gestational diabetes mellitus. Journal of Clinical Endocrinology and Metabolism, 90, 3983–3988. Herrera, E. (2000). Metabolic adaptations in pregnancy and their implications for the availability of substrates to the fetus. European Journal of Clinical Nutrition, 54, S47. Hubel, C. A., McLaughlin, M. K., Evans, R. W., Hauth, B. A., Sims, C. J., & Roberts, J. M. (1996). Fasting serum triglycerides, free fatty acids, and malondialdehyde are increased in preeclampsia, are positively correlated, and decrease within 48 hours post partum. American Journal of Obstetrics and Gynecology, 174, 975–982. Hubel, C., Shakir, Y., Gallaher, M., McLaughlin, M., & Roberts, J. (1998a). Low-density lipoprotein particle size decreases during normal pregnancy in association with triglyceride increases. Journal of the Society for Gynecologic Investigation, 5, 244–250. 162 Hubel, C. A., Lyall, F., Weissfeld, L., Gandley, R. E., & Roberts, J. M. (1998b). Small low-density lipoproteins and vascular cell adhesion molecule-1 are increased in association with hyperlipidemia in preeclampsia. Metabolism, 47, 1281–1288. Hubel, C. A., Powers, R., Snaedal, S., et al. (2006). C-reactive protein is increased 30 years after eclamptic pregnancy. J Soc Gynecol Invest, 13(2 Suppl), 292A. Hubel, C. A., Powers, R. W., Snaedal, S., et al. (2008). C-reactive protein is elevated 30 years after eclamptic pregnancy. Hypertension, 51, 1499–1505. Irgens, H. U., Reisaeter, L., Irgens, L. M., & Lie, R. T. (2001). Long term mortality of mothers and fathers after pre-eclampsia: Population based cohort study. BMJ (Clinical research ed), 323, 1213–1217. James-Todd, T. K. A., Hibert, E., Mason, S., Vadnais, M., Hu, F., & Rich-Edwards. J. (2010). Gestation length, birth weight and subsequent risk of type 2 diabetes in mothers. Oral presentation. Presented at American Diabetes Association’s 70th Scientific Sessions, June 2010, Orlando, FL. Jónsdóttir, L., Arngrimsson, R., Geirsson, R. T., Slgvaldason, H., & Slgfússon, N. (1995). Death rates from ischemic heart disease in women with a history of hypertension in pregnancy. Acta Obstetricia et Gynecologica Scandinavica, 74, 772–776. Kanagalingam, M. G., Nelson, S. M., Freeman, D. J., et al. (2009). Vascular dysfunction and alteration of novel and classic cardiovascular risk factors in mothers of growth restricted offspring. Atherosclerosis, 205, 244–250. Kelly, R., Holzman, C., Senagore, P., et al. (2009). Placental vascular pathology findings and pathways to preterm delivery. American Journal of Epidemiology, 170, 148–158. Kestenbaum, B., Seliger, S. L., Easterling, T. R., et al. (2003). Cardiovascular and thromboembolic events following hypertensive pregnancy. American Journal of Kidney Diseases, 42, 982–989. Kim, C., Newton, K. M., & Knopp, R. H. (2002). Gestational diabetes and the incidence of type 2 diabetes a systematic review. Diabetes Care, 25, 1862–1868. Kwak, S. H., Kim, S. H., Cho, Y. M., et al. (2012). A genome-wide association study of gestational diabetes mellitus in Korean women. Diabetes, 61, 531–541. Labarthe, D. R. (1999). Prevention of cardiovascular risk factors in the first place. Preventive Medicine, 29, S72–S78. Laivuori, H., Tikkanen, M. J., & Ylikorkala, O. (1996). Hyperinsulinemia 17 years after preeclamptic first pregnancy. The Journal of Clinical Endocrinology and Metabolism, 81, 2908–2911. Lauenborg, J., Mathiesen, E., Hansen, T., et al. (2005). The prevalence of the metabolic syndrome in a danish population of women with previous gestational diabetes mellitus is three-fold higher than in the general population. The Journal of Clinical Endocrinology and Metabolism, 90, 4004–4010. A. Fraser et al. Lauenborg, J., Grarup, N., Damm, P., et al. (2009). Common type 2 diabetes risk gene variants associate with gestational diabetes. The Journal of Clinical Endocrinology and Metabolism, 94, 145–150. Lawlor, D. A., Emberson, J. R., Ebrahim, S., et al. (2003). Is the association between parity and coronary heart disease due to biological effects of pregnancy or adverse lifestyle risk factors associated with child-rearing? Circulation, 107, 1260–1264. Lawlor, D. A., Relton, C., Sattar, N., & Nelson, S. M. (2012). Maternal adiposity—a determinant of perinatal and offspring outcomes? Nature Reviews Endocrinology, 8, 679–688. Lie, R. T., Wilcox, A. J., & Skjaerven, R. (2006). Maternal and paternal influences on length of pregnancy. Obstetrics and Gynecology, 107, 880–885. Lin, Y.-S., Tang, C.-H., Yang, C.-Y. C., et al. (2011). Effect of pre-eclampsia–eclampsia on major cardiovascular events among peripartum women in Taiwan. The American Journal of Cardiology, 107, 325–330. Lykke, J. A., Langhoff-Roos, J., Sibai, B. M., Funai, E. F., Triche, E. W., & Paidas, M. J. (2009). Hypertensive pregnancy disorders and subsequent cardiovascular morbidity and type 2 diabetes mellitus in the mother. Hypertension, 53, 944–951. Lykke, J. A., Langhoff-Roos, J., Lockwood, C. J., Triche, E. W., & Paidas, M. J. (2010a). Mortality of mothers from cardiovascular and non-cardiovascular causes following pregnancy complications in first delivery. Paediatric and Perinatal Epidemiology, 24, 323–330. Lykke, J., Paidas, M., Damm, P., Triche, E., Kuczynski, E., & Langhoff-Roos, J. (2010b). Preterm delivery and risk of subsequent cardiovascular morbidity and type-II diabetes in the mother. BJOG: An International Journal of Obstetrics & Gynaecology, 117, 274–281. Lykke, J. A., Paidas, M. J., Triche, E. W., & LANGHOFFROOS, J. (2012). Fetal growth and later maternal death, cardiovascular disease and diabetes. Acta Obstetricia et Gynecologica Scandinavica, 91, 503–510. Macdonald-Wallis, C., Lawlor, D. A., Fraser, A., May, M., Nelson, S. M., & Tilling, K. (2012). Blood pressure change in normotensive, gestational hypertensive, preeclamptic, and essential hypertensive pregnancies. Hypertension, 59, 1241–1248. Magnussen, E. B., Vatten, L. J., Lund-Nilsen, T. I., Salvesen, K. A., Smith, G. D., & Romundstad, P. R. (2007). Prepregnancy cardiovascular risk factors as predictors of pre-eclampsia: Population based cohort study. BMJ (Clinical research ed), 335, 978. Magnussen, E. B., Vatten, L. J., Smith, G. D., & Romundstad, P. R. (2009). Hypertensive disorders in pregnancy and subsequently measured cardiovascular risk factors. Obstetrics and Gynecology, 114, 961–970. Magnussen, E. B., Vatten, L. J., Myklestad, K., Salvesen, K. Å., & Romundstad, P. R. (2011). Cardiovascular risk factors prior to conception and the length of Pregnancy Characteristics and Women’s Cardiovascular Health pregnancy: Population-based cohort study. American Journal of Obstetrics and Gynecology, 204, 526. e521–526.e528. Mahendru, A. A., Everett, T. R., Wilkinson, I. B., Lees, C. C., & McEniery, C. M. (2014). A longitudinal study of maternal cardiovascular function from preconception to the postpartum period. Journal of Hypertension, 32, 849–856. Manten, G. T., Sikkema, M. J., Voorbij, H. A., Visser, G. H., Bruinse, H. W., & Franx, A. (2007). Risk factors for cardiovascular disease in women with a history of pregnancy complicated by preeclampsia or intrauterine growth restriction. Hypertension in Pregnancy, 26, 39–50. Martinez, G., Daniels, K., & Chandra, A. (2012). Fertility of men and women aged 15–44 years in the United States: National survey of family growth, 2006–2010. Natl Health Stat Report, 12, 1–28. Mathers, C., Boerma, J. T., Fat, D. M., & World Health Organization. (2008). The global burden of disease : 2004 update. Geneva: World Health Organization. McDonald, S. D., Malinowski, A., Zhou, Q., Yusuf, S., & Devereaux, P. J. (2008). Cardiovascular sequelae of preeclampsia/eclampsia: A systematic review and meta-analyses. American Heart Journal, 156, 918–930. Metzger, B. E., Cho, N. H., Roston, S. M., & Radvany, R. (1993). Prepregnancy weight and antepartum insulin secretion predict glucose tolerance five years after gestational diabetes mellitus. Diabetes Care, 16(1wik), 42–1605. Meyers-Seifer, C. H., & Vohr, B. R. (1996). Lipid levels in former gestational diabetic mothers. Diabetes Care, 19, 1351–1356. MMWR. (1989). Chronic disease reports in the Morbidity and Mortality Weekly Report (MMWR). MMWR Morb Mortal Wkly Rep, 38, 1–8. Monga, M., & Creasy, R. (1994). Cardiovascular and renal adaptation to pregnancy. In R. Creasy, R. Resnik, J. D. Iams, et al. (Eds.), Maternal-fetal medicine: Principles and practice (pp. 758–767). Philadelphia: WB Saunders. Mongraw-Chaffin, M. L., Cirillo, P. M., & Cohn, B. A. (2010). Preeclampsia and cardiovascular disease death. Hypertension, 56, 166–171. Mosca, L., Benjamin, E. J., Berra, K., et al. (2011). Effectiveness-based guidelines for the prevention of cardiovascular disease in women – 2011 update: A guideline from the american heart association. Circulation, 123, 1243–1262. Nanda, S. S. M., Syngelaki, A., Akolekar, R., & Nicolaides, K. H. (2011). Prediction of gestational diabetes mellitus by maternal factors and biomarkers at 11 to 13 weeks. Prenatal Diagnosis, 31, 135–141. Nardi, O., Zureik, M., Courbon, D., Ducimetière, P., & Clavel-Chapelon, F. (2006). Preterm delivery of a first child and subsequent mothers’ risk of ischaemic heart disease: A nested case–control study. European Journal of Cardiovascular Prevention & Rehabilitation, 13, 281–283. 163 Ness, R. B., Harris, T., Cobb, J., et al. (1993). Number of pregnancies and the subsequent risk of cardiovascular disease. The New England Journal of Medicine, 328, 1528–1533. Ness, R. B., Cobb, J., Harm, T., & D’Agostino, R. B. (1995). Does number of children increase the rate of coronary heart disease in men? Epidemiology, 6, 442–445. North, R. A., McCowan, L. M., Dekker, G. A., et al. (2011). Clinical risk prediction for pre-eclampsia in nulliparous women: Development of model in international prospective cohort. BMJ (Clinical research ed), 342, d1875. O’Brien, T. E., Ray, J. G., & Chan, W. S. (2003). Maternal body mass index and the risk of preeclampsia: A systematic overview. Epidemiology, 14, 368–374. Oblast, T. I. (1999). Decline in deaths from heart disease and stroke—United States, 1900–1999. Heart Disease and Stroke, 63(1900), 593–597. OECD Family D. (2014). Childlessness (SF 2.5) In: Social Policy Division, Organisation for Economic Co-operation and Development. Paris, France: 2010. SF2.5 report on childlessness. www.oecd.org/social/ family/database. Accessed 24 June, 2013. Ostlund, I., Haglund, B., & Hanson, U. (2004). Gestational diabetes and preeclampsia. European Journal of Obstetrics, Gynecology, and Reproductive Biology, 113, 12–16. Parikh, N. I., Cnattingius, S., Dickman, P. W., Mittleman, M. A., Ludvigsson, J. F., & Ingelsson, E. (2010). Parity and risk of later-life maternal cardiovascular disease. American Heart Journal, 159, 215–221. e216. Pell, J. P., Smith, G. C., & Walsh, D. (2004). Pregnancy complications and subsequent maternal cerebrovascular events: A retrospective cohort study of 119,668 births. American Journal of Epidemiology, 159, 336–342. Pitiphat, W., Gillman, M. W., Joshipura, K. J., Williams, P. L., Douglass, C. W., & Rich-Edwards, J. W. (2005). Plasma C-reactive protein in early pregnancy and preterm delivery. American Journal of Epidemiology, 162, 1108–1113. Portelinha, A., Cerdeira, A. S., Belo, L., et al. (2009). Haemostatic factors in women with history of Preeclampsia. Thrombosis Research, 124, 52–56. Qiu, C., Williams, M. A., Leisenring, W. M., et al. (2003). Family history of hypertension and type 2 diabetes in relation to preeclampsia risk. Hypertension, 41, 408–413. Regitz-Zagrosek, V., Lundqvist, C. B., Borghi, C., et al. (2011). ESC Guidelines on the management of cardiovascular diseases during pregnancy. The Task Force on the Management of Cardiovascular Diseases during Pregnancy of the European Society of Cardiology (ESC). European Heart Journal, 32, 3147–3197. Retnakaran, R., & Shah, B. R. (2009a). Abnormal screening glucose challenge test in pregnancy and future risk of diabetes in young women. Diabetic Medicine, 26, 474–477. 164 Retnakaran, R., & Shah, B. R. (2009b). Mild glucose intolerance in pregnancy and risk of cardiovascular disease: A population-based cohort study. Canadian Medical Association Journal, 181, 371–376. Retnakaran, R., Austin, P. C., & Shah, B. R. (2011). Effect of subsequent pregnancies on the risk of developing diabetes following a first pregnancy complicated by gestational diabetes: A population-based study. Diabetic Medicine, 28, 287–292. Rich-Edwards, J. W. (2009). Reproductive health as a sentinel of chronic disease in women. Women’s Health (London, England), 5, 101–105. Rich-Edwards, J. W., McElrath, T. F., Karumanchi, S. A., & Seely, E. W. (2010). Breathing life into the lifecourse approach: Pregnancy history and cardiovascular disease in women. Hypertension, 56, 331–334. Rich-Edwards, J. W. K. K., Wilcox, A., & Skjaerven, R. (2012). Duration of first pregnancy predicts maternal cardiovascular death, whether delivery was medically indicated or spontaneous. American Journal of Epidemiology, 175(Suppl 11), S64. Rich-Edwards, J. W., Fraser, A., Lawlor, D. A., & Catov, J. M. (2014). Pregnancy characteristics and women’s future cardiovascular health: An underused opportunity to improve women’s health? Epidemiologic Reviews, 36, 57–70. Riskin-Mashiah, S., Damti, A., Younes, G., & Auslender, R. (2010). First trimester fasting hyperglycemia as a predictor for the development of gestational diabetes mellitus. European Journal of Obstetrics, Gynecology, and Reproductive Biology, 152, 163–167. Roberts, C. L., Algert, C. S., Morris, J. M., Ford, J. B., & Henderson-Smart, D. J. (2005). Hypertensive disorders in pregnancy: A population-based study. The Medical Journal of Australia, 182, 332–335. Romero, R., Espinoza, J., Gonçalves, L. F., Kusanovic, J. P., Friel, L., & Hassan, S. (2007). The role of inflammation and infection in preterm birth. Seminars in Reproductive Medicine, 25, 021–039. Romundstad, P. R., Davey Smith, G., Nilsen, T. I., & Vatten, L. J. (2007). Associations of prepregnancy cardiovascular risk factors with the offspring’s birth weight. American Journal of Epidemiology, 166, 1359–1364. Romundstad, P. R., Magnussen, E. B., Smith, G. D., & Vatten, L. J. (2010). Hypertension in pregnancy and later cardiovascular risk: Common antecedents? Circulation, 122, 579–584. Sacks DA, C. W., Wolde-Tsadik, G., & Buchanan, T. A. (2003). Fasting plasma glucose test at the first prenatal visit as a screen for gestational diabetes. Obstetrics and Gynecology, 101, 1197–1203. Sarwar, N., Gao, P., Seshasai, S. R., et al. (2010). Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: A collaborative meta-analysis of 102 prospective studies. Lancet, 375, 2215–2222. Sattar, N. (2004). Do pregnancy complications and CVD share common antecedents? Atherosclerosis Supplements, 5, 3–7. A. Fraser et al. Sattar, N., & Greer, I. A. (2002). Pregnancy complications and maternal cardiovascular risk: Opportunities for intervention and screening? British Medical Journal, 325, 157–160. Sattar, N., Bendomir, A., Berry, C., Shepherd, J., Greer, I. A., & Packard, C. J. (1997). Lipoprotein subfraction concentrations in preeclampsia: Pathogenic parallels to atherosclerosis. Obstetrics and Gynecology, 89, 403–408. Sattar, N., Greer, I., Galloway, P., et al. (1999). Lipid and lipoprotein concentrations in pregnancies complicated by intrauterine growth restriction. Journal of Clinical Endocrinology and Metabolism, 84, 128–130. Sattar, N., Ramsay, J., Crawford, L., Cheyne, H., & Greer, I. A. (2003). Classic and novel risk factor parameters in women with a history of preeclampsia. Hypertension, 42, 39–42. Savvidou, M. D., Hingorani, A. D., Tsikas, D., Frölich, J. C., Vallance, P., & Nicolaides, K. H. (2003). Endothelial dysfunction and raised plasma concentrations of asymmetric dimethylarginine in pregnant women who subsequently develop pre-eclampsia. The Lancet, 361, 1511–1517. Savvidou, M. D., Kaihura, C., Anderson, J. M., & Nicolaides, K. H. (2011). Maternal arterial stiffness in women who subsequently develop pre-eclampsia. PLoS ONE, 6, e18703. Scarborough, P., & Weissberg, P. (2011). Trends in coronary heart disease, 1961–2011. London: British Heart Foundation. Schwarz, E. B., Ray, R. M., Stuebe, A. M., et al. (2009). Duration of lactation and risk factors for maternal cardiovascular disease. Obstetrics and Gynecology, 113, 974–982. Shaat, N. L. A., Karlsson, E., Ivarsson, S., Parikh, H., Berntorp, K., & Groop, L. (2007). A variant in the transcription factor 7-like 2 (TCF7L2) gene is associated with an increased risk of gestational diabetes mellitus. Diabetologia, 50, 972–979. Shah, B. R., Retnakaran, R., & Booth, G. L. (2008). Increased risk of cardiovascular disease in young women following gestational diabetes mellitus. Diabetes Care, 31, 1668–1669. Shah, R. U., Klein, L., & Lloyd-Jones, D. M. (2009). Heart failure in women: Epidemiology, biology and treatment. Women’s Health (London, England), 5, 517–527. Shaw, L. J., Bairey Merz, C. N., Pepine, C. J., et al. (2006). Insights from the NHLBI-Sponsored Women’s Ischemia Syndrome Evaluation (WISE) Study: Part I: Gender differences in traditional and novel risk factors, symptom evaluation, and gender-optimized diagnostic strategies. Journal of the American College of Cardiology, 47, S4–S20. Shay, C. M., Ning, H., Allen, N. B., et al. (2012). Status of cardiovascular health in US adults clinical perspective prevalence estimates from the National Health and Nutrition Examination Surveys (NHANES) 2003– 2008. Circulation, 125, 45–56. Skjaerven, R., Wilcox, A. J., Klungsoyr, K., et al. (2012). Cardiovascular mortality after pre-eclampsia in one Pregnancy Characteristics and Women’s Cardiovascular Health child mothers: Prospective, population based cohort study. BMJ (Clinical research ed), 345, e7677. Smith, G. C., Pell, J. P., & Walsh, D. (2001). Pregnancy complications and maternal risk of ischaemic heart disease: A retrospective cohort study of 129,290 births. Lancet, 357, 2002–2006. Smith, G. C. S., Shah, I., Pell, J. P., Crossley, J. A., & Dobbie, R. (2007). Maternal obesity in early pregnancy and risk of spontaneous and elective preterm deliveries: A retrospective cohort study. American Journal of Public Health, 97, 157–162. Smith, G. N., Walker, M. C., Liu, A., et al. (2009). A history of preeclampsia identifies women who have underlying cardiovascular risk factors. American Journal of Obstetrics and Gynecology, 200(58), e51–e58. Solomon, C. G., Willett, W. C., Carey, V. J., et al. (1997). A prospective study of pregravid determinants of gestational diabetes mellitus. JAMA, 278, 1078–1083. Steenland, K., Lally, C., & Thun, M. (1996). Parity and coronary heart disease among women in the American Cancer Society CPS II population. Epidemiology, 7, 641–643. Stepan, H., Faber, R., & Walther, T. (1999). Expression of low density lipoprotein receptor messenger ribonucleic acid in placentas from pregnancies with intrauterine growth retardation. British Journal of Obstetrics and Gynaecology, 106, 1221–1222. Tarim, E., Yigit, F., Kilicdag, E., et al. (2006). Early onset of subclinical atherosclerosis in women with gestational diabetes mellitus. Ultrasound in Obstetrics & Gynecology, 27, 177–182. United Nations DoEaSA, Population Division. (2009). World fertility report. New York: United Nations Publications. Verma, A., Boney, C. M., Tucker, R., & Vohr, B. R. (2002). Insulin resistance syndrome in women with prior history of gestational diabetes mellitus. The Journal of Clinical Endocrinology and Metabolism, 87, 3227–3235. Wadsack, C., Tabano, S., Maier, A., et al. (2007). Intrauterine growth restriction is associated with alterations in placental lipoprotein receptors and maternal lipoprotein composition. American Journal of Physiology - Endocrinology and Metabolism, 292, E476–E484. 165 Wallis, A. B., Saftlas, A. F., Hsia, J., & Atrash, H. K. (2008). Secular trends in the rates of preeclampsia, eclampsia, and gestational hypertension, United States, 1987–2004. American Journal of Hypertension, 21, 521–526. Weintraub, W. S., Daniels, S. R., Burke, L. E., et al. (2011). Value of primordial and primary prevention for cardiovascular disease: A policy statement from the American Heart Association. Circulation, 124, 967–990. Wikström, A. K., Haglund, B., Olovsson, M., & Lindeberg, S. N. (2005). The risk of maternal ischaemic heart disease after gestational hypertensive disease. BJOG: An International Journal of Obstetrics & Gynaecology, 112, 1486–1491. Wilcox, A. J., Skjaerven, R., & Lie, R. T. (2008). Familial patterns of preterm delivery: Maternal and fetal contributions. American Journal of Epidemiology, 167, 474–479. Williams, M. A., Qiu, C., Muy-Rivera, M., Vadachkoria, S., Song, T., & Luthy, D. A. (2004). Plasma adiponectin concentrations in early pregnancy and subsequent risk of gestational diabetes mellitus. The Journal of Clinical Endocrinology and Metabolism, 89, 2306–2311. Wilson, B. J., Watson, M. S., Prescott, G. J., et al. (2003). Hypertensive diseases of pregnancy and risk of hypertension and stroke in later life: Results from cohort study. British Medical Journal, 326, 845. Winzer, C., Wagner, O., Festa, A., et al. (2004). Plasma adiponectin, insulin sensitivity, and subclinical inflammation in women with prior gestational diabetes mellitus. Diabetes Care, 27, 1721–1727. Wolf, M., Kettyle, E., Sandler, L., Ecker, J. L., Roberts, J., & Thadhani, R. (2001). Obesity and preeclampsia: The potential role of inflammation. Obstetrics and Gynecology, 98, 757–762. Wolf, M., Hubel, C. A., Lam, C., et al. (2004). Preeclampsia and future cardiovascular disease: Potential role of altered angiogenesis and insulin resistance. The Journal of Clinical Endocrinology and Metabolism, 89, 6239–6243. Yusuf, S., Reddy, S., Ôunpuu, S., & Anand, S. (2001). Global burden of cardiovascular diseases. Circulation, 104, 2746–2753. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Part III The Life Course Origins and Consequences of Select Major Health Conditions and Issues Early in the Life Course: Time for Obesity Prevention Summer Sherburne Hawkins, Emily Oken, and Matthew W. Gillman 1 Introduction Although the dramatic rise in obesity in the USA experienced in recent decades (Wang and Beydoun 2007) may have now stabilized (Ogden et al. 2014; Wen et al. 2012), the prevalence of obesity remains high. The most recent nationally representative data collected in 2012 showed that 17% of children ages 2–19 years were obese (Ogden et al. 2014). From 2003 through 2010, the prevalence of obesity in low-income children aged 2–4 years remained at approximately 15% (Pan et al. 2012), while one regional study suggested that from 2004 through 2008, obesity prevalence started to drop among 0 to 6-year-olds from non-low-income families (Wen et al. 2012). The reasons for this potential slowing or slight reversal of the trend among some subgroups in the USA remain unknown. However, obesity has not affected all segments of the population equally (Wang and Beydoun 2007; Wang et al. 2011), and racial/ethnic differences in obesity are already evident by the preschool years (Ogden et al. 2014). The short- and long-term consequences of obesity include conditions involving nearly every organ system, such as asthma, type 2 diabetes, high blood pressure, cardiovascular disease, depression, and orthopedic problems, starting in childhood and escalating among adults (Han et al. 2010; Lobstein et al. 2004). Children who are obese are much more likely to be obese adults, and obesity at any age is very difficult to treat (Lobstein et al. 2004). Furthermore, the population-level effects of obesity are substantial. Nationwide, 9.1% of annual medical spending is attributable to adult obesity, representing a cost of $147 billion per year (Finkelstein et al. 2009). This combination of evidence on the epidemiology, health consequences, and public health impact of obesity suggests that prevention is essential. 2 S.S. Hawkins (*) Boston College, Chestnut Hill, MA, USA e-mail: summer.hawkins@bc.edu E. Oken • M.W. Gillman Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA e-mail: emily_oken@harvardpilgrim.org; matthew.gillman@nih.gov Conceptual Framework One of the reasons for the intractability of childhood obesity is the failure to take into account the complexity and interconnectedness of contributing factors ranging from the social, built, and economic environments to behavior, physiology, and epigenetics. These factors may also interact with each other creating a self-perpetuating cycle © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_9 169 S.S. Hawkins et al. 170 of obesity, as we illustrate using gestational diabetes mellitus (Sect. 4.3; Gillman 2016). Based on the life course health development approach to chronic disease epidemiology, biological, behavioral, and psychosocial exposures that occur at particular stages in the life course may have differential and/or lasting effects on later outcomes (Ben-Shlomo and Kuh 2002; Halfon and Forrest 2017). The effects of these exposures can be cumulative across an individual’s life as well as operate across generations to influence future generation’s risk for chronic disease. Factors can also interact with each other over the life course and be more or less important at particular stages. These periods of particular influence are often termed critical or sensitive (Ben-Shlomo and Kuh 2002). Throughout the chapter, we demonstrate that multiple critical and sensitive periods for obesity risk exist across the life course, although practically they may be hard to identify. Given the plasticity inherent in early human development, the preand perinatal periods may present the most important opportunity for critical or sensitive period effects. Added to the fact that treatment of obesity is impeded by cultural, behavioral, and physiological feedback loops, modifying environment, behaviors, and physiology early in life is likely an especially effective strategy for preventing obesity and its consequences. Consistent with LCHD principles (see Halfon and Forrest 2017), Glass and McAtee (2006) propose that the multilevel approach shown in Fig. 1 places obesity prevention in a complex system with individual risk factors being influenced by multiple “above-water” levels (families, neighborhood, policies) as well as by the interaction with biology and “underwater” levels (genes, epigenetics, physiology) over time. This framework adds to a life course health development approach by emphasizing feedback loops and cross-level influences, such as gene-environment interactions, thus highlighting the need for methodology that takes into account these complex relationships to help identify the important and modifiable levers of change (Huang and Glass 2008). We have organized this chapter to reflect the current thinking on periods in the life course that appear to be most important for the development of obesity. We focus primarily on the early portion of the life course, but also discuss later periods that may be key time points for intervening. These periods often represent times of active growth or turning points when the primary sources of influence change. At each stage of the life course, we discuss specific macro-level factors if they are directly relevant to that time period. Most macro-level factors, however, either indirectly or directly influence all age groups, so we present them together after reviewing each life course stage individually. While this chapter does not represent a systematic review of the literature, we present key examples at each stage of the life course to illustrate important risk factors, mechanisms, and gaps in research. We conclude with recommendations for future work on methodology, research in emerging areas, and implications for practice and policy. 3 Measurement of Overweight and Obesity Body mass index (BMI; weight/height2) is the most commonly used indirect measure of adiposity, or fatness, at the population level. The US Centers for Disease Control and Prevention (CDC) recommends monitoring growth in children aged 0–2 years using the World Health Organization weight for recumbent length growth standards and defines excess weight as at or above the 97.7th percentile (Grummer-Strawn et al. 2010). The CDC defines obesity in children aged 2–19 years as a BMI at or above the 95th percentile for age and sex, with overweight between the 85th and 95th percentiles, using the CDC sex-specific BMIfor-age growth charts from 2000 (Kuczmarski et al. 2002; Ogden and Flegal 2010). 4 Prenatal Period 4.1 Birth Weight Numerous studies have confirmed the associations of higher birth weight with risk of later obesity and type 2 diabetes mellitus; lower birth Early in the Life Course: Time for Obesity Prevention 171 Fig. 1 Conceptual framework for etiology of childhood obesity (Reproduced from Glass and McAtee (2006)) weight is consistently associated with increased risk for central adiposity and its metabolic correlates, including type 2 diabetes mellitus and cardiovascular disease (Parsons et al. 1999; Yu et al. 2011). Birth weight, however, is not an etiologic factor in itself. Some have used birth weight as a marker of in utero programming during sensitive periods of development (Gillman 2005). However, many factors could explain the relationship between birth weight and later obesity. For example, maternal obesity, a risk factor for higher birth weight, is also highly related to childhood obesity (Perng et al. 2014). This association may be due to genomic inheritance and shared postnatal environment of eating habits and physical activity or inactivity in addition to in utero effects (Gillman and Poston 2012). However, even after controlling for maternal obesity, sociodemographic characteristics, and other risk factors, the birth weight-obesity association remains, raising the possibility of a lasting effect of fetal programming on later health (Gillman 2004; Oken and Gillman 2003). 4.2 Maternal Prepregnancy Obesity Many women start their pregnancy already overweight or obese. In 2009, a representative survey from 20 states found that 21% of women were obese prepregnancy and an additional 25% were overweight (Fisher et al. 2013). Mothers who are overweight or obese prepregnancy are more likely to have infants with higher birth weight and an increased risk for being large-for-gestational age (LGA; >90th percentile of weight-for-gestational age) or macrosomic (birth weight > 4000 g) (Institute of Medicine 2009). S.S. Hawkins et al. 172 Women who are overweight or obese going into pregnancy are at higher risk for developing gestational diabetes mellitus (Torloni et al. 2009) and more likely to gain excessive amounts of weight during pregnancy (Battista et al. 2011; Dalenius et al. 2012). Studies must take into account these related factors to tease apart the roles of potentially modifiable risk factors. Furthermore, these risk factors may also be markers for shared genes and/or the postnatal environment (including lifestyle behaviors), which could also influence children’s risk for obesity. This issue of interrelated factors—which may serve as mediators, moderators, or confounders of each other—is applicable not just to the prenatal period, but also to all stages of life course health development. 4.3 Gestational Diabetes Mellitus (GDM) GDM is defined as diabetes first diagnosed in pregnancy, typically between 24 and 28 weeks gestation. Data on 59 million births from the National Hospital Discharge Survey showed that GDM increased from 1.9% to 4.2% from 1989 through 2004 (Getahun et al. 2008). Over this time period, the relative increase in GDM was 94% for white women (from 2.2% to 4.2%), but 260% for black women (from 0.6% to 2.1%) (Getahun et al. 2008). GDM may be fueling the obesity epidemic (Battista et al. 2011; Herring and Oken 2011). Figure 2 shows the interrelationships of maternal, fetal, and child factors across the life course that may propagate the intergenerational transmission of obesity and diabetes (Gillman 2016). Both maternal prepregnancy obesity and excessive gestational weight gain (GWG) independently increase mothers’ risk for developing GDM. A systematic review found that higher maternal prepregnancy BMI was associated with an increase in risk for GDM, such that overweight, moderately obese, and morbidly obese women were 2, 3, and 5.5 times more likely to develop GDM compared to women with normal BMI (Torloni et al. 2009). Excess GWG during preg- nancy may also increase mothers’ risk for GDM, independent of women’s prepregnancy weight (Hedderson et al. 2010). Compared to women with adequate levels of weight gain, women with excessive GWG are also more likely to have greater postpartum weight retention (Nehring et al. 2011). All of these factors increase women’s risk for subsequent health problems. Women with a history of GDM are more likely to develop type 2 diabetes mellitus, particularly within the first decade after delivery (Kim et al. 2002). Developing GDM in one pregnancy increases mothers’ risk for the recurrence of GDM in subsequent pregnancies (Kim et al. 2007). If women do not lose the excess weight they gained before they become pregnant again, this increased adiposity reinforces the cycle in the subsequent pregnancy. Prepregnancy obesity, excessive GWG, and GDM are associated with higher fetal growth and subsequent increases in children’s adiposity and risk for obesity (Fig. 2) (Gillman 2016). Infants born to mothers with prepregnancy obesity or GDM are more likely to be of higher birth weight or be macrosomic (Battista et al. 2011; Institute of Medicine 2009), and trials of GDM treatment show reductions in macrosomia (Crowther et al. 2005; Gillman et al. 2010; Landon et al. 2009). A study found that children born to mothers with GDM had higher levels of adiposity and insulin secretion at age 5–10 years, independent of current BMI (Chandler-Laney et al. 2012). Boney et al. (2005) found that children who were LGA at birth and exposed to either GDM or maternal obesity in utero were at an increased risk for developing metabolic syndrome. The cycle continues when girls who are obese mature and have children of their own. Plausible developmental mechanisms exist for the influence of GDM on childhood obesity. Pancreatic β-cells normally increase their insulin secretion to compensate for the insulin resistance during pregnancy. Glucose intolerance results when the β-cells are not able to respond to this increasing demand (Battista et al. 2011). Since glucose crosses the placenta but insulin does not, the fetus is exposed to greater levels of glucose (Freinkel 1980). The developing fetal pancreas Early in the Life Course: Time for Obesity Prevention 173 Postpartum Pregnancy Prepregnancy obesity Fetus Postpartum weight retention Excess gestational weight gain Mother Later Life Type 2 diabetes, cardiovascular disease,etc. Gestational diabetes Altered fetal growth & metabolism Child Altered body composition Child obesity Adult obesity, type 2 diabetes, CVD, etc. Fig. 2 Intergenerational transmission of obesity and gestational diabetes mellitus (Reproduced from Gillman (2016)) responds to the additional glucose by producing insulin, which can increase fetal adiposity (Freinkel 1980). Current research on the mechanisms of GDM is focusing on the areas of genetics, glucose, amino acid, lipid transport, and adipokines (Harlev and Wiznitzer 2010). Methodological challenges abound in studying the association between GDM and later obesity. Kim et al. (2011) have argued that many studies combine pregestational diabetes mellitus and GDM into one category, examine only exposed or high-risk infants without an appropriate control group, and fail to control for potential confounding factors, most notably BMI. Their systematic review identified 12 studies of children exposed prenatally to GDM (excluding pregestational diabetes), with resulting crude odds ratios of 0.7–6.3 for offspring obesity. However, in many studies, it is not possible to distinguish women with GDM with true onset during pregnancy from undiagnosed pregestational diabetes (Herring and Oken 2011; Kim et al. 2011). In the two studies that adjusted for maternal prepreg- nancy BMI, results attenuated and were no longer significant after the covariate was included (Kim et al. 2011), which suggests that GDM may be a marker for preexisting maternal factors. Randomized controlled trials (RCTs) of risk factor manipulation in pregnancy with childhood follow-up are especially valuable because they minimize the effect of confounding and thus are the most direct assessment of in utero programming in humans. In a US-based RCT of the treatment for mild GDM on pregnancy-related outcomes, Landon et al. (2009) found reduction in LGA and macrosomia; however, no childhood follow-up has been done. Gillman et al. (2010) followed up a subset of children who participated in an Australian-based multicenter RCT of treatment for mild GDM. Although the parent trial showed halving of macrosomia at birth, there were no differences in child BMI at age 4–5 years. One possible explanation for the null result is that offspring of mothers with GDM appear to lose their excess fat in the first year of life and do not again begin to diverge from their unexposed S.S. Hawkins et al. 174 peers until school age; thus, continued follow-up of these children may be needed to demonstrate a longer-lasting effect of prenatal GDM treatment. Furthermore, as this study included women with mild GDM only, a more dramatic benefit of treatment may be experienced among offspring of women with more extreme hyperglycemia (Gillman et al. 2010). Another method for minimizing confounding is sibling-pair designs, which partially control for differences in the pre- and postnatal environment (Brion 2010). This method relies on discrepant in utero exposure between siblings, such that the mother experienced GDM during her pregnancy with one sibling but not the other. Lawlor and colleagues (2011b) conducted a sibling-pair study of over 280,000 Swedish men through a record linkage study of their military conscription medical examination with birth information. They found that BMI at age 18 was higher in men whose mothers had GDM during pregnancy than those who did not; these differences were still evident in the within-sibling analyses and independent of maternal BMI. The authors concluded that GDM may influence later obesity risk through intrauterine mechanisms (Lawlor et al. 2011b). 4.4 Gestational Weight Gain (GWG) In 1990, the Institute of Medicine (IOM) (1990) issued guidelines for the appropriate amount of weight women should gain during pregnancy. However, over the last two decades, excessive GWG has been common and increasing. In 2010, based on the Pregnancy Nutrition Surveillance System (PNSS) from 29 states and the District of Columbia, approximately 48% of low-income women gained more weight than recommended (Dalenius et al. 2012). Excessive weight gain is associated with adverse infant outcomes, including macrosomia and LGA infants, as well as postpartum weight retention in mothers (SiegaRiz et al. 2009). In 2009, the IOM published new guidelines, which for the first time include a weight gain range for obese women that recom- mends lower gains than for women with lower weight status (Institute of Medicine 2009). GWG may also have a lasting effect on body size across the life course through in utero conditioning. Although some evidence suggests that extreme undernutrition during pregnancy may be associated with higher obesity risk in offspring, the more common occurrence is overnutrition during pregnancy (Herring et al. 2012). Recent systematic reviews found that excessive GWG was associated with an increased risk for offspring obesity compared to adequate GWG (Lau et al. 2014; Mamun Mamun et al. 2014; Nehring et al. 2013), with an overall pooled odds ratio of 1.4 (Mamun et al. 2014; Nehring et al. 2013). For example, Perng et al. (2014) found that every 5 kilograms of GWG was associated with greater adiposity (measured total fat and trunk fat) and higher leptin in children at ages 6–10 years, independent of maternal prepregnancy BMI. GWG may influence childhood obesity through several potential developmental pathways. Mothers who are more prone to gain weight through genetic risk, poor diet, or other behavioral factors may have children who are also themselves exposed to these same risks. However, associations still remain after adjustment for maternal and paternal BMI, reducing some influence of shared genes and the postnatal environment. An alternative explanation may be through fetal conditioning (Gillman 2005), similar to that proposed for GDM. In animal models, overnutrition during pregnancy has resulted in insulin resistance, increased adiposity, and hypertension in offspring (Alfaradhi and Ozanne 2011). One methodology to test causality is through an intervention aimed at influencing maternal weight gain during pregnancy through positive lifestyle changes. Three recent meta-analyses summarized the evidence on RCTs for prenatal dietary, physical activity, and behavioral or lifestyle interventions on maternal weight gain and infant outcomes (Agha et al. 2014; Oteng-Ntim et al. 2012; Thangaratinam et al. 2012). The reviews found that dietary and lifestyle interventions resulted in a small reduction in GWG (1.42–2.21 kg), but together the interventions did Early in the Life Course: Time for Obesity Prevention not appear to influence birth weight or LGA (Agha et al. 2014; Oteng-Ntim et al. 2012; Thangaratinam et al. 2012). However, none of the trials to date have looked at longer-term outcomes of child adiposity. Several ongoing RCTs targeted at changing GWG will follow offspring for development of obesity and its consequences (Dodd et al. 2011; Moholdt et al. 2011; Vesco et al. 2012). Vesco et al. (2014) found that obese women randomized to a dietary intervention gained less weight during pregnancy and had a lower proportion of babies born LGA, with further follow-up planned. Two separate sibling-pair analyses have examined GWG and offspring obesity. Lawlor and colleagues (2011a) examined the association of maternal weight gain during pregnancy with obesity in men at age 18 years. They found that among mothers with a normal BMI, there was no relationship between GWG and later obesity. However, among mothers who were already overweight or obese, GWG was associated with an increased risk for later obesity, even in the withinsibling analyses, suggesting an influence of the intrauterine environment (Lawlor et al. 2011a). Although Branum et al. (2011) found that women with higher prepregnancy BMI and gestational weight gain had children with a higher BMI at age 4, differences were no longer significant in the sibling fixed-effects analysis. The interaction between maternal prepregnancy BMI and GWG on childhood obesity risk is an area of active investigation (Institute of Medicine 2009). 4.5 Maternal Smoking During Pregnancy In 2010, approximately 9% of US women smoked during pregnancy, with white mothers and mothers with lower education more likely to smoke during pregnancy (Hawkins and Baum 2014). Many short- and long-term health effects of smoking during pregnancy on mothers and infants are well known, including lower fetal growth (U.S. Department of Health and Human Services 2004). It may seem paradoxical then that numerous studies, as summarized in recent 175 meta-analyses, have shown that prenatal smoking exposure is associated with an increased risk for later obesity, even in studies that adjusted for potential confounding factors (Oken et al. 2008; Weng et al. 2012). Although mechanisms for the relationship between maternal smoking during pregnancy and childhood obesity are not fully understood, animal studies have shown an association between prenatal nicotine exposure and increased adiposity in offspring (Gao et al. 2005). However, the extent to which this association, seen mainly in observational studies, is causal remains uncertain, especially since smoking is so strongly socially patterned. Several approaches have been used to minimize confounding, including accounting for paternal smoking, a proxy for sociodemographic risk that is likely to provide little direct exposure to the fetus. One study with information on both maternal smoking during pregnancy and paternal smoking status postpartum found that the association remained after adjustment for sociodemographic characteristics and paternal smoking (von Kries et al. 2008), while in another study the association was no longer evident after adjustment (Fleten et al. 2012). Others have used alternative study designs to test whether the association may be causal. Although RCTs have demonstrated that smoking cessation during pregnancy reduces rates of low birth weight and preterm birth (Lumley et al. 2009), none have examined later child obesity risk. Two studies have used sibling-pair designs to examine smoking during pregnancy (Gilman et al. 2008; Iliadou et al. 2010). Although both found a relationship between smoking and later obesity, the association was no longer evident after including the sibling fixed-effect. These results raise the possibility that the association may be confounded by unmeasured factors that are shared within families rather than being causal. 4.6 Hormonal Influences: Leptin In 1973, Douglas L. Coleman published his seminal work in two papers, discussing an unknown circulating factor responsible for the obese/diabetic S.S. Hawkins et al. 176 state in the ob/ob mouse, later called leptin (Grayson and Seeley 2012). Leptin is a hormone primarily produced by adipose tissue known to be responsible for the regulation of appetite, energy expenditure, and neuroendocrine function (Hauguel-de Mouzon et al. 2006). It is sometimes called the satiety hormone because of its effects on inducing a sense of fullness. The developmental role of leptin in the perinatal period may differ from that later in the life course. The placenta releases leptin into maternal and fetal circulation, which may influence appetite and weight regulation. Maternal plasma leptin concentrations during gestation are two times higher than during non-gravid periods (Hauguel-de Mouzon et al. 2006). Fetuses born to obese mothers and mothers with GDM have higher cord leptin levels than fetuses of lean mothers or mothers without GDM, respectively (Catalano et al. 2009; Okereke et al. 2002). Umbilical cord leptin levels are positively associated with fetal fat mass, percent body fat, and birth weight (Okereke et al. 2002). Sensitive period(s) may exist for the role of leptin in later obesity risk. A series of studies from Project Viva, a US pre-birth cohort, has examined relationships of leptin with early growth. Lower cord blood leptin levels were associated with smaller size at birth, but higher weight gain from birth to 6 months and higher BMI at age 3 years (Mantzoros et al. 2009; Parker et al. 2011). In a follow-up study, Boeke et al. (2013) found that lower leptin levels during pregnancy and in cord blood were associated with more adiposity at age 3 years; however, higher leptin levels at age 3 were associated with greater weight gain and higher adiposity through age 7. These findings were independent of maternal BMI and birth weight. The authors concluded there may be a sensitive period of leptin influence during the prenatal period followed by accumulation of leptin tolerance during early childhood, with different effects of leptin exposure by timing (Boeke et al. 2013). These observations are consistent with animal studies showing conditioning impact of heightened leptin sensitivity on later obesity, with differences evident across the life course (Bouret et al. 2004). There is great interest in identifying the correct sensitive period(s) for leptin to influence fetal development and reduce later obesity risk. To date, however, little is known about modifiable factors influencing fetal leptin regulation in humans (Boeke et al. 2013). 5 Infancy 5.1 Rapid Weight Gain Infants grow in both length and weight, and weight changes include growth in both fat-free mass and fat mass. Many studies have used change in weight as a proxy for gain in adiposity, which may or may not be a valid assumption. There are many different definitions of rapid growth, which are often based on country- or population-specific references (Monteiro and Victora 2005). Several meta-analyses have demonstrated that infants who gain weight more quickly than average during the first 2 years of life are at higher risk for later obesity (Baird et al. 2005; Monteiro and Victora 2005; Weng et al. 2012). Taveras and colleagues (2011c) examined the number of major weight-for-length percentiles crossed on the CDC 2000 growth chart during each of the 6-month periods from birth to 2 years with outcomes of obesity at ages 5 and 10 years. They found that crossing upward 2 or more percentiles in the first 2 years was associated with an increased risk for obesity at both ages. The highest prevalence of later obesity was seen in children with the crossing upward of 2 or more weight-for-length percentiles within the first 6 months of life (Taveras et al. 2011c). Debate continues as to the exact time frame that rapid weight gain matters the most for later obesity risk. A major limitation of current research is the lack of repeated measurements at small intervals during infancy. Studies with repeat detailed measurements often include small, homogeneous samples of children, and thus their generalizability is limited. An important area of future research is to more precisely define the critical window for rapid gain in adiposity (or length) as well as modifiable determinants of rapid weight gain. Early in the Life Course: Time for Obesity Prevention Although based on the research we summarize here a potential conclusion might be to limit rapid weight gain in infants to prevent later obesity, an important consideration is the potential detrimental effects of restricting weight gain for other organ systems. For infants born preterm, more rapid postnatal weight gain has an important benefit for neurodevelopment and attained size (Ehrenkranz et al. 2006) Although no association was seen between slower infant weight gain and poorer neurodevelopmental outcomes in a study of healthy, term infants (Belfort et al. 2008), this area requires more study to help determine optimal length and BMI patterns to promote healthy neurodevelopment as well as reduce childhood obesity. One important modifiable determinant of the rate of infant weight gain is infant feeding, both the type and the approach. Although exclusively breastfed infants gain weight more rapidly in the early postnatal period, infants who are formula fed or fed a combination of breast milk and formula gain BMI more rapidly during the latter half of the first year of life (Kramer et al. 2004). In an Australian RCT promoting positive feeding practices, Mihrshahi et al. (2011) found that formula feeding and feeding on a schedule were independently associated with rapid weight gain between birth and age 4–7 months. Although the association between breastfeeding and childhood obesity is discussed in the next section, infant gain in length or adiposity and nutrition are closely linked and, in fact, may be challenging to tease apart. 5.2 Breastfeeding Despite the numerous demonstrated health benefits of breastfeeding (Section on Breastfeeding 2012), according to the CDC’s 2014 Breastfeeding Report Card, only 79% of US mothers initiated breastfeeding and only 49% breastfed for at least 6 months (Centers for Disease Control and Prevention 2014). Moreover, racial/ethnic disparities in breastfeeding are substantial. Using national data from 2004 to 2008, there was a 20% point differential between black and white moth- 177 ers for both breastfeeding initiation (54% versus 74%) and breastfeeding for at least 6 months (27% versus 43%) (Centers for Disease Control and Prevention 2010). Systematic reviews and meta-analyses of observational studies have demonstrated an inverse association of breastfeeding initiation or duration with later obesity (Arenz et al. 2004; Harder et al. 2005; Owen et al. 2005b). Two reviews found that an inverse relationship still remained after adjusting for confounding factors, such as parental obesity or social class (Arenz et al. 2004; Owen et al. 2005b). However, a third showed attenuation of the association with BMI to null after such adjustment (Owen et al. 2005a). There was also evidence for a dose-response relationship, such that a longer duration of breastfeeding conferred a greater reduction in obesity risk (Harder et al. 2005; Owen et al. 2005b). As the majority of study samples were often homogeneous with children of mostly white European descent, there is debate about the extent to which these relationships apply to all racial/ethnic groups (Gillman 2011; Harder et al. 2005). Plausible mechanisms abound for the relationship between breastfeeding and childhood obesity (Bartok and Ventura 2009). Breastfed infants may be better at self-regulation than bottle-fed infants because they come off the breast when they are full, which may help them learn to regulate energy intake (Li et al. 2010). Breast milk contains hormones, such as leptin, involved in regulating growth and development during infancy (Savino and Liguori 2008). After the first 3 months of life, breastfed infants have slower weight gain for the remainder of the year than formula-fed infants (Savino and Liguori 2008). Nevertheless, evidence is mounting to question the extent to which this relationship is causal (Gillman 2011). The majority of research has been based on observational studies, and associations may still be due to unmeasured factors. The few studies using a sibling-pair design showed that breastfeeding duration reduced risk for later obesity, but because mothers often breastfeed similarly across siblings, there are a limited number of discordant pairs to detect effects (Gillman et al. 2006; Metzger and McDade 2010; Nelson et al. S.S. Hawkins et al. 178 2005; O’Tierney et al. 2009). Furthermore, these designs may not eliminate the role of infant growth and behavior as a predictor—rather than result— of breastfeeding intensity and duration. Another methodological design to infer causality is to examine the association in a context with different social gradients for both the exposure and outcome measures (Brion 2010; Gillman 2011). Brion et al. (2011) compared the association between breastfeeding duration and BMI from two cohorts—in England where breastfeeding is associated with more advantaged social circumstances and in Brazil where there is little social patterning. There was an inverse association between breastfeeding duration and obesity risk for children in England, but no such association for children in Brazil, from which the authors concluded the effects are likely due to residual confounding (Brion et al. 2011). Similarly, a study in Hong Kong also found no association between breastfeeding and childhood obesity, where breastfeeding and obesity follow different social patterns (Kwok et al. 2010). The Promotion of Breastfeeding Intervention Trial (PROBIT) in Belarus is a cluster-randomized trial of breastfeeding promotion (Kramer et al. 2001). Mothers who started breastfeeding were randomly assigned to an intervention group that provided additional breastfeeding support or a standard care control group. Although mothers in the breastfeeding promotion group showed increases in duration and exclusivity of breastfeeding, there was no evidence for a protective effect of breastfeeding on skinfold thickness or obesity in children at age 6.5 or 11 years (Kramer et al. 2007; Martin et al. 2013). Based on the existing evidence, breastfeeding may only confer modest protection against obesity rather than being a major determinant (Gillman 2011). 5.3 Disparities in Obesity Partially Explained by Early-Life Factors Beyond breastfeeding, many other early-life obesogenic exposures may be socially patterned (Dixon et al. 2012). Several investigators have attempted to investigate whether observed racial/ ethnic disparities in obesity rates (Ogden et al. 2014) are related to early-life factors. Taveras and colleagues (2010) have shown that compared to white children, black and Hispanic children were more likely to have risk factors for childhood obesity including excessive weight gain during infancy and early introduction of solid foods, while after 2 years, they were more likely to have a television in their bedrooms, shorter sleep duration, and higher intake of sugarsweetened beverages and fast food (Taveras et al. 2010). In contrast, they were less likely to be exposed to factors associated with protection against obesity, including exclusive breastfeeding (Taveras et al. 2010). The same investigators more recently examined whether racial/ethnic differences in childhood obesity at age 7 were explained by these early risk factors (Taveras et al. 2013). Although black and Hispanic children had higher BMI z-scores than white children, there were no longer differences in BMI after adjustment for infancy- and childhoodrelated risk factors. The authors conclude that racial/ethnic disparities in obesity may be determined by modifiable factors in early life (Taveras et al. 2013). 6 Early to Mid-childhood 6.1 Family Although family members have similar levels of adiposity, meaning that children are more likely to have a high BMI if their parents have a high BMI (Patel et al. 2011), the influence of parents on child adiposity almost certainly extends beyond shared genes. Parents (including caregivers) also share a similar family, neighborhood, and social environment. During the early years, parents are the primary influence on children’s dietary and physical activity/inactivity choices. Parents not only physically provide children with food and opportunities for physical activity, but they also influence children’s preferences through modeling or other experiences (Birch and Davison 2001; Van Der Horst et al. 2007). In the next two sections on early childhood to midchildhood and adolescence, we focus on “above- Early in the Life Course: Time for Obesity Prevention water” macro-level factors where there is potential for population-level interventions and public health impact. 6.2 Diet, Physical Activity, and Inactivity In the simplest terms, excessive weight gain occurs when there is more “energy in” than “energy out.” The dietary and physical activity patterns of children and adolescents in the USA have changed substantially over the last few decades. Total energy intake and portion sizes from energy-dense, nutrient-poor foods have increased, and more meals are being eaten away from home (Duffey and Popkin 2013; Piernas and Popkin 2011). Among school-aged children, daily calories from sugar-sweetened beverages have increased from 130 to 212 kcal/day over the last 20 years (Lasater et al. 2011). Although children and adolescents are recommended to participate in 60 min or more of physical activity daily (U.S. Department of Health and Human Services 2008), many children are not achieving this goal. In 2013, nationwide, 15% of students did not engage in at least 60 min of physical activity on any of the prior 7 days (Kann et al. 2014). As children transition from childhood through adolescence, physical activity levels decrease (Kahn et al. 2008), and media exposure and sedentary behaviors increase (Kann et al. 2014; Rideout and Hamel 2006). In recent decades, screen time and media use have increased. Currently, 41% of students play video or computer games 3 or more hours per day, and an additional 33% watch television 3 or more hours per day (Kann et al. 2014). Even by age 1, children are using approximately 50 min of screen media daily, which increases to nearly 2 hours by age 4–6 years (Rideout and Hamel 2006). As media use has increased, so has children’s exposure to advertisements and food marketing. Food advertising has been linked to influencing children’s food preferences, purchasing requests, and consumption patterns (McGinnis et al. 2006), suggesting a potential mechanism by which television and media use may increase children’s risk for 179 obesity. US guidelines for the responsible advertising to children are voluntary self-regulatory initiatives, and the Rudd Center for Food Policy and Obesity (2013) suggested that loopholes in industry pledges may provide for more public relations benefits than health benefits. 6.3 Food Insecurity In 2013, 14% of US households with children were food insecure at least once during the year, meaning they were unable to provide adequate food for one or more household members due to insufficient means (Coleman-Jensen et al. 2014). Black and Hispanic households, low-income households, and single-parent households with children had rates of food insecurity higher than the national average (Coleman-Jensen et al. 2014). Since children from lower-income families are at higher risk for obesity, the challenge for many families is to dependably provide nutritious and high-quality food rather than obtaining enough food (Ludwig et al. 2012). The Supplemental Nutrition Assistance Program (SNAP) has no regulations on the quality of foods purchased (US Department of Agriculture, 2013b), and families may be spending their limited food budget on foods that are high in calories but with low nutrient quality such as sugarsweetened beverages. Three systematic reviews found that although food insecurity was associated with an increased risk for obesity in adults, particularly women, the evidence was mixed for children (Dinour et al. 2007; Eisenmann et al. 2011; Franklin et al. 2012). A challenge for studies is identifying whether the measure of food insecurity is at the family or child level as parents may protect children from being food insecure. Echoing the conclusion from a review by Eisenmann et al. (2011), even if the association is not causal, both food insecurity and obesity exist in low-income households. It is essential, therefore, to learn more about purchasing patterns, diet, and physical activity in these families to learn why food insecurity and obesity coexist and identify areas for intervention. S.S. Hawkins et al. 180 6.4 Child Care and School Policies and programs in child care and school can influence children’s dietary intake, physical activity patterns, and risk for obesity. Approximately 30% of children are in centerbased programs by age 2 and 40% of children by age 3, spending nearly 30 hours in nonparental care each week (National Center for Education Statistics 2005). Thus, child care is a setting in which obesogenic behaviors can be allowed or prevented (Larson et al. 2011a). A review by Larson and colleagues (2011b) identified 18 obesity prevention interventions that take place in child care centers/preschools and 2 of the 5 interventions that used weight status as an outcome found evidence for reduced weight. In 2007, Benjamin et al. (2008) reviewed state child care regulations for policies related to nutrition and physical activity that may contribute to childhood obesity. They found that 80% of states had regulations to ensure that water is freely available in child care centers, 33% regulated screen time, 18% had regulations for the provision of breast milk, 14% restricted sugarsweetened beverages, and only three states required a specified number of minutes of physical activity. Four states had no policies related to obesogenic practices in child care. The proportion of states with these regulations for family child care homes—as opposed to centers—was either the same or less. As of 2013, up to one quarter of states did not comply with any of the five recommendations from the IOM to promote physical activity among infants in child care centers or family child care homes (Slining et al. 2014). Benjamin et al. (2009) showed that infants who attended home-based child care early in life had an increased weight for length at 1 year and BMI at 3 years, suggesting that more research is needed into the food and physical activity policies of family child care homes. Additional studies in Denmark (Neelon et al. 2015) and Finland (Lehto et al. 2015) have demonstrated an association between starting child care before age 1 and an increased risk for later obesity. In 2011, the White House Task Force on Childhood Obesity supported the development of new national stan- dards for healthy eating, encouraging breastfeeding, promoting physical activity, and limiting screen time in early-care settings (American Academy of Pediatrics, American Public Health Association, and National Resource Center for Health and Safety in Child Care and Early Education 2012). Since children spend most of their time in school and may eat two daily meals there, the school environment can play an important role in shaping health behaviors (Institute of Medicine 2007; Story et al. 2009). A systematic review and meta-analysis by Waters et al. (2011) identified 55 interventions for preventing obesity in children aged 0–18 years, with 5 of the 6 most promising strategies related to the school environment: school curricula that include healthy eating and physical activity, increases in opportunities for physical activity throughout the school week, improvements in the nutritional quality of the food served in schools, environments and cultural practices that support children eating healthier foods and being active throughout the day, and support for teachers and staff to implement health promotion strategies and activities. They also concluded that interventions did not increase disparities. There have been a number of reviews that examined the impact of school-based interventions overall or school food and physical activity, separately, on obesity risk (Gonzalez-Suarez et al. 2009; Katz et al. 2008; Kropski et al. 2008). Food and beverages available in schools are either part of the federal school lunch and breakfast programs or are competitive foods sold outside the federal programs. Meals served in the National School Lunch Program and School Breakfast Program must adhere to federally defined nutrition standards in order for schools to be eligible for federal subsidies (Institute of Medicine 2007). In 2013, the US Department of Agriculture issued the new “Smart Snacks in Schools” nutrition standards for competitive foods and beverages, which limit calories, fat, sugar, and sodium (U.S. Department of Agriculture 2013a). A study by Masse et al. (2013) found that between 2003 and 2008, states significantly strengthened their school nutritionrelated laws, particularly those related to the sale Early in the Life Course: Time for Obesity Prevention of competitive foods. Overall, laws for competitive food policies were stronger for elementary schools than for middle and high schools. As of 2008, 7 states had no school nutrition laws across the 16 categories they examined (Masse et al. 2013). The National Association for Sport and Physical Education (NASPE) sets standards for physical education, including time allotment, curriculum, and staffing (National Association for Sport and Physical Education 2004). Perna et al. (2012) found that public schools in states with specific and stringent physical education laws reported more weekly time for physical education: specifically, elementary schools reported 40 more minutes and middle schools reported 60 more minutes than schools within states with no laws. There were no differences between high schools. However, overall, only 8.5% of schools fully met the NASPE guidelines for physical education time (Perna et al. 2012). Without federal legislation for physical education, policies vary widely by state and even by schools themselves. 7 Adolescence 7.1 Social Influences Adolescence is a developmental period characterized by hormonal changes and a period of rapid growth both physically and psychologically (Viner et al. 2012). As children become more independent, their sphere of influence moves from parents to peers with increasing autonomy in making choices. Although influences of dietary and physical activity habits on obesity risk are similar to those described during the early- to mid-childhood periods, these habits are often influenced by peer norms. Strong peer relationships are an important developmental change during adolescence, and peer groups can influence health behaviors both positively and negatively (Viner et al. 2012). Using social network theory and analysis, Christakis and colleagues (2007) found that weight gain in one person spread through social ties and influenced risk of obesity in a friend, sib- 181 ling, or spouse among adults in the Framingham Study. Trogdon et al. (2008) used the National Longitudinal Study of Adolescent Health (Add Health) to examine the influence of peer effects on adolescents’ own BMI by constructing a detailed definition of peer groups based on nominated friendship relationships. They found that mean peer BMI was associated with adolescent BMI, females were more sensitive to peer BMI and overweight status than males, and peer weight was more influential among adolescents with the highest BMI (Trogdon et al. 2008). Ali et al. (2011) also used data from the Add Health study to understand potential mechanisms of peer effects on obesity-related health behaviors. They found evidence for the influence of peers on adolescents pursuing an active sport, regular exercise, and eating in fast-food restaurants, but no consistent relationships with television viewing, short sleep duration, or other dietary factors (Ali et al. 2011). Although additional research is needed to confirm these findings, the potential implications are that obesity prevention programs aimed at adolescents will need to consider the influence of peer groups. 8 Macro-level Factors Although we have touched upon policies and social issues that have their strongest influence in particular age groups, this section presents macro-level factors that either indirectly or directly influence obesity risk across all stages in life course health development. 8.1 Environment The term “obesogenic environment” often refers to features of the built and natural environments that limit healthful behaviors related to eating, sleeping, screen time, and physical activity. The definition used to study the built environment varies widely across studies (Dunton et al. 2009; Lovasi et al. 2009; Papas et al. 2007). The most common measures of access to food are distances to the nearest fast-food restaurants or grocery 182 stores or density of food outlets within a defined area. The most common measures of access to physical activity resources are measured distance to facilities for physical activity such as parks, density of such facilities, walkability, or aspects of community design. Related measures included assessments of aesthetics or neighborhood safety. Papas et al. (2007) found that 17 of the 20 studies identified found a significant association between some aspect of the built environment and risk for obesity across the life course, with the majority of studies looking at features of the environment related to physical activity. Dunton et al. (2009) examined aspects of the built environment related to physical activity and childhood obesity. While they found few consistent findings in children, in adolescents obesity was associated with access to equipment and facilities, neighborhood type (urban/rural), and urban sprawl. Lovasi et al. (2009) examined how the built environment may be contributing to disparities in obesity and its risk factors. They focused on studies that included individuals who were poor or of low socioeconomic status, black, or Hispanic. The authors found that lack of access to supermarkets, exercise facilities or places to be active, and safety because of crime or traffic was all associated with BMI or related behaviors, and they concluded that prevention strategies should focus on these areas (Lovasi et al. 2009). There are many methodological challenges to understanding the effects of the neighborhood environment on children because of the diversity of populations, measures, and outcomes across studies (Dunton et al. 2009; Lovasi et al. 2009; Papas et al. 2007). A further limitation is that nearly all of the studies have been cross-sectional, so the temporality of associations is not clear. Effects of the built environment on health vary across the life course because exposure to and interaction with the environment change by age. For example, distance to playgrounds matters more for children than adolescents, whereas walkability may have less impact on young children. While subjective measures of the built environment are often from parents or children themselves, more studies are using objective measures of the environ- S.S. Hawkins et al. ment, such as geographic information system (GIS) mapping or even personal devices to determine time, place, and activity simultaneously. The most important aspect is the definition of the built environment. Studies that examine the same construct, such as walkability, may be using different definitions and measurements, which limit the ability to synthesize the evidence. As Dunton et al. (2009) conclude, it is imperative to identify “modifiable and specific” features of the built environment to inform the development of interventions. Neighborhoods vary in many ways, most of which are unmeasured but likely interrelated; observed associations that remain after adjustment are still subject to residual confounding. RCTs, which balance both measured and unmeasured confounders, are uncommon in this area primarily due to the feasibility of manipulating aspects of the built environment. The Department of Housing and Urban Development conducted a unique social experiment from 1994 through 1998 to better understand the effects of neighborhood characteristics on health and social outcomes (Sanbonmatsu et al. 2011). Women with children living in public housing in high-poverty areas were randomly assigned to one of three groups: experimental housing vouchers redeemable only if they moved to a low-poverty area, unrestricted traditional vouchers, and a control group that offered neither opportunity (Ludwig et al. 2011). Ludwig et al. (2011) followed up the participants from 2008 to 2010 and found that women assigned to the experimental group had lower levels of extreme obesity and diabetes than women in the control group, although there were no baseline measures of these outcomes. However, among youth, there were no differences in physical health outcomes, including obesity (Sanbonmatsu et al. 2011). The authors concluded that although the mechanisms for these reductions were unknown, the intervention provides some causal evidence for the impact of the neighborhood environment on health (Ludwig et al. 2011). A methodology for future research in this area is quasi-experimental designs that evaluate a clearly defined change—often a wide-ranging Early in the Life Course: Time for Obesity Prevention policy—that are referred to as natural experiments. Taking measurements of residents before and after this change, or using secondary measures such as data from electronic medical records, especially in comparison with an unexposed control group, can help assess the impact of neighborhood factors on obesity itself or related health behaviors. Such alternative study designs are needed to identify important levers of change to help inform the development of interventions conducted at the neighborhood or community levels. 8.2 Local and State In recent years, city and state governments have taken bold actions to change the environment and encourage healthy choices. New York City (NYC) has been a pioneer in this effort by using a multiagency approach to tackle obesity and its risk factors (Mello 2009). In 2006, NYC passed the first regulation in the USA banning trans fat use in restaurants. An evaluation comparing food purchase data before and after the ban found a significant decrease in the trans fat content of purchases from fast-food chains after the law, with benefits for patrons from both high- and low-poverty neighborhoods (Angell et al. 2012). In 2008, NYC required chain restaurants to post calorie information prominently on menu boards and menus. Although children and adolescents’ reported they noticed the posted information, there were no differences in calories purchased after versus before the introduction of calorie postings (Elbel et al. 2011). NYC initiatives have also extended to schools and the built environment (Office of the Mayor 2012). NYC established nutritional standards for every city agency that purchases or serves meals to clients, including the 1.1 million students that attend city schools, as well as standards for city vending machines. The most controversial proposal was to limit the size of sugar-sweetened beverages sold in food service establishments to 16 ounces, which caused much debate both for and against this measure and was ultimately blocked (Grynbaum 2012). 183 Although these policies in NYC provide assessable natural experiments to allow for evaluation of specific environmental factors that might promote weight gain (or loss), a challenge is learning what may be the critical lever(s) for change. When policies and programs are implemented simultaneously or within short time periods, it may not be possible to tease apart the individual effects of each policy. Furthermore, these efforts may not be generalizable; budgets are often limited, and other cities or states may not have the resources to implement the whole suite of policies that have been enacted in NYC. Nevertheless, these efforts provide valuable data to clinicians and policy makers to help elucidate what policies or programs will have the biggest effect on obesity levels. A relatively new area is using legislation to financially penalize the purchase and consumption of obesogenic foods and beverages. One approach draws from the lessons of tobacco control and the success of cigarette taxes. Taxes on sugar-sweetened beverages are a prime example, although taxes have also been proposed to reduce the consumption of fast food (Powell et al. 2013). The consumption of sugar-sweetened beverages is strongly related to increased body weight (Te Morenga et al. 2012). As of January 2014, 34 states and DC had a sales tax on regular soda sold in food stores, with a mean tax rate of 5.2% (Chriqui et al. 2014). A systematic review by Powell et al. (2013) found that soda taxes had little impact on weight outcomes, but the authors argued that they were based on state-level sales taxes that were relatively low and therefore unlikely to effect substantial change in behavior. Brownell et al. (2009) have proposed a national excise tax on sugar-sweetened beverages as a public health strategy to address the obesity epidemic. They recommended implementing an excise tax of 1 cent per ounce for beverages that have any added caloric sweetener, which would increase the cost of a 20-ounce soda by 15–20% (Brownell et al. 2009). This type of tax, which is similar to cigarette excise taxes, is preferable to a sales tax because it provides an incentive to reduce the amount of sugar per ounce of sugarsweetened beverage (Brownell et al. 2009). Since S.S. Hawkins et al. 184 the cost would be passed onto retailers, they would likely increase the retail price, and consumers would be aware of the cost when they are deciding to purchase the product. Andreyeva et al. (2011) estimated that a 1 cent per ounce tax on sugar-sweetened beverages could reduce consumption by 24% and daily per capita caloric intake from sugar-sweetened beverages from current levels of 190–200 calories to 145–150 calories. Brownell et al. (2009) suggest that the public health impact could be greater for groups at higher risk for obesity, such as children and lowincome groups, who consume greater amounts of sugar-sweetened beverages. An alternative approach to promoting a healthful diet is a subsidy to reduce price and encourage the consumption of healthful foods. Unfortunately, in recent decades, just the opposite situation has occurred. The real inflationadjusted price of fruits and vegetables has increased, while soda prices have declined and fast-food prices have remained stable (Powell et al. 2013). In the USA, subsidies on food have been designed to alleviate food insecurity for low-income families rather than increasing consumption of healthful foods by everyone. Powell et al. (2013) found that lower fruit and vegetable prices were associated with lower body weight among low-income populations; however, cohort studies are mixed when it comes to the effects of fruits and vegetables on weight gain (Casazza et al. 2013). Based on the long history of tobacco control, the area of taxes and subsidies related to obesity is still in its infancy. 8.3 ing healthy foods in schools; improving access to healthy, affordable food; and getting children more physically active. The strategy emphasized that changes are needed at many macro levels to promote healthy behaviors, including improvements to schools and the built environment. The White House Task Force reported some progress on many of these areas after just 1 year (White House Task Force on Childhood Obesity 2011). The IOM’s Standing Committee on Childhood Obesity Prevention (Institute of Medicine 2011) and the US Department of Health and Human Services’ National Prevention Strategy (National Prevention Council 2011) echo these priorities. 9 Recommendations 9.1 Major Themes A multilevel approach for obesity prevention is needed, which takes into account individual risk factors that operate at multiple levels (“abovewater” and “underwater” influences) and recognizes that these factors also interact across the life course (Fig. 1). We reviewed phases of life course health development, prenatal through adolescence, that appear to be most important for the development of obesity. We also presented alternative methodological approaches to observational studies that can help disentangle causal associations. The following sections outline research priorities and data/methods development priorities, and conclude with recommendations for practice and policy. National 9.2 Reducing and preventing obesity is a federal priority. At a national level, the White House Task Force on Childhood Obesity and the accompanying Let’s Move! campaign aim to reduce childhood obesity and raise a healthier generation through an intra-agency collaboration (White House Task Force on Childhood Obesity 2010). The 70 recommendations were summarized into five themes: getting children a healthy start on life; empowering parents and caregivers; provid- Research Priorities Of the many emerging risk factors for obesity, we briefly highlight sleep duration, endocrinedisrupting chemicals, epigenetic markers, and microbial colonization. • Sleep duration and quality: Disruption of sleep may have adverse health consequences, including childhood obesity. Biologically plausible mechanisms exist for why short sleep duration Early in the Life Course: Time for Obesity Prevention could increase children’s risk for obesity: tiredness and fatigue lead to reduced physical activity; hormone changes associated with insufficient sleep, particularly lower leptin levels and higher ghrelin levels, result in increased hunger and eating; and with less sleep, there is more awake time to eat (Taheri 2006). Although most of the studies in this area have been cross-sectional, prospective cohort studies do provide some evidence for causality. Chen et al. (2008) identified 17 studies from 1980 to 2007 on sleep duration and adiposity in children, of which 3 were prospective cohort studies. Nielsen et al. (2011) reviewed the literature from 2007 to 2009 and identified an additional 13 studies in children. Of the eight prospective cohort studies, all found a significant inverse relationship between duration of sleep and adiposity (Nielsen et al. 2011). Recent studies from Project Viva have shown that chronic sleep curtailment from infancy was associated with an increase in adiposity and metabolic risk at age 7 (Cespedes et al. 2014; Taveras et al. 2014). • Early relational environment: Two aspects of children’s early relational environment—the quality of parental relationships and children’s exposure to adverse experiences—have been identified as potential risk factors for child and adolescent obesity. The psychological and physiological consequences of insecure attachments and trauma are well established, including poor emotional regulation and heightened stress responses, which may directly lead to obesity through biological changes (Glaser and Kiecolt-Glaser 2005) or indirectly through emotional eating (Michopoulos et al. 2015; Torres and Nowson 2007) and sleep disturbances (Vgontzas et al. 2008). Studies in children and adolescents have found that those with poor-quality maternal-child relationships (Anderson et al. 2012) or insecure attachment (Anderson and Whitaker 2011) in early childhood were more likely to be obese compared to those with higher-quality relationships. Danese and Tan (2014) conducted a meta-analysis of 41 studies and found that childhood maltreatment was associated with an increased risk of obesity across the life course with an overall pooled odds ratio of 1.36 and associations 185 remained after adjustment for socioeconomic status and health behaviors. An additional meta-analysis of 23 cohort studies found that adults who reported physical, emotional, sexual, or general abuse during childhood were more likely to be obese (pooled odds ratios of 1.28–1.45) and four studies reported doseresponse relationships (Hemmingsson et al. 2014). While this evidence suggests a role of children’s early relational environment in the development of obesity, further research is needed to better understand the mechanisms to help inform interventions. • Endocrine-disrupting chemicals (EDCs): EDCs are compounds that mimic or interfere with the normal actions of endocrine hormones, including estrogens, androgens, and thyroid and pituitary hormones (Newbold et al. 2007). Although some EDCs are naturally occurring, man-made organic compounds pose greater risks to human health and include flame retardants, bisphenol A (BPA), pesticides, and polychlorinated biphenyls (PCBs). EDCs that are lipophilic, resistant to metabolism, and/or able to bioconcentrate up the food chain become stored in body fats and are of particular concern (Elobeid and Allison 2008). Thus, in utero exposure to environmental chemicals, including EDCs, during critical periods may play a role in the development of obesity through fetal programming (Newbold et al. 2007). Only recently has evidence been synthesized from animal models and epidemiologic studies in humans to suggest a possible link between EDCs and later obesity (Elobeid and Allison 2008). Trasande et al. (2012) found that urinary BPA concentrations were associated with obesity in children and adolescents, although alternative explanations cannot be ruled out because the study was cross-sectional. • Epigenetics: The notion that early environmental influences, such as maternal diet, or toxic substances, like air pollution, alter offspring outcomes through epigenetic changes that influence gene regulation could unite several strands of human and animal observations about the origins of obesity. Proof of principle emanates from agouti mice (Waterland and Jirtle 2004). The epigenetic mechanism associated with dif- S.S. Hawkins et al. 186 ferences in body fat and cardiometabolic disease risk of the offspring involves switching off the agouti gene by methyl groups from a dietary supplement (Waterland and Jirtle 2004). These and similar findings show, in principle, how genetically identical individuals raised in similar postnatal environments can nonetheless develop widely differing phenotypes. • Gut microbiota: Infants get their first priming dose of microbes in utero via the placenta, followed by a more thorough colonization as they pass through the birth canal and are exposed to the mother’s skin, so that by the time they are a few days or weeks old, their gastrointestinal tracts are colonized by a population of microbes notable not only for their abundance but also variety. Gut microbiota are likely to affect many organs and systems, including the regulation of energy balance and weight gain. Although research on animal models suggests a role of gut microbiota in the development of obesity, studies in humans are limited (DiBaise et al. 2008). One model system for evaluating the role of gut microbiota is the route of delivery. Microbes from the mother and the environment colonize the infant’s intestinal tract during delivery (Neu and Rushing 2011). During cesarean delivery, the direct contact with maternal vaginal and intestinal flora is absent. The intestinal microbial composition of infants via cesarean delivery resembles that of the mother’s skin, whereas the intestinal flora of infants born vaginally resembles the mother’s vaginal flora and intestinal tract (Dominguez-Bello et al. 2010). A systematic review by Li et al. (2013) found that delivery by cesarean section was associated with an increased risk for overweight/obesity across the life course compared to vaginal delivery, with an overall pooled odds ratio of 1.33. 9.3 Data/Methods Development Priorities There are a number of major challenges to understanding causal influences on obesity risk throughout the life course. Here, we discuss some of these challenges as well as approaches to overcoming or minimizing them. • One recurrent issue is the extent to which associations described in observational studies are causal. One notable example is breastfeeding (Gillman 2011). Mothers who choose to breastfeed often have substantially different social and economic circumstances from mothers who do not breastfeed their infants. Furthermore, factors that predict successful initiation and long-term maintenance of breastfeeding, such as maternal obesity and cesarean delivery, are themselves putative causes of child obesity. Also, it is possible that infant characteristics themselves predict breastfeeding duration, since mothers may be more likely to supplement faster growing infants, who appear hungrier. • In cross-sectional studies, both confounding and reverse causation are important considerations. Longitudinal cohort studies with adjustment for multiple measured characteristics can go part of the way in minimizing confounding, but other approaches such as sibling-pair design, maternal versus paternal effects, cohorts with different confounding structures, and RCTs can help control for unmeasured confounding. Others have applied Mendelian randomization, a method that takes advantage of variation in genes of known function to examine the causal effect of a modifiable exposure on disease in nonexperimental studies (Smith and Ebrahim 2003). The genotype must affect the disease status only via its effect on the exposure of interest and should be randomly distributed with respect to other covariates (Ding and Hu 2008). This approach can produce unbiased estimates of the effects of a putative causal variable without a traditional RCT. One example is the study of the maternal and offspring FTO genotype and offspring obesity risk, which suggests that maternal obesity affects childhood obesity only through pathways other than the intrauterine environment (Lawlor et al. 2008). However, many times these approaches are not feasible. For example, data may not be readily available on Early in the Life Course: Time for Obesity Prevention large numbers of siblings with discordant exposures; RCTs are costly and may not be ethical; and the few common genes that influence obesity risk do not have strong associations and also may influence multiple metabolic pathways. • Another challenge is that of appropriate exposure assessment, which is relevant for multiple factors of great interest including diet, physical activity, and the toxic, built, and social environments. Multiple factors may interact among each other. For example, breastfeeding may particularly affect childhood obesity risk only in the presence of an obesogenic environment, whereas two important null studies of breastfeeding and obesity (Brion et al. 2011; Kramer et al. 2007; Martin et al. 2013) were both conducted in middle-income countries with relatively low population obesity rates (Belarus and Brazil). • Current statistical techniques typically cannot take into account the multitude of factors both hierarchically and across the life course that impact obesity. Traditional longitudinal analyses, even those that account for multiple levels of influence, are often not powerful enough to account for the complexity and interconnectedness of obesity. Systems science approaches such as agent-based and system dynamics modeling can include not only longitudinal and multiple levels but also more complex features of relationships like nonlinearity, path dependence, loops, and tipping (Hammond 2009; Huang et al. 2009). These approaches have just recently been imported into public health from disciplines such as engineering (Mani et al. 2010) and evolutionary biology (Kitano 2002) and may very well contribute to understanding and ultimately solving childhood obesity. 9.4 Translational Priorities The key to reducing childhood obesity is finding the right level and time in the life course to intervene for the maximal effectiveness and efficiency. Many intervention studies early in the life course are getting underway or are ongoing, and will be invaluable in informing not only what fac- 187 tors to change but how to change them. Such interventions may be complex and costly, but ultimately may be what is required to reverse the tide of obesity. • Interventions to modify determinants of obesity through life course health development may invoke multiple settings, e.g., medical care, homes, child care, and school (Foster et al. 2010; Taveras et al. 2011a, b, 2012); involve multiple components, e.g., system redesign and individual behavior change strategies including e-technology (Lubans et al. 2010; Taveras et al. 2012); and target single or multiple factors (Dodd et al. 2011; Taveras et al. 2011b; Vesco et al. 2012). Interventions within medical care may be especially valuable during pregnancy and infancy, when individuals see their providers more often than any other time during the life course. • Some recommendations are already clear. For prenatal factors, smoking avoidance has been a priority for decades (U.S. Department of Health and Human Services 2004). Guidelines exist for appropriate GWG (Institute of Medicine 2009). For mild-moderate GDM, treatment with lifestyle and insulin reduces neonatal complications (Crowther et al. 2005; Landon et al. 2009); the protocol and criteria for diagnosing GDM are undergoing new scrutiny. • Among early childhood factors, “feeding up” small-for-gestational age infants should be abandoned for most because such infants who gain weight rapidly in infancy are at higher risk of chronic disease and derive no neurocognitive benefits (Belfort and Gillman 2011). The World Health Organization recommends exclusive breastfeeding for 6 months, but in the developed world, recommendations are moving toward 4–6 months (Section on Breastfeeding 2012); that interval appears to be appropriate for introducing solid foods for obesity prevention (Huh et al. 2011; Pearce et al. 2013). • Among dietary factors, evidence is strongest for intake of sugary beverages at many ages. Avoiding introducing these into infant diets may be especially valuable, given most 188 humans’—especially children’s—inherent “sweet tooth” (Ventura and Mennella 2011). • The American Academy of Pediatrics recommends zero screen time under age 2 years and no more than 2 hours/day of screen time for child age 2 years and older (Strasburger 2011). Avoiding from the outset having a TV in the room where a child sleeps appears to be key to reducing screen time (Schmidt et al. 2012). • Among the newer risk factors, improving sleep duration and quality may be an especially effective maneuver—and preliminary studies suggest it is feasible (Taveras et al. 2011a)—because all parents want more sleep! Funding Funded by grants from NIH R00 HD068506 to Dr. Hawkins, K24 HD069408 and P30 DK092924 to Dr. Oken, and K24 HL060841 to Dr. Gillman. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. References Agha, M., Agha, R. A., & Sandell, J. (2014). Interventions to reduce and prevent obesity in pre-conceptual and pregnant women: A systematic review and metaanalysis. PloS One, 9(5), e95132. Alfaradhi, M. Z., & Ozanne, S. E. (2011). Developmental programming in response to maternal overnutrition. Frontiers in Genetics, 2, 27. Ali, M. M., Amialchuk, A., & Heiland, F. W. (2011). Weight-related behavior among adolescents: The role of peer effects. PloS One, 6(6), e21179. American Academy of Pediatrics, American Public Health Association, and National Resource Center for Health and Safety in Child Care and Early Education. (2012). Preventing childhood obesity in early care and education: Selected standards from caring for our children; National health and safety performance standards; Guidelines for early care and education programs, 3rd edition. http://nrckids.org/CFOC3/PDFVersion/ preventing_obesity.pdf. Accessed 8 Feb 2013. Anderson, S. E., Gooze, R. A., Lemeshow, S., & Whitaker, R. C. (2012). Quality of early maternal–child relationship and risk of adolescent obesity. Pediatrics, 129(1), 132–140. Anderson, S. E., & Whitaker, R. C. (2011). Attachment security and obesity in US preschool-aged children. Archives of Pediatrics & Adolescent Medicine, 165(3), 235–242. S.S. Hawkins et al. Andreyeva, T., Chaloupka, F. J., & Brownell, K. D. (2011). Estimating the potential of taxes on sugarsweetened beverages to reduce consumption and generate revenue. Preventive Medicine, 52(6), 413–416. Angell, S. Y., Cobb, L. K., Curtis, C. J., Konty, K. J., & Silver, L. D. (2012). Change in trans fatty acid content of fast-food purchases associated with New York City’s restaurant regulation: A pre-post study. Annals of Internal Medicine, 157(2), 81–86. Arenz, S., Ruckerl, R., Koletzko, B., & von Kries, R. (2004). Breast-feeding and childhood obesity–a systematic review. International Journal of Obesity and Related Metabolic Disorders, 28(10), 1247–1256. Baird, J., Fisher, D., Lucas, P., Kleijnen, J., Roberts, H., & Law, C. (2005). Being big or growing fast: Systematic review of size and growth in infancy and later obesity. BMJ, 331(7522), 929. Bartok, C. J., & Ventura, A. K. (2009). Mechanisms underlying the association between breastfeeding and obesity. International Journal of Pediatric Obesity, 4(4), 196–204. Battista, M. C., Hivert, M. F., Duval, K., & Baillargeon, J. P. (2011). Intergenerational cycle of obesity and diabetes: How can we reduce the burdens of these conditions on the health of future generations? Experimental Diabetes Research, 2011, 596060. Belfort, M. B., & Gillman, M. W. (2011). Healthy infant growth: What are the trade-offs in the developed world? In M. W. Gillman, P. D. Gluckman, & R. G. Rosenfeld (eds): Recent Advances in Growth Research: Nutritional, Molecular and Endocrine Perspectives. Nestlé Nutr Inst Workshop Ser, vol 71, pp 171–184 Belfort, M. B., Rifas-Shiman, S. L., Rich-Edwards, J. W., Kleinman, K. P., Oken, E., & Gillman, M. W. (2008). Infant growth and child cognition at 3 years of age. Pediatrics, 122(3), e689–e695. Ben-Shlomo, Y., & Kuh, D. (2002). A life course approach to chronic disease epidemiology: Conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology, 31(2), 285–293. Benjamin, S. E., Cradock, A., Walker, E. M., Slining, M., & Gillman, M. W. (2008). Obesity prevention in child care: A review of U.S. state regulations. BMC Public Health, 8, 188. Benjamin, S. E., Rifas-Shiman, S. L., Taveras, E. M., Haines, J., Finkelstein, J., Kleinman, K., & Gillman, M. W. (2009). Early child care and adiposity at ages 1 and 3 years. Pediatrics, 124(2), 555–562. Birch, L. L., & Davison, K. K. (2001). Family environmental factors influencing the developing behavioral controls of food intake and childhood overweight. Pediatric Clinics of North America, 48(4), 893–907. Boeke, C. E., Mantzoros, C. S., Hughes, M. D., L RifasShiman, S., Villamor, E., Zera, C. A., & Gillman, M. W. (2013). Differential associations of leptin with adiposity across early childhood. Obesity, 21(7), 1430–1437. Early in the Life Course: Time for Obesity Prevention Boney, C. M., Verma, A., Tucker, R., & Vohr, B. R. (2005). Metabolic syndrome in childhood: Association with birth weight, maternal obesity, and gestational diabetes mellitus. Pediatrics, 115(3), e290–e296. Bouret, S. G., Draper, S. J., & Simerly, R. B. (2004). Trophic action of leptin on hypothalamic neurons that regulate feeding. Science, 304(5667), 108–110. Branum, A. M., Parker, J. D., Keim, S. A., & Schempf, A. H. (2011). Prepregnancy body mass index and gestational weight gain in relation to child body mass index among siblings. American Journal of Epidemiology, 174(10), 1159–1165. Brion, M. J. (2010). Commentary: Assessing the impact of breastfeeding on child health: Where conventional methods alone fall short for reliably establishing causal inference. International Journal of Epidemiology, 39(1), 306–307. Brion, M. J., Lawlor, D. A., Matijasevich, A., Horta, B., Anselmi, L., Araujo, C. L., Menezes, A. M., Victora, C. G., & Smith, G. D. (2011). What are the causal effects of breastfeeding on IQ, obesity and blood pressure? Evidence from comparing high-income with middle-income cohorts. International Journal of Epidemiology, 40(3), 670–680. Brownell, K. D., Farley, T., Willett, W. C., Popkin, B. M., Chaloupka, F. J., Thompson, J. W., & Ludwig, D. S. (2009). The public health and economic benefits of taxing sugar-sweetened beverages. The New England Journal of Medicine, 361(16), 1599–1605. Casazza, K., Fontaine, K. R., Astrup, A., Birch, L. L., Brown, A. W., Bohan Brown, M. M., Durant, N., Dutton, G., Foster, E. M., Heymsfield, S. B., McIver, K., Mehta, T., Menachemi, N., Newby, P. K., Pate, R., Rolls, B. J., Sen, B., Smith, D. L., Jr., Thomas, D. M., & Allison, D. B. (2013). Myths, presumptions, and facts about obesity. The New England Journal of Medicine, 368(5), 446–454. Catalano, P. M., Presley, L., Minium, J., & Hauguel-de Mouzon, S. (2009). Fetuses of obese mothers develop insulin resistance in utero. Diabetes Care, 32(6), 1076–1080. Centers for Disease Control and Prevention. (2010). Racial and ethnic differences in breastfeeding initiation and duration, by state – National Immunization Survey, United States, 2004–2008. MMWR. Morbidity and Mortality Weekly Report, 59(11), 327–334. Centers for Disease Control and Prevention. (2014). Breastfeeding report card: United States, 2014. http://www.cdc.gov/breastfeeding/ pdf/2014breastfeedingreportcard.pdf. Accessed 2 Dec 2014. Cespedes, E. M., Rifas-Shiman, S. L., Redline, S., Gillman, M. W., Pena, M. M., & Taveras, E. M. (2014). Longitudinal associations of sleep curtailment with metabolic risk in mid-childhood. Obesity, 22(12), 2586–2592. Chandler-Laney, P. C., Bush, N. C., Granger, W. M., Rouse, D. J., Mancuso, M. S., & Gower, B. A. (2012). Overweight status and intrauterine exposure to ges- 189 tational diabetes are associated with children’s metabolic health. Pediatr Obes, 7(1), 44–52. Chen, X., Beydoun, M. A., & Wang, Y. (2008). Is sleep duration associated with childhood obesity? A systematic review and meta-analysis. Obesity, 16(2), 265–274. Chriqui, J. F., Eidson, S. S., & Chaloupka, F. J. (2014). State sales taxes on regular soda (as of January 2014) – BTG Fact Sheet. http://www.bridgingthegapresearch.org/_asset/s2b5pb/BTG_soda_tax_fact_ sheet_April2014.pdf. Accessed 3 Dec 2014. Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. The New England Journal of Medicine, 357(4), 370–379. Coleman-Jensen, A., Gregory, C., & Singh, A. (September 2014). Household food security in the United States in 2013, ERR-173. Washington, DC: U.S. Department of Agriculture, Economic Research Service. Crowther, C. A., Hiller, J. E., Moss, J. R., McPhee, A. J., Jeffries, W. S., & Robinson, J. S. (2005). Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. The New England Journal of Medicine, 352(24), 2477–2486. Dalenius, K., Brindley, P., Smith, B. L., Reinold, C. M., & Grummer-Strawn, L. (2012). Pregnancy nutrition surveillance 2010 report. Atlanta, GA: US Department of health and human services; Centers for Disease Control and Prevention. Danese, A., & Tan, M. (2014). Childhood maltreatment and obesity: Systematic review and meta-analysis. Molecular Psychiatry, 19(5), 544–554. DiBaise, J. K., Zhang, H., Crowell, M. D., KrajmalnikBrown, R., Decker, G. A., & Rittmann, B. E. (2008). Gut microbiota and its possible relationship with obesity. Mayo Clinic Proceedings, 83(4), 460–469. Ding, E. L., & Hu, F. B. (2008). Determining origins and causes of childhood obesity via Mendelian randomization analysis. PLoS Medicine, 5(3), e65. Dinour, L. M., Bergen, D., & Yeh, M. C. (2007). The food insecurity-obesity paradox: A review of the literature and the role food stamps may play. Journal of the American Dietetic Association, 107(11), 1952–1961. Dixon, B., Pena, M. M., & Taveras, E. M. (2012). Lifecourse approach to racial/ethnic disparities in childhood obesity. Advances in Nutrition, 3(1), 73–82. Dodd, J. M., Turnbull, D. A., McPhee, A. J., Wittert, G., Crowther, C. A., & Robinson, J. S. (2011). Limiting weight gain in overweight and obese women during pregnancy to improve health outcomes: The LIMIT randomised controlled trial. BMC Pregnancy and Childbirth, 11, 79. Dominguez-Bello, M. G., Costello, E. K., Contreras, M., Magris, M., Hidalgo, G., Fierer, N., & Knight, R. (2010). Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proceedings of the National Academy of Sciences of the United States of America, 107(26), 11971–11975. Duffey, K. J., & Popkin, B. M. (2013). Causes of increased energy intake among children in the U.S., 1977–2010. 190 American Journal of Preventive Medicine, 44(2), e1–e8. Dunton, G. F., Kaplan, J., Wolch, J., Jerrett, M., & Reynolds, K. D. (2009). Physical environmental correlates of childhood obesity: A systematic review. Obesity Reviews, 10(4), 393–402. Ehrenkranz, R. A., Dusick, A. M., Vohr, B. R., Wright, L. L., Wrage, L. A., & Poole, W. K. (2006). Growth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics, 117(4), 1253–1261. Eisenmann, J. C., Gundersen, C., Lohman, B. J., Garasky, S., & Stewart, S. D. (2011). Is food insecurity related to overweight and obesity in children and adolescents? A summary of studies, 1995–2009. Obesity Reviews, 12(5), e73–e83. Elbel, B., Gyamfi, J., & Kersh, R. (2011). Child and adolescent fast-food choice and the influence of calorie labeling: A natural experiment. International Journal of Obesity, 35(4), 493–500. Elobeid, M. A., & Allison, D. B. (2008). Putative environmental-endocrine disruptors and obesity: A review. Current Opinion in Endocrinology, Diabetes, and Obesity, 15(5), 403–408. Finkelstein, E. A., Trogdon, J. G., Cohen, J. W., & Dietz, W. (2009). Annual medical spending attributable to obesity: Payer-and service-specific estimates. Health Affairs, 28(5), 822–831. Fisher, S. C., Kim, S. Y., Sharma, A. J., Rochat, R., & Morrow, B. (2013). Is obesity still increasing among pregnant women? Prepregnancy obesity trends in 20 states, 2003–2009. Preventive Medicine, 56(6), 372–378. Fleten, C., Nystad, W., Stigum, H., Skjaerven, R., Lawlor, D. A., Davey Smith, G., & Naess, O. (2012). Parent-offspring body mass index associations in the Norwegian Mother and Child Cohort Study: A familybased approach to studying the role of the intrauterine environment in childhood adiposity. American Journal of Epidemiology, 176(2), 83–92. Foster, G. D., Linder, B., Baranowski, T., Cooper, D. M., Goldberg, L., Harrell, J. S., Kaufman, F., Marcus, M. D., Treviño, R. P., & Hirst, K. (2010). A schoolbased intervention for diabetes risk reduction. The New England Journal of Medicine, 363(5), 443–453. Franklin, B., Jones, A., Love, D., Puckett, S., Macklin, J., & White-Means, S. (2012). Exploring mediators of food insecurity and obesity: A review of recent literature. Journal of Community Health, 37(1), 253–264. Freinkel, N. (1980). Banting lecture 1980. Of pregnancy and progeny. Diabetes, 29(12), 1023–1035. Gao, Y. J., Holloway, A. C., Zeng, Z. H., Lim, G. E., Petrik, J. J., Foster, W. G., & Lee, R. M. (2005). Prenatal exposure to nicotine causes postnatal obesity and altered perivascular adipose tissue function. Obesity Research, 13(4), 687–692. Getahun, D., Nath, C., Ananth, C. V., Chavez, M. R., & Smulian, J. C. (2008). Gestational diabetes in the United States: Temporal trends 1989 through 2004. American Journal of Obstetrics and Gynecology, 198(5), e521–e525. S.S. Hawkins et al. Gillman, M. W. (2004). A life course approach to obesity. In D. Kuh (Ed.), A life course approach to chronic disease epidemiology. Oxford: Oxford University Press. Gillman, M. W. (2005). Developmental origins of health and disease. The New England Journal of Medicine, 353(17), 1848–1850. Gillman, M. W. (2011). Commentary: Breastfeeding and obesity–the 2011 Scorecard. International Journal of Epidemiology, 40(3), 681–684. Gillman, M. W. (2016). Interrupting intergenerational cycles of maternal obesity. In M. S. Fewtrell, F. Haschke, & S. L. Prescott (eds): Preventive Aspects of Early Nutrition. Nestlé Nutr Inst Workshop Ser, vol 85, pp 59–69. Gillman, M. W., Oakey, H., Baghurst, P. A., Volkmer, R. E., Robinson, J. S., & Crowther, C. A. (2010). Effect of treatment of gestational diabetes mellitus on obesity in the next generation. Diabetes Care, 33(5), 964–968. Gillman, M. W., & Poston, L. (2012). Maternal obesity. Cambridge, UK: Cambridge University Press. Gillman, M. W., Rifas-Shiman, S. L., Berkey, C. S., Frazier, A. L., Rockett, H. R., Camargo, C. A., Jr., Field, A. E., & Colditz, G. A. (2006). Breast-feeding and overweight in adolescence: Within-family analysis [corrected]. Epidemiology, 17(1), 112–114. Gilman, S. E., Gardener, H., & Buka, S. L. (2008). Maternal smoking during pregnancy and children’s cognitive and physical development: A causal risk factor? American Journal of Epidemiology, 168(5), 522–531. Glaser, R., & Kiecolt-Glaser, J. K. (2005). Stress-induced immune dysfunction: Implications for health. Nature Reviews Immunology, 5(3), 243–251. Glass, T. A., & McAtee, M. J. (2006). Behavioral science at the crossroads in public health: Extending horizons, envisioning the future. Social Science & Medicine, 62(7), 1650–1671. Gonzalez-Suarez, C., Worley, A., Grimmer-Somers, K., & Dones, V. (2009). School-based interventions on childhood obesity: A meta-analysis. American Journal of Preventive Medicine, 37(5), 418–427. Grayson, B. E., & Seeley, R. J. (2012). Deconstructing obesity: The face of fatness before and after the discovery of leptin. Diabetologia, 55(1), 3–6. Grummer-Strawn, L. M., Reinold, C., & Krebs, N. F. (2010). Use of World Health Organization and CDC growth charts for children aged 0–59 months in the United States. MMWR – Recommendations and Reports, 59(RR-9), 1–15. Grynbaum, M. M. (2012). Will soda restrictions help New York win the war on obesity? BMJ, 345, e6768. Halfon, N., & Forrest, C. B. (2017). The emerging theoretical framework of life course health development. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health-development science. Cham: Springer. Hammond, R. A. (2009). Complex systems modeling for obesity research. Preventing Chronic Disease, 6(3), A97. Han, J. C., Lawlor, D. A., & Kimm, S. Y. (2010). Childhood obesity. Lancet, 375(9727), 1737–1748. Early in the Life Course: Time for Obesity Prevention Harder, T., Bergmann, R., Kallischnigg, G., & Plagemann, A. (2005). Duration of breastfeeding and risk of overweight: A meta-analysis. American Journal of Epidemiology, 162(5), 397–403. Harlev, A., & Wiznitzer, A. (2010). New insights on glucose pathophysiology in gestational diabetes and insulin resistance. Current Diabetes Reports, 10(3), 242–247. Hauguel-de Mouzon, S., Lepercq, J., & Catalano, P. (2006). The known and unknown of leptin in pregnancy. American Journal of Obstetrics and Gynecology, 194(6), 1537–1545. Hawkins, S. S., & Baum, C. F. (2014). Impact of state cigarette taxes on disparities in maternal smoking during pregnancy. American Journal of Public Health, 104(8), 1464–1470. Hedderson, M. M., Gunderson, E. P., & Ferrara, A. (2010). Gestational weight gain and risk of gestational diabetes mellitus. Obstetrics and Gynecology, 115(3), 597–604. Hemmingsson, E., Johansson, K., & Reynisdottir, S. (2014). Effects of childhood abuse on adult obesity: A systematic review and meta-analysis. Obesity Reviews, 15(11), 882–893. Herring, S. J., & Oken, E. (2011). Obesity and diabetes in mothers and their children: Can we stop the intergenerational cycle? Current Diabetes Reports, 11(1), 20–27. Herring, S. J., Rose, M. Z., Skouteris, H., & Oken, E. (2012). Optimizing weight gain in pregnancy to prevent obesity in women and children. Diabetes, Obesity & Metabolism, 14(3), 195–203. Huang, T. T., Drewnosksi, A., Kumanyika, S., & Glass, T. A. (2009). A systems-oriented multilevel framework for addressing obesity in the 21st century. Preventing Chronic Disease, 6(3), A82. Huang, T. T., & Glass, T. A. (2008). Transforming research strategies for understanding and preventing obesity. JAMA, 300(15), 1811–1813. Huh, S. Y., Rifas-Shiman, S. L., Taveras, E. M., Oken, E., & Gillman, M. W. (2011). Timing of solid food introduction and risk of obesity in preschool-aged children. Pediatrics, 127(3), e544–e551. Iliadou, A. N., Koupil, I., Villamor, E., Altman, D., Hultman, C., Langstrom, N., & Cnattingius, S. (2010). Familial factors confound the association between maternal smoking during pregnancy and young adult offspring overweight. International Journal of Epidemiology, 39(5), 1193–1202. Institute of Medicine. (1990). Nutrition during pregnancy. Part I, weight gain. Washington, DC: National Academies Press. Institute of Medicine. (2007). Nutrition standards for foods in schools: Leading the way toward healtheir youth. Washington, DC: National Academies Press. Institute of Medicine. (2009). Weight gain during pregnancy: Reexamining the guidelines. Washington, DC: National Academies Press. 191 Institute of Medicine. (2011). Early childhood obesity prevention policies. Washington DC: National Academies Press. Kahn, J. A., Huang, B., Gillman, M. W., Field, A. E., Austin, S. B., Colditz, G. A., & Frazier, A. L. (2008). Patterns and determinants of physical activity in U.S. adolescents. The Journal of Adolescent Health, 42(4), 369–377. Kann, L., Kinchen, S., Shanklin, S. L., Flint, K. H., Kawkins, J., Harris, W. A., Lowry, R., Olsen, E. O., McManus, T., Chyen, D., Whittle, L., Taylor, E., Demissie, Z., Brener, N., Thornton, J., Moore, J., Zaza, S., & Centers for Disease Control and Prevention (CDC). (2014). Youth risk behavior surveillance– United States, 2013. MMWR Surveillance Summaries, 63(4), 1–168. Katz, D. L., O’Connell, M., Njike, V. Y., Yeh, M. C., & Nawaz, H. (2008). Strategies for the prevention and control of obesity in the school setting: Systematic review and meta-analysis. International Journal of Obesity, 32(12), 1780–1789. Kim, C., Berger, D. K., & Chamany, S. (2007). Recurrence of gestational diabetes mellitus: A systematic review. Diabetes Care, 30(5), 1314–1319. Kim, C., Newton, K. M., & Knopp, R. H. (2002). Gestational diabetes and the incidence of type 2 diabetes: A systematic review. Diabetes Care, 25(10), 1862–1868. Kim, S. Y., England, J. L., Sharma, J. A., & Njoroge, T. (2011). Gestational diabetes mellitus and risk of childhood overweight and obesity in offspring: A systematic review. Experimental Diabetes Research, 2011, 541308. Kitano, H. (2002). Systems biology: A brief overview. Science, 295(5560), 1662–1664. Kramer, M. S., Chalmers, B., Hodnett, E. D., Sevkovskaya, Z., Dzikovich, I., Shapiro, S., Collet, J. P., Vanilovich, I., Mezen, I., Ducruet, T., Shishko, G., Zubovich, V., Mknuik, D., Gluchanina, E., Dombrovsky, V., Ustinovitch, A., Ko, T., Bogdanovich, N., Ovchinikova, L., & Helsing, E. (2001). Promotion of Breastfeeding Intervention Trial (PROBIT): A randomized trial in the Republic of Belarus. JAMA, 285(4), 413–420. Kramer, M. S., Guo, T., Platt, R. W., Vanilovich, I., Sevkovskaya, Z., Dzikovich, I., Michaelsen, K. F., Dewey, K., & Promotion of Breastfeeding Interven tion Trials Study Group. (2004). Feeding effects on growth during infancy. The Journal of Pediatrics, 145(5), 600–605. Kramer, M. S., Matush, L., Vanilovich, I., Platt, R. W., Bogdanovich, N., Sevkovskaya, Z., Dzikovich, I., Shishko, G., Collet, J. P., Martin, R. M., Davey Smith, G., Gillman, M. W., Chalmers, B., Hodnett, E., Shapiro, S., & PROBIT Study Group. (2007). Effects of prolonged and exclusive breastfeeding on child height, weight, adiposity, and blood pressure at age 6.5 y: Evidence from a large randomized trial. The American Journal of Clinical Nutrition, 86(6), 1717–1721. 192 Kropski, J. A., Keckley, P. H., & Jensen, G. L. (2008). School-based obesity prevention programs: An evidence-based review. Obesity, 16(5), 1009–1018. Kuczmarski, R. J., Ogden, C. L., Guo, S. S., GrummerStrawn, L. M., Flegal, K. M., Mei, Z., Curtin, L. R., Roche, A. F., & Johnson, C. L. (2002). 2000 CDC growth charts for the United States: Methods and development. Vital and Health Statistics, 11(246), 1–190. Kwok, M. K., Schooling, C. M., Lam, T. H., & Leung, G. M. (2010). Does breastfeeding protect against childhood overweight? Hong Kong’s ‘Children of 1997’ birth cohort. International Journal of Epidemiology, 39(1), 297–305. Landon, M. B., Spong, C. Y., Thom, E., Carpenter, M. W., Ramin, S. M., Casey, B., Wapner, R. J., Varner, M. W., Rouse, D. J., Thorp, J. M., Jr., Sciscione, A., Catalano, P., Harper, M., Saade, G., Lain, K. Y., Sorokin, Y., Peaceman, A. M., Tolosa, J. E., Anderson, G. B., & Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. (2009). A multicenter, randomized trial of treatment for mild gestational diabetes. The New England Journal of Medicine, 361(14), 1339–1348. Larson, N., Ward, D., Neelon, S. B., & Story, M. (2011a). Preventing obesity among preschool children: How can child-care settings promote healthy eating and physical activity? Research synthesis. Princeton, NJ: Robert Wood Johnson Foundation. Larson, N., Ward, D. S., Neelon, S. B., & Story, M. (2011b). What role can child-care settings play in obesity prevention? A review of the evidence and call for research efforts. Journal of the American Dietetic Association, 111(9), 1343–1362. Lasater, G., Piernas, C., & Popkin, B. M. (2011). Beverage patterns and trends among school-aged children in the US, 1989–2008. Nutrition Journal, 10, 103. Lau, E. Y., Liu, J., Archer, E., McDonald, S. M., & Liu, J. (2014). Maternal weight gain in pregnancy and risk of obesity among offspring: A systematic review. Journal of Obesity, 2014, 524939. Lawlor, D. A., Lichtenstein, P., Fraser, A., & Langstrom, N. (2011a). Does maternal weight gain in pregnancy have long-term effects on offspring adiposity? A sibling study in a prospective cohort of 146,894 men from 136,050 families. The American Journal of Clinical Nutrition, 94(1), 142–148. Lawlor, D. A., Lichtenstein, P., & Langstrom, N. (2011b). Association of maternal diabetes mellitus in pregnancy with offspring adiposity into early adulthood: Sibling study in a prospective cohort of 280,866 men from 248,293 families. Circulation, 123(3), 258–265. Lawlor, D. A., Timpson, N. J., Harbord, R. M., Leary, S., Ness, A., McCarthy, M. I., Frayling, T. M., Hattersley, A. T., & Smith, G. D. (2008). Exploring the developmental overnutrition hypothesis using parentaloffspring associations and FTO as an instrumental variable. PLoS Medicine, 5(3), e33. Lehto, R., Mäki, P., Ray, C., Laatikainen, T., & Roos, E. (2015). Childcare use and overweight in Finland: S.S. Hawkins et al. Cross-sectional and retrospective associations among 3- and 5-year-old children. Pediatric Obesity, 11(2), 136–143. doi:10.1111/ijpo.12036. Li, H. T., Zhou, Y. B., & Liu, J. M. (2013). The impact of cesarean section on offspring overweight and obesity: A systematic review and meta-analysis. International Journal of Obesity, 37(7), 893–899. Li, R., Fein, S. B., & Grummer-Strawn, L. M. (2010). Do infants fed from bottles lack self-regulation of milk intake compared with directly breastfed infants? Pediatrics, 125(6), e1386–e1393. Lobstein, T., Baur, L., & Uauy, R. (2004). Obesity in children and young people: A crisis in public health. Obesity Reviews, 5(1), 4–104. Lovasi, G. S., Hutson, M. A., Guerra, M., & Neckerman, K. M. (2009). Built environments and obesity in disadvantaged populations. Epidemiologic Reviews, 31, 7–20. Lubans, D. R., Morgan, P. J., Dewar, D., Collins, C. E., Plotnikoff, R. C., Okely, A. D., Batterham, M. J., Finn, T., & Callister, R. (2010). The Nutrition and Enjoyable Activity for Teen Girls (NEAT girls) randomized controlled trial for adolescent girls from disadvantaged secondary schools: Rationale, study protocol, and baseline results. BMC Public Health, 10, 652. Ludwig, D. S., Blumenthal, S. J., & Willett, W. C. (2012). Opportunities to reduce childhood hunger and obesity: Restructuring the supplemental nutrition assistance program (the food stamp program). JAMA, 308(24), 2567–2568. Ludwig, J., Sanbonmatsu, L., Gennetian, L., Adam, E., Duncan, G. J., Katz, L. F., Kessler, R. C., Kling, J. R., Lindau, S. T., Whitaker, R. C., & McDade, T. W. (2011). Neighborhoods, obesity, and diabetes–a randomized social experiment. The New England Journal of Medicine, 365(16), 1509–1519. Lumley, J., Chamberlain, C., Dowswell, T., Oliver, S., Oakley, L., & Watson, L. (2009). Interventions for promoting smoking cessation during pregnancy. Cochrane Database of Systematic Reviews, 3, CD001055. Mamun, A. A., Mannan, M., & Doi, S. A. (2014). Gestational weight gain in relation to offspring obesity over the life course: A systematic review and bias-adjusted meta-analysis. Obesity Reviews, 15(4), 338–347. Mani, M., Mandre, S. B., & M. (2010). Events before droplet splashing on a solid surface. Journal of Fluid Mechanics, 647, 163. Mantzoros, C. S., Rifas-Shiman, S. L., Williams, C. J., Fargnoli, J. L., Kelesidis, T., & Gillman, M. W. (2009). Cord blood leptin and adiponectin as predictors of adiposity in children at 3 years of age: A prospective cohort study. Pediatrics, 123(2), 682–689. Martin, R. M., Patel, R., Kramer, M. S., Guthrie, L., Vilchuck, K., Bogdanovich, N., Sergeichick, N., Gusina, N., Foo, Y., Palmer, T., Rifas-Shiman, S. L., Gillman, M. W., Smith, G. D., & Oken, E. (2013). Effects of promoting longer-term and exclusive breastfeeding on adiposity and insulin-like growth factor-I Early in the Life Course: Time for Obesity Prevention at age 11.5 years: A randomized trial. JAMA, 309(10), 1005–1013. Masse, L. C., Perna, F., Agurs-Collins, T., & Chriqui, J. F. (2013). Change in school nutrition-related laws from 2003 to 2008: Evidence from the school nutrition-environment state policy classification system. American Journal of Public Health, 103(9), 1597–1603. McGinnis, J. M., Gootman, J. A., & Kraak, V. I. (2006). Food marketing to children and youth: Threat or opportunity? Washington, DC: National Academies Press. Mello, M. M. (2009). New York City’s war on fat. The New England Journal of Medicine, 360(19), 2015–2020. Metzger, M. W., & McDade, T. W. (2010). Breastfeeding as obesity prevention in the United States: A sibling difference model. American Journal of Human Biology, 22(3), 291–296. Michopoulos, V., Powers, A., Moore, C., Villarreal, S., Ressler, K. J., & Bradley, B. (2015). The mediating role of emotion dysregulation and depression on the relationship between childhood trauma exposure and emotional eating. Appetite, 91, 129–136. Mihrshahi, S., Battistutta, D., Magarey, A., & Daniels, L. A. (2011). Determinants of rapid weight gain during infancy: Baseline results from the NOURISH randomised controlled trial. BMC Pediatrics, 11, 99. Moholdt, T. T., Salvesen, K., Ingul, C. B., Vik, T., Oken, E., & Morkved, S. (2011). Exercise Training in Pregnancy for obese women (ETIP): Study protocol for a randomised controlled trial. Trials, 12, 154. Monteiro, P. O., & Victora, C. G. (2005). Rapid growth in infancy and childhood and obesity in later life–a systematic review. Obesity Reviews, 6(2), 143–154. National Association for Sport and Physical Education. (2004). Moving into the future: National standards for physical education (2nd ed.). Reston, VA: McGraw-Hill. National Prevention Council. (2011). National prevention strategy. Washington, DC: U.S. Department of Health and Human Services, Office of the Surgeon General. Neelon, S. B., Andersen, C. S., Morgen, C. S., KamperJørgensen, M., Oken, E., Gillman, M. W., & Sørensen, T. I. A. (2015). Early child care and obesity at 12 months of age in the Danish National Birth Cohort. International Journal of Obesity, 39(1), 33–38. Nehring, I., Lehmann, S., & von Kries, R. (2013). Gestational weight gain in accordance to the IOM/ NRC criteria and the risk for childhood overweight: A meta-analysis. Pediatric Obesity, 8(3), 218–224. Nehring, I., Schmoll, S., Beyerlein, A., Hauner, H., & von Kries, R. (2011). Gestational weight gain and longterm postpartum weight retention: A meta-analysis. The American Journal of Clinical Nutrition, 94(5), 1225–1231. Nelson, M. C., Gordon-Larsen, P., & Adair, L. S. (2005). Are adolescents who were breast-fed less likely to be overweight? Analyses of sibling pairs to reduce confounding. Epidemiology, 16(2), 247–253. 193 Neu, J., & Rushing, J. (2011). Cesarean versus vaginal delivery: Long-term infant outcomes and the hygiene hypothesis. Clinics in Perinatology, 38(2), 321–331. Newbold, R. R., Padilla-Banks, E., Snyder, R. J., Phillips, T. M., & Jefferson, W. N. (2007). Developmental exposure to endocrine disruptors and the obesity epidemic. Reproductive Toxicology, 23(3), 290–296. Nielsen, L. S., Danielsen, K. V., & Sorensen, T. I. (2011). Short sleep duration as a possible cause of obesity: Critical analysis of the epidemiological evidence. Obesity Reviews, 12(2), 78–92. Office of the Mayor. (2012). Reversing the epidemic: The New York City obesity task force plan to prevent and control obesity. http://www.nyc.gov/html/om/ pdf/2012/otf_report.pdf. Accessed 5 Feb 2013. O’Tierney, P. F., Barker, D. J., Osmond, C., Kajantie, E., & Eriksson, J. G. (2009). Duration of breast-feeding and adiposity in adult life. The Journal of Nutrition, 139(2), 422S–425S. Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA, 311(8), 806–814. Ogden, C. L., & Flegal, K. M. (2010). Changes in terminology for childhood overweight and obesity. National Health Statistics Reports, 25, 1–5. Oken, E., & Gillman, M. W. (2003). Fetal origins of obesity. Obesity Research, 11(4), 496–506. Oken, E., Levitan, E. B., & Gillman, M. W. (2008). Maternal smoking during pregnancy and child overweight: Systematic review and meta-analysis. International Journal of Obesity, 32(2), 201–210. Okereke, N. C., Uvena-Celebrezze, J., Hutson-Presley, L., Amini, S. B., & Catalano, P. M. (2002). The effect of gender and gestational diabetes mellitus on cord leptin concentration. American Journal of Obstetrics and Gynecology, 187(3), 798–803. Oteng-Ntim, E., Varma, R., Croker, H., Poston, L., & Doyle, P. (2012). Lifestyle interventions for overweight and obese pregnant women to improve pregnancy outcome: Systematic review and meta-analysis. BMC Medicine, 10, 47. Owen, C. G., Martin, R. M., Whincup, P. H., DaveySmith, G., Gillman, M. W., & Cook, D. G. (2005a). The effect of breastfeeding on mean body mass index throughout life: A quantitative review of published and unpublished observational evidence. The American Journal of Clinical Nutrition, 82(6), 1298–1307. Owen, C. G., Martin, R. M., Whincup, P. H., Smith, G. D., & Cook, D. G. (2005b). Effect of infant feeding on the risk of obesity across the life course: A quantitative review of published evidence. Pediatrics, 115(5), 1367–1377. Pan, L., Blanck, H. M., Sherry, B., Dalenius, K., & Grummer-Strawn, L. M. (2012). Trends in the prevalence of extreme obesity among US preschool-aged children living in low-income families, 1998–2010. JAMA, 308(24), 2563–2565. 194 Papas, M. A., Alberg, A. J., Ewing, R., Helzlsouer, K. J., Gary, T. L., & Klassen, A. C. (2007). The built environment and obesity. Epidemiologic Reviews, 29, 129–143. Parker, M., Rifas-Shiman, S. L., Belfort, M. B., Taveras, E. M., Oken, E., Mantzoros, C., & Gillman, M. W. (2011). Gestational glucose tolerance and cord blood leptin levels predict slower weight gain in early infancy. The Journal of Pediatrics, 158(2), 227–233. Parsons, T. J., Power, C., Logan, S., & Summerbell, C. D. (1999). Childhood predictors of adult obesity: A systematic review. International Journal of Obesity and Related Metabolic Disorders, 23(8), S1–107. Patel, R., Martin, R. M., Kramer, M. S., Oken, E., Bogdanovich, N., Matush, L., Smith, G. D., & Lawlor, D. A. (2011). Familial associations of adiposity: Findings from a cross-sectional study of 12,181 parental-offspring trios from Belarus. PloS One, 6(1), e14607. Pearce, J., Taylor, M. A., & Langley-Evans, S. C. (2013). Timing of the introduction of complementary feeding and risk of childhood obesity: A systematic review. International Journal of Obesity, 37(10), 1295–1306. Perna, F. M., Oh, A., Chriqui, J. F., Masse, L. C., Atienza, A. A., Nebeling, L., Agurs-Collins, T., Moser, R. P., & Dodd, K. W. (2012). The association of state law to physical education time allocation in US public schools. American Journal of Public Health, 102(8), 1594–1599. Perng, W., Gillman, M. W., Mantzoros, C. S., & Oken, E. (2014). A prospective study of maternal prenatal weight and offspring cardiometabolic health in midchildhood. Annals of Epidemiology, 24(11), 793–800 e791. Piernas, C., & Popkin, B. M. (2011). Increased portion sizes from energy-dense foods affect total energy intake at eating occasions in US children and adolescents: Patterns and trends by age group and sociodemographic characteristics, 1977–2006. The American Journal of Clinical Nutrition, 94(5), 1324–1332. Powell, L. M., Chriqui, J. F., Khan, T., Wada, R., & Chaloupka, F. J. (2013). Assessing the potential effectiveness of food and beverage taxes and subsidies for improving public health: A systematic review of prices, demand and body weight outcomes. Obesity Reviews, 14(2), 110–128. Rideout, V. J., & Hamel, E. (2006). The media family: Electronic media in the lives of infants, toddlers, preschoolers, and their parents. Menlo Park, CA: Kaiser Family Foundation. Rudd Center for Food Policy & Obesity. (2013). Industry self-regulation of food marketing.. http://www.yaleruddcenter.org/what_we_do.aspx?id=25. Accessed 5 Feb 2013. Sanbonmatsu, L., Ludwig, J., Katz, L. F., Gennetian, L. A., Duncan, G. J., Kessler, R. C., Adam, E., TW, M. D., & Lindau, S. T. (2011). Moving to opportunity for fair housing demonstration program – Final impacts evaluation. Washington, DC: U.S. Department of Housing and Urban Development. S.S. Hawkins et al. Savino, F., & Liguori, S. A. (2008). Update on breast milk hormones: Leptin, ghrelin and adiponectin. Clinical Nutrition, 27(1), 42–47. Schmidt, M. E., Haines, J., O’Brien, A., McDonald, J., Price, S., Sherry, B., & Taveras, E. M. (2012). Systematic review of effective strategies for reducing screen time among young children. Obesity, 20(7), 1338–1354. Section on Breastfeeding. (2012). Breastfeeding and the use of human milk. Pediatrics, 129(3), e827–e841. Siega-Riz, A. M., Viswanathan, M., Moos, M. K., Deierlein, A., Mumford, S., Knaack, J., Thieda, P., Lux, L. J., & Lohr, K. N. (2009). A systematic review of outcomes of maternal weight gain according to the Institute of Medicine recommendations: Birthweight, fetal growth, and postpartum weight retention. American Journal of Obstetrics and Gynecology, 201(4), 339.e331–339.e314. Slining, M. M., Benjamin Neelon, S. E., & Duffey, K. J. (2014). A review of state regulations to promote infant physical activity in child care. International Journal of Behavioral Nutrition and Physical Activity, 11(1), 139. Smith, G. D., & Ebrahim, S. (2003). ‘Mendelian randomization’: Can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology, 32(1), 1–22. National Center for Education Statistics. (2005). Number of children under 6 years old and not yet enrolled in kindergarten, percentage in center-based programs, average weekly hours in nonparental care, and percentage in various types of primary care arrangements, by selected child and family characteristics: 2005. http://nces.ed.gov/programs/digest/d09/tables/ dt09_044.asp. Accessed 5 Feb 2013. Story, M., Nanney, M. S., & Schwartz, M. B. (2009). Schools and obesity prevention: Creating school environments and policies to promote healthy eating and physical activity. The Milbank Quarterly, 87(1), 71–100. Strasburger, V. C. (2011). Children, adolescents, obesity, and the media. Pediatrics, 128(1), 201–208. Taheri, S. (2006). The link between short sleep duration and obesity: We should recommend more sleep to prevent obesity. Archives of Disease in Childhood, 91(11), 881–884. Taveras, E. M., Blackburn, K., Gillman, M. W., Haines, J., McDonald, J., Price, S., & Oken, E. (2011a). First steps for mommy and me: A pilot intervention to improve nutrition and physical activity behaviors of postpartum mothers and their infants. Maternal and Child Health Journal, 15(8), 1217–1227. Taveras, E. M., Gillman, M. W., Kleinman, K., RichEdwards, J. W., & Rifas-Shiman, S. L. (2010). Racial/ ethnic differences in early-life risk factors for childhood obesity. Pediatrics, 125(4), 686–695. Taveras, E. M., Gillman, M. W., Kleinman, K. P., RichEdwards, J. W., & Rifas-Shiman, S. L. (2013). Reducing racial/ethnic disparities in childhood obesity: The role of early life risk factors. JAMA Pediatrics, 167(8), 731–738. Early in the Life Course: Time for Obesity Prevention Taveras, E. M., Gillman, M. W., Pena, M. M., Redline, S., & Rifas-Shiman, S. L. (2014). Chronic sleep curtailment and adiposity. Pediatrics, 133(6), 1013–1022. Taveras, E. M., Gortmaker, S. L., Hohman, K. H., Horan, C. M., Kleinman, K. P., Mitchell, K., Price, S., Prosser, L. A., Rifas-Shiman, S. L., & Gillman, M. W. (2011b). Randomized controlled trial to improve primary care to prevent and manage childhood obesity: The high five for kids study. Archives of Pediatrics & Adolescent Medicine, 165(8), 714–722. Taveras, E. M., McDonald, J., O’Brien, A., Haines, J., Sherry, B., Bottino, C. J., Troncoso, K., Schmidt, M. E., & Koziol, R. (2012). Healthy habits, happy homes: Methods and baseline data of a randomized controlled trial to improve household routines for obesity prevention. Preventive Medicine, 55(5), 418–426. Taveras, E. M., Rifas-Shiman, S. L., Sherry, B., Oken, E., Haines, J., Kleinman, K., Rich-Edwards, J. W., & Gillman, M. W. (2011c). Crossing growth percentiles in infancy and risk of obesity in childhood. Archives of Pediatrics & Adolescent Medicine, 165(11), 993–998. Te Morenga, L., Mallard, S., & Mann, J. (2012). Dietary sugars and body weight: Systematic review and metaanalyses of randomised controlled trials and cohort studies. BMJ, 346, e7492. Thangaratinam, S., Rogozinska, E., Jolly, K., Glinkowski, S., Roseboom, T., Tomlinson, J. W., Kunz, R., Mol, B. W., Coomarasamy, A., & Khan, K. S. (2012). Effects of interventions in pregnancy on maternal weight and obstetric outcomes: Meta-analysis of randomised evidence. BMJ, 344, e2088. Torloni, M. R., Betran, A. P., Horta, B. L., Nakamura, M. U., Atallah, A. N., Moron, A. F., & Valente, O. (2009). Prepregnancy BMI and the risk of gestational diabetes: A systematic review of the literature with meta-analysis. Obesity Reviews, 10(2), 194–203. Torres, S. J., & Nowson, C. A. (2007). Relationship between stress, eating behavior, and obesity. Nutrition, 23(11–12), 887–894. Trasande, L., Attina, T. M., & Blustein, J. (2012). Association between urinary bisphenol a concentration and obesity prevalence in children and adolescents. JAMA, 308(11), 1113–1121. Trogdon, J. G., Nonnemaker, J., & Pais, J. (2008). Peer effects in adolescent overweight. Journal of Health Economics, 27(5), 1388–1399. U.S. Department of Agriculture. (2013a). National school lunch program and school breakfast program: Nutrition standards for all foods sold in school as required by the Healthy, Hunger-Free Kids Act of 2010; Interim Final Rule. Federal Register, 78(125), 39067–39120. U.S. Department of Agriculture. (2013b). Supplemental Nutrition Assistance Program (SNAP). http://www. fns.usda.gov/snap. Accessed 5 Feb 2013. U.S. Department of Health and Human Services. (2004). The health consequences of smoking: A report of the surgeon general. Atlanta, GA: U.S. Department of Health and Human Services; Centers for Disease Control and Prevention; National Center for Chronic 195 Disease Prevention and Health Promotion; Office on Smoking and Health. U.S. Department of Health and Human Services. (2008). 2008 physical activity guidelines for Americans. Washington, DC: U.S. Department of Health and Human Services. Van Der Horst, K., Paw, M. J., Twisk, J. W., & Van Mechelen, W. (2007). A brief review on correlates of physical activity and sedentariness in youth. Medicine and Science in Sports and Exercise, 39(8), 1241–1250. Ventura, A. K., & Mennella, J. A. (2011). Innate and learned preferences for sweet taste during childhood. Current Opinion in Clinical Nutrition and Metabolic Care, 14(4), 379–384. Vesco, K. K., Karanja, N., King, J. C., Gillman, M. W., Leo, M. C., Perrin, N., CT, M. E., Eckhardt, C. L., Smith, K. S., & Stevens, V. J. (2014). Efficacy of a group-based dietary intervention for limiting gestational weight gain among obese women: A randomized trial. Obesity, 22(9), 1989–1996. Vesco, K. K., Karanja, N., King, J. C., Gillman, M. W., Perrin, N., McEvoy, C., Eckhardt, C., Smith, K. S., & Stevens, V. J. (2012). Healthy moms, a randomized trial to promote and evaluate weight maintenance among obese pregnant women: Study design and rationale. Contemporary Clinical Trials, 33(4), 777–785. Vgontzas, A. N., Lin, H. M., Papaliaga, M., Calhoun, S., Vela-Bueno, A., Chrousos, G. P., & Bixler, E. O. (2008). Short sleep duration and obesity: The role of emotional stress and sleep disturbances. International Journal of Obesity, 32(5), 801–809. Viner, R. M., Ozer, E. M., Denny, S., Marmot, M., Resnick, M., Fatusi, A., & Currie, C. (2012). Adolescence and the social determinants of health. Lancet, 379(9826), 1641–1652. von Kries, R., Bolte, G., Baghi, L., & Toschke, A. M. (2008). Parental smoking and childhood obesity–is maternal smoking in pregnancy the critical exposure? International Journal of Epidemiology, 37(1), 210–216. Wang, Y., & Beydoun, M. A. (2007). The obesity epidemic in the United States–gender, age, socioeconomic, racial/ethnic, and geographic characteristics: A systematic review and meta-regression analysis. Epidemiologic Reviews, 29, 6–28. Wang, Y. C., Gortmaker, S. L., & Taveras, E. M. (2011). Trends and racial/ethnic disparities in severe obesity among US children and adolescents, 1976–2006. International Journal of Pediatric Obesity, 6(1), 12–20. Waterland, R. A., & Jirtle, R. L. (2004). Early nutrition, epigenetic changes at transposons and imprinted genes, and enhanced susceptibility to adult chronic diseases. Nutrition, 20(1), 63–68. Waters, E., de Silva-Sanigorski, A., Hall, B. J., Brown, T., Campbell, K. J., Gao, Y., Armstrong, R., Prosser, L., & Summerbell, C. D. (2011). Interventions for preventing obesity in children. Cochrane Database of Systematic Reviews, 12, CD001871. Wen, X., Gillman, M. W., Rifas-Shiman, S. L., Sherry, B., Kleinman, K., & Taveras, E. M. (2012). Decreasing 196 prevalence of obesity among young children in Massachusetts from 2004 to 2008. Pediatrics, 129(5), 823–831. Weng, S. F., Redsell, S. A., Swift, J. A., Yang, M., & Glazebrook, C. P. (2012). Systematic review and meta-analyses of risk factors for childhood overweight identifiable during infancy. Archives of Disease in Childhood, 97(12), 1019–1026. White House Task Force on Childhood Obesity. (2011). One year progress report. http://www.letsmove.gov/ sites/letsmove.gov/files/Obesity_update_report.pdf. Accessed 8 Feb 2013. S.S. Hawkins et al. White House Task Force on Childhood Obesity. (2010). Solving the problem of childhood obesity within a generation: The White House task force on childhood obesity report to the president http://www.letsmove.gov/ sites/letsmove.gov/files/TaskForce_on_Childhood_ Obesity_May2010_FullReport.pdf. Accessed 19 July 2012. Yu, Z. B., Han, S. P., Zhu, G. Z., Zhu, C., Wang, X. J., Cao, X. G., & Guo, X. R. (2011). Birth weight and subsequent risk of obesity: A systematic review and meta-analysis. Obesity Reviews, 12(7), 525–542. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development Framework Pamela Salsberry, Rika Tanda, Sarah E. Anderson, and Manmohan K. Kamboj 1 Introduction Estimates project that one in three US adults may have diabetes in 2050 (Boyle et al. 2010). The rates are even higher in some subgroups; for example, in Hispanic females it is 1 in 2 (Venkat Narayan et al. 2003). This problem is not limited to the USA. Shaw et al. 2010 estimate that 7.7% P. Salsberry, PhD, RN, FAAN (*) College of Public Health, Division of Health Behavior, Health Promotion, Institute for Population Health, The Ohio State University, 1841 Neil Avenue, 353 Cunz Hall, Columbus, OH 43210, USA e-mail: Salsberry.1@osu.edu R. Tanda, PhD, RN College of Health Science and Professions, Ohio University, Grover Center W133B, Athens, OH 45701, USA e-mail: tandar@ohio.edu S.E. Anderson, PhD The Ohio State University, College of Public Health, 336 Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA e-mail: sanderson@cph.osu.edu M.K. Kamboj, MD The Ohio State University, College of Medicine, Endocrinology, Metabolism and Diabetes, Nationwide Children’s Hospital, 700 Children’s Drive (ED425), Columbus, OH 43205, USA e-mail: Manmohan.Kamboj@Nationwidechildrens.org or 434 million adults will have diabetes by 2030 worldwide (Shaw et al. 2010). Until very recently, type 2 diabetes mellitus (T2DM) was a disease diagnosed in adults, but as the childhood obesity epidemic has spread in both magnitude and severity, the diagnosis of T2DM in adolescents and young adults has become more common. The prevalence of prediabetes in 12–19 years, estimated using National Health and Nutrition Examination Survey (NHANES) data, increased from 9% in 1999–2000 to 23% in 2007–2008 (May et al. 2012). Analyses using data from the SEARCH for Diabetes in Youth Study (SEARCH) indicate a 30.5% overall increase in the prevalence of T2DM in children/adolescents from 2001 to 2009 (Dabelea et al. 2014). Because of the newness of this diagnosis in children and adolescents, very little is known about the natural history of the disease, and it will likely take decades to fully understand the determinants and consequences of this new epidemic. Early indicators suggest that the disease may be more severe (Elder et al. 2012) and more difficult to manage when diagnosed in adolescents (Zeitler et al. 2012). A recent analysis estimates that adolescents with T2DM will lose approximately 15 years of life and will experience severe and chronic complications by their 40s as a result of the disease (Rhodes et al. 2012). The mean direct cost for medical care for a man diagnosed with T2DM for less than 15 years is estimated at $2465. The economic burden of T2DM rises significantly as the © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_10 197 P. Salsberry et al. 198 years with disease increase. For those with disease 15 years or more, the costs are estimated to be 10–50% higher. This grim forecast highlights that not only will the disease burden be significant but the economic burden will be high in youth diagnosed with T2DM (Li et al. 2013). Metabolic comorbidities occur with alarming frequency (West et al. 2009). Within the SEARCH study, a significant portion of adolescents with T2DM had hypertension, high LDL cholesterol, or high triglycerides (Lawrence et al. 2009a; Mayer-Davis et al. 2009). It is clear that there is much to do to understand the epidemic, to determine effective prevention strategies, and to develop interventions for those diagnosed with the disease. It is also clear from what we are learning that preventive and treatment strategies need to begin early. The risk for a child developing T2DM begins in utero. Individual health development trajectories emerge from this starting point and are the result of factors that accumulate across time and/or are the result of biological conditioning during sensitive periods of development. Understanding how these trajectories emerge, and which factors and events result in disease-causing pathways, is key to not only understanding the onset and variable natural history of the disease but in determining ways to prevent and treat it. There is an extensive and emergent body of literature that examines the mechanisms, pathways, and determinants of T2DM in children and adolescents. Genetic and epigenetic mechanisms are clearly a part of the story, but several studies also demonstrate the influence and importance of the social context on developing biological processes (Kempf et al. 2008). These include behaviors and psychological stressors nested within families, neighborhoods, and communities. Consistent with the principles of life course health development that are presented by Halfon and Forrest (2017), there is a growing consensus that T2DM is the result of the multilevel interaction of genetic, behavioral, social, and economic factors with the timing, sequence, and dose of exposure central to long-term outcomes. A life course health development approach that details risks and protective factors, as well as preventive and treatment strategies, contextualized within the developmental stage of the child, is required. While one’s genetic code may establish susceptibility toward T2DM development, its development is the result of a complex process of person-environment interactions that are multidimensional, multidirectional, and multilevel. A major aim of this chapter is to demonstrate that a life course health development approach to pediatric T2DM is critical to the development of a sound national strategy to prevent and treat T2DM in children. We review major factors known to influence the development of pediatric T2DM and track these across childhood. This set of factors will likely change over the next decade as our understanding of disease mechanisms deepens, but what will not change is the importance of placing these factors into context and recognizing that T2DM development is highly sensitive to the timing and social structuring of multiple environmental exposures. Research recommendations relevant to the field of maternal and child health regarding T2DM will be discussed through a life course lens. This chapter is organized around four key developmental stages: preconception and intrauterine life, infancy, childhood (early and middle), and adolescence. These health development stages unfold against the “backdrop” of each child’s genetics, race/ethnicity, and family economic status. 2 The Backdrop 2.1 Genetics Having a relative with T2DM is an established risk factor for the disease (Rosenbloom et al. 1999). Through a series of twin studies, heritability of fasting glucose levels has been estimated to range from 38% to 51% (Katoh et al. 2005; Snieder et al. 1999). Heritability is a population parameter that measures the fraction of variation, i.e., fasting glucose levels, among individuals in a population that is attributable to their genotypes (Visscher et al. 2008). At the individual level, a positive family history of T2DM has been associated with reduced insulin sensitivity at younger ages in children and adolescents when compared Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… with children with no family history (Arslanian et al. 2005). There is a 40% increased lifetime risk for T2DM for children if one parent has T2DM; this increases to 70% if both parents have T2DM (Groop and Tuomi 1997). Studies have found that as many as 80% of youth with T2DM have a close relative with diabetes (Arslanian et al. 2005; Copeland et al. 2011). Most of the genetic studies of T2DM have been carried out with adults; genetic studies of T2DM in children and youth are limited because of low number of subjects (Morgan 2012). An exception to this is a large meta-analysis that combined data from 6 European cohorts to test whether associations of common genetic variants identified for fasting glucose or insulin levels in nondiabetic adults were detectable in healthy children and adolescents. They found that the majority of novel fasting glucose loci identified in genome-wide associations studies (GWAS) in adults were detectable in childhood and at similar effect sizes (Barker et al. 2011). Work is progressing to examine whether associations between adult-identified susceptibility genes with T2DM hold in pediatric T2DM. For example, transcription factor 7-like 2 (TCF7L2) has been found to be associated with an increased risk of T2DM in African-American youth but not in non-Hispanic whites (Dabelea et al. 2011). There is clear evidence that genetics plays a role in childhood susceptibility to T2DM, but significant research is needed. This research will require substantial collaboration across sites to achieve sample sizes large enough to test hypotheses. While progress is being made in elucidating underlying gene variants in adults, testing to determine if the same variants are operating in children and adolescents is required (Morgan 2012). 2.2 Prevalence and Incidence of T2DM Related to Age, Race, and Ethnicity 199 rates are noted for adolescents aged 15–19 years, while incidence in children below 10 years in age is low. These results come from the SEARCH study, a multiethnic, population-based study designed to estimate rates of diabetes (both type 1 and type 2) based upon health-care provider assessment and in a subpopulation with further blood analyses. Data from SEARCH indicated a prevalence of diagnosed T2DM among 10–14year-olds of 0.23 cases per 1000 children in 2009, up from 0.15 per 1000 in 2001 (Dabelea et al. 2014). Comparable prevalence estimates for adolescents (15–19 years) in 2001 and 2009 were respectively 0.54 and 0.68 per 1000 (Dabelea et al. 2014). Data from 1999 to 2010 NHANES, representative of US youth 12–19 years, indicated a total prevalence of T2DM of 0.36 per 1000 (Demmer et al. 2013). This estimate combines diagnosed and undiagnosed cases of T2DM, with approximately 1/3 of the total being undiagnosed but having a fasting glucose level of ≥126 mg/dL (Demmer et al. 2013). The prevalence of pediatric T2DM is higher for females and among minorities in the USA (Dabelea et al. 2014). SEARCH researchers modeling T2DM incidence by age and race/ethnicity report increasing incidence during the elementary school years with a peak in incidence at approximately age 14 years for all racial-ethnic groups (Imperatore et al. 2012). At any age, the incidence of T2DM for African-American, Hispanic, and Asian/Pacific Islander youth is higher than for non-Hispanic whites (Imperatore et al. 2012; Dabelea et al. 2007), and the incidence for children under 10 years old is very low (annual incidence <1 per 100,000) (Dabelea et al. 2007). However, because T2DM develops gradually, can be asymptomatic, and may be misdiagnosed as type 1 diabetes mellitus (T1DM) in children, population estimates of prevalence and incidence must be interpreted cautiously (Amed et al. 2010a). 2.3 Economic Status The prevalence of T2DM among children has risen with population increases in obesity, but compared to adolescents, T2DM in preadolescent children occurs less frequently. The highest Data on the relationship between economic status (of parents) and children and adolescents diagnosed with T2DM are limited, but there are P. Salsberry et al. 200 indications of a positive correlation. The treatment options for T2DM in adolescents and youth study (TODAY Study) found that 41.5% of their subjects came from households with an annual income of less than $25,000, and 26.3% were in families in which the highest education level of the parent/guardian was less than a high school degree (Copeland et al. 2011). The association of economic status measured by educational level, occupation, and income with T2DM in adults has been studied extensively and likely offers clues to risk in children and adolescents. A systematic review and meta-analysis that was global in scope found associations with low levels of education (relative risk (RR) = 1.41, 95% CI: 1.28–1.51), with occupation (RR = 1.31, 95% CI: 1.09–1.57), and with low income (RR = 1.31, 95% CI: 1.09–1.57). While the data were limited from middle- and low-income countries in this study, the increased risks were independent of the income levels of countries (Agardh et al. 2011). Another systematic review examining childhood socioeconomic status (SES) as a risk for T2DM found a strong relationship; although this relationship was attenuated by adult SES, a significant relationship remained (Tamayo et al. 2010). Parental education has been found to be associated with insulin resistance in the CARDIA study participants, and the association differed by race and gender. Low parental education was associated with greater insulin resistance in AfricanAmerican and white females and in African-American males, but not in white males (Tamayo et al. 2012). Smith et al. 2011 found similar results in the Framingham Offspring Study where an inverse relation between cumulative economic status and T2DM in women, but not men, was noted (Smith et al. 2011). These results highlight why untangling the pathways for risk will be difficult. 2.4 Social (Psychological) Stressors There is strong evidence that acute or chronic stress during critical periods in early life, childhood, and/or adolescence has long-term risks for altered metabolic function including the develop- ment of T2DM (Pervanidou and Chrousos 2012). Obesity likely mediates this relation brought on by unhealthy behaviors related to eating and activity patterns as coping mechanisms for chronic stress. Furthermore, chronic stress leads to the dysregulation of the hypothalamicpituitary-adrenal (HPA) axis, with increased cortisol, catecholamine, and elevated insulin concentrations (Pervanidou and Chrousos 2011). Key stressors in children are recognized as those related to the creation of a safe, stable, and nurturing environment, and when these are disrupted, the child’s long-term health suffers (Shonkoff and Garner 2012). 2.5 Summary These “backdrop” factors are the platform upon which health develops and emerges over time, influenced by dynamic interactions at multiple levels (biological, behavioral, social/cultural) at sensitive periods transacting simultaneously. The next sections illustrate this principle by highlighting key factors within developmental stage that have been linked to pediatric T2DM. 3 Preconception and Intrauterine Life Over the last 20 years, a significant body of evidence from epidemiological and animal studies has established a strong link between the intrauterine environment and long-term health (Dyer and Rosenfeld 2011; Gluckman et al. 2008; McMillen and Robinson 2005; Sebert et al. 2011; Sinclair et al. 2007). The notion of biological embedding is now widely accepted, even though the mechanisms are not completely understood (Tarry-Adkins and Ozanne 2011). Maternal nutritional status, gestational diabetes, high levels of maternal stress, hypoxia/placental insufficiency, and environmental toxins have been associated with the development of T2DM in children and adolescents (Warner and Ozanne 2010; Boekelheide et al. 2012). Furthermore, the relationship of these high-risk maternal factors to Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… intrauterine growth restriction (IUGR) and small-for-gestational-age (SGA) newborns with their predisposition for obesity, insulin resistance, and T2DM later in life has been established as well. These factors are briefly reviewed below. Several excellent papers review the underlying mechanisms of these relationships in greater depth (Dyer and Rosenfeld 2011; Armitage et al. 2004; Berends and Ozanne 2012; Martin-Gronert and Ozanne 2012; Thayer et al. 2012; Prince et al. 2014). 3.1 Maternal Nutrition: Prior to and During Pregnancy 3.1.1 Maternal Under Nutrition A series of studies linking low birth weight, maternal undernutrition, and the development of cardiovascular disease has fueled much work on the impact of caloric restriction on the developing fetus (Hales and Barker 2001; Barker et al. 1993). Animal studies have found endocrine and metabolic abnormalities associated with food restrictions (Warner and Ozanne 2010). Data suggest that undernutrition alters the structure and/or function of the developing endocrinemetabolic axis, leading to an insulin resistance and energy conservation in light of reduced calories. An overactivation of the HPA axis leads to increased cortisol levels in growth-restricted fetuses, a situation that mimics chronic stress (Kanaka-Gantenbein 2010; Kapoor et al. 2006). The effects of the undernutrition seem to depend on the timing and extent of the deprivation. In a rodent study, a 50% caloric restriction at the end of the fetal period resulted in reduced β-cell mass (Bertin et al. 2002), and if the caloric restriction continued through early life, the reduction in β-cell mass was permanent (Garofano et al. 1998). Furthermore, the type of diet seems to be important as well. Because of the importance of amino acids in fetal development, studies on the effects of low-protein diets on fetal growth have been pursued with high interest (FernandezTwinn et al. 2005). Studies have shown that when dams were fed a low-protein diet, the results were growth-restricted pups, and if the maternal diets 201 remained low protein during suckling, the growth restriction was permanent. Most importantly, these offspring then went on to develop glucose intolerance and T2DM (Hoet et al. 1992). 3.1.2 Maternal Overnutrition Maternal obesity at the time of pregnancy is known to be associated with a greater risk for child obesity (Salsberry and Reagan 2005), placing them at increased risk for the development of T2DM. Fetal overnutrition has also been associated with insulin resistance and adult diabetes (Armitage et al. 2008). In animal studies, offspring born to obese maternal rats had higher fat-to-lean ratios at birth, and by 3 months of age, these pups were insulin resistant (Samuelsson et al. 2008). When pregnant dams are fed a highfat content, the offspring are noted to develop a phenotype resembling the human metabolic syndrome (Armitage et al. 2008). 3.2 Maternal Stress Increased maternal stress is associated with adverse birth outcomes, in particular low birth weight and preterm birth (Christian 2012). Studies demonstrate that maternal exposure to glucocorticoids reduces birth weight in sheep and rats (Tarry-Adkins and Ozanne 2011). Meaney et al. (2007) have suggested that these birth outcomes are mediated by effects on the HPA axis. Stressors in the environment alter maternal physiology and behavior, which then programs HPA activity in the offspring (Meaney et al. 2007). These changes have been linked to the development of T2DM and other components of the metabolic syndrome in the offspring (Brunton 2010). 3.3 Maternal Hypoxia/Placental Insufficiency Fetal oxygenation is also an independent risk factor for fetal growth abnormalities and long-term development of T2DM. Reduction in fetal oxygenation has also been associated with IUGR, a known risk factor for metabolic syndrome. P. Salsberry et al. 202 Multiple underlying pathophysiological reasons for fetal hypoxia include placental insufficiency, uterine placental ligation, and smoking. These effects all result in IUGR (Tarry-Adkins and Ozanne 2011). 3.4 Environmental Exposures Exposures to environmental toxins during pregnancy are under increased scrutiny as the evidence builds suggesting significant life course health affects with early-life exposures to a range of chemicals (Thayer et al. 2012). In particular, nicotine, arsenic, and endocrine disrupting chemicals (EDC) are tied to metabolic outcomes. For example, high levels of arsenic exposure have been linked to T2DM in adults, and there is some suggestion that even low doses may also confer an increased T2DM risk (Navas-Acien et al. 2008; Maull et al. 2012). The causal mechanisms include altered glucose metabolism, insulin resistance, and obesity as a result of exposure (Seki et al. 2012). Exposure to EDC, particularly bisphenol A (BPA), is thought to lead to functional changes in gene expression and increased risk for development of obesity and T2DM (Barouki et al. 2012; Liu et al. 2013). This is an area of intense activity that will likely result in significant new knowledge over the next decade. 3.5 Microbiome There is growing recognition of the importance of the human microbiome in pediatrics (Johnson and Versalovic 2012). During pregnancy a woman’s microbiome undergoes significant change from the first to third trimester, changes thought to be beneficial to the mother and the establishment of the neonatal microbiome (Prince et al. 2014; Koren et al. 2012). The infant’s microbiota is influenced by the mode of delivery, with colonization in vaginally delivered newborns a function of maternal vaginal and intestinal microbiota, while cesarean delivered newborns exhibit the microbiome of the maternal skin microbiota. The infant’s environment and nutrition further influence the infant’s developing microbiome (Johnson and Versalovic 2012). These environmental influences on the developing microbiome are critical, as the microbiome is thought to play a role in health and disease (Cenit et al. 2014; Devaraj et al. 2013). Evidence from research in adults suggests that the microbiome plays a critical role in T2DM. Ongoing research is beginning to demonstrate implications for prevention and treatment of metabolic conditions based upon knowledge of the microbiome; extending this work to children is of high importance (Szajewska 2013; Versalovic 2013). 3.6 Gestational Diabetes (GDM) GDM represents a form of impaired glucose tolerance that is first recognized during pregnancy. Insulin resistance normally occurs in pregnancy. This allows the metabolic needs of the fetus to be met, while an increased maternal insulin secretion compensates for the insulin resistance. But in GDM, this increased resistance is coupled with a failure of increased insulin secretion, resulting in hyperglycemia (Battista et al. 2011). GDM is harmful to the mother and to the fetus. GDM has been shown to alter pancreatic development and insulin sensitivity in the fetus (Warner and Ozanne 2010). Fetal growth is often affected, resulting in macrosomia or large-for-gestational-age infants (Dyer and Rosenfeld 2011). The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study found a strong linear relation between fasting glucose and post challenge glucose with macrosomia and neonatal adiposity (Metzger et al. 2008). Macrosomic infants born to diabetic mothers have been found to be more glucose intolerant between the ages of 10–16 years, are more likely to be obese, and are at a greater risk for T2DM (Silverman et al. 1998). Because of methodological differences in current studies, the long-term effect of GDM on child outcomes is not fully understood, but studies do suggest that increased rates of obesity, T2DM, and other cardiovascular conditions may result (Kim et al. 2011; Simeoni and Barker 2009). Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… Screening and Diagnosis Given the convincing evidence that diabetes and gestational diabetes are harmful to both the mother and fetus, considerable effort has been directed toward establishing consensus criteria for the diagnosis of diabetes in pregnancy. There is controversy over the “correct” diabetes threshold, that is, the level at which maternal blood glucose conveys a significant risk to the fetus. The HAPO study, a multisite and multinational study, was designed to address some of these questions. HAPO study results showed a linear relation between increasing levels of fasting and 1 and 2 hour plasma glucose levels post a 75 g oral glucose tolerance test (OGTT) on the primary study outcomes, including birth weight above 90th percentile, cord blood C-peptide level above the 90th percentile, primary cesarean delivery, and neonatal hypoglycemia (Hadar and Hod 2010). These results have led to guidelines issued in 2010 by the International Association of Diabetes and Pregnancy Study Groups (IADPSG) for the detection and diagnosis of hyperglycemic disorders in pregnancy. These guidelines include the determination of the diabetes status of the pregnant woman at the first prenatal visit. Following these guidelines is expected to increase the number of all women with diabetes during pregnancy. These guidelines, however, remain controversial (Moses 2010). There are concerns over increasing costs associated with the identification of a greater number of women with diabetes resulting in an increased number of women receiving treatment. The American College of Obstetricians and Gynecologists and the National Diabetes Education program have joined together in a call for action, specifically recommending follow-up testing during the postpartum period in women who experienced GDM. Currently only approximately one-half of these women are tested. Recommendations include the following: If the postpartum test is normal, retest every 3 years and at the first prenatal visit for subsequent pregnancies. If prediabetes is diagnosed at the post- 203 partum check, then the recommendation is to test annually (Gabbe et al. 2012). Treatment Intrauterine conditions are being targeted in the HAPO study. The initial results showed that close control of glucose improved birth outcomes, leading to the new recommendations for the diagnosis and classification of hyperglycemia during pregnancy (Metzger et al. 2010). The HAPO study is also following the outcomes in children born to women with GDM; this longitudinal follow-up may yield important insights for the health of the offspring as well. Early results, however, on offspring at ages 4 and 5 show no difference in body mass index (BMI) in the treatment group (Gillman et al. 2010). Further work with longer follow-up is required to determine if there are latent effects of treatment that will potentially influence outcomes at a later age. 3.7 Summary Intrauterine life is a sensitive period for T2DM risk development. Building upon the backdrop of genetic susceptibility and within the social and economic context of the mother, the fetus develops an underlying propensity toward T2DM development. Much of this comes through epigenetic changes influenced by maternal nutrition, life stressors, chemical exposures, and maternal behaviors. The occurrence of higher levels of risk is strongly correlated with maternal economic disadvantage and the result of molecular, physiological, behavioral, cultural, and evolutionary processes that interact across time and space. Consistent with the principles of life course health development (LCHD), development of a phenotype at high risk for T2DM development is thus the result of person-environment interactions that are multidimensional, multidirectional, and multilevel, which are highly sensitive to the timing and social structure of environmental exposures. P. Salsberry et al. 204 4 4.1 Infancy Feeding Multiple advantages of breastfeeding are well accepted (American Academy of Pedicatrics 2012; James and Lessen 2009), but clarifying the relationship between breastfeeding and future metabolic function is not simple and requires consideration of multiple factors that have effects over one’s life course. Controlling social and environmental factors between infancy and some later age is difficult because information for such a long interval is often missing. Despite the limitations, there are several studies that have examined infant feeding type or breastfeeding duration and their relation to dietary habits or cardiometabolic risks. For example, a study that used the Hertfordshire cohort has found an independent association between exclusive breastfeeding (vs. mixed or bottle-feeding) and adherence to healthy dietary recommendations and less intake of processed meat in middle to late adulthood (Robinson et al. 2013). Socioeconomic factors were not associated with the dietary recommendation adherence. The authors speculate breast milk may have provided significant impact on dietary flavor learning during infancy. In a recent study by Martin et al. (2014), the relationship between cardiometabolic risks and long-term exclusive breastfeeding was examined. This study was a follow-up study of a randomized control study where one group of new mothers was encouraged to undertake exclusive breastfeeding and the other nonexclusive breastfeeding. While the original intervention significantly prolonged breastfeeding duration and exclusiveness, the intervention effect did not influence cardiometabolic factors, such as blood pressure, fasting glucose, insulin, insulin resistance, and adiponectin, when children were at age 11.5 years. A previous follow-up study of this cohort also failed to show intervention effects on anthropometry (height, weight, BMI, and skinfold) and blood pressure at age 6.5 years (Kramer et al. 2009). Investigating the effects of breastfeeding is complex as there are likely multiple biological and behavioral ways that breastfeeding may influence the infant’s health development. Nutrition-focused research studies that examine the importance of the nutrients within breast milk are ongoing. But there are also suggestions that breastfeeding may impact an infant’s ability to self-regulate behavior. For example, Taveras et al. (2004), using the US mixed-race cohort, have found that restrictive feeding style is associated with bottle-feeding and shorter breastfeeding duration, which could disrupt the infant’s ability to self-regulate feeding behavior. Maternal restrictive feeding style was also found to be associated with increased child’s weight for age at 18 months among an African-American sample (Thompson and Bentley 2013). There has been particular interest in the effects of breastfeeding on children who are exposed to maternal diabetes in utero. The evidence is mixed regarding the effects of breastfeeding among offspring of diabetic mothers (ODM) and development of obesity and T2DM during childhood. A study from Germany found that high consumption of breastmilk during the first 7 days of life among ODM may pose deleterious effects on body weight and glucose tolerance measured at age 2 years (Plagemann et al. 2002). These authors suggest that certain substances contained in early milk of mother’s with diabetes may program for metabolic dysregulation in their offspring. In the Nurses’ Health Study II (Mayer-Davis et al. 2006), researchers found that exclusive breastfeeding compared to exclusive formula feeding, or longer duration of breastfeeding (>9 months) compared to never breastfed, was associated with lower odds of being overweight at ages 9–14 years, controlling for child’s age, gender, and pubertal development. However, further controlling for other maternal and child factors resulted in null association, and a separate analysis for ODM did not reach statistical significance. A case control study using the SEARCH participants found that longer duration of breastfeeding resulted in a reduction in T2DM, controlling for maternal, child’s, and social factors. When the child’s weight (measured using BMIz) was included in the analysis, the association was attenuated to null, suggesting that the Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… child’s BMIz may mediate the pathway to a diagnosis of T2DM (Mayer-Davis et al. 2008). The mechanisms involved in the effects of breastfeeding on child’s future weight status or risks for T2DM are unclear. However, insulin and other metabolic hormones that are present in breast milk likely play some role. Insulin is present at more than threefold concentration in early milk collected within the first weeks of the postpartum period than in mature milk collected 3 months postpartum (Ley et al. 2012). The insulin concentration in early milk does not appear to be affected by the mother’s gestational diabetes status. While the insulin concentration in mature milk of nondiabetic women is not different from that of women with GDM (Ley et al. 2012), maternal prenatal metabolic indicators, such as BMI, fasting glucose, insulin resistance, insulin sensitivity, and adiponectin levels, can predict insulin concentration in mature milk (Ley et al. 2012; Whitmore et al. 2012). Other research efforts have been focused on infant feeding practices, such as timing of solid food introduction and amount of sugar-sweetened drink during infancy and early childhood, and their effects on childhood obesity development (Gaffney et al. 2012). Another area is investigation of micro- and macronutrient constituents of breast milk. A randomized clinical trial demonstrated that health modifications made to the diets of pregnant and lactating women improved infant metabolic health (Aaltonen et al. 2011). More research is needed to investigate how infant feeding may influence metabolic health development over the life course. An excellent review article summarizes the critical next questions in lactation and neonatal nutrition, providing a road map for this necessary research (Neville et al. 2012). 4.2 Infant Growth Patterns Several studies (Fabricius-Bjerre et al. 2011; Ekelund et al. 2007; Eriksson et al. 2006) highlight significant associations between infant/ childhood growth patterns and development of T2DM and later cardiovascular disease. Because growth is orchestrated by a complex series of 205 interactions between growth hormone, sex steroids, and insulin, tracking growth over time is an important global health measure. Understanding high-risk growth patterns may assist in earlier identification of high-risk children with the potential to begin interventions at an earlier age. The Helsinki Birth Cohort Study (HBCS) 1934– 1944 has shown that a small body size at birth with thinness during the first year followed by higher BMIs later in childhood is associated with the development of T2DM (Eriksson 2006). Also, this study found that an early age at adiposity rebound was associated with a markedly increased risk of T2DM in adulthood (Eriksson 2011). An important recent finding suggests that breastfeeding may alter childhood growth even in children exposed to overnutrition during the pregnancy. In the Exploring Perinatal Outcomes among Children (EPOCH) study, breastfeeding was associated with long-term effects on childhood BMI growth extending into late childhood. Slower BMI growth velocity was seen among adequately breastfed (breastfeeding >6 months) children compared with infants who had low breastfeeding status, and these benefits lasted through age 9 years (Crume et al. 2012). 4.3 Sleep Duration Shortened sleep duration is suggested as a risk factor for obesity, insulin resistance, and Type 2 diabetes among adults. Long-term effects of shortened sleep during infancy on metabolic function are unknown. A limited number of studies that examined the relationship between sleep duration during infancy and subsequent obesity in early childhood have produced conflicting results. For example, studies of US children (Taveras et al. 2008, 2014) have found chronic sleep curtailment from infancy to age 7 years was associated with increased BMI z-scores. Because short sleep hours were associated with longer TV viewing time, the investigators suggested the screen time as one of the potential pathways for the association. Similarly, Bell and Zimmerman (2010) using sample from the Panel Survey of Income DynamicsChild Development Supplement, reported that P. Salsberry et al. 206 shortened night time sleep (less than 9 hours) during early childhood (ages 0–59 months) but not during school age was associated with increased likelihood of being overweight or obese at followup at age 5–10 years. Both US studies include relatively large number of minority population (30–50%) whose nighttime sleep is on average shorter than white children. However, studies of Dutch children and Australian children have shown no such association (Hiscock et al. 2011; Klingenberg et al. 2013). The latter two studies utilized objective measures of sleep duration such as actinography, while the US studies have relied on parental report. Improvement in measurement of child’s sleep duration and other behavioral markers in longitudinal studies are needed to advance the knowledge in this area. 4.4 Parenting and Postpartum Depression The impact of maternal postpartum depression on parenting is significant during the first year of life. Mothers who suffer from postpartum depression may lack responsiveness to infant cues for hunger or satiety. However, the studies examining the association between maternal depressive symptoms and child weight status have produced mixed results. Several studies conducted in the USA have shown the association between maternal postpartum depressive symptoms and inappropriate parenting practices such as shorter or less intense breastfeeding (McLearn et al. 2006; Gaffney et al. 2014), inappropriate early feeding practices (Gaffney et al. 2014), and fewer healthy interactions with infants (McLearn et al. 2006). Exposure to chronic maternal depressive symptoms has also been associated with increased risk of childhood overweight (Wang et al. 2013). Cumulative exposure to maternal depressive symptoms in early infancy can lead to disruption of behavioral and metabolic cues for sensation of hunger and satiety. A study involving five European countries, however, has shown no relationship between maternal depressive symptoms and adiposity measures at age 24 months (Grote et al. 2010). The investigators of the latter study noted that prevalence of high depressive symptom scores differed significantly by the participating countries, and therefore, the instrument to measure depressive symptoms used in this study may not be comparably used by the participants across different study sites. More prospective cohort studies focused on the relationship between infant/child development and maternal lifestyles as well as infant’s surrounding environment are needed. Cohort studies such as the Amsterdam Born Children and their Development (ABCD) study (van Eijsden et al. 2011) may provide further knowledge on this area. 4.5 Summary Infant development occurs within a context of “backdrop” factors and as an extension of what has happened during the prior period (fetal life). Because evolutionary life-history theory suggests that development during fetal life prepares the neonate for a particular external environment, when conditions in utero match the conditions in infancy, development unfolds as an extension of pathways begun in utero. When a mismatch occurs between these environments, aspects of development may be compromised. This may be the case when undernutrition in fetal life is followed by an abundance of nutrients postnatally, a particularly risky circumstance for metabolic health. The important point is that infant health development must be seen as conditioned by the interaction of processes and conditions during intrauterine life, and that this wider lens brings into sharper focus the multiple interacting risks contributing to the development of T2DM. Our examinations of how experiences in infancy feed forward into this continuously evolving health developmental process suggest that long-term health behaviors are beginning to be set through complex transactions between maternal behaviors and preferences regarding feeding and sleep (both of which are influenced by the mother’s own environment and psychological state) and the infant’s developing behavioral response patterns. Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… 5 5.1 Childhood Childhood Overweight and Obesity Almost all children and adolescents with T2DM are also overweight or obese, and excess adiposity, particularly visceral adiposity, is associated with insulin resistance (Dabelea et al. 2007; Haines et al. 2007; Amed et al. 2010b). Studies with US and Canadian youth have found that 75–95% of those with T2DM are obese (BMI percentile > 95th for age and sex) (Mayer-Davis et al. 2009; Amed et al. 2010b; Liu et al. 2009; Lawrence et al. 2009b; Bell et al. 2009). Another study in obese children found that 25% of obese children 4–10 years of age and 21% of 11–18 years of age were found to have an impaired glucose tolerance. Silent T2DM was identified in 4% of the obese adolescents (Sinha et al. 2002). Researchers in the UK using the International Obesity Task Force (IOTF) cut point for defining obesity (which is higher than the US reference (Rolland-Cachera 2011)) found that 83% of youth with T2DM were obese (Haines et al. 2007). These results clearly point to the importance of understanding the underlying factors that contribute to childhood obesity, and also point toward the complex path that underlies the development of T2DM. Further understanding of how multiple factors interact, with a key emphasis toward the timing of these factors in reference to the life course is needed (Ong 2010; Tounian 2011). 5.2 Impaired Glucose Tolerance and Progression to T2DM Obese children may have deficits in glucose regulation that predict future T2DM, as well as less favorable risk factor profiles for cardiovascular disease (West et al. 2009; Burns et al. 2014). Evidence of impaired glucose tolerance, elevated fasting glucose, insulin resistance, and/or previously undiagnosed “silent” T2DM has been documented in multiple samples of obese youth 207 (Wabitsch et al. 2004; Sinha et al. 2002; Morrison et al. 2012). Morrison et al. (2012) studied 259 overweight and obese children and adolescents (aged 5–17 years) who were seeking weight-loss treatment and found that >20% had prediabetes—impaired glucose tolerance as measured by an oral glucose tolerance test or elevated fasting glucose (Morrison et al. 2012). Among these youth with prediabetes, a majority with impaired glucose tolerance did not have elevated fasting glucose levels (Morrison et al. 2012), which is consistent with findings from an Italian study (Morandi et al. 2014). The current American Diabetes Association (ADA) guidelines (American Diabetes Association 2010) recommend that overweight children ≥10 years or pubertal with two or more additional risk factors for T2DM have their fasting glucose measured (see Table 1). ADA guidelines do not recommend screening youth with an oral glucose tolerance test. Morrison et al. found that the sensitivity and specificity of the ADA criteria to identify youth with prediabetes were respectively 39% and 70%. Furthermore, in their sample the prevalence of prediabetes in children 5–9 years old was not significantly different from that of youth ≥10 years old (Morrison et al. 2012). Some evidence indicates that elevated fasting triglycerides may help to identify obese children and adolescents who would benefit from an oral glucose tolerance test (Morrison et al. 2012; Morandi et al. 2014). More longitudinal studies of children at risk for T2DM are needed to elucidate the natural history and progression of the disorder. In a sample of 117 obese children and adolescents (4–18 years) in which two oral glucose tolerance tests were administered between 18 and 24 months apart, Weiss et al. 2005 found that of 38 youth with impaired glucose tolerance at the first time point, 8 developed T2DM, 10 still had impaired glucose tolerance, and 15 (46%) had normal glucose tolerance at the second time point (Weiss et al. 2005). Among the 84 youth with normal glucose tolerance initially, 8 had impaired glucose tolerance at the follow-up assessment (Weiss et al. 2005). Progression from prediabetes to T2DM has been studied among adults (Morris P. Salsberry et al. 208 Table 1 Summary of screening guidelines for T2DM: Childhood overweight and obesity Year Recommendations Other risk factors 2007 BMI > 85th% and aged ≥ 10 years with ≥2 other risk factors FGT or 2 h. GTT; BMI ≥ 95th% and aged ≥ 10 years FGT every 2 years ADA (Amercian Diabetes Association 2000) 2012 BMI > 85th% or weight > % of ideal for height with ≥2 other risk factors screened every 3 years beginning at age 10 years or onset of puberty, whichever is earlier. Testing done every 2 years ISPADa (International Society for Pediatric and Adolescent Diabetes) 2011 BMI > 85th% with other risk factors. In Asian children, screen with low or high birth weight with 1 risk factor, regardless of BMI Parental obesity Family history Current lifestyle BMI trajectory CVD risk factors (HBP, cholesterol) Family history of T2DM in first or second-degree relatives Certain race/ethnic groupsb Signs of insulin resistance or conditions associated with insulin resistance (AN, HTN, dyslipidemia, PCOS, SGA) Maternal diabetes or GDM Immediate family history of T2DM, early family history of CVD, or Signs of insulin resistance (AN, dyslipidemia, HBP, PCOS) Diabetes screening Expert Committee (Barlow 2007) Overweight/Obesity screening 2010 USPSTF (Barton 2010) Expert Committee 2007 (Barlow 2007) Screen at 6 year and older for obesity Every well child visit: height and weight, calculate and plot BMI a The ISPAD guidelines are also sensitive to country resources, so that screening decisions will likely be tied directly to the health-care resources with a country b Native American, African-American, Latino, Asian American, Pacific Islander et al. 2013), and although less is currently known about how the disease develops in children and adolescents, this is an area of active investigation. 5.3 Diabetes Prevention A number of lifestyle interventions to prevent T2DM in youth at high risk have been evaluated in randomized trials. The Yale Bright Bodies Healthy Lifestyle program is an intensive 6-month behavioral intervention for obese youth and their families that has been evaluated in multiple randomized controlled trials (Savoye et al. 2007, 2011, 2014). Among 174 ethnically diverse children and adolescents aged 8–16 years—69 assigned to the control condition—the youth in the intervention group showed reductions in adiposity at 6 months that were maintained at 1 year as well as reductions in fasting insulin levels, which were seen at 6 months and also maintained for 1 year (Savoye et al. 2007). These youth were also assessed 24 months after the start of the trial and the favorable changes in body composition and insulin sensitivity seen at 6 and 12 months were sustained at similar levels at 24 months (Savoye et al. 2011). A subsequent trial among 10–16-year-olds with prediabetes (n = 38 receiving the Bright Bodies intervention and n = 37 standard clinical care) also demonstrated improvements in glucose metabolism and insulin sensitivity as well as reductions in adiposity and improved fitness (Savoye et al. 2014). Such results are encouraging, and although the intervention is intensive, it uses a standardized curriculum and is likely to be cost-effective long term (Savoye et al. 2014). The HEALTHY Study was a national 3-year middle-school intervention program designed to reduce risk factors for type 2 diabetes. It used a randomized trial designed to determine whether Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… changes to the school environment of middleschool students over a 3-year period could impact their risk for T2DM (Foster et al. 2010; Hirst et al. 2009). Prevention of T2DM in youth involves encouraging changes in eating and activity behaviors that should also reduce risk for obesity. Although many large and well-conducted randomized trials of school-based interventions to prevent obesity have had limited impact on obesity prevalence (Luepker et al. 1996; Caballero et al. 2003; Brown and Summerbell 2009), such interventions may improve glucose homeostasis and other risk factors for T2DM (Trevino et al. 2004; Rosenbaum et al. 2007). The Healthy Study was a group randomized trial conducted in 42 middle schools (21 intervention, 21 control) in 7 areas of the USA between fall 2006 and spring 2009 (Hirst et al. 2009). Students were in 6th grade when enrolled and in 8th grade when outcomes were assessed. The interventions in the Healthy Study included changes to the school environment with regard to nutrition (Gillis et al. 2009), physical activity (McMurray et al. 2009), and health messaging (Venditti et al. 2009). The primary outcome was prevalence of BMI ≥85th percentile (overweight and obesity), and at baseline about 50% of students in intervention and control schools were overweight or obese (Kaufman et al. 2009). Over the course of the study, this prevalence decreased in intervention schools to 45.8% and in control schools to 45.2%, and thus there was not an effect of the intervention on the prevalence of overweight and obesity (Foster et al. 2010). However, the intervention did have a modest and statistically impact on the secondary outcomes of obesity, mean BMI z-score, high waist circumference, and fasting insulin levels (Foster et al. 2010). These results suggest that changes to the school environment regarding nutrition, physical activity, and health messaging may reduce the risk of childhood onset of T2DM (Foster et al. 2010) Some evidence suggests that interventions targeting physical fitness may be as effective in reducing risk for T2DM in youth as multicomponent interventions. Among obese 7–11-year-old children, a program of 40 min of aerobic exercise 5 days per week for 4 months reduced adiposity 209 and insulin levels, but these effects were not sustained in the absence of the program (Ferguson et al. 1999). A dose-response effect for the benefit of time spent in exercise on fitness and cardiometabolic measures of T2DM risk was demonstrated in a subsequent study of overweight and obese 7–11-year-olds randomized to a 10–15-week program of 20 or 40 min per school day of aerobic activity or to a control group (Davis et al. 2012). Similar findings were noted in a pilot randomized study in which physical activity was added to a family-based weight control treatment. The exercise group had lower visceral abdominal fat posttreatment, a result that may be key in future work to reduce diabetes risk (Saelens et al. 2011). In a YMCA-based program targeted to prevent the development of T2DM in high-risk inner-city African-American children (TAT: Taking Action Together), initial results were encouraging with improvement in glucoregulation in boys (but not girls) and a decrease in BMI z-scores in the treatment group (Ritchie et al. 2010; Raman et al. 2010). 5.4 Obesity Prevention Because of the strong association between obesity and T2DM, much of the literature that discusses prevention of T2DM focuses on weight reduction or healthy weight maintenance throughout childhood. In a systematic review of the effectiveness of weight management interventions in children, the US Preventative Services Task Force (USPSTF) results support at least in the short term benefits of comprehensive medium-to-high intensity behavioral interventions in children 6 years and older (Whitlock et al. 2010). The outcomes studied in the USPSTF review were limited to childhood weight and not the progression to T2DM (see Table 1). Recent evidence points to the importance and benefit of intervening early in children’s lives to reduce obesity (Knowlden and Sharma 2012), and a number of randomized controlled treatment trials in preschool-aged children have demonstrated success. An intensive 16-week multidisciplinary program for overweight or obese Dutch 210 children aged 3–5 years and their parent was compared to a “usual-maintained care” control, and results showed reductions in BMI and waist circumference that were for 12 months (Bocca et al. 2012a). Among 33 obese preschool-aged (2–5 years) children in the Midwest USA, a pilot randomized trial compared two 6-month multidisciplinary treatment programs (one in which all sessions were held at a clinic and another program that alternated between clinic sessions and individual home-based visits by a pediatric psychology fellow); both interventions were consistent with Expert Committee (Barlow 2007) stage 3 recommendations and were compared to a control condition of pediatrician counseling as recommended by the Expert Committee as stage 1 treatment (Stark et al. 2014). Compared to pediatrician counseling, children in the intervention with home visits group had greater reductions in BMI z-score at 6 months (end of intervention) and at 12 months than did children in the clinic only intervention (Stark et al. 2014). These results suggest that parents of obese preschoolaged children may benefit from in-home guidance regarding implementation and maintenance of parenting behaviors related to weight reduction. In an observational study of children aged 6–16 years seeking treatment for obesity in Sweden, researchers evaluated whether age and severity of obesity impacted the likelihood of treatment success over a 3-year period (Danielsson et al. 2012). They found that younger children (6–9 years) were more likely to have clinically meaningful reductions in weight status than older children and adolescents, and that treatment success was rare for adolescents particularly if they were severely obese (Danielsson et al. 2012). The difficulties associated with treating obesity once it is established underscore the need to prevent excess weight gain in young children before they become obese. It is well established that parents play a key role in obesity treatment efforts directed at youth (Kitzmann et al. 2010), and for obvious reasons parents are the main audience for obesity prevention efforts in early childhood (Skouteris et al. 2011, 2012; Hesketh and Campbell 2010). P. Salsberry et al. Interventions directed at parents of infants have shown promise in reducing children’s risk for obesity (Campbell et al. 2013; Wen et al. 2012), and parents of infants may be receptive to messaging aimed at increasing adoption of healthy eating and activity behaviors. Emerging evidence also suggests that interventions to promote positive parenting in general may help to reduce risk for childhood obesity (Harvey-Berino and Rourke 2003; Brotman et al. 2012). Maternal warmth and sensitivity are associated in prospective cohort studies with lower risk for obesity (Anderson et al. 2012, 2014). An explanatory mechanism may involve the impact of responsive parenting on the child’s developing stress response system and regulatory pathways in the limbic areas of the brain (McEwen 2008; Schore 2005). In a national sample of preschool-aged children, the household routines of having family meals, ensuring that children get adequate sleep, and limiting time spent watching television were associated with lower prevalence of obesity (Anderson and Whitaker 2010). A randomized trial of a 6-month home-based intervention to promote these household routines in a sample of low-income families of 2–5-year-old children reduced children’s BMI (Haines et al. 2013). Recent evidence points to the importance of intervening early. In a randomized clinical trial targeting 3–5-year-old overweight or obese children, a multidisciplinary program reduced BMI, BMI z-scores, waist circumference, and waist circumference z-scores compared to a usual treatment control group. The treatment results were still present 12 months after the start of the intervention (Bocca et al. 2012b). A 6-month intensive home-based behavioral intervention program aimed at obese preschoolers (ages 2–5 years) has also shown promise in reducing BMI z-scores, both during the treatment period and at 12 months follow-up (Saelens et al. 2011). In an observational study of obese children ages 6–16 years, all were exposed to a behavioral treatment; changes in BMIs across a 3-year treatment window showed that younger aged children (6–9 compared with 14–16 years) were more successful in improving outcomes (Danielsson et al. 2012). Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… 5.5 Screening Guidelines Given what we know about factors associated with diabetes, screening for T2DM in children and adolescents is linked to weight status of the child, age of the child, and pubertal status. Several groups have published guidelines for T2DM screening in children. These guidelines are summarized in Table 1. 5.5.1 Growth in Childhood Birth weight and growth trajectories have both been recognized as factors that are associated with the life course risk of T2DM. Given this, it seems key that individuals from the start understand their risk and that primary-care providers collect these data for all children. Surprisingly few guidelines currently recommend including birth weight as a component of a child’s risk assessment for T2DM. An exception to this is the ISPAD screening guidelines that include birth size in their guidelines for Asian children. No recommendations were found that considered the actual growth of the child with respect to possible high-risk trajectories. Including birth size and growth as screeners for high-risk children has yet to be fully examined. A better understanding of the growth trajectory may be useful in identifying high-risk children earlier, but further work is needed in this area. 5.6 Summary Childhood is the period where health behaviors are formed. While there is evidence that these behaviors are influenced by experience in the prior developmental periods, there is also evidence that significant change is still possible, especially in children under 5 years. Increasing evidence from both observational and intervention studies suggests that behavioral change is possible early on, but by middle childhood it becomes increasingly more difficult to affect real behavior change. These findings are pushing the research community to design studies that include young children, and the evidence is accumulating that interventions at the youngest ages achieve the best outcomes. During this period the child’s 211 world widens as he/she transitions into preschool and kindergarten, raising further the need to widen the focus of intervention beyond the family. 6 6.1 Adolescence Transition into Adolescence Puberty is an important phase in the life course that needs to be considered in studying the risks for T2DM. During puberty there is a known physiological insulin resistance and hyperinsulinemia that is thought to facilitate pubertal weight gain and growth (Burt Solorzano and McCartney 2010). The timing of the start of this insulin resistance has been reported to begin in mid-childhood, some years before puberty (Jeffery et al. 2012). Understanding this insulin resistance as it relates to the pubertal process is of high importance, as the rates of childhood diabetes continue to rise. Obesity exaggerates this pubertal insulin resistance, especially in girls, and may partially explain advanced maturation in obese females (Burt Solorzano and McCartney 2010). 6.2 Diagnosis of T2DM The rising prevalence of both type 1 and type 2 among children and adolescents, in conjunction with increasing obesity throughout the population, has complicated the diagnosis of diabetes type. Past reliance on phenotype to distinguish and diagnose type of diabetes is not reliable (Amed et al. 2010a; Rosenbloom et al. 2008). In obese adolescents who present with new-onset diabetes, measurement of pancreatic autoantibodies is recommended as necessary to differentiate the type of diabetes (Zeitler 2010). 6.3 Treatment Overall, there are few published evidence-based treatment studies for pediatric T2DM. The ADA and the ISPAD have published treatment 212 recommendations, but these are primarily consensus based and built on current guidelines for adult T2DM (Amed et al. 2010a; Herbst et al. 2014). There are a handful of studies that have either been conducted or are ongoing specifically to address pediatric T2DM, studies which address both questions of prevention and treatment. Treatment for T2DM has been addressed in a large multicenter randomized clinical trial, the Treatment Options for T2DM in Adolescents and Youth (known as the TODAY Study) (Copeland et al. 2011). Subjects were between the ages of 10 and 17, diagnosed with T2DM for less than 2 years, and had a BMI percentile at or above the 85th percentile for age and sex. Subjects were randomized into one of 3 comparison groups: metformin monotherapy, metformin plus rosiglitazone, or metformin plus an intensive family lifestyle program. Rates of failure (defined as poor glycemic control) were 52% in metformin alone, 39% in metformin plus rosiglitazone, and 47% in metformin plus the lifestyle program. Greatest weight loss was seen in the metformin plus the lifestyle program (Zeitler et al. 2012). These results highlight the difficulty that the majority of adolescents have in maintaining good glycemic control and suggest that many will require insulin within a few years of diagnosis. Ongoing research is needed to determine optimal management strategies in adolescents with T2DM. The goal of therapy is to achieve euglycemia. The blood glucose levels should be monitored based on individual needs. Self- monitored blood glucose testing may be done at premeal times, or limited to before breakfast and before dinner only. Postprandial blood glucose levels, obtained 2 h after dinner, also offer helpful data for dose titration. The hemoglobin A1c is monitored at the time of clinic follow-up every 3 months. This reflects the average blood glucose over the past 3-month period. The aim of treatment is to achieve a hemoglobin A1c of less than 6.5% and the target blood glucose range = 70–120 mg/dl. The first intervention for treatment of T2DM remains healthy lifestyle modifications including dietary modifications (including decreasing P. Salsberry et al. caloric intake) and increasing physical activity and exercise regimens to achieve weight stabilization and ultimately weight loss. Data from a recent study suggests that regular exercise was associated with lowering of the hemoglobin A1c (Herbst et al. 2014). Initiating changes in portion control as well as food choices and implementing modest amounts of exercise often prove difficult. Often adolescents have long-standing overweight and obesity concerns. Lifestyle changes tend to be more successful if initiated by the whole family instead of the particular individual being targeted. If the diagnostic parameters are consistent with T2DM, pharmacologic treatment will need to be initiated. The particular therapeutic regimen initiated is determined by the initial presentation of T2 DM. If the adolescent presents in diabetic ketoacidosis, insulin therapy is instituted, initially intravenously, with subsequent transition to subcutaneous insulin therapy after the resolution of the acute diabetes ketoacidosis phase. Oral medication, specifically metformin, is initiated as well once the ketosis has resolved. If the presentation of T2DM is less acute, with the diagnosis being made on routine office testing, and hemoglobin A1c is less than 9%, medication therapy may be initiated first with metformin. In patients with symptomatology in between these 2 scenarios, where hemoglobin A1c is more than 9% in the presence of typical symptomatology, but no diabetes ketoacidosis, therapy may be initiated with metformin along with basal insulin (Zeitler et al. 2014). Other medications used for treatment of T2DM in adults are not approved for adolescents below 16 years of age. Adherence to therapeutic interventions is key for achieving adequate diabetes control and is strongly emphasized. Modifications to regimen are based on hemoglobin A1c and review of blood glucose data. Adolescents who are on metformin therapy as well as basal insulin may need additional rapid acting insulin to cover their meals. Adolescents who were initiated on insulin and metformin, and achieve goal A1c, are feasible to gradually decrease the insulin dose, after they are on maximal metformin dosing. If adolescents are able to modify lifestyle factors as well Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… as manage medication therapy adequately, it may be feasible to discontinue insulin altogether, while continuing on metformin therapy, albeit done very gradually with close monitoring. 6.3.1 Bariatric Surgery Bariatric surgery for weight loss or uncontrolled T2DM is not commonly undertaken in the adolescent population. Results obtained in studies with adults that report weight loss and improvement in T2DM after bariatric surgery procedures (Courcoulas et al. 2014) has caused increased interest in its use in adolescents with morbid obesity. When bariatric surgery has been used as a treatment strategy for extreme obesity in adolescents, it has been found to be effective in improving or reversing abnormal glucose metabolism, but long-term outcome data in this age group is not yet available. This treatment option is limited to a very specific population because of potential complications (Brandt et al. 2010). 6.4 213 screening in youth diagnosed with T2DM. These guidelines are summarized in Table 2. In a study examining adherence to ADA recommendations for lipid levels, blood pressure, hemoglobin A1c, microalbuminuria, and eye examinations, 95% of the participants reported having their blood pressure checked at all or most visits, 88% had lipid levels measured, 83% had kidney function tested, 68% underwent HbA1c testing, and 66% underwent an eye examination (Waitzfelder et al. 2011). Participants aged 18 years or older, particularly those with T2DM, tended to have fewer tests of all kinds performed. Age and family income emerged as important correlates of overall quality of care in multivariate models: older age and lower income were associated with not meeting the guidelines. Overall, while there was good compliance with the ADA-recommended guidelines, efforts to improve HbA1c testing and eye examinations were needed, especially among older adolescents and young adults (Waitzfelder et al. 2011). Surveillance 6.5 Comorbidities Comorbidities associated with T2DM should be investigated and treated as needed based on individual clinical presentation. The ADA and the ISPAD have published guidelines for comorbidity The TODAY study has demonstrated that a significant portion of adolescents with T2DM develop comorbidities. Careful attention to these Table 2 Summary of surveillance guidelines for comorbidities ADA (Amercian Diabetes Association 2000) ISPAD (ISPAD 2011) Year 2012 2011 Recommendations Blood pressure measurement annually Fasting lipid profile Microalbuminuria assessment Dilated eye examination performed at the time of diagnosis Evaluation of polycystic ovarian disease and other comorbidities such as sleep apnea, hepatic steatosis, orthopedic complications, and psychosocial concerns Blood pressure measured at least annually Initial eye examination within 3 months of diagnosis and then annually from 11 years on after 2 years of diabetes duration Annual screening for microalbuminuria from age 11 and after 2 years of diabetes duration Fasting blood lipids after initial diagnosis and stabilization of diabetes ≥12 years; if family history of hypercholesterolemia, early CVD, or family history is unknown, screening should start at 2 years Peripheral and autonomic neuropathy assessed by history and physical exam from age 11 years with 2 years of diabetes duration P. Salsberry et al. 214 must be part of any management plan for these youths. Common comorbidities include hypertension and other metabolic conditions. Table 2 summarizes guidelines for monitoring the development of comorbidities in children/adolescents with T2DM. 6.6 Care Transition into Adult Practice The emerging adult period (ages 18–30 years) is a high-risk time for youth with T2DM. These transitions from the pediatric to adult care setting often result in loss to follow-up at a time when the emergence of complications is occurring and the youth are engaging in high-risk health behaviors (Peters and Laffel 2011). In the majority of health-care institutions, there are no wellstructured, seamless processes in place to ensure smooth transitions of care for any chronic care condition. Furthermore, there is little evidencebased research to guide practice on the management of this care transition in youth with T2DM. In the absence of evidenced-based programs and because of concerns about this transition, many programs are in the process of designing and/or implementing transition of care initiatives (Lee 2013). In 2011, a consensus-based document outlining recommendations for transition from pediatric and to adult diabetes care systems was published. Part of this document specifically highlighted the need for research to determine the best strategies for transition to diabetic adult care to assure optimal outcomes for the youth (Peters and Laffel 2011). 6.7 Summary Because T2DM is most likely diagnosed during or shortly after puberty, the emphasis in adolescence is focused on understanding the components of care for the disease, including the surveillance for and management of comorbidities. In adolescence the consequences of the child’s early environments are becoming clear and the window for prevention has narrowed. 7 Summary and Recommendations 7.1 Summary • Pediatric T2DM is a life course problem—this chapter demonstrates that no one factor and no one time period provides the explanation; prevention and treatment is not simple. To optimize childhood metabolic health, we must begin prior to conception and at every age seize the opportunities to shape the child’s environment to enhance metabolic health development. • Significant progress has been made in describing the growing pediatric T2DM epidemic and in identifying individual risk factors that contribute to its development. Work is ongoing to understand the underlying mechanisms of pediatric T2DM. This knowledge base will likely grow rapidly over the next decade. • A major limitation of much of this work is that the studies tend to be singularly focused on exploring and understanding a particular risk, and are completed using a specific disciplinary approach. What is missing from most of this literature is the recognition that these risks exist within a dynamic developmental context with implications from micro- to macrosystems. The next generation of studies needs to consider how risks play out differently depending upon prior or current exposures and environments. What are the risk modifiers and mediators? • Accounting for the influence of time-specific exposures during sensitive developmental periods in conjunction with exposures across time is limited in the current research, and must be addressed more thoroughly in future studies. • Another major task facing pediatric T2DM researchers is to understand the developmental similarities and differences in T2DM between children and adults. When T2DM “threatens” or occurs in children/adolescents, the challenges of understanding and intervening are different from those in the adult population, both biologically and behaviorally. Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… • Pediatric T2DM must be understood within the dynamic context of the child’s overall health development. Research on the factors and developmental processes occurring early in life has shown how the emergence of T2DM children and adolescents is conditioned by prior experiences, exposures, and response patterns earlier in the life course. • Translating the current knowledge into practice-relevant evidence is lagging and must be accelerated. 7.2 7.2.1 Recommendations Recommendation 1: To Continue to Elucidate Mechanisms that Contributes to Pediatric T2DM by Focusing the Research in Key Areas There are several parts to this recommendation including the identification of substantive research areas to guide further work, as well as recommendations about the process for doing the work. Life course health development research will require transdisciplinary teams of scientists that examine phenomenon across different levels (genetic, cellular, biological systems, families, neighborhoods, and large and complex macrosystems) at different developmental stage of the child. No one individual, and no one discipline, can achieve this knowledge by working in a disciplinary silo. There are several areas that will require further investigation, and in particular we need to increase our understanding of factors that may mitigate the effects of adverse early-life programming. Childhood adversity, including the type of adversity (e.g., resource lack compared with family chaos), and chronic stress are noted as likely to be involved in the biological programming of several chronic health conditions including pediatric T2DM, but this is poorly accounted for in most of the current studies. Inclusion of poverty and family stress get at some of these factors, but better conceptual and empirical models are needed. Furthermore, the timing of the exposure for these risks is also not well 215 documented, and their time-specific impacts are not well understood (i.e., is it poverty during pregnancy, during early childhood, or both?). On the positive side, there is a small but growing literature on the importance of parenting style, particularly nurturing behaviors, as possibly ways of mitigating the negative effects of various types of adverse environmental contexts. Research priorities include: • Understanding the health capital and health development capacity that mothers (and fathers) bring to conception and determine which of these factors are most important for child health development • Determining how adult-recognized T2DM genetic susceptibility applies in pediatric T2DM • Increased work to identify the epigenetic changes that occur in response to risk exposures (e.g., environmental toxins, stressors) • Nutrition research, at every stage of childhood • Understanding how health behaviors develop across time But none of these are factors will provide a satisfactory explanation in isolation; each must be conditioned on prior and co-occurring factors. A life course health development perspective suggests that research must be embedded within the life stage of the child, taking into account the child’s full developmental history, including the intrauterine period. Exploring contemporaneous factors, e.g., physical activity, and their association with T2DM should be done conditional on prior life course exposures, not simply on current factors; for example, knowing that the child was exposed to diabetes in utero and the birth size of the infant may be key markers for understanding differential effects of contemporaneous factors. Figure 1 integrates these ideas to display the life stages along the horizontal axis and prevention, screening, and treatment/surveillance along the y axis. Embedded within this figure but not displayed because of the complexity is that at each point multiple factors from multiple levels are transacting simultaneously to bring about an outcome. P. Salsberry et al. 216 Rx of DM – T2DM or GDM (HAPO Study) Diet quality Prevenon, Treatment & Surveillance Longitudinal follow-up of children born to mothers with DM, GDM, with targeted intervenons aimed at high risk groups Preschool obesity Tx DM Screening @ 1st prenatal visit, then risk stratified Set optimal wt gain target Smoking cessation Screening Determinants, Risk Factors for T2DM NOTE: Green indicates recommendaons based upon consensus documents (the what we know). Red indicates areas for further study (that is, areas where we need more knowledge). Maternal GDM, Family DM history. BMI, smoking, acvity level, diet Preconception GDM, Family DM history, BMI, Smoking, High level of stress, Diet quality of mother; Race/ ethnicity Pregnancy TODAY STUDY (Treatment) HEALTHY Study (prevenon) Community-Based Programs (YMCA) TAT Transioning to adult care Surveillance for DM & Met Syndrome complicaons: renopathy, HBP, Lipids, triglycerides Exercise Trials Bariatric surgery “Stress” Assessment of Family as well as individual child High risk groups: BMI %le>85th , starng at 1 0 or earlier if reached puberty screen for T2DM Growth Trajectories: Height and Weight Diet/feeding – nutrient composion Lifestyle factors: Diet, acvity condioned on family history, individual risk for diabetes Feeding-processes First Year Years 2 & 3 Preschool School – age Adolescent Fig. 1 A life course approach for understanding the risks, prevention and treatment of type 2 diabetes 7.2.2 Recommendation 2: To Strengthen Data and Methods Used to Address Identified Research Priorities and Explore the Feasibility of an International Effort to Harmonize Ongoing Birth Cohorts for the Purpose of Developing a Better Understanding of the Timing, Dose, and Interaction of Factors that Contribute to Pediatric T2DM Worldwide Few data sets used to study the onset and impacts of T2DM include significant information on the broader context of the family, school, or neighborhood. In studies with good social data, biological data are often not included. A wider (micro to macro contexts) and longer (birth through adolescence) lens is needed to study T2DM. The need for longitudinal studies that can provide this lens is widely noted in the research literature, but because of the costs and subject burden, these long-term studies are difficult to carry out. However, there are a large number of longitudinal birth cohort studies ongoing worldwide that may have the potential for improving the understanding of pediatric T2DM. The recommendation here is to explore the feasibility of an international effort to harmonize ongoing birth cohorts to develop a better understanding of the timing, dose, and interactions of factors that contribute to T2DM worldwide. An added benefit of the development of such a data set is that it would provide the data to generate and test hypotheses, not just about T2DM but data directly relevant to the testing of the life course health development theoretical framework. Table 3 presents an overview of international birth cohort studies. Of these studies, one dates to the 1950s, one to the 1960s, two during the 1970s, and another two in the 1980s. Twelve of the birth cohorts began data collection in the 1990s and 21 began data collection between 2000 and 2011. From this list, there are a handful of studies with data elements (birth data, multiple data collection points throughout childhood, including adolescent health and height and weight) pertinent to informing a life course picture of key pathways in this epidemic. Six birth cohorts were identified as having collected data beyond 1995 and included at least one data collection point in adolescence (defined as data Table 3 Birth cohort longitudinal studies Cohort/country Early Childhood Longitudinal Study—Birth cohort/US (ECLS) National Child Development Study (NCDS): UK 1958 Cohort 1970 British Cohort Study Years Births in 2001 Births in 1958 Births in 1970 Ages/data collection points Birth through kindergarten entry: 9 months, 2 years, 4 years, and kindergarten 1958, 7, 11, 16, 23, 33, 42, 46, 50; in 2003 @45, 9000 in biomedical screens 1970, 5, 10, 16, 30, 34, and 42 (in 2012) Sample characteristics 14,000 diverse nationally rep. sample; oversampling of API, AI, and Alaskan natives, twins, LBW Measures Cognitive, social, emotional, physical development—assessments, parents, child care settings, and KGT Comments: PI/Managing entity// website National Center for Education Statistics http://nces.ed.gov/ecls 17,000 born during 1 week in 1958 in England, Scotland, Wales Physical, educational, economic, employment, family life, health behaviors, well-being, social participation, attitudes Centre for Longitudinal Studies, Institute of Education: Jane Elliot, PI http://www.cls.ioe.ac.uk/ 17,000 people born in England, Scotland, Wales in single week in 1970 Health, physical educational and social development, economic circumstances Sibling and parent info; parenting, childcare, school choice, child behavior and cognitive development, child and parent health, parent’s employment and education, income, poverty, neighborhood, and residential mobility; social capital and ethnicity Education, social work/support for parents, health, childcare, social inclusion Managed by Centre for Longitudinal Studies, Institute of Education; Alice Sullivan, PI http://www.cls.ioe.ac.uk/ Managed by Centre for Longitudinal Studies, Institute of Education Lucinda Platt, PI http://www.cls.ioe.ac.uk/ Millennium Cohort Study Births in 2000–2001 9 months, 3, 5, 7, 11 (in 2012) plus health visitor survey report and fertility survey report 19,000 people in the UK Growing up in Scotland (GUS) Births in Scotland: 2003/2004 birth cohort 1 and 2010/2011 birth cohort 2 Data collection yearly on the early cohorts; for the 2010 cohorts—only at key transitions Growing up in Ireland Births in 1999/2000 for older cohort; 2007/2008 for infant cohort Two rounds of data collections with each group 8000 children were enrolled into the study in 2005–20065000 were babies (~10 months) born between 6/ 2004 and 5/ 20053000 were toddlers (~34 months) born between 6/2002 and 5/ 2003. 6000 children born between 3/2010 and 2/ 2011 will be enrolled during 2010–2011; data collection at 10 months old. 8500 9-year-olds and 11,000 9-month-olds interviewed during September 2008 to April 2009. Second round data collection took place between January and August 2011. The main aim of the study is to paint a full picture of children in Ireland and how they are developing in the current social, economic, and cultural environment Centre for Research onFamilies and Relationships—University of Edinburghhttp://growingupinscotland. org.uk/ PI: Professor James Williams at Trinity College Dublin. http://www. growingup.ie/ (continued) Table 3 (continued) Cohort/country NLSY: Children of the ‘79 Years US Panel study of income dynamics (PSID) US Birth 1968 and ongoing National Longitudinal Survey of Children and Youth Canada 1994 and ongoing Ages/data collection points The NLSY child sample is comprised of all children born to the NLSY79 mothers; mothers have been interviewed biennially; data on child begins at birth through adulthood Birth through adulthood. Annually from 1968 to 1997, biennially 1998. Child development supplement 1n 1997 from birth −12 and 2002/2003 for 5–18 years. 2007/2008 at 10–18 years. 18 years transition to adulthood at ‘05,‘07, ‘09, ‘11 Birth to early adulthood—eight cycles with varying age ranges (0–25 years) Comments: PI/Managing entity// website Access to in data available at Bureau of Labor Statistics web site www.bls. gov/nls/nlsy79.htm Sample characteristics More than 11,000 children have been identified; birth years range from 1979 through 2010 Measures Cognitive, behavioral, health status, home environment, psychosocial, parental involvement, economic conditions of the family, risky behaviors 70,000 individuals over four decades. Includes initial participants from 1968 and their biological/adopted descendants. 1997 and 1999 included 511 immigrants to the USA Cognitive, behavioral and health status. Afterschool physical activities, home environment, psychosocial, parental involvement, schooling and school environment http://psidonline.isr.umich.edu/Guide/ default.aspx Cycle 8: 35,795 children Child health, family psychosocial, emotional, development, teenage risk behaviors http://www.statcan.gc.ca/start-debuteng.html Danish Longitudinal Survey of Children Denmark 1995 and 2003 Birth to adulthood: Birth 1995, 1996, 1999, 2003, 2007 (11 years old) 6011 children (born Sept 15-Oct 31, 1995 and mothers are Danish citizens); 611 children (born April 1-Dec 31, 1995 and mothers had been residing in Demark for 3 years); 6000 children born in 1995 who were placed in group homes/foster care—data collection began in 2003 Growing up in France (ELFE) France 2011 (children born on specific days of year 2009 [4 days in each of the four different quarters]) Birth to adulthood: birth, 2 months, 3, 6, 11 1000 children (goal is 20,000) Effect of differences in the socioeconomic situation, way of life, ethnicity, upbringing, and organization of home life within the family and its effects on childhood development. Consequences of the institutionalized childhood. Effectiveness of societal support of families and children. Effect of leave-of-absence schemes on the parents’ perception of the father/mother role, or their perception of the role of the family/”the public sector.” Effect of parents’ mutual relations (conflict/harmony) on their children’s daily life. Effect of parental education, way of life, cultural background and working conditions of the parents on the way they prioritize family life. Conditions in childhood/adolescence that effect social integrate. What social mechanisms influence child resiliency To study the various factors interacting all over the life course up to adulthood (family structure, social and physical environment, schooling, health, and nutritional behavior) and to clarify the impact of the experience lived during infancy on the individual’s physical, psychological, social, and professional development. Measure the cumulative exposures to specific environmental conditions or behaviors year after year and to analyze their consequences on social and health inequalities http://www.sfi.dk/the_danish_ longitudinal_survey_of_ children-3214.aspx http://www.cls.ioe.ac.uk/page.aspx?& sitesectionid=326&sitesectiontitle=EL FE+%28Growin%20g+up+in+France (continued) Table 3 (continued) Cohort/country European Longitudinal Study of Pregnancy and Childhood (ELSPAC) Years Europe born 1991 to 1995: Great Britain, Isle of Man, Czech Republic, Slovakia, Russia, Ukraine, Croatia and Estonia; recently added: Spain and Greece. Growing up in Australia Australia 2004 Ages/data collection points Pregnancy, birth to 19 years: 6 and 18 months; 3, 5, 7, 11, 15, 18 and 19 years Two cohorts— families with 4–5-year-old children (4–19 years) and families with 0–1-year-old infants (birth to 16 years) Interviewed biennially Sample characteristics 7589 children 10,000 children – infant cohort (Dabelea et al. 2014; Grote et al. 2010) and child cohort (4991) Measures Detect biological, psychological, and social factors as well as factors of external environment associated with survival and health of the fetus, infant and a child. Biopsychosocial determinants of the child’s development from conception until the end of school age, while taking into consideration a lifestyle of the family, environment, care, or relationships among family members as well as personalities of the mother and the father. Furthermore, we are aiming to find the links between biopsychosocial determinants as well as origin and development of health disorders of a fetus, newborn, and a child. Medical issues during pregnancy—bleeding, eclampsia, infections, adolescent mothers. Specific termination of pregnancy—preterm delivery, intrauterine growth retardation of the fetus, abortions, child of diabetic mother. Growth— weight; height; length of long bones; circumference of head, waist, and arm; skin folders; and other anthropometric data. Development—developmental landmarks, motoric coordination, intellectual abilities Children’s social, economic, and cultural environments to their adjustment and well-being Comments: PI/Managing entity// website Research Institute of Preventive and Social Paediatrics at the Faculty of Medicine, Masaryk University Brno, Czech Republic: Lubomír Kukla, PI; Website: http://www.aloe-app.com/ index-en.php Daryl Higgins, PI Website: http:// www.aifs.gov.au/growingup/index. html Birth to twenty South Africa 1990 Birth through adulthood Birth to 14 years annually and 14 through adulthood biennially; multigenerational 3273 children (1594 recruited antenatal); 3273 women (singleton births) The Avon Longitudinal Study of Parents and Children 1992 (ALSPAC)— Children of the ‘90s study UK Birth to early adulthood 14,000 mothers in the former county of Avon enrolled during pregnancy in 1991 and 1992. The health and development of their children has been followed in great detail ever since Lifecycle approach and attempts to integrate historical, personal, social, and economic information with data collected during all major developmental stages. In the early years, the emphasis was on physical growth, health and health service usage; in the preschool years attention turned to social behavior, and home and center support for learning; during primary school, data has been collected on educational progress; in the teen years the study is concentrating on precursors to adult health and adjustment, including sexual behavior, school achievement, substance use, diet, activity, and conflict with the law. Regularly assessments are made of children’s residential circumstances, household composition, and socioeconomic conditions. Growth measurements are taken and blood and urine samples subjected to a number of biochemical tests. Body composition, blood pressure and physical activity are also assessed During pregnancy: medical history and physical health of mother and grandparents, exposure to environmental pollutants, sleeping patterns, diet, caffeine and alcohol consumption, smoking, illegal drug taking, psychological well-being of parents, their expectations and attitudes, social support networks. Early childhood: health, lifestyle, and development. At focus clinics, physical measurements and psychological tests, regular biological samples, DNA extraction, school performance on SATS and GCSEs. ALSPAC links to additional sources of data on participants include national pupil database and school census data, and information on death and cancer Child Youth Family and Social Development, Human Sciences Research Council: Linda Richter, PI http://www.wits.ac.za/academic/ health/research/birthto20/10274/ home.html http://www.bristol.ac.uk/alspac/ (continued) Table 3 (continued) Cohort/country Childhood Obesity – Early Programming by Infant Nutrition (CHOPIN) Years Germany: 2002 to 2004 enrollment All Babies in Southeast Sweden Sweden: Oct 1st 1997 to Oct 1st 1999 Aarhus Birth Cohort Births beginning in 1990 Denmark Amsterdam Born Children and their Development (ABCD study) Babies After SCOPE: Evaluating the Longitudinal Impact using Neurological and Nutritional Endpoints Ages/data collection points Follow through to age 11; data collection yearly through 8 and then again at 11 Sample characteristics 1678 infants in Belgium, Germany, Italy, Poland, Spain Measures Birth outcomes (wt, length, gestational age, Apgar, sex); child exposures infancy through age 11; development; child health, biological samples. Also included a 1 year multicenter intervention trial on newborn infants, to test whether feeding infant formulae (differing in protein content), can influence growth trajectories during the first year of life Multiple—beginning during fetal life, including bio-samples during pregnancy through age 14 years Pre-pregnancy, second and third trimester 17,000 infants Main exposure: nutrition, physical activity, sleep pattern, infections, psychosocial factors, child care, stress, drugs, vaccinations, genetics, Outcomes: autoimmunity (Type 1 DM, celiac disease, RA), allergy, obesity, Type 2 DM Fetal programming hypothesis and on various aspects of pregnancy, birth, and neonatal care. Birth outcomes (wt, lt, GA at birth, Apgar, congenital malformations, stillbirth, sex) 2003–2004 Pre-pregnancy, pregnancy (1st, 2nd, 3rd trimester), birth, 5 and 6 years to adulthood 6161 infants and 8266 mothers Ireland: Aug 1st 2008 to Oct 31 2011 Birth to 5 years, birth to 1, 2, and 5 years 2185 children born in Ireland 100,000 children and 100,000 mothers who gave birth at the Dept of Obst and Gyn, Aarhus Univ Hospital, Skejby Association between lifestyle, psychosocial conditions and nutritional status during pregnancy, and the child’s health at birth (birth weight, pregnancy duration, perinatal mortality) and in later life (growth, physical development, behavior and cognitive functioning) Maternal health, fetal growth, and childhood nutrition, growth and development in the first 2 years of life. Birth outcomes (wt, lt, GA at birth, sex) Comments: PI/Managing entity// website PI: Berthold Koletzko, Div. Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians-University of Munich Medical http://www.ist-world.org/ ProjectDetails.aspx?ProjectId=c63efc 2bdd954069bed34a6d65d33dd4&Sou rceDatabaseId=e4fcfde0182a45898e8 741a1abae3984 PI Johnny Ludvigsson; Dept of Clin Exp Medicine, Linköping university, Sweden http://www.researchweb.org/is/lio/ ansokan/56531 Dept. of Pediatrics and Perinatal Epidemiology Research Unit, Aarhus University Hospital, Skejby, Denmark:Tine Brink Henriksen, PI http://fetotox.au.dk/the-fetotox-birthcohorts/ aarhus-birth-cohort-biobank-abcb/ Academic Medical Centre/ University of Amsterdam: Manon van Eijsden; Tanja G.M. Vrijkotte, PI http://www.abcd-study.nl/ University College Cork: PI Deirdre Murray, PI http://www. nationalchildrensresearchcentre.ie/ research/ baseline-study-babies-after-scopeevaluating-longitundinal-impactusing-neurological-and-nutritionalendpoints/ Bologna Birth Cohort Italy 2004–2005 Birth to adulthood: birth, 1, 2, 3, and 7 years 654 children, 651 mothers, 593 fathers delivering at the University Hospital of Bolonga Born in Bradford UK Jan 1st 2007 to Dec 24th 2010 Birth to adulthood: 1, 2, and 3 years 14,000 children, 14,000 mothers, and 3000 fathers Copenhagen Child Cohort 2000 (CCC2000) Denmark Births in 2000 Pregnancy, birth to adulthood: birth, 1, 6, and 11 years 6090 children, 6090 mothers Danish National Birth Cohort (DNBC) Denmark Births 1996–2002 Pregnancy, birth to adulthood: birth—7 years, 11 years 95,000 children, 100,418 mothers Dortmund Nutritional and Anthropometric Longitudinally Designed Study (DONALD) Germany Births from 1985 and ongoing Infants 3–6 months old to adulthood: birth—18 years 1300 each children, mothers, and fathers; open cohort study. Annual recruitment of 30–40 infants Exposure: passive smoking, breastfeeding, nutrition, allergens, indoor pollution, motor vehicles pollution Outcome: wheezing, asthma, eczema, atopic dermatitis, sleep, cognitive development. Birth outcomes (wt, lt, GA at birth, Apgar, stillbirth, sex) Describe health and ill-health within a largely bi-ethnic population; identify modifiable causal pathways that promote well-being, or contribute to ill-health; develop, design and evaluate interventions to promote health and well-being; Develop a model for integrating operational, epidemiological, and evaluative research into practice within the NHS and other health-related systems; build and strengthen the local research capacity Developmental trajectories of psychopathology and mental disorders from birth in a general population birth cohort and to determine at risk states of mental illness in childhood and adolescence. Birth outcomes (wt, lt, GA at birth, Apgar, congenital malformations, stillbirth, sex) Pregnancy complications and diseases in offspring as a function of factors operating in pregnancy/fetal and early life. Fetal growth as an outcome and as a determinant of later health. Main exposures: infection, alcohol, tobacco, environments, medication, psychosocial factors. Nutrition in pregnancy, in general but with focus upon fish intake Outcome measures: life-long follow-up of children—mortality and morbidity. Birth outcomes (wt, lt, GA at birth, Apgar, congenital malformations, stillbirth, sex) Environmental exposure is not the main focus of the birth outcomes (wt, lt, GA at birth, apgar, sex). Child exposures: child care, second hand smoke, smoking, alcohol, breastfeeding, diet, PA, medications, head circumference, sexual maturation, allergies, infections, cardiac, cancer, DM, BP, sleep, urine samples Maria Pia Fantini, PI Bradford Institute for Health Research: John Wright, PI http://www.borninbradford.nhs.uk/ Child and Adolescent psychiatric Centre, University Hospital of Copenhagen: Anne Mette Skovgaard, PI Statens Serum Institut, Copenhagen: Jørn Olsen, PI http://www.ssi.dk/English/RandD/ Research%20areas/Epidemiology/ DNBC/ Research Insitute of Child Nutrition: Anette Buyken, Thomas Remer, Ute Alexy, Mathilde Kersting – Pis (continued) Table 3 (continued) Ages/data collection points Birth to adulthood: birth, 1, 7, 9, and 12 years Cohort/country Gateshead Millennium Study Years UK births from June 1st 1999 to May 31st 2000 Sample characteristics 1029 children and 1011 mothers GECKO Drenthe cohort Netherlands born April 1st 2006 to April 1st 2007 Pregnancy, birth to adulthood: birth—5 years 2997 children born in Drenthe, Netherlands. Pregnant mothers recruited in the third trimester Generation R Netherlands birth Jan 21st 2002 to Feb 1st 2006 Pregnancy, birth to adulthood: birth—5, 7, and 9 years 10,000 children, 97,780 mothers, and 6500 fathers Measures Feeding, weaning, and growth, including characteristics of maternal feeding style, eating attitudes, and depression. Childhood aspects of developmental psychopathology, behavioral and emotional development, and repetitive behaviors. Origins of childhood obesity, with particular reference to food intake, physical activity and sedentary behavior. Original purpose was investigation of weight faltering The overall aim of the GECKO Drenthe cohort is to study the prevalence and early risk factors for the development of childhood overweight and fat distribution at very young age. Within this birth cohort the growth of the children is closely followed, starting at birth until adulthood. The children are followed using questionnaires that the parents have to answer and the regular visits to the Well Baby Clinics, where length, weight, head circumference, and hip and waist circumference are measured. Furthermore, physical activity is measured using accelerometers. At birth, cord blood was sampled and stored for (epi)genetic analysis and biomarkers. During the first 9 months of life, the children were measured every month (incl. questionnaire); thereafter this frequency decreased until once every 2 years from 3 years onward. The children are now 5 years old Identification of early critical periods and causal pathways for growth, development, and health. Birth outcomes (wt, length, GA at birth, Apgar, congenital malformation, still birth, sex) Comments: PI/Managing entity// website Newcastle University: Ashley Adamson, PI http://www.cls.ioe.ac.uk/page.aspx?& sitesectionid=332&sitesectiontitle=Ga teshead+Millennium+Study University Medical Center Groningen: E. Corpeleijn, PI Erasmus Medical Center Rotterdam: Vincent Jaddoe, PI http://research.lunenfeld.ca/ eagle/?page=841 Italy June 1st 2003 to Oct 31st 2004 Birth to 20 years: birth, 1, 3, and 8 years 708 children, 693 mothers (>18 years), and 63 fathers Environmental exposures, diet, air pollution asthma and allergies, accidents and injuries, obesity, minor birth defects, neurodevelopment. Birth outcomes (wt, length, GA at birth, Apgar, congenital malformation, still birth, sex) Department of Epidemiology Regional Health Service lazio Region: Daniela Porta, PI Germany births Jan 1st 1995 to Jan 1st 1998 Birth to adulthood: birth—4, 6, 10, and15 years 5991 children, maternal pregnancy outcomes HelmholtzZentrum München: D. Berdel; A.v. Berg; C.P. Bauer; U. Krämer; S. Koletzko; J. Heinrich – PIs Denmark births April 1st 1984 to April 1st 1987 Pregnancy, birth annually through 18 to 20 years KOALA Birth Cohort Study (KOALA) Netherlands Birth Jan 1st 2000 to Oct 1st 2002 Pregnancy, Birth to adulthood: birth—2, 5–9 years 11,144 children of live birth singleton pregnancy, 11,980 mothers who attended 2 midwife in Aalborg or Odense during week 35 to 38 of pregnancy 2843 children, 2900 mothers >14 weeks in a pregnancy pelvic pain or from an alternate lifestyle practices, 2900 fathers Intervention substudy; double -blinded randomized trial for hypoallergenic formulae, observational substudy: natural course of atopic diseases and metabolic disorders; mental health, nutrition, body weight and physical activity; Birth outcomes (wt, lt, GA at birth) Birth outcomes (wt, lt, GA at birth, Apgar, congenital malformations, sex). Converted from a trial to a birth cohort Maastricht University: Carel Thijs, PI http://www.koala-study.nl/ Lifeways CrossGeneration Cohort Study Ireland 2001 to 2003 Pregnancy, birth: birth—3, 5, and 10 years Main outcomes: 1. allergy and asthma development, 2. growth and overweight development (inflammatory, immunological, metabolic, cardiorespiratory and cardiovascular aspects) Main exposures: microbial (infections, gut microbiota), breastfeeding and breast milk composition, nutrition, physical activity, parenting practices and parenting styles; home, neighborhood, and childcare environment. Birth outcomes (wt, lt, GA at birth, Apgar, congenital malformations, sex) Physical and psychological health status, socioeconomic circumstances, birth, childhood, early adulthood and early middle age. Birth outcomes (wt, GA at birth, Apgar, congenital malformations, stillbirth, sex) Genetic and Environment: Prospective Study on Infancy in Italy (GASPII) German Infant Study on the influence of Nutrition Intervention (GINIplus) Healthy Habits for two (HHf2) 1074 children, 1061 mothers, 325 fathers The Danish Epidemiology Science Centre: Jørn Olsen, PI University College Dublin: Cecily Kelleher, PI http://www.ucd.ie/phpps/research/ clinicalepidemiologygroup/ lifewayscross-generationcohortstudy/ (continued) Table 3 (continued) Ages/data collection points Pregnancy, birth to 16 years: birth—4 years Sample characteristics 1590 children; 1610 mothers >16 years and 9–13 wks gestation; 37 fathers Norway 2003 to 2009 Birth to adulthood: Birth −2 years, 8 years 2500 children breastfeeding at 2 wks of age, 2500 mothers, 2400 fathers Norway Jan 1st 1999 to Dec 31 2008 Pregnancy, birth to adulthood: birth, 1, 3, 5, 7, 8 years 10,500 children, 90,700 mothers 17–20 wks pregnant, 72,100 fathers Cohort/country Mother Child Cohort in Crete (RHEA) Years Greece Feb 1st 2007 to Feb 29th 2008 Norwegian Human Milk Study (HUMIS) Norwegian Mother and Child Cohort Study (MoBa) Measures 1. The evaluation of nutritional, environmental, biological, and psychosocial exposures in the prenatal period and in early childhood. 2. The evaluation of the association of these exposures with the development of the fetus and the child, reproductive outcomes, endocrine-related outcomes, neurodevelopment, obesity, and the occurrence of chronic diseases including asthma and allergies, metabolic syndrome, and endocrine disorders that appear first in childhood but that may perpetuate to adulthood. 3. The evaluation of mother’s health during and after pregnancy. 4. The evaluation of genetic susceptibility and the interactions between genetic and environmental factors affecting child health. Birth outcomes (wt, lt, GA at birth, Apgar, stillbirth, sex) Exposures: environmental toxicants, gut microbiota, pops, Pfos, organotins, gut microbiota, Bifidobacterium, Lactobacillus, early nutrition, human milk composition, duration of breastfeeding. Main outcomes: child health, reproductive health, asthma and allergy, ADHD, neuropsychological development, depression, overweight. Birth outcomes (wt, lt, GA at birth, Apgar, congenital malformations, sex) Causes of diseases. Additional aims are to detect early signs of disease and to describe the development of diseases. Birth outcomes (wt, lt, GA at birth, apgar, congenital malformations, stillbirth) Comments: PI/Managing entity// website Dept of Social Medicine, Medical School, Heraklion, University of Crete, Greece and CREAL, Barcelona, Spain: Manolis Kogevinas and Leda Chatzi – PIs Norwegian Institute of Public Health: Merete Eggesbø, PI Norwegian Institute of Public Health: Per Magnus, PI http://www.fhi.no/eway/default.aspx? pid=240&trg=Main_6664&Main_666 4=6894:0:25,7372:1:0:0:::0:0&MainC ontent_6898=6671:0:25,7899:1:0:0::: 0:0&List_6673=6674:0:25,7905:1:0:0 :::0:0 Odense Child Cohort Denmark Births beginning in 2010 Infants 0–5 months to 18 years 1650 children, 2249 mothers, 2102 fathers—living in Odense, Denmark Interaction between the fetus/infant/child during pregnancy, birth, and childhood and the social and environmental determinants for health and disease. Birth outcomes (wt, lt, GA at birth, Apgar, stillbirth, sex) Study on the pre- and early postnatal determinants of child health and development (EDEN – France) France Births 2003 to 2005 Pregnancy, birth to 8 years: birth to 5 years and 8 years 1900 children, 2000 mothers >18 years and <24 wks pregnant, 1800 fathers Pre- and postnatal determinant of the child’s development and health. Birth outcomes (wt, lt, GA at birth, Apgar, congenital malformations, stillbirth, sex) Odense University Hospital, Hans Christian Andersen Children’s Hospital, Paediatric Research Unit: Steffen Husby, PI http://www.sdu.dk/en/Om_SDU/ Institutter_centre/Klinisk_institut/ Forskning/Forskningsenheder/ Paediatri/Odense+Børnekohorte CESP INSERM: Heude Barbara, PI The cohorts outlined here met the following criteria: Enrollment of the mother/child into the cohort was either prior to the pregnancy, during the pregnancy, or at birth; there was data collected with some frequency in childhood; the studies were not single/narrow focused) Key source is http://www.birthcohorts.net; this site is an inventory of birth cohort longitudinal studies maintained by Department of Public Health, University of Copenhagen, Professor Anne-Marie Nybo Andersen P. Salsberry et al. 228 collection at 15 years or beyond). These cohorts also gathered data for childhood weight, height, health assessment, and various social parameters. The cohorts were the Canadian National Longitudinal Survey of Children and Youth (NLSCY, data collected at various age points from birth through age 25 over the course of 8 cycles), the European Longitudinal Study of Pregnancy and Childhood (ELSPAC, data collected at nine points from birth through 19 years and includes the countries of Great Britain, Isle of Man, Czech Republic, Slovakia, Russia, Ukraine, Croatia, Estonia, Spain and Greece), the Growing Up in Australia survey (data collected through two separate cohorts with one beginning at birth to 1 year and the second at 4/5 years with follow-up to age 19 years), the South Africa’s Birth to 20 longitudinal study (data collected through two separate cohorts that followed children ages birth to 14 years and age 14 years through adulthood), the UK’s Avon Longitudinal Study of Parents and Children (ALSPAC) (data collected on children born between 1990 and 1992 from birth through adulthood), and finally, the German Infant Study on the Influence of Nutrition Intervention (GINIplus), collected data on children born between 1995 and 1998 annually from birth through 4 years and then at 6, 10, and 15 years of age. In addition to the data limitations currently facing the pediatric T2DM research community, there are also methodological/analytical challenges. To make progress in all of these research priorities, large amounts of data will be needed. These data will be diverse ranging from genetic and other biologicals to social structure factors. Management and analyses of such diverse data will require new methods and collaborations that will include basic scientists, mathematicians, data engineers, practitioners, and translational scientists. Researchers will need to employ big data analytics and a complex system perspective that can address multiple causes and outcomes. The challenges are significant, but the opportunities vast to make a real difference to the health development of all children 7.2.3 Recommendation 3: To Translate the Growing Body of Knowledge into EvidencedBased Prevention and Treatment Strategies There are clear needs for both prevention and treatment trials in children and adolescents. There is a dearth of evidence-based studies and guidelines available to pediatric practitioners and pediatric endocrinologist to address the multiple management issues faced when treating patients with T2DM. Areas of clear need include prevention strategies for children born at high risk. For example, low birth weight newborns, offspring of diabetic mothers, could be placed in programs to test interventions to modify risk. This is a very important area of pediatric and diabetes research with implications on health status and care for large number of children into adulthood and should be of high priority for funding agencies. Prevention trials are needed that can identify strategies to reduce the risk for developing T2DM, and as recent work demonstrates, understanding the age at intervention is key. Prevention strategies may be gleaned from longitudinal data collected for other reasons. While not a prospective trial, the primary data elements could be supplemented either through new data collection efforts (e.g., during adolescence collecting insulin, glucose, etc.), or by combining the primary data with other administrative data—child electronic health records of heights and weights throughout childhood—or both. With home address information, child contexts could be tracked over time and neighborhoods accounted for in the analyses. Finally, there is much work to be done to translate the knowledge into policy at every level. Pediatric practitioners need up-to-date guidelines to identify children at high risk for T2DM at young ages. Families must be given the information about the possible long-term consequences of decisions made in early life. Local, state, and national governments also need policies in place that enhance child health development throughout childhood. Only then will we be able to achieve the goal of optimal health development for every child. Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… References Aaltonen, J., Ojala, T., Laitinen, K., Poussa, T., Ozanne, S., & Isolauri, E. (2011). Impact of maternal diet during pregnancy and breastfeeding on infant metabolic programming: A prospective randomized controlled study. European Journal of Clinical Nutrition, 65(1), 10–19. Agardh, E., Allebeck, P., Hallqvist, J., Moradi, T., & Sidorchuk, A. (2011). Type 2 diabetes incidence and socio-economic position: A systematic review and meta-analysis. International Journal of Epidemiology, 40(3), 804–818. Amed, S., Daneman, D., Mahmud, F. H., & Hamilton, J. (2010a). Type 2 diabetes in children and adolescents. Expert Review of Cardiovascular Therapy, 8(3), 393–406. Amed, S., Dean, H. J., Panagiotopoulos, C., Sellers, E. A., Hadjiyannakis, S., Laubscher, T. A., et al. (2010b). Type 2 diabetes, medication-induced diabetes, and monogenic diabetes in Canadian children: A prospective national surveillance study. Diabetes Care, 33(4), 786–791. Amercian Diabetes Association. (2000). Type 2 diabetes in children and adolescents. Pediatrics, 105(3 Pt 1), 671–680. American Academy of Pedicatrics. (2012). Breastfeeding and the use of human milk. Pediatrics, 129(3), e827–e841. American Diabetes Association. (2010). Diagnosis and classification of diabetes mellitus. Diabetes Care, 33(Suppl 1), S62–S69. Anderson, S. E., & Whitaker, R. C. (2010). Household routines and obesity in US preschool-aged children. Pediatrics, 125(1), 420–428. Anderson, S. E., Gooze, R. A., Lemeshow, S., & Whitaker, R. C. (2012). Quality of early maternal-child relationship and risk of adolescent obesity. Pediatrics, 129(1), 132–140. Anderson, S. E., Lemeshow, S., & Whitaker, R. C. (2014). Maternal-infant relationship quality and risk of obesity at age 5.5 years in a national US cohort. BMC Pediatrics, 14(1), 54. Armitage, J. A., Khan, I. Y., Taylor, P. D., Nathanielsz, P. W., & Poston, L. (2004). Developmental programming of the metabolic syndrome by maternal nutritional imbalance: How strong is the evidence from experimental models in mammals? The Journal of Physiology, 561(Pt 2), 355–377. Armitage, J. A., Poston, L., Taylor, P., & D. (2008). Developmental origins of obesity and the metabolic syndrome: The role of maternal obesity. Frontiers of Hormone Research, 36, 73–84. Arslanian, S. A., Bacha, F., Saad, R., & Gungor, N. (2005). Family history of type 2 diabetes is associated with decreased insulin sensitivity and an impaired balance between insulin sensitivity and insulin secretion in white youth. Diabetes Care, 28910, 115–119. Barker, D. J., Hales, C. N., Fall, C. H., Osmond, C., Phipps, K., & Clark, P. M. (1993). Type 2 (non-insulin- 229 dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): Relation to reduced fetal growth. Diabetologia, 36(1), 62–67. Barker, A., Sharp, S. J., Timpson, N. J., Bouatia-Naji, N., Warrington, N. M., Kanoni, S., et al. (2011). Association of genetic loci with glucose levels in childhood and adolescence: A meta-analysis of over 6,000 children. Diabetes, 60(6), 1805–1812. Barlow, S. E. (2007). Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: Summary report. Pediatrics, 120(Suppl 4), S164–S192. Barouki, R., Gluckman, P. D., Grandjean, P., Hanson, M., & Heindel, J. J. (2012). Developmental origins of non-communicable disease: Implications for research and public health. Environmental Health : A Global Access Science Source, 11, 42. Barton, M. (2010). Screening for obesity in children and adolescents: US preventive services task Force recommendation statement. Pediatrics, 125(2), 361–367. Battista, M. C., Hivert, M. F., Duval, K., & Baillargeon, J. P. (2011). Intergenerational cycle of obesity and diabetes: How can we reduce the burdens of these conditions on the health of future generations? Experimental Diabetes Research, 2011, 596060. doi:10.1155/2011/596060. Bell, J. F., & Zimmerman, F. J. (2010). Shortened nighttime sleep duration in early life and subsequent childhood obesity. Archives of Pediatrics & Adolescent Medicine, 164(9), 840–845. Bell, R. A., Mayer-Davis, E. J., Beyer, J. W., D’Agostino, R. B., Jr., Lawrence, J. M., Linder, B., et al. (2009). Diabetes in non-Hispanic white youth: Prevalence, incidence, and clinical characteristics: The SEARCH for diabetes in youth study. Diabetes Care, 32(Suppl 2), S102–S111. Berends, L. M., & Ozanne, S. E. (2012). Early determinants of type-2 diabetes. Best Practice & Research. Clinical Endocrinology & Metabolism, 26(5), 569–580. Bertin, E., Gangnerau, M. N., Bellon, G., Bailbe, D., Arbelot De Vacqueur, A., & Portha, B. (2002). Development of beta-cell mass in fetuses of rats deprived of protein and/or energy in last trimester of pregnancy. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 283(3), R623–R630. Bocca, G., Corpeleijn, E., Stolk, R. P., & Sauer, P. J. J. (2012a). Results of a multidisciplinary treatment program in 3-year-old to 5-year-old overweight or obese children: A randomized controlled clinical trial. Archives of Pediatrics & Adolescent Medicine, 166(12), 1109–1115. Bocca, G., Corpeleijn, E., Stolk, R. P., & Sauer, P. J. (2012b). Results of a multidisciplinary treatment program in 3-year-old to 5-year-old overweight or obese children: A randomized controlled clinical trial. Archives of Pediatrics & Adolescent Medicine, 166(12), 1109–1115. Boekelheide, K., Blumberg, B., Chapin, R. E., Cote, I., Graziano, J. H., Janesick, A., et al. (2012). Predicting later-life outcomes of early-life expo- 230 sures. Environmental Health Perspectives, 120(10), 1353–1361. Boyle, J. P., Thompson, T. J., Gregg, E. W., Barker, L. E., & Williamson, D. F. (2010). Projection of the year 2050 burden of diabetes in the US adult population: Dynamic modeling of incidence, mortality, and prediabetes prevalence. Population Health Metrics, 8, 29. doi:10.1186/1478-7954-8-29. Brandt, M. L., Harmon, C. M., Helmrath, M. A., Inge, T. H., McKay, S. V., & Michalsky, M. P. (2010). Morbid obesity in pediatric diabetes mellitus: Surgical options and outcomes. Nature reviews. Endocrinology, 6(11), 637–645. Brotman, L. M., Dawson-McClure, S., Huang, K. Y., Theise, R., Kamboukos, D., Wang, J., et al. (2012). Early childhood family intervention and long-term obesity prevention among high-risk minority youth. Pediatrics, 129(3), e621–e628. Brown, T., & Summerbell, C. (2009). Systematic review of school-based interventions that focus on changing dietary intake and physical activity levels to prevent childhood obesity: An update to the obesity guidance produced by the National Institute for health and clinical excellence. Obesity Reviews, 10(1), 110–141. Brunton, P. J. (2010). Resetting the dynamic range of hypothalamic-pituitary-adrenal axis stress responses through pregnancy. Journal of Neuroendocrinology, 22(11), 1198–1213. Burns, S. F., Lee, S., Bacha, F., Tfayli, H., Hannon, T. S., & Arslanian, S. A. (2014). Pre-diabetes in overweight youth and early atherogenic risk. Metabolism, 63(12), 1528–1235. Burt Solorzano, C. M., & McCartney, C. R. (2010). Obesity and the pubertal transition in girls and boys. Reproduction, 140(3), 399–410. Caballero, B., Clay, T., Davis, S. M., Ethelbah, B., Rock, B. H., Lohman, T., et al. (2003). Pathways: A schoolbased, randomized controlled trial for the prevention of obesity in American Indian schoolchildren. American Journal of Clinical Nutrition, 78(5), 1030–1038. Campbell, K. J., Lioret, S., McNaughton, S. A., Crawford, D. A., Salmon, J., Ball, K., et al. (2013). A parentfocused intervention to reduce infant obesity risk behaviors: A randomized trial. Pediatrics, 131(4), 652–660. doi:10.1542/peds.2012-2576. Cenit, M. C., Matzaraki, V., Tigchelaar, E. F., & Zhernakova, A. (2014). Rapidly expanding knowledge on the role of the gut microbiome in health and disease. Biochimica et Biophysica Acta, 1842(10), 1981–1992. Christian, L. M. (2012). Physiological reactivity to psychological stress in human pregnancy: Current knowledge and future directions. Progress in Neurobiology, 99(2), 106–116. doi:10.1016/j.pneurobio.2012.07.003. Copeland, K. C., Zeitler, P., Geffner, M., Guandalini, C., Higgins, J., Hirst, K., et al. (2011). Characteristics of adolescents and youth with recent-onset type 2 diabetes: The TODAY cohort at baseline. Journal of Clinical Endocrinology and Metabolism, 96(1), 159–167. Courcoulas, A., Goodpaster, B., Eagleton, J., Belle, S., Kalarchian, M., Lang, W., et al. (2014). Surgical vs P. Salsberry et al. medical treatments for type 2 diabetes mellitus: A randomized clinical trial. JAMA Surgery, 7(149), 707–715. Crume, T. L., Ogden, L. G., Mayer-Davis, E. J., Hamman, R. F., Norris, J. M., Bischoff, K. J., et al. (2012). The impact of neonatal breast-feeding on growth trajectories of youth exposed and unexposed to diabetes in utero: The EPOCH study. International Journal of Obesity, 36(4), 529–534. Dabelea, D., Bell, R. A., D’Agostino, R. B., Jr., Imperatore, G., Johansen, J. M., Linder, B., et al. (2007). Incidence of diabetes in youth in the United States. JAMA, 297(24), 2716–2724. Dabelea, D., Dolan, L. M., D’Agostino, R., Jr., Hernandez, A. M., McAteer, J. B., Hammon, R. F., et al. (2011). Association testing of TCF7L2 polymorphisms with type 2 diabetes in multi-ethnic youth. Diabetologia, 54(3), 535–539. Dabelea, D., Mayer-Davis, E. J., Saydah, S., Imperatore, G., Linder, B., et al. (2014). Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009. JAMA, 311(17), 1778–1786. Danielsson, P., Kowalski, J., Ekblom, O., & Marcus, C. (2012a). Response of severely obese children and adolescents to behavioral treatment. Archives of Pediatrics & Adolescent Medicine, 166(12), 1103–1108. Danielsson, P., Kowalski, J., Ekblom, O., & Marcus, C. (2012b). Response of severely obese children and adolescents to behavioral treatment. Archives of Pediatrics & Adolescent Medicine, 166(12), 1103–1108. Davis, C. L., Pollock, N. K., Waller, J. L., Allison, J. D., Dennis, B. A., Bassali, R., et al. (2012). Exercise dose and diabetes risk in overweight and obese children: A randomized controlled trial. JAMA, 308(11), 1103–1112. Demmer, R. T., Zuk, A. M., Rosenbaum, M., & Desvarieux, M. (2013). Prevalence of diagnosed and undiagnosed type 2 diabetes mellitus among US adolescents: Results from the continuous NHANES, 1999-2010. American Journal of Epidemiology, 178(7), 1106–1113. Devaraj, S., Hemarajata, P., & Versalovic, J. (2013). The human gut microbiome and body metabolism: Implications for obesity and diabetes. Clinical Chemistry, 59(4), 617–628. Dyer, J. S., & Rosenfeld, C. R. (2011). Metabolic imprinting by prenatal, perinatal, and postnatal overnutrition: A review. Seminars in Reproductive Medicine, 29(3), 266–276. van Eijsden, M., Vrijkotte, T. G., Gemke, R. J., & van der Wal, M. F. (2011). Cohort profile: The Amsterdam born children and their development (ABCD) study. International Journal of Epidemiology, 40(5), 1176–1186. Ekelund, U., Ong, K. K., Linne, Y., Neovius, M., Brage, S., Dunger, D. B., et al. (2007). Association of weight gain in infancy and early childhood with metabolic risk in young adults. The Journal of Clinical Endocrinology and Metabolism, 92(1), 98–103. Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… Elder, D. A., Herbers, P. M., Weis, T., Standiford, D., Woo, J. G., & D’Alessio, D. A. (2012). Beta-cell dysfunction in adolescents and adults with newly diagnosed type 2 diabetes mellitus. Journal of Pediatrics, 160(6), 904–910. Eriksson, J. G. (2006). Early growth, and coronary heart disease and type 2 diabetes: Experiences from the Helsinki birth cohort studies. International Journal of Obesity, 30(Suppl 4), S18–S22. Eriksson, J. G. (2011). Early growth and coronary heart disease and type 2 diabetes: Findings from the Helsinki birth cohort study (HBCS). The American Journal of Clinical Nutrition, 94(6 Supplement), 1799S–1802S. Eriksson, J. G., Osmond, C., Kajantie, E., Forsen, T. J., & Barker, D. J. (2006). Patterns of growth among children who later develop type 2 diabetes or its risk factors. Diabetologia, 49(12), 2853–2858. Fabricius-Bjerre, S., Jensen, R. B., Faerch, K., Larsen, T., Molgaard, C., Michaelsen, K. F., et al. (2011). Impact of birth weight and early infant weight gain on insulin resistance and associated cardiovascular risk factors in adolescence. PloS One, 6(6), e20595. doi:10.1371/ journal.pone.0020595. Ferguson, M. A., Gutin, B., Le, N. A., Karp, W., Litaker, M., Humphries, M., et al. (1999). Effects of exercise training and its cessation on components of the insulin resistance syndrome in obese children. International Journal of Obesity and Related Metabolic Disorders, 23(8), 889–895. Fernandez-Twinn, D. S., Wayman, A., Ekizoglou, S., Martin, M. S., Hales, C. N., & Ozanne, S. E. (2005). Maternal protein restriction leads to hyperinsulinemia and reduced insulin-signaling protein expression in 21-mo-old female rat offspring. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 288(2), R368–R373. Foster, G. D., Linder, B., Baranowski, T., Cooper, D. M., Goldberg, L., Harrell, J. S., et al. (2010). A schoolbased intervention for diabetes risk reduction. New England Journal of Medicine, 363(5), 443–453. Gabbe, S. G., Landon, M. B., Warren-Boulton, E., & Fradkin, J. (2012). Promoting health after gestational diabetes: A National Diabetes Education Program call to action. Obstetrics and Gynecology, 119(1), 171–176. Gaffney, K. F., Kitsantas, P., & Cheema, J. (2012). Clinical practice guidelines for feeding behaviors and weight-for-age at 12 months: A secondary analysis of the infant feeding practices study II. Worldviews on Evidence- Based Nursing, 9(4), 234–242. Gaffney, K. F., Kitsantas, P., Brito, A., & Swamidoss, C. S. (2014). Postpartum depression, infant feeding practices, and infant weight gain at six months of age. Journal of Pediatric Health Care, 28(1), 43–50. Garofano, A., Czernichow, P., & Breant, B. (1998). Betacell mass and proliferation following late fetal and early postnatal malnutrition in the rat. Diabetologia, 41(9), 1114–1120. Gillis, B., Mobley, C., Stadler, D. D., Hartstein, J., Virus, A., Volpe, S. L., et al. (2009). Rationale, design and methods of the HEALTHY study nutrition interven- 231 tion component. International Journal of Obesity, 33(Suppl 4), S29–S36. Gillman, M. W., Oakey, H., Baghurst, P. A., Volkmer, R. E., Robinson, J. S., & Crowther, C. A. (2010). Effect of treatment of gestational diabetes mellitus on obesity in the next generation. Diabetes Care, 33(5), 964–968. Gluckman, P. D., Hanson, M. A., Cooper, C., & Thornburg, K. L. (2008). Effect of in utero and early-life conditions on adult health and disease. New England Journal of Medicine, 359(1), 61–73. Groop, L. C., & Tuomi, T. (1997). Non-insulin-dependent diabetes mellitus–a collision between thrifty genes and an affluent society. Annals of Medicine, 29, 37–53. Grote, V., Vik, T., von Kries, R., Luque, V., Socha, J., Verducci, E., et al. (2010). Maternal postnatal depression and child growth: A European cohort study. BMC Pediatrics, 10, 14. doi:10.1186/1471-2431-10-14. Hadar, E., & Hod, M. (2010). Establishing consensus criteria for the diagnosis of diabetes in pregnancy following the HAPO study. Annals of the New York Academy of Sciences, 1205, 88–93. Haines, L., Wan, K. C., Lynn, R., Barrett, T. G., & Shield, J. P. (2007). Rising incidence of type 2 diabetes in children in the U.K. Diabetes Care, 30(5), 1097–1101. Haines, J., McDonald, J., O’Brien, A., Sherry, B., Bottino, C. J., Schmidt, M. E., et al. (2013). Healthy habits, happy homes: Randomized trial to improve household routines for obesity prevention among preschool aged children. JAMA Pediatrics, 167(11), 1072–1079. Hales, C. N., & Barker, D. J. (2001). The thrifty phenotype hypothesis. British Medical Bulletin, 60, 5–20. Halfon, N., & Forrest, C. B. (2017). The emerging theoretical framework of life course health development. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health-development science. Cham: Springer. Harvey-Berino, J., & Rourke, J. (2003). Obesity prevention in preschool native-American children: A pilot study using home visiting. Obesity Research, 11(5), 606–611. Herbst, A., Kapellen, T., Schober, E., Graf, C., Meissner, T., & Holl, R. (2014). Impact of regular physical activity on blood glucose control and cardiovascular risk factors in adolescents with type 2 diabetes mellitus – a multicenter study of 578 patients from 225 centres. Pediatric Diabetes, 16(3), 204–210. Hesketh, K. D., & Campbell, K. J. (2010). Interventions to prevent obesity in 0-5 year olds: An updated systematic review of the literature. Obesity, 18(Supple 1), S27–S35. Hirst, K., Baranowski, T., DeBar, L., Foster, G. D., Kaufman, F., Kennel, P., et al. (2009). HEALTHY study rationale, design and methods: Moderating risk of type 2 diabetes in multi-ethnic middle school students. International Journal of Obesity, 33(Suppl 4), S4–20. Hiscock, H., Scalzo, K., Canterford, L., & Wake, M. (2011). Sleep duration and body mass index in 0-7year olds. Archives of Disease in Childhood, 96(8), 735–739. 232 Hoet, J. J., Dahri, S., Snoeck, A., Reusens-Billen, B., & Remacle, C. (1992). Importance of diets and their effect on fetal development: Function and structure of the endocrine pancreas following protein deficiency during intrauterine life. Bulletin et Mémoires de l’Académie Royale de Médecine de Belgique, 147 (3–5), 174–181. discussion 81–83. Imperatore, G., Boyle, J. P., Thompson, T. J., Case, D., Dabelea, D., Hamman, R. F., et al. (2012). Projections of type 1 and type 2 diabetes burden in the U.S. population aged <20 years through 2050: Dynamic modeling of incidence, mortality, and population growth. Diabetes Care, 35(12), 2515–2520. ISPAD. (2011). Global IDF/ISPAD guideline for childhood and Pediatric diabetes. Brussels, Belgium: International Diabetes Foundation. James, D. C., & Lessen, R. (2009). Position of the American dietetic association: Promoting and supporting breastfeeding. Journal of the American Dietetic Association, 109(11), 1926–1942. Jeffery, A. N., Metcalf, B. S., Hosking, J., Streeter, A. J., Voss, L. D., & Wilkin, T. J. (2012). Age before stage: Insulin resistance rises before the onset of puberty: A 9-year longitudinal study (EarlyBird 26). Diabetes Care, 35(3), 536–541. Johnson, C. L., & Versalovic, J. (2012). The human microbiome and its potential importance to pediatrics. Pediatrics, 129(5), 950–960. Kanaka-Gantenbein, C. (2010). Fetal origins of adult diabetes. Annals of the New York Academy of Sciences, 1205, 99–105. Kapoor, A., Dunn, E., Kostaki, A., Andrews, M. H., & Matthews, S. G. (2006). Fetal programming of hypothalamo-pituitary-adrenal function: Prenatal stress and glucocorticoids. The Journal of Physiology, 572(Pt 2), 31–44. Katoh, S., Lehtovirta, M., Kaprio, J., Harjutsalo, V., Koskenvuo, M., Eriksson, J., et al. (2005). Genetic and environmental effects on fasting and postchallenge plasma glucose and serum insulin values in Finnish twins. Journal of Clinical Endocrinology and Metabolism, 90(5), 2642–2647. Kaufman, F. R., Hirst, K., Linder, B., Baranowski, T., Cooper, D. M., Foster, G. D., et al. (2009). Risk factors for type 2 diabetes in a sixth-grade multiracial cohort: The HEALTHY study. Diabetes Care, 32(5), 953–955. Kempf, K., Rathmann, W., & Herder, C. (2008). Impaired glucose regulation and type 2 diabetes in children and adolescents. Diabetes/Metabolism Research and Reviews, 24, 427–437. Kim, S. Y., England, J. L., Sharma, J. A., & Njoroge, T. (2011). Gestational diabetes mellitus and risk of childhood overweight and obesity in offspring: A systematic review. Experimental Diabetes Research, 2011, 541308. Kitzmann, K. M., Dalton, W. T., 3rd, Stanley, C. M., Beech, B. M., Reeves, T. P., et al. (2010). Lifestyle interventions for youth who are overweight: A metaanalytic review. Health Psychology, 29(1), 91–101. P. Salsberry et al. Klingenberg, L., Christensen, L. B., Hjorth, M. F., Zangenberg, S., Chaput, J. P., Sjodin, A., et al. (2013). No relation between sleep duration and adiposity indicators in 9-36 months old children: The SKOT cohort. Pediatric Obesity, 8(1), e14–e18. Knowlden, A. P., & Sharma, M. (2012). Systematic review of family and home-based interventions targeting paediatric overweight and obesity. Obesity Reviews, 13(6), 499–508. Koren, O., Goodrich, J. K., Cullender, T. C., Spor, A., Laitinen, K., Backhed, H. K., et al. (2012). Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell, 150(3), 470–480. Kramer, M. S., Matush, L., Vanilovich, I., Platt, R. W., Bogdanovich, N., Sevkovskaya, Z., et al. (2009). A randomized breast-feeding promotion intervention did not reduce child obesity in Belarus. The Journal of Nutrition, 139(2), 417S–421S. Lawrence, J., Mayer-Davis, E., Ryeynolds, K., Beyer, J., Pettitt, D., D’Agostino, R. B., et al. (2009a). Diabetes in Hispanic American youths. Diabetes Care, 32(Supplement 2), S123–S132. Lawrence, J. M., Mayer-Davis, E. J., Reynolds, K., Beyer, J., Pettitt, D. J., D’Agostino, R. B., et al. (2009b). Diabetes in Hispanic American youth: Prevalence, incidence, demographics, and clinical characteristics: The SEARCH for diabetes in youth study. Diabetes Care, 32(Suppl 2), S123–S132. Lee, Y. (2013). Diabetes care for emerging adults: Transition from pediatric to adult diabetes care systems. Annals of Pediatric Endocrinology & Metabolism, 18, 106–110. Ley, S. H., Hanley, A. J., Sermer, M., Zinman, B., & O’Connor, D. L. (2012). Associations of prenatal metabolic abnormalities with insulin and adiponectin concentrations in human milk. The American Journal of Clinical Nutrition, 95(4), 867–874. Li, R., Bilik, D., Brown, M. B., Zhang, P., Ettner, S. L., Ackerman, R. T., et al. (2013). Medical costs associated with type 2 diabetes complications and comorbidities. The American Journal of Managed Care, 19(5), 421–430. Liu, L. L., Yi, J. P., Beyer, J., Mayer-Davis, E. J., Dolan, L. M., Dabelea, D. M., et al. (2009). Type 1 and type 2 diabetes in Asian and Pacific Islander U.S. youth: The SEARCH for diabetes in youth study. Diabetes Care, 32(Suppl 2), S133–S140. Liu, J., Yu, P., Qian, W., Li, Y., Zhao, J., Huan, F., et al. (2013). Perinatal bisphenol a exposure and adult glucose homeostasis: Identifying critical windows of exposure. PloS One, 8(5), e64143. Luepker, R. V., Perry, C. L., McKinlay, S. M., Nader, P. R., Parcel, G. S., Stone, E. J., et al. (1996). Outcomes of a field trial to improve children’s dietary patterns and physical activity. The child and adolescent trial for cardiovascular health. CATCH collaborative group. The Journal of the American Medical Association, 275(10), 768–776. Martin, R. M., Patel, R., Kramer, M. S., Vilchuck, K., Bogdanovich, N., Sergeichick, N., et al. (2014). Effects Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… of promoting longer-term and exclusive breastfeeding on cardiometabolic risk factors at age 11.5 years: A cluster-randomized, controlled trial. Circulation, 129(3), 321–329. Martin-Gronert, M. S., & Ozanne, S. E. (2012). Mechanisms underlying the developmental origins of disease. Reviews in Endocrine & Metabolic Disorders, 13(2), 85–92. Maull, E. A., Ahsan, H., Edwards, J., Longnecker, M. P., Navas-Acien, A., Pi, J., et al. (2012). Evaluation of the association between arsenic and diabetes: A National Toxicology Program workshop review. Environmental Health Perspectives, 120(12), 1658–1670. May, A. L., Kuklina, E. V., & Yoon, P. W. (2012). Prevalence of cardiovascular disease risk factors among US adolescents, 1999–2008. Pediatrics, 129(6), 1035–1041. Mayer-Davis, E. J., Rifas-Shiman, S. L., Zhou, L., Hu, F. B., Colditz, G. A., & Gillman, M. W. (2006). Breastfeeding and risk for childhood obesity: Does maternal diabetes or obesity status matter? Diabetes Care, 29(10), 2231–2237. Mayer-Davis, E. J., Dabelea, D., Lamichhane, A. P., D’Agostino, R. B., Jr., Liese, A. D., et al. (2008). Breast-feeding and type 2 diabetes in the youth of three ethnic groups: The SEARCh for diabetes in youth case-control study. Diabetes Care, 31(3), 470–475. Mayer-Davis, E. J., Beyer, J., Bell, R. A., Dabelea, D., D’Agostino, R., Jr., Imperatorek, G., et al. (2009). Diabetes in African American youth: Prevalence, incidence, and clinical characteristics: The SEARCH for diabetes in youth study. Diabetes Care, 32(Supplement 2), S112–S122. McEwen, B. S. (2008). Understanding the potency of stressful early life experiences on brain and body function. Metabolism Clinical and Experimental, 57(Suppl 2), S11–S15. McLearn, K. T., Minkovitz, C. S., Strobino, D. M., Marks, E., & Hou, W. (2006). Maternal depressive symptoms at 2 to 4 months post partum and early parenting practices. Archives of Pediatrics & Adolescent Medicine, 160(3), 279–284. McMillen, I. C., & Robinson, J. S. (2005). Developmental origins of the metabolic syndrome: Prediction, plasticity, and programming. Physiological Reviews, 85(2), 571–633. McMurray, R. G., Bassin, S., Jago, R., Bruecker, S., Moe, E. L., Murray, T., et al. (2009). Rationale, design and methods of the HEALTHY study physical education intervention component. International Journal of Obesity, 33(Suppl 4), S37–S43. Meaney, M. J., Szyf, M., & Seckl, J. R. (2007). Epigenetic mechanisms of perinatal programming of hypothalamic-pituitary-adrenal function and health. Trends in Molecular Medicine, 13(7), 269–277. Metzger, B. E., Lowe, L. P., Dyer, A. R., Trimble, E. R., Chaovarindr, U., Coustan, D. R., et al. (2008). Hyperglycemia and adverse pregnancy outcomes. 233 The New England Journal of Medicine, 358(19), 1991–2002. Metzger, B. E., Gabbe, S. G., Persson, B., Buchanan, T. A., Catalano, P. A., Damm, P., et al. (2010). International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care, 33(3), 676–682. Morandi, A., Maschio, M., Marigliano, M., Miraglia Del Giudice, E., Moro, B., Peverelli, P., et al. (2014). Screening for impaired glucose tolerance in obese children and adolescents: A validation and implementation study. Pediatric Obesity, 9(1), 17–25. Morgan, A. R. (2012). Determining genetic risk factors for pediatric type 2 diabetes. Current Diabetes Reports, 12(1), 88–92. Morris, D. H., Khunti, K., Achana, F., Srinivasan, B., Gray, L. J., Davies, M. J., et al. (2013). Progression rates from HbA(1c) 6.0-6.4% and other prediabetes definitions to type 2 diabetes: A meta-analysis. Diabetologia, 56(7), 1489–1493. Morrison, K. M., Xu, L., Tarnopolsky, M., Yusuf, Z., Atkinson, S. A., & Yusuf, S. (2012). Screening for dysglycemia in overweight youth presenting for weight management. Diabetes Care, 35(4), 711–716. Moses, R. G. (2010). New consensus criteria for GDM: Problem solved or a pandora’s box? Diabetes Care, 33(3), 690–691. Navas-Acien, A., Silbergeld, E. K., Pastor-Barriuso, R., & Guallar, E. (2008). Arsenic exposure and prevalence of type 2 diabetes in US adults. JAMA, 300(7), 814–822. Neville, M. C., Anderson, S. M., McManaman, J. L., Badger, T. M., Bunik, M., Contractor, N., et al. (2012). Lactation and neonatal nutrition: Defining and refining the critical questions. Journal of Mammary Gland Biology and Neoplasia, 17(2), 167–188. doi:10.1007/ s10911-012-9261-5. Ong, K. K. (2010). Early determinants of obesity. Endocrine Development, 19, 53–61. Pervanidou, P., & Chrousos, G. P. (2011). Stress and obesity/metabolic syndrome in childhood and adolescence. International Journal of Pediatric Obesity, 6(Supplement 1), 21–28. Pervanidou, P., & Chrousos, G. P. (2012). Metabolic consequences of stress during childhood and adolescence. Metabolism, 61(5), 611–619. Peters, A., & Laffel, L. (2011). Diabetes care for emerging adults: Recommendations for transition from pediatric to adult diabetes care systems: A position statement of the American Diabetes Association. Diabetes Care, 34(11), 2477–2485. Plagemann, A., Harder, T., Franke, K., & Kohlhoff, R. (2002). Long-term impact of neonatal breast-feeding on body weight and glucose tolerance in children of diabetic mothers. Diabetes Care, 25(1), 16–22. Prince, A. L., Antony, K. M., Ma, J., & Aagaard, K. M. (2014). The microbiome and development: A mother’s perspective. Seminars in Reproductive Medicine, 32(1), 14–22. 234 Raman, A., Ritchie, L. D., Lustig, R. H., Fitch, M. D., Hudes, M. L., & Fleming, S. E. (2010). Insulin resistance is improved in overweight African American boys but not in girls following a one-year multidisciplinary community intervention program. Journal of Pediatric Endocrinology & Metabolism, 23(1–2), 109–120. Rhodes, E. T., Prosser, L. A., Hoerger, T. J., Lieu, T., Ludwig, D. S., & Laffel, L. M. (2012). Estimated morbidity and mortality in adolescents and young adults diagnosed with type 2 diabetes mellitus. Diabetic Medicine, 29(4), 453–463. Ritchie, L. D., Sharma, S., Ikeda, J. P., Mitchell, R. A., Raman, A., Green, B. S., et al. (2010). Taking action together: A YMCA-based protocol to prevent type-2 diabetes in high-BMI inner-city African American children. Trials, 11, 60. Robinson, S., Ntani, G., Simmonds, S., Syddall, H., Dennison, E., Sayer, A. A., et al. (2013). Type of milk feeding in infancy and health behaviours in adult life: Findings from the Hertfordshire cohort study. The British Journal of Nutrition, 109(6), 1114–1122. Rolland-Cachera, M. F. (2011). Childhood obesity: Current definitions and recommendations for their use. International Journal of Pediatric Obesity, 6(5– 6), 325–331. Rosenbaum, M., Nonas, C., Weil, R., Horlick, M., Fennoy, I., Vargas, I., et al. (2007). School-based intervention acutely improves insulin sensitivity and decreases inflammatory markers and body fatness in junior high school students. The Journal of Clinical Endocrinology and Metabolism, 92(2), 504–508. Rosenbloom, A. L., Joe, J. R., Young, R. S., & Winter, W. E. (1999). Emerging epidemic of type 2 diabetes in youth. Diabetes Care, 22(2), 345–354. Rosenbloom, A. L., Silverstein, J. H., Amemiya, S., Zeitler, P., & Klingensmith, G. J. (2008). ISPAD clinical practice consensus guidelines 2006-2007. Type 2 diabetes mellitus in the child and adolescent. Pediatric Diabetes, 9(5), 512–526. Saelens, B. E., Grow, H. M., Stark, L. J., Seeley, R. J., & Roehrig, H. (2011). Efficacy of increasing physical activity to reduce children’s visceral fat: A pilot randomized controlled trial. International Journal of Pediatric Obesity, 6(2), 102–112. Salsberry, P. J., & Reagan, P. B. (2005). Dynamics of early childhood overweight. Pediatrics, 116(6), 1329–1338. Samuelsson, A. M., Matthews, P. A., Argenton, M., Christie, M. R., McConnell, J. M., et al. (2008). Dietinduced obesity in female mice leads to offspring hyperphagia, adiposity, hypertension, and insulin resistance: A novel murine model of developmental programming. Hypertension, 51(2), 383–392. Savoye, M., Shaw, M., Dziura, J., Tamborlane, W. V., Rose, P., Guandalini, C., et al. (2007). Effects of a weight management program on body composition and metabolic parameters in overweight children: A randomized controlled trial. JAMA, 297(24), 2697–2704. P. Salsberry et al. Savoye, M., Nowicka, P., Shaw, M., Yu, S., Dziura, J., Chavent, G., et al. (2011). Long-term results of an obesity program in an ethnically diverse pediatric population. Pediatrics, 127(3), 402–410. Savoye, M., Caprio, S., Dziura, J., Camp, A., Germain, G., Summers, C., et al. (2014). Reversal of early abnormalities in glucose metabolism in obese youth: Results of an intensive lifestyle randomized controlled trial. Diabetes Care, 37(2), 317–324. Schore, A. N. (2005). Back to basics: Attachment, affect regulation, and the developing right brain: Linking developmental neuroscience to pediatrics. Pediatrics in Review, 26(6), 204–217. Sebert, S., Sharkey, D., Budge, H., & Symonds, M. E. (2011). The early programming of metabolic health: Is epigenetic setting the missing link? The American Journal of Clinical Nutrition, 94(6 Supplement), 1953S–1958S. Seki, Y., Williams, L., Vuguin, P. M., & Charron, M. J. (2012). Minireview: Epigenetic programming of diabetes and obesity: Animal models. Endocrinology, 153(3), 1031–1038. Shaw, J. E., Socree, R. A., & Zimmet, P. Z. (2010). Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Research and Clinical Practice, 87, 4–14. Shonkoff, J. P., & Garner, A. S. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129(1), e232–e246. Silverman, B. L., Rizzo, T. A., Cho, N. H., & Metzger, B. E. (1998). Long-term effects of the intrauterine environment. The Northwestern University diabetes in pregnancy Center. Diabetes Care, 21(Suppl 2), B142–B149. Simeoni, U., & Barker, D. J. (2009). Offspring of diabetic pregnancy: Long-term outcomes. Seminars in Fetal & Neonatal Medicine, 14(2), 119–124. Sinclair, K. D., Lea, R. G., Rees, W. D., & Young, L. E. (2007). The developmental origins of health and disease: Current theories and epigenetic mechanisms. Society of Reproduction and Fertility Supplement, 64, 425–443. Sinha, R., Fisch, G., Teague, B., Tamborlane, W. V., Banyas, B., Allen, K., et al. (2002). Prevalence of impaired glucose tolerance among children and adolescents with marked obesity. The New England Journal of Medicine, 346(11), 802–810. Skouteris, H., McCabe, M., Swinburn, B., Newgreen, V., Sacher, P., & Chadwick, P. (2011). Parental influence and obesity prevention in pre-schoolers: A systematic review of interventions. Obesity Reviews, 12(5), 315–328. Skouteris, H., McCabe, M., Ricciardelli, L., Milgrom, J., Baur, L., Aksan, N., et al. (2012). Parent–child interactions and obesity prevention: A systematic review of the literature. Early Child Development and Care, 182(2), 153–174. Smith, B. T., Lynch, J. W., Fox, C. S., Harper, S., Abrahamowicz, M., Almeida, N. D., et al. (2011). Life-course socioeconomic position and type 2 dia- Pediatric Type 2 Diabetes: Prevention and Treatment Through a Life Course Health Development… betes mellitus: The Framingham offspring study. American Journal of Epidemiology, 173(4), 438–447. Snieder, H., Boomsma, D. I., van Doornen, L. J., & Neale, M. C. (1999). Bivariate genetic analysis of fasting insulin and glucose levels. Genetic Epidemiology, 16(4), 426–446. Stark, L. J., Clifford, L. M., Towner, E. K., Filigno, S. S., Zion, C., Bolling, C., et al. (2014). A pilot randomized controlled trial of a behavioral familybased intervention with and without home visits to decrease obesity in preschoolers. Journal of Pediatric Psychology, 39(9), 1001–1012. doi:10.1093/jpepsy/ jsu059. Szajewska, H. (2013). Microbiota modulation: Can probiotics prevent/treat disease in pediatrics? Nestle Nutrition Institute Workshop Series, 77, 99–110. Tamayo, T., Christian, H., & Rathmann, W. (2010). Impact of early psychosocial factors (childhood socioeconomic factors and adversities) on future risk of type 2 diabetes, metabolic disturbances and obesity: A systematic review. BMC Public Health, 10, 525. doi:10.1186/1471-2458-10-525. Tamayo, T., Jacobs, D. R., Jr., Strassburger, K., Giani, G., Seeman, T. E., Matthews, K., et al. (2012). Race- and sex-specific associations of parental education with insulin resistance in middle-aged participants: The CARDIA study. European Journal of Epidemiology, 27(5), 349–355. Tarry-Adkins, J. L., & Ozanne, S. E. (2011). Mechanisms of early life programming: Current knowledge and future directions. The American Journal of Clinical Nutrition, 94(6 Supplement), 1765S–1771S. Taveras, E. M., Scanlon, K. S., Birch, L., Rifas-Shiman, S. L., Rich-Edwards, J. W., & Gillman, M. W. (2004). Association of breastfeeding with maternal control of infant feeding at age 1 year. Pediatrics, 114(5), e577–e583. Taveras, E. M., Rifas-Shiman, S. L., Oken, E., Gunderson, E. P., & Gillman, M. W. (2008). Short sleep duration in infancy and risk of childhood overweight. Archives of Pediatrics & Adolescent Medicine, 162(4), 305–311. Taveras, E. M., Gillman, M. W., Pena, M. M., Redline, S., & Rifas-Shiman, S. L. (2014). Chronic sleep curtailment and adiposity. Pediatrics, 133(6), 1013–1022. Thayer, K. A., Heindel, J. J., Bucher, J. R., & Gallo, M. A. (2012). Role of environmental chemicals in diabetes and obesity: A National Toxicology Program workshop review. Environmental Health Perspectives, 120(6), 779–789. Thompson, A. L., & Bentley, M. E. (2013). The critical period of infant feeding for the development of early disparities in obesity. Social Science & Medicine, 97, 288–296. Tounian, P. (2011). Programming towards childhood obesity. Annals of Nutrition & Metabolism, 58(Suppl 2), 30–41. Trevino, R. P., Yin, Z., Hernandez, A., Hale, D. E., Garcia, O. A., & Mobley, C. (2004). Impact of the Bienestar school-based diabetes mellitus prevention program on fasting capillary glucose levels: A randomized 235 controlled trial. Archives of Pediatrics & Adolescent Medicine, 158(9), 911–917. Venditti, E. M., Elliot, D. L., Faith, M. S., Firrell, L. S., Giles, C. M., Goldberg, L., et al. (2009). Rationale, design and methods of the HEALTHY study behavior intervention component. International Journal of Obesity, 33(Suppl 4), S44–S51. Venkat Narayan, K. M., Boyle, J. P., Thompson, T. J., Sorensen, S. W., & Willimason, D. F. (2003). Lifetime risk for riabetes mellitus in the United States. JAMA, 290(14), 1884–1889. Versalovic, J. (2013). The human microbiome and probiotics: Implications for pediatrics. Annals of Nutrition & Metabolism, 63(Suppl 2), 42–52. Visscher, P. M., Hill, W. G., & Wray, N. R. (2008). Heritability in the genomics era–concepts and misconceptions. Nat Rev Genet, 9(4), 255–266. Wabitsch, M., Hauner, H., Hertrampf, M., Muche, R., Hay, B., Mayer, H., et al. (2004). Type II diabetes mellitus and impaired glucose regulation in Caucasian children and adolescents with obesity living in Germany. International Journal of Obesity and Related Metabolic Disorders, 28(2), 307–313. Waitzfelder, B., Pihoker, C., Klingensmith, G., Case, D., Anderson, A., Bell, R. A., et al. (2011). Adherence to guidelines for youths with diabetes mellitus. Pediatrics, 128(3), 531–538. Wang, L., Anderson, J. L., Dalton, W. T., Wu, T., Liu, X., & Zheng, S. (2013). Maternal depressive symptoms and the risk of overweight in their children. Maternal and Child Health Journal, 17(5), 940–948. Warner, M. J., & Ozanne, S. E. (2010). Mechanisms involved in the developmental programming of adulthood disease. The Biochemical Journal, 427(3), 333–347. Weiss, R., Taksali, S. E., Tamborlane, W. V., Burgert, T. S., Savoye, M., & Caprio, S. (2005). Predictors of changes in glucose tolerance status in obese youth. Diabetes Care, 28(4), 902–909. Wen, L. M., Baur, L. A., Simpson, J. M., Rissel, C., Wardle, K., & Flood, V. M. (2012). Effectiveness of home based early intervention on children’s BMI at age 2: Randomised controlled trial. BMJ, 344, e3732. doi:10.1136/bmj.e3732. West, N. A., Hamman, R. F., Mayer-Davis, E. J., D’Agostino, R. B., Jr., Marcovina, S. M., Liese, A. S., et al. (2009). Cardiovascular risk factors among youth with and without type 2 diabetes: Differences and possible mechanisms. Diabetes Care, 32(1), 175–180. Whitlock, E. P., O’Connor, E. A., Williams, S. B., Beil, T. L., & Lutz, K. W. (2010). Effectiveness of weight management interventions in children: A targeted systematic review for the US preventive service task Force. Pediatrics, 125(2), e396–e418. Whitmore, T. J., Trengove, N. J., Graham, D. F., & Hartmann, P. E. (2012). Analysis of insulin in human breast milk in mothers with type 1 and type 2 diabetes mellitus. International Journal of Endocrinology, 2012, 296368. doi:10.1155/2012/296368. 236 Zeitler, P. (2010). Approach to the obese adolescent with new-onset diabetes. The Journal of Clinical Endocrinology and Metabolism, 95(12), 5163–5170. Zeitler, P., Hirst, K., Pyle, L., Linder, B., Copeland, K., Arslanian, S., et al. (2012). A clinical trial to maintain P. Salsberry et al. glycemic control in youth with type 2 diabetes. New England Journal of Medicine, 366(24), 2247–2256. Zeitler, P., Tandon, N., Nadeau, K., Urakami, T., Barrett, T., & Maahs, D. (2014). Type 2 diabetes in the child and adolescent. Pediatric Diabetes, 20(Supplement), 20–26. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Life Course Health Development in Autism Spectrum Disorders Irene E. Drmic, Peter Szatmari, and Fred Volkmar 1 Introduction Health development results from dynamic personenvironment interactions that begin before conception and continue throughout the lifespan (Halfon et al. 2014; Russ et al. 2014). Life course health development (LCHD) incorporates theories and empirical evidence from the biological, physical, and social sciences to formulate a framework that explains how health develops over the life course. The principles of life course health development (see Halfon and Forrest 2017) describe health as an emergent set of capacities that develop continuously over the lifespan in complex, nonlinear processes that result from multilevel interactions of individuals and their environments. Furthermore, health is sensitive to the timing and social structuring of environmental exposures, as well as to timing and synchronization I.E. Drmic (*) Hospital for Sick Children, Toronto, ON, Canada e-mail: Irene.drmic@sickkids.ca P. Szatmari Centre for Addiction and Mental Health, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada e-mail: Peter.Szatmari@utoronto.ca F. Volkmar Child Study Center, Yale University School of Medicine, New Haven, CT, USA e-mail: Fred.Volkmar@yale.edu of molecular, physiological, behavioral, social, and cultural functions. Finally, evolution enables and constrains the range of adaptive plastic responses and health developmental pathways associated with these phenotypes. The life course health development conceptual framework can be applied to autism spectrum disorders (ASD) to help prioritize a research agenda and improve health development across the lifespan for individuals with ASD, their families, and communities. Novel approaches to understanding ASD are welcome given that ASD represents a major public health challenge. The estimated annual cost of caring for individuals with ASD in the USA is $137 billion, with a lifetime cost per individual estimated to be $2.4 million for those with co-occurring intellectual disability and $1.4 million for those without intellectual disability (Dawson and Bernier 2013). The following chapter is not an exhaustive review of the literature in ASD; rather, we highlight some issues and findings that are pertinent to an understanding of ASD using the lens of the life course health development principles. 1.1 What Is Autism Spectrum Disorder (ASD): The Clinical Phenotype Autism spectrum disorders are a group of neuropsychiatric conditions all of which share © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_11 237 238 difficulties in social engagement and social interaction as their major diagnostic feature. As a result of this basic problem, difficulties arise in learning, cognitive organization, attention, and adaptive skills. Autism, the prototypic disorder, was described by Kanner in 1943 as an “inborn disturbance” of social contact. Autism was first included in a new category of pervasive developmental disorders when first recognized officially in the third edition of Diagnostic and Statistical Manual of Mental Disorders (DSM;APA 1980). Autism is a condition of early onset and poses multiple problems in health development. Many parents are concerned about affected children’s development by age 1 with the vast majority of parents worried by age 2 (Chawarska et al. 2008). An increasing body of work has focused on autism as it first develops in infancy (Rogers 2009). In a minority of cases (about 20%), parents report some period of normal development followed by loss of skills; the clinical significance of regression continues to be debated (Parr et al. 2011; Shattuck et al. 2009). Problems in social interaction are a hallmark of autism and related conditions. Social difficulties are distinctive even considering any associated developmental delays. Similarly, communication problems are present and persistent even in the more cognitively able children who lack verbal and nonverbal social communication skills such as gestures, facial expression, and eye contact. Even when children do speak, they exhibit a number of difficulties including echolalia, pronoun use difficulties, abnormal prosody, and in the social use of language (Paul 2008). Kanner noted the range of unusual behaviors frequent in autism including motor mannerisms (stereotyped movement), difficulties with change, and insistence on routines. The apparent over-engagement with the nonsocial world arises in the general lack of social engagement. This suggests a potential route both as a mechanism for learning difficulties and for intervention. The original impression that autism was not associated with cognitive problems rested, in large part, on the unusual abilities of children in some areas; in some cases, these skills are highly circumscribed and may arise even in association with overall intellectual delay (Hermelin 2001). I.E. Drmic et al. Even for the most able individuals, scatter in abilities is often present with a relative deficit in verbal as opposed to perceptual abilities. It is also the case that there is frequently a gap between abilities in a formal testing situation and “realworld” or adaptive skills – the latter reflecting the significant difficulties individuals have in generalization and in tasks that involved fast-paced decision making, executive functioning, and social interaction (Robinson et al. 2009). In more classical autism, nonverbal skills tend to be much stronger than verbal abilities; the opposite profile may hold true for Asperger’s disorder although this has been debated (Klin et al. 1996; Meyer and Minshew 2002; Noterdaeme et al. 2010). As noted above in addition to classic autism of the type described by Kanner, a number of other conditions with some apparent similarity to autism have been recognized, although their validity apart from autism continues to be hotly debated. Asperger’s disorder was described (Asperger 1944) the year after Kanner’s report first appeared. Although the children (all boys) had major problems in social interaction, in contrast to Kanner’s (1943) cases, they demonstrated good language abilities and were more likely to exhibit motor difficulties, unusual and allconsuming esoteric special interests, and their problems seemed to come to clinical attention rather later (Woodbury-Smith and Volkmar 2009). Although included in DSM-IV and International Statistical Classification of Diseases and Related Health Problems (ICD)-10 as a distinctive disorder, Asperger’s disorder has been the focus of much debate and is not considered a diagnosable disorder in DSM-V. A subthreshold pervasive developmental disorder category was included in DSM since 1980 to encompass individuals whose difficulties appear to fall close to autism but do not achieve thresholds for diagnosis of that, or related, conditions. Interestingly this concept also was described in the 1940s although not with great precision. It has as always been recognized to significantly outnumber more classical autism (Fombonne 2005a, b) by probably four or five times although research in this area has, paradoxically, been limited. In recent years, the tendency to equate pervasive developmental disorder not Life Course Health Development in Autism Spectrum Disorders otherwise specified with more classic autism has led to a broader autism spectrum concept although it must be emphasized that the DSM-5 autism spectrum disorder concept would likely exclude many cases that in the past had been said to have pervasive developmental disorder not otherwise specified (Volkmar and McPartland 2014). 1.2 Epidemiology It is clear that ASD has a strong genetic component and is likely related to the interaction of multiple genes (Devlin and Scherer 2012); ASD invariably originates in the first years of life. Poverty and lack of access to adequate resources and intervention clearly impact identification, service provision, and likely outcome (Liptak et al. 2008; Thomas et al. 2007), but this area of work remains one that is relatively little studied. Various factors complicate the study of prevalence rates in ASD. There has been a concern that rates have risen over time although in his review of the topic Fombonne (2005a, b) suggests that a median rate of strictly defined autism would be about 1 per 800 to 1000 people with the much broader pervasive developmental disorder category being much more common. Several recent studies have reported higher rates (e.g., Brugha et al. 2011; Elsabbagh et al. 2012), but studies of adults in community settings report similar rates to those estimated by Fombonne and others (Brugha et al. 2011; Elsabbagh et al. 2012; Rutter 1973) and various factors which might account for apparent increases in rates have been identified (King and Bearman 2009; Rutter 1982) with potential overestimation due to methodology (i.e., case-confirmation strategies) and small sample sizes resulting in high estimates (Levy et al. 2009). Such factors include increased public awareness and increased recognition of more cognitively able individuals, applying the diagnosis to individuals with specific syndromes such as Down syndrome and widening of diagnostic criteria, sundry methodological problems, and “diagnostic substitution” – the latter referring to an issue, particularly in the USA, where an edu- 239 cational label of autism may be used to obtain services in preference to other labels such as mental retardation (Fombonne 2005a, b). Most studies have reported significantly higher rates of ASD in boys than in girls although girls with more strictly defined autism are also more likely to have intellectual disability (Fombonne 2005a, b). The three- to fivefold increase in boys is similar to other neurodevelopmental disorders such as language disorders and attention deficit hyperactivity disorder (ADHD). It is possible that milder expression in some girls is underdiagnosed (Landa et al. 2007). There has been speculation that greater severity sometimes observed in girls is a function of a greater genetic risk for the condition (Fombonne 2005a, b) and that observed sex differences might relate to fundamental cognitive mechanisms (Baron-Cohen 2011). In reality, sex differences have been remarkably little studied and our understanding of this phenomenon remains very limited. Potential social class correlates of autism first noted by Kanner (1943) in his original sample have not generally held up in subsequent research, presumably reflecting a referral bias in Kanner’s original cases (Fombonne 2005a, b; Wing 1980). However, disparities in diagnosis rates are observed with children from backgrounds of poverty or minority status often going undiagnosed (LaFramboise et al. 2009). Although diagnostic presentation is rather similar around the world, cultural factors may impact both diagnosis and treatment (Brown and Rogers 2003). 1.3 Classification and Diagnosis Several approaches were undertaken in the late 1970s to provide more operational definitions of autism. Rutter’s approach (1978) emphasized difficulties in social interaction, communication, and unusual behaviors of early onset that were not solely attributable to mental retardation. Both family genetic and longitudinal studies provided important information on the stability and validity of the diagnosis apart from other conditions and led to the inclusion of autism in DSM-III (APA 1980). The DSM-III approach was modified I.E. Drmic et al. 240 in 1987 when DSM-III-R appeared and yet again with DSM-IV which appeared in 1994. The DSM-IV definition was based on results of a large international field trial and became consistent with the tenth revision of the ICD leading to two decades of diagnostic stability (Volkmar and McPartland 2014). For DSM-5, a number of changes were made and have proven controversial. The adoption of the term autism spectrum disorder seems well justified; however, the criteria proposed appear to really be more relevant to classic “Kanner’s autism,” and many individuals, particularly the more cognitively able, would likely lose their diagnosis (Volkmar and McPartland 2014). As a result, cases with “wellestablished” diagnosis of Asperger’s or pervasive developmental disorder not otherwise specified (PDD-NOS; in DSM-IV terms) could retain an ASD diagnosis – thus effectively creating two diagnostic systems in current use; the advent of ICD-11 and the Research Domain Criteria (RDoC) approach suggest that for some time there may be considerable controversy about definitional issues (see Baker et al. 2012; Brand et al. 2012; Mattilla et al. 2011; McGrew et al. 2012; Watts 2012; Huerta et al. 2012). 2 Pathogenesis 2.1 Hereditability ASD is now considered a disorder caused at least in part by genetic factors (Szatmari et al. 1998). The evidence for this comes from studies showing that ASD runs within families, from twin studies that show that this familial aggregation is due to genetic and possibly to shared environmental risk factors (Sullivan et al. 2012) and to the recent identification of several different networks of genes responsible for synaptic and axonal development and neuronal migration (Malhotra and Sebat 2012). Early family studies looked at the recurrence risk to a sibling if one child in the family had ASD. Those sibling risks were around 2% which were considered to be very much higher than the general population rate estimated at that time to be 4 per 10,000 (Szatmari et al. 1998). With better study designs and with a better appreciation of the breadth of the ASD phenotype, these family studies now suggest that the risk to sibling is closer to 9% (Ritvo et al. 1989). This needs to be seen in the context thought of more recent prevalence estimates of ASD of around 1%. Thus, it seems the sibling recurrence risk is roughly ten times the population base rate (Constantino et al. 2010). The baby sibs paradigm provides a more valid estimate of sibling recurrence risk. In these studies, the infant sibling of an older child with ASD is followed from birth to 3 years of age. In this context, the emergence of autistic signs and symptoms can be carefully documented prospectively, and the presence or absence of an ASD diagnosis is determined by 3 years of age. These studies have shown that signs and symptoms of the ASD phenotype begin to emerge around 6–12 months of age, and upward of 20% of siblings will have a diagnosis of ASD at 3 years of age (Chawarska et al. 2008; Zwaigenbaum et al. 2012). Interestingly, despite the fact that these studies have demonstrated that ASD can be diagnosed as early as 3 years and that best practice in ASD, such as the 2007 American Academy of Pediatrics guideline that all children should be screened for ASD at 18 and 24 months, the average age of diagnosis in the USA is still close to 5 years and higher in those with milder forms of ASD (CDC 2014). Twin studies are used to determine the mechanism of familial aggregation. Earlier studies suggested very sharp discrepancies between monozygous and dizygous concordance rates suggesting not only that the mechanism of heritability was genetic but that there was little room for environmental risk factors in playing an important role in causation. The twin study with the largest sample size and the one carried out with the most methodological rigor was recently published (Hallmayer et al. 2011). In that study, monozygous concordance rates were closer to 60%, and dizygous rates were 20% rate seen in the recent infant sibling studies. While the Hallmayer et al. twin study also provides support for the importance of genetic factors in Life Course Health Development in Autism Spectrum Disorders the etiology of the disorder, the higher than expected dizygous concordance rate also suggests a significant role for shared environmental risk factors. 2.2 Genetic Contribution Hundreds of different genes have been described in ASD that have been disrupted by either deletions or duplications of copy number. These genes all appear to be related to different aspects of neuronal development (largely synaptic functioning) or neuronal migration (Li et al. 2012). This is an important finding as it steers mechanisms of etiology away from neurotransmitters and monoamines, a theory which had dominated the field for several decades (McDougle et al. 2005). With the discovery of copy number variants as important structural genomic risk factors for a variety of human diseases has come the realization that these variants might play an important role in the etiology of ASD (Marshall et al. 2008). Copy number variants represent deletions or duplications covering more than 1000 kbs of DNA. In general, copy number variants can be common or rare, and they can be either de novo or inherited from parents. The first copy number variant identified in ASD in a genome-wide study was a rare deletion in the neurexin gene (Szatmari et al. 2007), and this has been followed up with many reports of other copy number variants affecting important brain-expressed genes (for a review see Malhotra and Sebat 2012). These copy number variants tend to be extremely rare, found in less than 1% of the ASD samples, and tend to rise de novo. This would explain the frequent lack of family history in ASD families and would provide a mechanism whereby the prevalence of the disorder remains stable in the context of a condition associated with reduced fertility (Power et al. 2012). Common genetic variants (either polymorphisms or copy number variants) have not been reliably identified in ASD, although such variants likely do exist and will require very large sample sizes for their identification (Devlin et al. 2011). If they do exist, their effect size must 241 be very small or else they operate as modifying factors as opposed to susceptibility genes. Currently, copy number variants explain less than 10% of cases in ASD, and the population attributable risk is less than 5% (Pinto et al. 2010). The etiology of the vast majority of cases of ASD remains to be explained. The next generation of genetic studies will focus on much smaller copy number variants or DNA sequence variants identified either in exomes or in whole genome sequencing. 2.3 Nongenetic Contributions The search for environmental risk factors has been underway for many decades. There seems reasonably good evidence that certain maternal anticonvulsants such as carbamazepine, valproic acid, and perhaps other anticonvulsants increase risk for ASD (Palac and Meador 2011). Other putative environmental risk factors that may have been more common in earlier decades such as congenital rubella have now virtually disappeared. A complication in all this work is disentangling the sometimes complex issues of severe mental handicap and “autistic-like” behaviors, particularly since stereotyped mannerisms become much more common with severe intellectual deficiency, but characteristic socialcommunicative features may be either difficult to judge or not clearly present (Volkmar et al. 2005). Pregnancy and birth complications have been studied extensively, and other than low birth weight or being small for gestational age, it is difficult to identify a risk factor (see Gardener et al. 2009 for a recent meta-analysis). It should be noted, however, that low birth weight may in fact represent a consequence of genetic vulnerability rather than an environmental risk factor interacting with genetic susceptibility (Rutter 2005). Other potential environmental markers signaled by inflammatory indices are of interest but remain highly speculative at this point (Muller et al. 2015). Perhaps the most convincing evidence for an environmental risk factor is that of advanced parental age (Shelton et al. 2010). Since paternal and maternal age is so highly correlated, it is I.E. Drmic et al. 242 difficult to determine if both are risk factors or if only one. This is important as the mechanism by which the risk factor might operate will be quite different based on the maternal/paternal risk factor (Veltman and Brunner 2012). De novo copy number variants are more common with advanced paternal age, while chromosomal abnormalities (like trisomies) are more common with advanced maternal age. 2.4 Epigenetic Contributions Epigenetic studies of non-syndromic ASD are in their infancy but do hold promise once technological and methodical issues associated with this area of study are resolved. Genes that regulate DNA methylation have been implicated in the etiology of ASD, and single gene disorders associated with ASD have implicated epigenetic mechanisms (Loke et al. 2015). It is anticipated that this will be an active field in the future and will yield novel insights in how personenvironment interactions lead to the development of ASD. 2.5 Physical Biomarkers Magnetic resonance imaging and diffusion tensor imaging studies have provided some insight into brain structure, circuitry, and connectivity in ASD. Courchesne and colleagues followed toddlers from 12 months to 48 months and found an abnormal brain growth trajectory in autism (Schumann et al. 2010). Toddlers, who were ultimately diagnosed with ASD, showed enlargement of cerebral gray and white matter, which was most pronounced in the frontal, temporal, and cingulate cortices, and some gray matter regions showed an abnormal growth rate. In addition, in those infants who later developed ASD, there was a difference in the development of white matter tracks between 6 and 12 months of age (i.e., increases in fractional anisotropy), with a slowing in development by 24 months (Wolff et al. 2012). Thus, abnormal neurodevelopmental connectivity early in life could potentially serve as an important biomarker for ASD. 2.6 Genotype-Phenotype Contributions In addition, genotype-phenotype correlations have been virtually impossible to establish, and the fact that the genetic variants identified in ASD also appear to be playing a role in intellectual disability, epilepsy, schizophrenia, and ADHD has emphasized the nonspecificity of these susceptibility risk factors (State 2010). These genetic findings in ASD have influenced healthcare recommendation and practice, such that microarray testing is part of the standard of care for diagnosis of ASD (McGrew et al. 2012). It is likely that in the future, whole exome sequencing will also become standard clinical practice. However, to date, no studies have directly assessed the impact of genetic testing on health outcomes such as earlier age of diagnosis or reduced use of other diagnostic tools such as MRI (Sun et al. 2015). In the future, we anticipate that there will be an increased application of genetics and neuroimaging to the early identification of ASD (Dawson and Bernier 2013). It is also likely that the discovery of genetic variants that impact the development of the synapse will lead to clinical trials of new (or “repurposed”) drugs targeting those mechanisms. Such trials are already underway in fragile X and tuberous sclerosis but so far with disappointing results (Geschwind and State 2015). 3 Outcome Studies 3.1 An Overview of Developmental Course Before reviewing approaches to assessing outcome, an overview of what is known, and what is not known, about the course of autism spectrum disorders may be helpful. It must be emphasized that outcome is usually very specifically defined as adult independence and that little information is available on health-related outcomes and vanishingly little relates to older individuals with ASD. Variations in life course are, in part, one of the rationales used to support previous diagnostic distinctions between subtypes. In more “classical” Life Course Health Development in Autism Spectrum Disorders autism, it is clear that diagnosis in infancy is of great interest but also somewhat problematic until about age 3 when reasonable stability in clinical manifestations occurs. Before that time, the issue usually is between change from (or to) a more classic picture to a more “subthreshold” condition (an issue that may be mitigated by the broad approach adopted in DSM-5). Often the difficulty, using the DSM-IV, was that social and communication problems are present but the “restricted interests” criteria were not fully met (even though some likely precursor behaviors are present). Rarely a child moves off the autism spectrum entirely. There is good evidence (NRC 2001) that treatments are most effective when started early in life; although for reasons not well understood, some children do not improve as dramatically as others. It is important to emphasize that apart from a reasonably robust literature on adult outcome (i.e., early in adult life), the literature on adults with autism is very limited both in terms of actual numbers of studies and individuals studied and an absence of literature on the topic of aging in autism (Howlin 2013; Perkins and Berkman 2012). For example, few studies address the issue of rates of employment among adults with autism/ASD and rates of suicide or suicide attempts (even though depression is thought to be a frequent co-occurring condition), and no studies are available on the life course changes for individuals with autism/ ASD. Put another way (and viewed in the starkest possible light), it is as if adults with autism/ASD vanish from the scene once they’ve become adult and “had their outcome,” such as capacities in young adulthood for personal independence and self-sufficiency. School-age children with more “classic” autism often become more sociable as they get older, and gains in cognitive and communication skills also typically are made – sometimes dramatic ones and other times not. Social language use and peer relationships often remain areas of difficulties, and problems with agitation and selfstimulatory behavior may require behavioral or pharmacologic intervention. As first observed by Leo Kanner (Kanner 1971), in adolescence, some children make major gains, while another group unfortunately exhibits some degree of deterioration in functioning. A number of outcome studies 243 now suggest a gradual change in overall prognosis presumably reflecting both earlier and better intervention as well as some changes in diagnostic practice over the time (i.e., including recognizing autism in more cognitively able individuals) (Howlin 2005). An increasing number of individuals are able to live independently and self-sufficiently as adults, and many now go to college or pursue technical/vocational training. Major predictors of adult outcome include level of intellectual functioning and communication competence. Some adults require intermediate levels of support, and in many cases, affected individuals never attain independence and must live in highly structured residential settings. Agerelated vulnerabilities have been noted, e.g., the risk of seizure disorder is substantially increased both in early childhood and again in adolescence and possibly well into adulthood. More able adults with autism and related conditions also are apparently more vulnerable to anxiety and depression which can require specific treatments (Howlin 2013; Skokauskas and Gallagher 2010; Szatmari and McConnell 2011; White et al. 2009). Data on outcome in Asperger’s disorder are, expectedly, less extensive but suggest (also as might be expected) overall better outcomes in terms of independence, educational attainment, employment, and social independence. However, comorbid conditions, such as anxiety and depression, can be a complicating factors and negatively impact outcomes (Skokauskas and Gallagher 2010; Szatmari and McConnell 2011; White et al. 2009; Volkmar et al. 2014). There is some suggestion of increased risk for both psychosis and legal difficulties in adolescence and adulthood, but the evidence for this consists largely of case reports (Skokauskas and Gallagher 2010; Szatmari and McConnell 2011; White et al. 2009). Studies of children with “subthreshold” pervasive developmental disorder are very uncommon although, on balance, these individuals appear to have a better outcome than those with more classical autism (Towbin 2005). The outcome in childhood disintegrative disorder and Rett’s does appear, unfortunately, to be poor (Volkmar et al. 2005; Van Acker et al. 2005). In childhood disintegrative disorder, children lose I.E. Drmic et al. 244 skills but then plateau and make relatively minimal gains. In Rett’s, the course is well known with children becoming somewhat more sociable over time but also more incapacitated due to neurological and movement problems. 3.2 Adult Independence Information on long-term prognosis is limited and difficult to interpret. This review will focus primarily on long-term follow-up outcome studies that have been published over the past decade (for reviews of earlier outcome studies see Gillberg 1991; Henninger and Taylor 2012; Howlin 2000; Howlin and Moss 2012; Levy and Perry 2011; Nordin and Gillberg 1998). The majority of studies that report on “outcomes” for people with ASD have focused on a few domains (e.g., work status, residential situation, friendships; see more detailed discussion below). Predicting outcome has been difficult due to the wide variability in cognitive, linguistic, social, and behavioral abilities in this group of individuals. Consistent interpretation of different findings has also been difficult due to varied sample size (generally small), sampling procedures (clinical versus population based), differences in diagnostic criteria over time, wide range of age at diagnosis, differences in amount of time between initial and follow-up assessments, differences in measures used, imprecise and/or poor quality data on early intellectual functioning, and the lack of information about early intervention services. These factors may account, in part, for the substantial variability reported in adult outcomes for people with ASD. As noted previously, although psychosocial outcomes have generally been poor, there is a trend over time for higher rates of improvement (see Table 1 – description of how the outcome categories were defined is provided below; Beadle-Brown et al. 2002; Billstedt et al. 2005, 2007, 2011; Eaves and Ho 2008; Farley et al. 2009; Gillberg 1991; Hofvander et al. 2009; Howlin 1998; Howlin et al. 2004; Kobayashi et al. 1992; Lockyer and Rutter 1969, 1970; Lotter 1978; Lovaas 1987; Nordin and Gillberg 1998; Rutter et al. 1967; Rutter and Lockyer 1967; Szatmari et al. 1989). Studies conducted from the 1950s to 1970s reported that less than 15% of individuals had good/very good outcomes and the majority had poor/very poor outcomes (Eisenberg 1956; Rutter et al. 1967; Lotter 1974). Over the past three decades, outcomes have improved but remain variable; the proportion of good to very good outcomes ranges from 4% to 62% and poor to very poor outcome ranges from 3% to 78% (see Table 1). Outcomes have been classified into different outcome categories ranging from “very poor to very good” by various studies over the years. Of note, these ratings were more subjective in earlier studies (pre-2000), whereas more objective criteria have been used in recent studies (for a detailed discussion, see Henniger and Taylor 2012). The group of studies published within the last decade used quantifiable outcome criteria to classify individuals with ASD into these different outcome categories ranging from very poor to very good (see Howlin et al. 2004, Appendix 2, for detailed criteria). In general, the criteria used to determine outcome were based on a 5-point composite rating of overall social and independent living functioning that was derived by summing individual scores for work status (ranging from 0 = employed or self-employed to 3 = no occupation), residential situation (ranging from 0 = living independently to 5 = being in hospital care or at home because nowhere else would accept the individual), and number and quality of meaningful friendships (ranging from 0 = more than one friendship to 3 = no friendships or joint activities) . Based on the 5-point composite rating, a score of 0 indicated a “very good” outcome (i.e., total for all three areas between 0 and 2); 1 = “good” outcome (total 3–4); 2 = “fair” outcome (total 5–7); 3 = “poor” outcome (total 8–10); and 4 = “very poor” outcome (total 11). Thus, the concept of “outcome” is related to practical independence and autonomy achieved in adult life. Howlin and her colleagues (2004) surveyed 68 adults (average age of 29 years) with autism who had a performance IQ of 50 or above in childhood and found that the majority (58%) had Mean age at intake (IN) and follow-up (FU) (years) IN: 3.9 FU: 26.6 IN: <10 years FU: 25.5 (17–40) Authors Gillespie-Lynch et al. (2012) Billstedt et al. (2011)b N AUT = 20 Farley et al. (2009) AUT = 48 IN: 7.2 (3–25) FU: 32.5 (22–46) Eaves and Ho (2008) AUT = 48 ASD = 40 ASP = 70 AUT = 70 (includes AUT & ATY) IN: 6.8 (3–12) FU: 24.0 (19–31) ASP IN: 11.3 (5–24) FU: 21.5 (16–33) AUT IN: <10.0 FU: 24.5 (16–36) Billstedt et al. (2005) TOT = 108 (AUT = 73 ATY = 35) IN: <10.0 FU: 25.5 (17–40) Howlin et al. 2004 AUT = 68 IN 7.24 (3–15) FU 29.3 (21–48) Cederlund et al. (2008) TOT = 108 (AUT = 73 ATY = 35) Functioning level at intake (IN) and follow-up (FU) IN: 54.7a FU: n/a AUT IN: 21% IQ > 70 FU: 7% IQ > 70 ATY IN: 14% IQ > 70 FU: 3% IQ > 70 IN: IQ > 70 (IQ Estimate = 86.66) FU: FSIQ = 88.93 IN: 17% IQ ≥ 70 FU: n/a ASP: IN: 100% IQ ≥ 70 (FSIQ =101.4) FU: 98% IQ ≥ 70 (FSIQ = 103.0) AUT: IN: 19% IQ ≥ 70 FU: 7% IQ ≥ 70 AUT: IN: 21% IQ > 70 FU: 7% IQ > 70 ATY IN: 14% IQ > 70 FU: 3% IQ > 70 IN: PIQ = 80.21 VIQ = 61.49 FU: PIQ = 75.00 VIQ = 79.78 Mean follow-up Method of initial time (years) assessment – DSM-III Dx AUT Very poor to poor 50 Restricted to Good to very good fair 20 30 17.8 DSM-III-R AUT& ATY 12 26 62 24.4 DSM-III AUT 17 34 48 – DSM I–III; Rutter AUT 46 32 21 – Gillberg’s criteria, DSM-III, DSM-III-R ASP AUT 3 76 70 24 27 0 17.8 Rutter, DSM-III AUT& ATY 78 21 0 – Criteria applicable at time of diagnosis (1950–1979) AUT 58 19 22 Life Course Health Development in Autism Spectrum Disorders Table 1 Review of adult outcome studies (continued) 245 246 Table 1 (continued) Authors Engstrom et al. (2003) N ASP = 32 HFA = 10 Howlin (2000) and Mawhood et al. (2000) AUT = 19 Larsen and Mouridsen (1997) AUT = 9 ASP = 9 Ruble and Dalrymple (1996) Kobayashi et al. (1992) AUT = 46 AUT = 201 AUT = 23 Lotter 1974 AUT = 32 Rutter et al. (1967) and Rutter and Lockyer (1967) IP = 57 Eisenberg (1956) AUT = 63 IN: ASP = 9.2; AUT = 5.9 FU: ASP 39.1 (33–44); AUT 36.5 (32–39) IN: 5.2 FU: 17.1 IN: 6.4 FU: 21.5 IN: <6 FU: (16–23) IN: (8–10) FU: (16–18) IN: 5.9 (2–10) FU: 15.6 IN: 6 FU: 15 Very poor to poor 12 Restricted to Good to fair very good 75 12 AUT 74 10 16 ICD-10 ASP AUT 22 67 44 11 33 22 8.6 DSM-III-R AUT 100 0 0 15.4 DSM-III-R AUT 46 27 27 6–11 DSM-III; Kanner & Eisenberg; Rutter Creak (1961) AUT 44 48 4 AUT 63 24 13 Functioning level at intake (IN) and follow-up (FU) IN: n/a FU: 100% IQ > 70 Mean follow-up Method of initial time (years) assessment – DSM-IV IN: NVIQ ≥ 70 NVIQ = 94.28 VIQ = 66.56 FU: NVIQ = 82.78 VIQ = 82.33 IN: ASP: 100% IQ > 70 AUT: 56% IQ > 70 FU: N/A – Rutter 30 IN: 26.7% IQ >70 FU: 15.2% IQ > 70 IN: 23.4% IQ ≥ 70 FU: n/a IN: 26% IQ > 70 FU: n/a IN: 66.7% IQ < 55 FU: n/a IN: 29% IQ > 70 (mean IQ 62) FU: n/a n/a 8 Dx ASP& HFA 9.7 Agreement among psychiatrists IP 61 25 14 9 Unclear AUT 73 22 5 ASP Asperger syndrome, ATY atypical autism, AUT autism, HFA high-functioning autism, IP infantile psychosis a Developmental quotient (mental age divided by chronological age) b QOL scale I.E. Drmic et al. Gillberg and Steffenburg (1987) Mean age at intake (IN) and follow-up (FU) (years) IN: HFA 13.5 (4–20); ASP 28.0 (15–41) FU: 30.8 (18–49) IN: (7–8) FU: 23.8 (21–26) Life Course Health Development in Autism Spectrum Disorders poor or very poor outcomes, 19% had fair outcome, and 22% had good or very good outcome. Those individuals with a performance IQ of at least 70 in childhood had significantly better outcome than those with an IQ below 70. More specifically, of those individuals with a performance IQ between 50 and 69 (n = 23), only one individual had very good outcome, three were rated as fair, and the remaining had poor or very poor outcome. Individuals in this IQ category had much poorer prognosis with few having jobs, friends, or achieving independence as adults. In the group of individuals with near average to average IQ, outcome was generally better, although still quite variable. Of those individuals with an IQ of 70 or above (n = 45; parental data missing on one individual), 14 individuals were rated as good or very good, 10 as fair, and 20 as poor or very poor. Furthermore, there was no difference in outcome in those individuals with an IQ over 100 and those with and initial IQ between 70 and 99. Interestingly, many individuals with an IQ between 70 and 99 made better progress in terms of academics, jobs, and general level of functioning, than those with an IQ above 100. Thus, having an IQ of about 70 seemed to be a critical cutoff point for better outcome (e.g., level of independence); however, outcome in these individuals was still quite variable. Other studies found a similar pattern of results. Billstedt et al. (2005) examined a group of lowto middle-functioning individuals (82% had an IQ below 70) with autism or atypical autism and found that 91% had poor or very poor outcome and none were reported to have good outcome. Eaves and Ho (2008) reported somewhat better outcomes in a group of low- to middle-functioning individuals (majority had IQ < 70) with approximately half having fair to good outcome and half poor (but none very poor). Similar outcomes were reported by Gillespie-Lynch et al. (2012), with 50% reporting poor outcome and 50% fair to very good outcome. Engstrom, Ekstrom, and Emilsson (2003) reported on the psychosocial functioning of a systematically selected sample of patients with Asperger syndrome (AS) and high-functioning autism (HFA) all of whom had an IQ above 70. The majority were rated as hav- 247 ing a fair outcome (75%), with few being rated as good or poor (12% each) and none being rated as very poor. Farley et al. (2009) also examined adult outcomes for 41 individuals with average or near average cognitive abilities and found better outcomes compared to the other studies with 48% showing good or very good outcome, 34% with fair outcome, and only 17% with poor outcome (none with very poor outcome). Taken together, these studies suggest that outcomes for adults may be improving and that this is likely related to availability of services for individuals with ASD (Gillespie-Lynch et al. 2012; Kobayashi et al. 1992). However, it is important to also take into consideration the pattern of attrition in longitudinal studies. For instance, selective attrition of lower functioning individuals may account for the more positive outcomes in some studies (Eaves and Ho 2008; GillespieLynch et al. 2012). 3.3 Quality of Life Although the vast majority of studies have based outcomes on level of independence achieved in adulthood and IQ, symptom severity and language development are thought to be important factors in these outcomes; there is increasing interest in examining outcomes based on quality of life (QOL) indictors. QOL is a more comprehensive and multidimensional concept that also encompasses one’s subjective evaluations of their experiences (Burgess and Gutstein 2007). According to the World Health Organization (WHO), the concept of QOL not only includes level of independence and social relationships but also is composed of the person’s health, personal beliefs, and their relationship to salient features of their environment (Saxena and Orley 1997). Generally, studies show that overall QOL and health-related QOL is relatively low for people with ASD (Kamp-Becker et al. 2011; van Heijst and Geurts 2015; Potvin et al. 2015). van Heijst and Geurts (2015) conducted a meta-analysis on QOL studies that included children, adolescents, and adults with ASD and found that QOL is 248 lower in people with ASD compared to people without ASD and age did not have an effect. The lack of association with age has previously been reported (Kamino et al. 2012; Kamp-Becker et al. 2011), although it had also been reported that increasing age is associated with decreasing quality of life (Bennett et al. 2005). Characteristics that were relevant predictors in the traditional adult outcome studies, including symptom severity, IQ, and early language, were not examined because information was too sparse. Given the dearth of information about the elderly, the authors also conducted an empirical study examining QOL in intellectually able elderly individuals with ASD and again found large difference in QOL in people with versus without ASD with lower QOL in individuals with ASD. Interestingly, age, IQ, and symptom severity did not predict QOL in the elderly. Billstedt et al. (2011) examined aspects of quality of life (QOL) in adults diagnosed with autism or atypical autism in childhood and found that the majority of the ASD group remained dependent on parents for support in education, recreation, and occupational situations in late adolescents and early adulthood, but QOL was quite positive (independent of intellectual functioning). The QOL scale used in this study measured both objective and subjective (although by proxy) indicators of well-being. The authors who evaluated the participants completed a QOL scale assessing “autism-friendly environment” using a five-item scale (QOL-1). The item categories were (1) staff and caregivers have specific “autism knowledge,” (2) applied structured education implemented, (3) individual specific treatment/training plan for the person with autism implemented, (4) occupation or everyday life activity corresponding to his/her level of capacity, and (5) overall quality of life. Each item was rated on a 1 (very good) to 5 (very poor). A second QOL scale assessing “parent/care rating of individual’s well-being” was obtained to estimate how the individual with autism enjoyed or liked his/her residential conditions (QOL-2). Sixtytwo percent of individuals were rated as having a very good or good QOL by the authors (QOL-1), and 91% of families reported high or very high I.E. Drmic et al. residential well-being (QOL-2). This is consistent with reports among young adults with intellectual disabilities who reported satisfaction with their lives even though they remained quite dependent on caregivers (Keogh et al. 2004) and a greater sense of empowerment than those individuals who were still in the school system (Kraemer et al. 2003). Howlin (2013) emphasized that traditional definitions of “good outcome” or “independent living” may not be the most suitable goal to strive for individuals with ASD, because it may lead to a life with little support that is isolated and lacking in stimulation. More research examining QOL is needed in ASD. Raphael et al. (1996) conceptualize a QOL model, which is relevant to persons with developmental disabilities, as containing three domains that include “being, belonging, and becoming” (p. 28). The “being” domain includes being who one is or wants to become, “belonging” includes how one fits within their environment, and “becoming” involves pursuit of personal goals and wants. They also noted that within each domain, it is important to consider the perceived importance, enjoyment experienced, perceived control, and opportunities for change and enhancement. Thus, it is important to also examine and understand the subjective experience of the individuals with ASD and their families when assessing outcome. As with any chronic disability, having a condition like ASD may predispose to other problems – particularly difficulties with depression and anxiety disorder; the relative dearth of work on adult outcome and complexities of the problems of comorbidity and diagnosis limit our understanding of the developmental pathways that result in these outcomes. Similarly, even for the most cognitively able adults (e.g., those who attend college), challenges remain in various areas including adaptive (realworld) skills and generalization, social interaction, organization abilities, and so forth (Farley et al. 2009). As noted previously, the significant limitations of the available literature must be emphasized – issues of QOL have not frequently been assessed, even more cognitively able individuals may lead lives of social isolation with comorbid mental health conditions, medication Life Course Health Development in Autism Spectrum Disorders use is quite high, limitations in daily life can result from poor adaptive skills, and essentially no information is available on aging (Howlin 2013). Thus, given the large differences between quality of life in people with and without ASD, a great deal of work needs to be done to understand how to support optimal health development in individuals with ASD and provide their families with the kinds of interventions that will enable them to achieve a higher QOL and promote wellbeing. A shift in research focus is needed to understand factors that lead to a better QOL across the life course. There is some evidence indicating that a higher QOL is related to earlier diagnosis (Kamino et al. 2012), greater perceived support (Renty and Roeyers 2006), and supported employment, residential, and leisure programs (Garcia-Villamisar et al. 2002, García-Villamisar and Dattilo 2010; Gerber et al. 2011). Thus, considerable work is needed to improve QOL in people with ASD, and a better understanding of the developmental trajectory across the lifespan may help to better understand the need and interventions required to support these individuals. 3.4 Predictors of Adult Independence Intellectual functioning (IQ) and language have been most consistently identified as the best predictor of adult personal independence as outcome in ASD (Billstedt et al. 2005, 2007; Gillberg and Steffenburg 1987; Gillespie-Lynch et al. 2012; Howlin et al. 2004; Kobayashi et al. 1992; Lotter 1974; Nordin and Gillberg 1998; Rumsey et al. 1985), although reports have been variable (Farley et al. 2009; Gillespie-Lynch et al. 2012). Farley et al. (2009) reported that early childhood variables were weakly associated with adult outcomes, but the participants in their study all had communicative phrase speech or better language by age 6 and near average to average IQ. GillespieLynch et al. (2012) also found that early intellectual functioning did not predict adult outcomes but suggested that this may be because the average age of the first assessment was quite young (mean age of 3.9 years). Cognitive testing of chil- 249 dren with autism older than 4–5 years of age is reported to give more stable measures than in those children who are tested at younger ages (Lord and Schopler 1989; Howlin et al. 2004). In general, however, the presence of an intellectual disability (ID) is associated with poorer outcome in people with ASD in adult life (Cederlund et al. 2008; Gillberg and Steffenberg 1987; Howlin et al. 2004; Rumsey et al. 1985; Shattuck et al. 2007), with the poorest outcomes for those individuals with an IQ below 50 (Lord and Bailey 2002). Few people with this level of cognitive impairment (IQ < 50), regardless of an ASD diagnosis, achieve a high level of independence as adults (Keogh et al. 2004; Kraemer and Blacher 2001). Conversely, higher levels of intellectual functioning (IQ > 70) are associated with better outcome (Billstedt et al. 2007; Eaves and Ho 2008; Gillberg and Steffenberg 1987; Howlin et al. 2004; Rumsey et al. 1985; Szatmari et al. 1989), but outcome can still be quite variable in these individuals (Billstedt et al. 2005; Howlin et al. 2004). These data further underscore the importance of focusing on early language development and learning (IQ) in early intervention programs for children with ASD. With regard to verbal and performance (nonverbal) IQ, the results are more inconsistent. Some have reported that childhood verbal IQ is a better predictor of later functioning than performance IQ (Billstedt et al. 2005; Lord and Bailey 2002), whereas another study found neither to be a consistent prognostic indicator (Howlin et al. 2004). In addition, the presence of good early language skills before age 5–6 is crucial for positive outcome in ASD (Billstedt et al. 2007; Gillberg and Steffenberg 1987; Gillespie-Lynch et al. 2012; Lockyer and Rutter 1969; Lotter 1978; Szatmari et al. 2003). Mawhood et al. (2000) have shown that individuals who tended to have better linguistic outcome in adulthood were functioning at a higher cognitive level more generally. Other factors have also been suggested to be related to outcome, including symptom severity, repetitive/stereotyped behaviors in childhood, adaptive behavior, education, gender, the presence of a diagnosed medical disorder, onset of 250 epilepsy before age 5 years, and joint attention skills (Billstedt et al. 2007; De Meyer et al. 1973; Farley et al. 2009; Gillespie-Lynch et al. 2012; Howlin and Goode 1998; Totsika et al. 2010; Venter et al. 1992). For instance, Farley et al. (2009) reported that adaptive behavior measures (in particular the Daily Living Skills domain) evaluated at follow-up were most closely correlated to better outcomes. They described a group of individuals with a high IQ but poor practical living skills who felt frustrated by these limitations. Furthermore, they also described individuals who had a low IQ but who were able to manage with little assistance and obtained good or very good outcome rating despite poor cognitive skills. These results have important implications for intervention programs that foster these practical living skills in adulthood. The development of joint attention skills may be another important area to target in early intervention programs because of the relationship of this developmental skill set with later health development outcomes. Joint attention skills typically develop between 6 and 18 months of age and refer to ability of the infant to coordinate their attention with another individual during a social interaction. Joint attention skills are important because they are considered early markers of social cognitive development; that is, they reflect the early development of the infant’s ability to think about the perspective of another individual (Tomasello 1995). These skills are critical to social learning and social information processing (Mundy and Jarrold 2010), language development (Sigman and Kasari 1995), and the emergence of social and behavioral competence in childhood (Vaughan Van Hecke et al. 2007). Children with ASD present with deficits in joint attention regardless of their developmental or intellectual level, and these deficits are neither absolute nor uniform (Mundy et al. 2009). In a recent longitudinal study into adult outcomes, responsiveness to joint attention, but not initiation of joint attention (IJA), was found to predict later outcomes. Responsiveness to joint attention was found to predict gains in IQ from early childhood (M = 3.9 years) to later childhood (M = 11.7 years) and receptive language in I.E. Drmic et al. late adolescents/early adulthood (M = 18.3 years) (Sigman and McGovern 2005; Sigman and Ruskin 1999). In terms of adult outcomes, responsiveness to joint attention predicted adult nonverbal communication, social skills, and symptoms (Gillespie-Lynch et al. 2012). In addition, responsiveness to joint attention and early language both predicted a composite measure of adult social functioning and independence. Taken together, responsiveness to joint attention measured in early childhood may reflect early emerging social cognition, and preliminary evidence suggests that it may be a good predictor of social behaviors in adulthood. Gillespie-Lynch and colleagues also suggested examining the relationship with responsiveness to joint attention and early executive functioning in childhood on later outcomes given that executive functioning is related to adaptive functioning in children with autism (Gilotty et al. 2012). Gulsrud et al. (2014) examined the developmental trajectories of specific joint attention gestures (i.e., pointing, showing, coordinated joint looking) and expressive language over a 6-year period and found that coordinated joint looking and showing increased over time and pointing to share interest increased over the preschool years (3–5 years of age) and then decreased. They also reported a causal relationship between early pointing and later language development; that is, those children with high rates of pointing in the preschool years had better expressive language skills later. Hence, an understanding of the developmental course of joint attention skills allows for better consideration deciding “when” to intervene. Based on the data, the authors suggested that it may be useful to focus on pointing in early childhood when the child is preverbal and less useful when the child begins to use spoken language. Thus, the interval when early joint attention skills develop may represent a critical or sensitive period to intervene and potentially impact the developmental trajectory and outcomes for individuals with ASD. In addition, the children who received the joint attention intervention showed the fastest improvement compared to the control condition or child who received a different intervention. Participation in Life Course Health Development in Autism Spectrum Disorders the joint attention treatment placed these children on a different developmental trajectory. This highlights that “what” you target and “when” you intervene are important (Gulsrud et al. 2014). 3.5 Diagnostic Stability and Symptom Severity There is considerable debate in the literature regarding the stability of a diagnosis of autism and other forms of ASD over time. Some studies report that individuals diagnosed in childhood continue to meet criteria for the disorder at follow-up (Billstedt et al. 2005; Cederlund et al. 2008; Howlin et al. 2004; Lotter 1978); whereas others report a change in diagnosis or movement “off” the spectrum in some individuals (Daniels et al. 2011; Kleinman et al. 2008; Turner and Stone 2007). Two recent studies reported that a diagnosis of autism was reasonably stable, whereas for other forms of ASD such as pervasive developmental disorder, the results were more variable (Rondeau et al. 2011; Woolfenden et al. 2012). Rondeau et al. (2011) did a metaanalysis on eight longitudinal studies, published between 1996 and 2009, examining autism and pervasive developmental disorder. All diagnoses were made before 36 months of age, and the interval between the initial and follow-up assessment was short, ranging from 12 to 84 months. They reported that an autism diagnosis tended to be a more stable than pervasive developmental disorder. Of those with pervasive developmental disorder, only 35% retained the same diagnosis (versus the 76% reported for autism), 39% moved to autism, and 25% moved off the spectrum. The authors concluded that those children diagnosed with pervasive developmental disorder at a young age should be reassessed at an older age. The diagnosis of autism is relatively reliable and stable over time. Issues arise for very young children (the full syndrome picture sufficient for diagnosis may not be observed until age 3 years) and for individuals with “autistic-like” or “spectrum” conditions not so clearly satisfying strict diagnostic criteria. For example, Woolfenden et al. (2012) conducted a systematic review of 23 251 longitudinal studies (many overlapping with Rondeau et al. 2011) examining the stability of the diagnosis of children initially diagnosed with autism and other forms of ASD. The mean age of children at baseline ranged from 1.8 to 11.3 years, and mean duration at follow-up ranged from 2.1 to 32.5 years. Overall, autism was found to be a stable diagnosis although lower estimates (as low as 53%) were reported for children diagnosed in the preschool years (below 3 years of age). A small percentage of individuals diagnosed with autism, between 12% and 15%, move off the spectrum altogether. For other ASD’s (including pervasive developmental disorder, Asperger’s disorder, but not autism), based on the highest quality studies, the pattern of stability was more variable with 14–61% retaining their initial diagnosis at follow-up. A few studies reported on optimal outcome in autism (Fein group), and while these adults are indeed quite independent and functional, it appears that some autistic traits remain. Taken together, a diagnosis of autism is more stable than the other ASD subtypes such as pervasive developmental disorder (Rondeau et al. 2011; Woolfenden et al. 2012), which is consistent with the rationale for the DSM-5 (Szatmari 2011). Given that the ASD category is less stable due to the inclusion of pervasive developmental disorder individuals, this presents challenges to clinicians when sharing a diagnosis of ASD with parents in terms of making appropriate intervention recommendations (Woolfenden et al. 2012). Impairments in the core ASD symptoms (impairment in communication, reciprocal social interaction, and repetitive or stereotyped behaviors and interests) also tend to persist into adulthood (Billstedt et al. 2007; Howlin 2003; Matson et al. 2008), although symptom severity is reported to decrease in some individuals over time (Seltzer et al. 2003; Shattuck et al. 2007). Totsika et al. (2010) found that an older (50 years or older) group of adults with ASD and ID presented with fewer behavioral problems than the younger (<50 years) group, which is consistent with reports of decreases in behavior problems in the general adult intellectual disability population (Holden and Gitlesen 2006). Billstedt et al. I.E. Drmic et al. 252 (2007) found that social interaction problems were still present in the vast majority of adults with ASD, but the occurrence of behavioral impairments (maladaptive and stereotyped behaviors) was much more variable with only a few symptoms in each category affecting half or more of the study sample. Others have reported that behavioral problems continue into the adult years (Ballaban-Gil et al. 1996) and that unusual responses to sensory (e.g., touch, pain, auditory, and visual) stimuli also remain common in adulthood (Billstedt et al. 2007; Leekam et al. 2006). 3.6 Cognitive Stability There is considerable variability in measures of cognitive ability over time. Some have reported that IQ is relatively stable in ASD (e.g., BallabanGil et al. 1996; Begovac et al. 2009; Howlin et al. 2004; Lockyer and Rutter 1970; Venter et al. 1992), whereas others have reported gains and/or losses over time (Billstedt et al. 2005; Farley et al. 2009; Mawhood et al. 2000). Howlin and colleagues reported that IQ remained remarkably stable over the life course; however, stability tended to be greatest among individuals with an initial IQ of at least 70. On the other hand, Billstedt et al. (2005) found that many individuals who initially had near average intelligence (IQ between 71 and 85) were later diagnosed with a mild (IQ 50–70) or severe (IQ < 50) intellectual disability, and those individuals with a severe intellectual disability (IQ < 50) in the original diagnostic study were all still in this category at follow-up. Overall, they found a downward shift of IQ level from the diagnostic study to the follow-up evaluation. A decrease in IQ may be an important biological marker in a subset of individuals with ASD (Farley et al. 2009). Farley et al. (2009) reported that over half of the sample had either large gains or losses in cognitive ability of greater than 1 standard deviation. Cognitive gain was associated with better outcome, as was better adaptive functioning. Some of the variability in results may be, in part, accounted for by the way IQ was measured or assessed, the age of initial assessment (IQ less stable in young children), and level of functioning of the participant, and the sometimes highly varied cognitive profiles of individuals which may evolve over time. This variability in IQ trajectory may also be due to the cumulative or time-specific impact of other risk or protective factors on the plasticity of IQ development. 4 Transitions Over the Life Course 4.1 Childhood Children with ASD, like all other children, undergo several transitions throughout their school years. For instance, this may include the transition from early intervention to preschool, changing grades each year and moving into adolescence and then into adult life. Individuals with ASD also struggle with smaller transitions that are required on a daily basis, such as transitioning from one context to another. For example, this may include a transition between classes or transitions to unfamiliar settings (e.g., doctor’s office). It is important to plan for these transitions because ineffective transition planning can have a negative impact on social and academic progress (Adreon and Stella 2001). Thus, numerous studies have emphasized the importance of transition planning for all children with special needs (Polloway et al. 2001), and this is particularly significant for children with ASD who frequently struggle with transitions. The ease with which a child transitions depends on the strategies adopted by educators when working with the child with ASD and their family (Harris and Handelman 2000). Parents of young children with ASD identified a number of factors that support successful and effective transitions, such as good communication between the school and home, transition plans that are child-centered, and inclusion of parents (Stoner et al. 2007). Longitudinal studies of ASD have emphasized documenting the prevalence of certain outcomes (such as the stability of the diagnosis) rather than a more detailed look at developmentally related changes over time. Some studies are now taking Life Course Health Development in Autism Spectrum Disorders a life course health development perspective by focusing on trajectories (Kim et al. 2015). The Pathways in ASD study, for example, followed roughly 400 children from the point of diagnosis at 2–4 years of age to the transition into primary school at 6 years of age allowing for the possibility of heterogeneous trajectories of autism symptoms and adaptive functioning. The results suggested (Szatmari et al. 2015) that heterogeneity in the rate of change of both phenotypes occurred, with some children doing remarkably well in adaptive function, but autism symptom severity remained more stable. There was little overlap of the autistic symptom severity and adaptive functioning developmental trajectories so that the course of the two seemed relatively independent, thereby highlighting the importance of close surveillance and treatment of these two domains independently over time because improvement in one area does not ensure improvement in the other area. These data also suggested that at least for the transition into primary school, continued improvement is possible for the majority of children with ASD. A growing evidence base suggests that behavioral interventions can be associated with positive outcomes for children with ASD, with little evidence and generally poor quality studies available in adolescents and adults (Lounds Taylor et al. 2012; Weitlauf et al. 2014; National ASD Center National Standards Project 2009, 2015). Early interventions that were based on highintensity applied behavior analysis over extended periods of time were associated with improvements in cognitive functioning and language skills, although the magnitude of effects varied across studies (Weitlauf et al. 2014). This variability may be related to the scarcity of information about modifying factors related to subgroups (Weitlauf et al. 2014). For instance, the majority of evidence supports the efficacy of comprehensive early intensive behavioral interventions (EIBI), which is currently the standard of care in many clinics and schools (Peters-Scheffer et al. 2011; Reichow 2012). Meaningful gains have been shown in IQ, language skills, and adaptive outcomes with medium to large effects (Reichow 2012; Strauss et al. 2013) and a decrease in 253 autism severity over time (Zachor and Itzchak 2010). Gains are not universal, some making rapid progress and others only modest or no progress (Peters-Scheffer et al. 2011; Reichow and Wolery 2009). Inclusion of parents in skill generalization has led to overall higher effect sizes (Strauss et al. 2013). Early intensive parent programs have been shown to modify parent behavior during interactions, but there is less data about their ability to improve developmental skills beyond language gains for some children (Weitlauf et al. 2014). Predictors of positive outcome and/or changes in trajectory include younger age at entry into EIBI programs (between 18 months and 5 years), higher IQ or adaptive functioning or language (mild to moderate range of functioning), and less severe autism symptoms (Strauss et al. 2013). With regard to program characteristics, intensity of EIBI programs was related to gains in IQ, adaptive functioning, and language ability, whereas duration of the program was generally not related to gains with the exception of some evidence that it was related to language and adaptive behavior gains (Strauss et al. 2013). Contemporary early intervention ABA approaches are more naturalistic, childdirected, and guided by developmental theory and are currently referred to as naturalistic developmental behavioral interventions (NDBI’s) (for detailed discussion see Schreibman et al. 2015). Other behavioral interventions that have shown some positive effects include, but are not limited to, social skills, treatment targeting joint attention, and cognitive behavior therapy (Weitlauf et al. 2014). Research is beginning to show that behaviorally based interventions have an effect on patterns of brain activity, particularly in regions involved in social processing (Dawson et al. 2012), and that neural plasticity in response to intervention may exist throughout the lifespan (Faja et al. 2012). Thus, the evidence supports that early interventions are effective in ASD; however, less is known about how sustainable these intervention effects are after transitions from early intervention into preschool and elementary school. There is emerging evidence that young children with ASD who received early intervention sustained I.E. Drmic et al. 254 social, language, and/or nonverbal cognitive gains through a 6–37-month follow-up period (e.g., Kasari et al. 2010; Landa et al. 2011; Landa and Kalb 2012). For instance, Landa and Kalb (2012) examined outcomes in 2-year-old toddlers enrolled in a 6-month nursery school-based intervention immediately following intervention (M age = 35 months), at short-term (M age = 41 months) and long-term follow-up (M age = 72 months; 37 months after children completed intervention). At long-term follow-up, they found gains in IQ and communication; however, increases were not evenly distributed across the different time points. Gains in IQ and communication were robust initially from pre- to post-intervention, followed by a period of stabilization, which was then followed by another developmental burst. In contrast, the trajectory of autism severity was different with an initial decrease in severity form pre- to post-intervention, followed by an increase in severity to preintervention levels by the final time point. Magiati et al. (2011) reassessed outcomes even later at 5 years after departing preschool and found that children maintained standardized IQ scores, but adaptive functioning scores decreased. Longerterm outcome studies are needed to determine the sustainability of gains, as well as to determine whether there are sensitive periods in development as well as the developmental scaffolding that is necessary, especially during key life course transitions that are likely to promote optimal health development outcomes (e.g., age, type of intervention, duration, intensity, content, etc.) (Landa and Kalb 2012). 4.2 Transition from Adolescence into Adulthood For youth with ASD and their families, the transition into adulthood is a time of uncertainty and loss of entitlement to many services that were available while in the school system. In comparison to early childhood, there often is a dearth of appropriate supports and opportunities for adults with ASD. Leaving high school can have a disruptive effect on youth and young adults with ASD in a number of areas, including ASD symptom presentation, maladaptive behaviors, and family functioning (Taylor and Seltzer 2010, 2011a, b). Taylor and Seltzer (2010) examined the impact of exiting high school and observed that in general, autism symptoms and maladaptive behaviors continued to improve while adolescents were part of the secondary school system, but improvement significantly slowed (by over half) after exiting high school for internalizing behavior and all but one of the autism symptom domains (i.e., verbal communication impairments). The authors suggested that this change was likely related to the lack of disabilityrelated services or that occupational or day program services were less stimulating than those in the school system. Interestingly, youth without comorbid intellectual disability improved more in both autism symptoms and maladaptive behaviors while in high school compared to those with intellectual disability (Shattuck et al. 2007), but after exiting high school, improvement slowed more for those individuals without an ID (Taylor and Seltzer 2010). Indeed, only 6% of individuals with ASD without comorbid ID were receiving day services immediately after high school, compared to 74% with ASD and ID, and over one-quarter had no occupational, educational, or day activities. In addition, the improvement in the mother-youth relationship seen in high school slowed or stopped after high school (i.e., decreasing maternal warmth), with the greatest impact on relationships for youth with ASD and no ID (Taylor and Seltzer 2011a). Thus, leaving high school had a negative impact on individuals with ASD, with the most pronounced effect on those youth with ASD without an ID, suggesting a particular lack of adequate educational and occupational activities for this group of individuals in adulthood. It also highlights that individuals with ASD and no ID are often impaired in their adaptive skills and encounter difficulty generalizing these skills in daily life. When services are inadequate, parents will often take on this responsibility, leading some families to experience this transition period as a time of stress and anxiety (Howlin 2005). Other families view this period as an opportunity Life Course Health Development in Autism Spectrum Disorders for new and more positive possibilities, such as finding more appropriate occupational placements and leaving high school, which for some youth, was a negative experience (Taylor 2009). Access to autism-related services is also related to family income. Families with lower incomes and less parental education may have less access to autism-related services in childhood compared to families with higher incomes (Liptak et al. 2008; Thomas et al. 2007). Improvement in maladaptive behaviors slows after high school more so for those families with a lower income (Taylor and Seltzer 2011b). Thus, it is important to better understand the factors that promote optimal transition for individuals with ASD, as well as the impact on families. Taylor (2009) underscores the importance of examining contextual and environmental factors that are amenable to change or can be targeted in intervention in order to improve success with transition and suggests that exclusively focusing on characteristics such as IQ and early language that although are predictive of later outcome is more difficult to intervene once the individual with ASD becomes an adolescent or adult. For instance, the implementation of transition planning has been associated with more successful achievement of adult milestones (Frank and Siltington 2000). Indeed, factors such as the posthigh school goals for the student and parental expectations were predictive of participation in postsecondary education (Chiang et al. 2012). In addition, the development of appropriate educational, occupational, and day programs for individuals with ASD of all ability levels is important as is a lifelong emphasis on generalization of skills. 4.2.1 Educational Attainment With regard to education, many individuals in the follow-up studies published in the last decade attended specialized schools or classrooms (Billstedt et al. 2011; Farley et al. 2009, Howlin et al. 2004). Only a few individuals with ASD obtained formal qualifications (Eaves and Ho 2008; Howlin et al. 2004), although results of recent studies have been more promising (Hofvander et al. 2009; Farley 2009). For example, 255 Farley and colleagues reported that 44% completed high school at grade 12 with a diploma, and 39% went on to postsecondary education. With regard to those students who are capable of entry into college or university, many do not seek or gain entry or drop out prematurely (Glennon 2001; VanBergeijk et al. 2008) due to a number of factors including social isolation and loneliness, difficulty managing changing routines and schedules, difficulty with independent living, and the absence of monitoring and guidance (Howlin et al. 2004; Jobe and White 2007; White et al. 2011). Students with ASD are able to succeed both academically and socially in postsecondary education when the transition is carefully planned, appropriate accommodations are obtained, and supports are available (for detailed discussion, see VanBergeijk et al. 2008). Preparing students for the transition to postsecondary education is important for many adolescents but is particularly important for individuals with ASD, most of whom have difficulty with transitions. VanBergeijk and colleagues stress the importance of a good fit between the student and the prospective academic institution. For instance, this may include careful consideration of the size of the school and classes, level of supports, and receptiveness to serving students with disabilities – specifically ASD, access to mental health services, and availability of supports to address both academic concerns (e.g., organizational help, accommodations related to evaluation and test taking, support with complex assignments, etc.), as well as nonacademic concerns including life skills, issues of daily living (e.g., laundry, budgeting), organizational difficulties, communication limitations, and social skills (e.g., socializing with peers, getting along with roommates, romantic relationships). Indeed, adolescents with ASD reported greater concern about social acceptance and being able to function independently than about accessing academic accommodations and resources in postsecondary educational settings, and thereby identified a need for built in social supports and access to trained staff members as a resource (Camarena and Sarigiani 2009). Many individuals with ASD also have difficulty with issues related to sexuality and may 256 require support or direct instruction (see Volkmar 2004). Furthermore, postsecondary educational programs must also include planning and preparation for the transition to work and independent living that includes vocational training and life coaching (VanBergeijk et al. 2008). Families and educators are also encouraged to support the development of self-advocacy skills which are important to the transition process so that adolescent and young adults are able to navigate the journey through higher education, work, and independent living (Camarena and Sarigiani 2009). Thus, for students with aspirations for postsecondary education, careful planning and supports are required to help students with ASD achieve academic success. 4.2.2 Employment, Independent Living, and Social Life The research on transition into adulthood in individuals with ASD has primarily focused on the traditional view of transition which describes the degree of independence in living arrangements, ability to gain and maintain employment, and the presence of intimate relationships outside of the family unit (Fussell and Furstenberg 2005). The majority of individuals with ASD have difficulty achieving the developmental tasks of adulthood and remain dependent on parents/caregivers for support in employment and accommodation, and most do not have intimate relationships (friendship or romantic). Many persons with ASD are able to work successfully within the community (GarciaVillamisar et al. 2000; Mawhood and Howlin 1999); however, the majority experience difficulty securing meaningful employment (Billstedt et al. 2011; Eaves and Ho 2008; Howlin et al. 2004; Newman et al. 2011). Findings from the National Longitudinal Transition Study-2 (NLTS2) examined a nationally representative sample of youth and young adults with disabilities in the USA and found that up to 8 years posthigh school, only 63% of adults with autism had worked at some point after high school and only 37% were employed at the time of the survey (Newman et al. 2011). In a group of individuals I.E. Drmic et al. with near average to average intelligence, the outcomes were somewhat more promising with 54% in full-time or part-time independent paid jobs, 12% in supported employment or part-time volunteer positions, 24% in day programs, and 10% unemployed (Farley et al. 2009). Even among those who are employed, many worked few hours, and most jobs tended to be low level, were found by parental contacts (versus open job market), and were poorly paid such that individuals did not have adequate financial support to live independently (Cimera and Cowan 2009; Eaves and Ho 2008; Howlin et al. 2004). Furthermore, many individuals were in part-time, volunteer, supported, or sheltered employment; few worked independently with little or no support, and some had no daytime occupation at all. Jobs often ended prematurely because of social or behavioral difficulties or other work-related difficulties (Mawhood and Howlin 1999). Cimera and Cowan (2009) examined cost of services and employment outcomes achieved by adults with ASD in the vocational rehabilitation system. The study found that adults with autism were employed at higher rates than other disability groups; however, the rate of employment was only 41% (60% do not obtain employment), and as noted earlier, they worked fewer hours and earned lower wages. Adults with autism were also the most costly group of individual to serve, and services were difficult to implement. Howlin (2013) underscores that generic employment services do not meet the needs of individuals with ASD, and there is a need for more programs and research specific to individuals with ASD. Although limited, research is beginning to emerge showing that supported employment models for individuals with ASD are effective (Howlin et al. 2005; Keel et al. 1997; Shattuck et al. 2012; Taylor et al. 2012). Critical factors in finding and maintaining a long-term placement include a thorough assessment to appropriately match the skills and abilities of the person with ASD to the requirements of the job, as well as having the appropriate level and type of employment support (Mawhood and Howlin 1999; Smith and Paulippen 1999). Life Course Health Development in Autism Spectrum Disorders Hagner and Cooney (2005) found that a set of supervisory accommodation strategies were commonly associated with successful supervision, including maintaining a consistent schedule and set of job responsibilities, using organizers to structure the job, minimizing unstructured or idle time, being direct with communication, and providing reminders and reassurances. Longer job retention has been correlated with higher levels of social inclusion and acceptance on the job (Belcher and Smith 1994). Furthermore, engaging in work is often related to other meaningful outcomes, such as contributing to one’s selfworth and confidence, providing opportunities to shares one’s strengths that are valued by others, opportunities to develop friendships or other supportive relationships, as well as engagement in the larger community (Lee and Carter 2012). On the other hand, failure to find suitable work resulted in frustration and in some serious problems with depression and/or anxiety (Mawhood and Howlin 1999). With supported employment, individuals with ASD are able to find and maintain suitable employment (Mawhood and Howlin 1999). Most individuals with ASD live with their parents or in other supported arrangements (e.g., community-based group home), and few live independently either with or without some level of support (Billstedt et al. 2011; Farley et al. 2009). Eaves and Ho (2008) reported that 56% lived with their parents, 35% were in supported arrangements such as group homes or foster care, and the remainder lived more or less independently. With regard to friendship, it has been reported that up to approximately 30% of individuals have one or more friends (Eaves and Ho 2008; Howlin et al. 2004). Social contacts often result from special interests and skills rather than close friendships (Howlin 2000). Some individuals have been reported to want friendships but were unable to form these relationships, whereas others had no interest or did not understand the concept of friendship (Billstedt et al. 2011). Few individuals had close intimate relationships or were married at some point (Eaves and Ho 2008; Farley et al. 2009; Hofvander et al. 2009; Howlin 257 et al. 2004). In a group of higher functioning individuals with ASD, Farley and colleagues reported that 7% were married, 5% divorced, 7% were in long-term relationships, 44% never dated, 32% dated in both group and couple situations, and 22% dated only in group settings. When parents were asked whether they felt their adult child wished to have a romantic relationship, of those participants not already in relationships, 44% felt their son or daughter would like to be in a relationship. On the other hand, 41% did not want a romantic partner because parents thought their children were concerned about passing on autism to a child or themselves being too difficult for a partner to manage. 4.2.3 Summary Taken together, the research to date focuses primarily on the attainment of traditional skills that are indicative of successful transition into adulthood and less on more subjective or quality of life indicators. Although outcomes are quite variable, in general, outcomes related to level of independence, employment status, and intimate relationships are poor, suggesting that the transition to adulthood is fraught with many challenges. Evidence suggests that better outcomes are related to better cognitive abilities and earlier language skills, although even these individuals struggle with attaining the developmental tasks of adulthood. Furthermore, availability of appropriate resources, services, and supports for the individuals with ASD and their families are key ingredients for successful transition into adulthood and better outcomes throughout adult life. Lee and Carter (2012) identified seven elements that are important for high-quality transition services, which include individualized services that reflect the strengths of the individual, positive career development and early work experiences, collaboration and interagency involvement, family supports and expectations, fostering self-determination and independence, social- and employment-related skill instruction, and establishment of job-related supports. Thus, the impetus of future research is to examine factors that will support successful transition (e.g., environmental and contextual factors) I.E. Drmic et al. 258 and find other ways to define transition such as from a psychosocial or attachment perspective (for review, see Taylor 2009), especially as the epidemiology of ASD changes from autism with ID to a preponderance of ASD without ID. 4.3 Aging and ASD Autism is a lifelong condition; however, little is known about older adults with ASD. Indeed, much of the research to date has examined the aging ASD population but not the aged (65 plus) (Perkins and Berkman 2012). Although information is quite limited, research suggests that life expectancy is shortened compared to the general population and causes of death include high rates of seizures and accidental deaths (e.g., drowning) (Mouridsen et al. 2008; Piven and Rabins 2011). It is unclear whether deaths that are related to diseases (e.g., nervous and sensory disease) in ASD are due to a predisposition to certain health concerns or due to poor health surveillance leading to poor diagnosis and management (Perkins and Berkman 2012). A number of medical and psychiatric concerns have been reported in children and adolescents with ASD, such as sleep disorders, psychiatric symptoms, gastrointestinal disorders, feeding selectivity and aversions, metabolic conditions, and epilepsy; however, there is little information about these concerns as individuals age (Piven and Rabins 2011). Similarly, it is unclear whether treatments that are effective in children and adolescents would also benefit adults. Given the dearth of information of older adults with ASD, a group of experts were convened to characterize the gaps in knowledge and define a research agenda to bridge this gap (see Piven and Rabins 2011). Research priorities included developing diagnostic criteria and tools to diagnose older adults, conducting cross-sectional descriptive studies in adults of phenomenology and associated features, longitudinal studies of lifespan trajectories, neurobiological studies examining underlying changes over time, intervention studies, and mechanism to support clinical and research training in aging. This research is needed to improve the lives of individuals with ASD and to inform clinical practice and policies that can address these healthcare disparities (Piven and Rabins 2011). 5 Mental Health Issues in ASD 5.1 Prevalence Rates In terms of quality of adult life, there is increasing evidence suggesting that comorbid psychiatric symptoms and disorders are common in individuals with ASD, with estimated rates ranging from 30% to 70% (e.g., Bradley et al. 2004; Gjevik et al. 2011; Leyfer et al. 2006; Moseley et al. 2011; Simonoff et al. 2008; Skokauskas and Gallagher 2010). Understanding comorbid mental health problems is important not only because of the high prevalence but also because of the impact on daily functioning, long-term prognosis, and the added burden and challenges faced by individuals and their families. Individuals with ASD have higher rates of comorbidity compared to the general population (Ghaziuddin et al. 1998; Moseley et al. 2011), a psychiatrically referred population of youth without ASD (Joshi et al. 2010), as well as individuals with ID alone (Bradley et al. 2004; Brereton et al. 2006; LoVullo and Matson 2009), although the latter has not been consistently found (Tsakanikos et al. 2006). The most commonly reported psychiatric disorders include anxiety and depression (MacNeil et al. 2009; Sterling et al. 2008; White et al. 2009). ASD has also been found to co-occur with obsessive compulsive disorder (OCD), disruptive behavioral disorders, attention deficit hyperactivity disorder (ADHD), bipolar disorder, Tourette’s disorder, tic disorder, and eating disorders (Drmic amd Szatmari 2014; Skokauskas and Gallagher 2010; Szatmari and McConnell 2011; Volkmar et al. 2014; White et al. 2009). Studies examining schizophrenia have been mixed; some studies report high rates (Chang et al. 2003), whereas others have not found an increased rate (Seltzer et al. 2004; Volkmar and Cohen 1991). Although the two disorders are quite different, misclassification of ASD as schizophrenia may also contribute to the variability in reported rates (Perlman 2000; Life Course Health Development in Autism Spectrum Disorders Palucka, Bradley, Lunsky 2008). Furthermore, ASD often co-occurs with multiple psychiatric disorders (Leyfer et al. 2006; Simonoff et al. 2008). For instance, Simonoff et al. (2008), using a population-based cohort, conducted a structured psychiatric interview (not modified for ASD) with 112 10–14-year olds and found that 70% of ASD participants had at least one comorbid disorder and 41% had two or more. It is important to better understand this comorbidity given that many individuals with ASD are likely to struggle with mental health concerns, and more importantly, psychiatric problems may cause considerable distress and interference with daily functioning and significantly impact long-term prognosis and outcome (Kim et al. 2000; Mattila et al. 2010; Muris et al. 1998; Russell and Sofronoff 2005; White et al. 2009). To date, the majority of research on psychiatric comorbidity has been conducted in children and adolescents (Amr et al. 2012; Gjevik et al. 2011; Leyfer et al. 2006; Mattila et al. 2010; Skokauskas and Gallagher 2010). In a review conducted by Skokauskas and Gallagher (2010) examining comorbid disorders in children with autism and Asperger syndrome, wide ranges in rates of co-occurring symptoms or diagnoses were found: 0–6% for schizophrenia, 5–35% for generalized anxiety, 10–64% for simple phobias, 1–37% for OCD, and 0–50% for affective disorders or symptoms. Fewer studies have examined this issue in adults, although research in this area is beginning to emerge (Cederlund et al. 2010; Ghaziuddin and Zafar 2008; Hofvander et al. 2009; La Malfa et al. 2007; Lugnegard et al. 2011; Moseley et al. 2011), and a number of follow-up studies have reported on psychiatric outcomes (Billstedt et al. 2005; Eaves and Ho 2008; Farley et al. 2009). A study that examined young adults with Asperger syndrome found high rates of depression and anxiety, consistent with the literature in children and adolescents with ASD, whereas psychotic and substance-induced disorders were uncommon (Lugnegard et al. 2011). Hofvander et al. (2009) assessed 122 consecutively referred adults with ASD and normal intelligence ranging in age from 16 to 60 years. Lifetime axis I comorbidity was common, and 259 they also found that mood (53%) and anxiety disorders (50%) were most common, followed by ADHD (43%), OCD (34%), and chronic tic disorders (20%). With regard to the rate of lifetime axis II disorders, 62% met criteria for at least one personality disorder. Other studies have reported somewhat lower rates of anxiety (Farley et al. 2009; Moseley et al. 2011) and depression (Billstedt et al. 2005; Eaves and Ho 2008; Farley et al. 2009; Moseley et al. 2011). A study examining adolescents and young adults with autism (over three-quarters had an intellectual disability) found that 42% had an additional mental health disorder, a rate that is two to four times seen in typically developing young people (Moseley et al. 2011). There is some evidence that the rates of mood and anxiety disorders in parents of children with autism is higher than that in the general population and when compared to Down syndrome (Bolton et al. 1998; Mazefsky et al. 2008). This may reflect an effect of stress on family members. A more thorough examination of genetic variable expressivity would involve a study of mood disorders in second- and thirddegree relatives. 5.2 Mental Health Services Given the high rate of psychiatric comorbidity, there is a need for appropriate mental health services for individuals with ASD of all ages. This includes preventive interventions designed to optimize mental functioning and life course trajectories. In a study investigating the healthcare experiences of children with ASD and their families, it was found that 15% of families reported unmet mental health services, and provider lack of skills was a barrier to obtaining these services (Chiri and Warfield 2012). With regard to the utilization of mental health services by individuals with ASD, the reports are mixed, with some reporting underutilization of services (Bryson et al. 2008) and others reporting relatively high rates of service use (Narendorf et al. 2011). Narendorf et al. (2011) reported that of those youth who received mental health services (i.e., 46%), 49% received it through the school. I.E. Drmic et al. 260 African-American youth and youth from lowerincome families were more likely to use schoolbased services than accessing community-based services, underscoring the importance of schools in providing these services for underserved group and the importance of ensuring the continuation of services after high school. For students with ASD, there is emerging research examining the delivery of mental health services in schools for anxiety (Drmic, Aljunied, Reaven 2017; Luxford, Hadwin, Kovshoff 2016). Bryson et al. (2008) found that short-term crisis-oriented mental health services were effective at stabilizing children with ASD but that many remained highly symptomatic at discharge, suggesting that changes are needed to the current system in order to serve those individuals with chronic and long-term needs. Many individuals with ASD may be at risk for psychiatric hospitalization. Indeed, approximately 11% of children and youth aged 5–21 years experienced at least one psychiatric hospital inpatient admission (Mandell 2008), and 48% used psychiatric hospital services by age 40 (Mouridsen et al. 2008). Children with a diagnosis of Asperger’s disorder and pervasive developmental disorder, compared to children with autism, were more likely to experience inpatient hospitalization (Bryson et al. 2008). Lunsky et al. (2009) compared patients with ASD and ID to other individuals accessing tertiary level mental healthcare in Ontario and found that individuals with ASD and ID were younger, spent more days in hospitals, and were less likely to have a psychotic disorder diagnosis than both patients with and without ID. Interestingly, fewer than half of the individuals with ASD and ID had an additional psychiatric diagnosis, although almost all (91%) were prescribed psychotropic medication, which is consistent with previous studies (Tsakanikos et al. 2006, 2007a). Tsakanikos et al. (2007b) reported that behavioral management problems predicted psychotropic medication and use of psychiatric services in adults with pervasive developmental disorder and ID. A study examining referral trends to specialist mental health services in south London from 1983 to 2000 for individuals with ASD and ID reported an increase in diagnosable psychiatric disorders over time and a significant reduction of medication at time of referral, but no change in the use of other therapeutic interventions, which is in contrast to services received by younger children with ASD. Thus, there continues to be a need for specialized inpatient and outpatient mental health services for individuals with ASD, as well as more training for service providers. 6 Summary ASD is a neurodevelopmental disorder that represents a chronic, lifelong condition based on atypicalities in brain development. While there are changes in syndrome expression over time, new challenges often emerge. Much of the current research has focused on static or crosssectional outcomes as opposed to developmental trajectories and changes in those trajectories. This can include looking at the developmental course of the disorder, the developmental course of specific skills that are important to later outcomes such as joint attention skills, as well as trajectories, related to the effects of early intervention. Longer-term studies are critical to treatment research in order to determine the sustainability of early intervention treatment gains, to explore whether there are sensitive periods of child development when treatments are most effective, and to determine factors that promote treatment outcome. Research needs to include enough information about factors that might be important to outcome so that predictors can be better elucidated, as well as a shift to focus on mediators and moderators of outcome. There is a need for more longitudinal studies and some consensus about the type of information that should be gathered by all research groups so that findings are comparable among different studies and can be combined into meta-analyses. For instance, standardized IQ measures could be used to document intellectual abilities. It may be beneficial to convene a group of experts as well as parents and individuals with ASD to determine what basic set of factors are important. The current review highlights that outcomes for people with ASD have been improving over Life Course Health Development in Autism Spectrum Disorders time; although relative to typically developing children and adolescents, outcomes are still generally poor. Much of the work to date has focused on achieving the traditional developmental tasks of adulthood, including independence in living arrangements, gaining and maintaining employment, and developing intimate relationships outside of the family unit. Research examining quality of life is beginning to emerge, which is an important next step to further our understanding of how to help improve the lived experiences of individuals with ASD. QOL studies need to shift focus and include information about factors and developmental processes that are thought to be important to achieving better life course health development outcomes, such as IQ, symptom severity, and language development. In addition, examining other factors that promote thriving in individuals with ASD is needed. Understanding what those factors are is critical so that better programs and interventions can be developed, which include the developmental scaffolding that children and families need to optimize longterm health development outcomes. It is also important to understand what impact access to adequate resources and intervention has on the identification, service provision, and likely outcomes on people with ASD. Another key theme is the development and emergence of mental health comorbidities which often represent an enormous challenge for young adults with ASD and their families. Research needs to include information about the developmental pathways that lead to these outcomes so that risk and protective factors can be targeted early, with the intent of optimizing the trajectory of mental health outcomes. There is also a need for more basic research such as epigenetic studies of nonsyndromic ASD, genetic studies that focus on much smaller copy number variants or DNA sequence variants identified either in exomes or in whole genome sequencing, and continued research examining environmental risk factors. A number of recommendations to facilitate the transition periods and support individual with ASD and their families can be gleaned from the existing literature. Implementation of a transition plan is crucial since it is associated with more 261 successful transition from early intervention into preschool (Stoner et al. 2007) and attainment of adult milestones (Frank and Siltington 2000) and may help temper the disruptive effect of leaving high school (Taylor and Seltzer 2010, 2011a, b). Parents, clinicians, and educators are encouraged to begin the transition planning process for the transition to adulthood early and include planning for treatment, independence (i.e., daily living skills, skills for independent living), education, employment, living arrangements, mental health, and medical care. For instance, good practical living skills, even in those individuals who have a cognitive disability, led to better outcomes and quality of life (Farley et al. 2009). Therefore, focusing on developing independence in daily living skills starting from a young age is an important target for intervention programs and transition planning. This highlights the importance of planning early so that skills can be integrated into treatment and educational programs at younger ages, allowing for ample practice and supervision of these skills well before the individual is expected to be able to manage on their own. For many individuals and families, the transition to adulthood is a time of worry and uncertainty because many services that were available through the educational system are no longer available and there are few appropriate supports or services for adults with ASD. Taylor and Seltzer (2010) showed that the improvement slowed significantly after exiting high school, particularly for those youth with ASD and no ID. This change was likely related to a lack of services or that available services were less stimulating than those provided in the educational system. Thus, the development of new models of care that incorporate appropriate educational, occupational and day programs for adults with ASD of all ability levels is essential. However, simply having a program is not enough; it is also important to ensure that the appropriate training and support is provided on an ongoing basis to the individual with ASD and that service providers are adequately trained to work with this group of individuals. For instance, given the prevalence and impact of mental health issues in individuals with ASD and their I.E. Drmic et al. 262 families, there is a need for ongoing mental health services and staff who are trained in this area and able to support families. A worrying trend is the increased use of psychopharmacology in adults with ASD often as a result of lack of psychosocial services that might better meet those needs in a way that improves life course outcomes and promotes thriving. Another example relates to successfully obtaining and maintaining long-term employment, which may involve supportive employment programs that include training related to general job skills (e.g., resume writing, interviewing skills, proper social behavior), specific skills needed for a particular job (e.g., learning job requirements and skills, learning about the culture and expectations for that job), and ongoing support (e.g., job coach). Training and support should also be provided to the employers in order to educate them about ASD in general, help them understand the needs of a particular individual, as well as highlight their unique skills and abilities that can positively contribute to workplace. Indeed, parents, educators, employers, and other professionals are encouraged to appreciate the many strengths, skills, and unique talents of individuals with ASD and approach current programs and future planning from a strength-based approach, leveraging each individual’s unique set of assets, versus a deficits-based approach that focuses on what needs to be fixed. There is a stark lack of information about ASD in the aging and elderly population. Given this gap in knowledge, a group of experts were convened to identify and describe these gaps and define a research agenda (see Piven and Rabins 2011). The research proprieties included developing diagnostic criteria and tools to diagnose older adults, conducting cross-sectional descriptive studies in adults of phenomenology and associated features, longitudinal studies of lifespan trajectories, neurobiological studies examining underlying changes over time, and intervention studies and mechanism to support clinical and research training in aging. This change in perspective might be part of a shift from treatment of deficits to accommodation of unique characteristics of the adult with ASD. There comes a time when the relentless pursuit of treatment to “make things better” will have a negative impact on the person with ASD and their family. People with ASD should not always be considered “disabled,” and taking a lifespan perspective to developmental trajectories should be part of this switch for service providers as well as parents. 7 Research Priorities There has been a marked increase in ASD-related research over the past decades. Considerable advances have been made in key areas, including: the genetic basis of ASD and its diagnosis; studies of the social brain; the connections between timing of diagnosis and long-term outcomes; new treatment models, some pharmacological and behavioral treatments can now be viewed as evidence based; psychiatric co-morbidities with particular emphasis on anxiety and depression; and, research on basic issues like employment, living arrangements, medical survelillance, etc. Thus, a number of gaps remain that need to be addressed. The following summarizes a number of recommendations for research, some of which were noted earlier in the paper, to better understand life course health development in ASD. 7.1 Research Priorities (i) Basic mechanism research: (a) Better understanding of epigenetic mechanisms in non-syndromic ASD. (b) Continued research in the area of genetics and neuroimaging. For example, this could include: • Genetic studies that focus on much smaller copy number variants or DNA sequence variants identified either in exomes or in whole genome sequencing • Studies that assess the impact of genetic testing (e.g., microarrays) on health outcomes • Studies combining genetics and neuroimaging work Life Course Health Development in Autism Spectrum Disorders • Neuroimaging studies examining the trajectories of brain development • Neuroimaging studies examining the impact of behavioral treatments on the brain (c) Continued research examining environmental risk factors, as well as geneenvironment interactions. (d) Identification of biomarkers (e.g., genetics, neural circuitry) is a high priority. For instance, the National Institutes of Health Interagency Coordinating Committee Strategic Plan called for the identification of biological markers, which separately or in combination with behavioral markers accurately identified ASD before age 2 (Dawson and Bernier 2013). (e) Development of novel therapeutics, e.g., creating medicine that can target core ASD symptoms. (f) Focus on early developmental skills or early markers of development that may impact the developmental trajectory. For instance, there is evidence that early joint attention skills are important for social cognitive development and later language and adaptive skills. Further, interventions targeting joint attention have been shown to be effective. Thus, research further examining the role of joint attention skills in ASD would be beneficial. It is also important to identify other potential early developing skills that may provide early targets for intervention. (ii) Clinical Research: (a) Early diagnosis remains a treatment priority. The recent advances in technology (e.g., neuroimaging) and genetics suggest the next wave of advances in our ability to detect autism through the use of biomarkers and new screening tools. Thus, research examining the utility of these biomarkers in clinical contexts will be needed. The availability of predictive biomarkers could index risk for ASD before or at the onset of symptoms, 263 thereby preventing the symptoms from developing or alleviating symptoms and improving the trajectory of outcomes. (b) Development of more evidence-based treatment research throughout the lifespan is needed. • A growing body of literature has shown that interventions can be associated with positive outcomes in children; however, there is still a need for studies across setting and improvements in methodological rigor. • Little evidence is available about treatment in adolescents and adults. There is a need to develop treatment across the lifespan and determine “which” treatments are most needed and effective at the different life stages and “when” these treatments are most effective. • Substantial scientific advances are needed to improve our understanding of which interventions are most effective for specific children with ASD and to elucidate the factors or components of interventions most associated with positive outcomes. • Exciting potential to bring together basic science and treatment in development of new metrics/measures (e.g., predicting response to treatments – recent work on EEG/fMRI and treatment change). (c) Very little evidence is available for specific treatment approaches in adolescents and adults; this is especially the case for evidence-based approaches to support the transition of youth with autism to adulthood. (Lounds et al. 2012). Thus, more research is needed to focus transition period. (d) Little information exists about the aging population in ASD with virtually no information about the aged (65+). More research is needed in various areas from basic and clinical research to data/ method development and translational research. A research agenda spanning all these areas for the aging population was described in Piven and Rabins (2011). As noted earlier, the research I.E. Drmic et al. 264 priorities included developing diagnostic criteria and tools to diagnose older adults, conducting cross-sectional descriptive studies in adults of phenomenology and associated features, longitudinal studies of lifespan trajectories, neurobiological studies examining underlying changes over time, and intervention studies and mechanism to support clinical and research training in aging. (e) Studies examining quality of life and the person, environmental, and personenvironmental interactions that lead to a thriving life. (iii) Translational research: (a) Need innovative solutions for training physicians, clinicians, and other professionals working with individuals with ASD throughout the lifespan. In particular, there is a serious lack of welltrained healthcare professional to serve the large number of adults with ASD. This includes multiple areas, including diagnosis and assessment of adult, mental health and medical supports, and supports for daily life. (b) Research related to transition planning as it relates to treatment, independence, education, employment, living arrangements, mental health, and medical care. 7.2 Data/Method Development Priorities (a) A shift toward longitudinal studies to better understand developmental trajectories (with attention to child and family characteristics – e.g., SES, parental education) (b) Inclusion of characteristics that might be important to outcome (e.g., IQ, language ability, etc.) so that predictors of outcome can be better elucidated, as well as a shift to focus on mediators and moderators of outcome. (c) A shift in focus examining not only risk factors but also on factors that are protective and promote thriving. (d) Better metrics throughout the lifespan. There is an exciting potential to bring together basic science and treatment in development of new metrics/measures (e.g., predicting response to treatments as was seen in recent work on fMRI and treatment change). (e) Development of better measures to look at outcome determination and more focus on quality of life (QOL) measures for the individual and family. (f) Greater focus in critical or sensitive periods of development when environemntal exposures can have long-lasting effects on outcomes. 7.3 Translational Priorities (a) Early diagnosis. A wide gap remains between our knowledge about best practices in ASD (e.g., screening for ASD at 18 and 24 months) and Centers for Disease Control and Prevention (CDC) data showing that the average age of diagnosis in the USA is 5 years and higher for individuals with milder forms of ASD. This is a problem given that much of the literature on early intervention shows best outcomes when intervention is initiated early (between 18 months and 5 years). This is a major challenge and will require innovative solutions and a focus on dissemination and implementation science. (b) Better coordination of different systems related to children’s health development (e.g., school and health systems). (c) Development of physician guidelines for healthcare. (d) Challenge in providing access to behavioral, mental health, and medical healthcare and, thus, the need for improved and expanded healthcare services with care providers who are well trained in working with individuals with ASD and their families. (e) Innovative solutions to better serve those in remote regions or underserved population (e.g., individuals living in poverty). Life Course Health Development in Autism Spectrum Disorders (f) Incorporating independent living skills into treatment/educational programs starting at a young age. (g) Although our understanding of autism has increased, there is a major gap in translating this to public school settings. (h) Development of new models/systems of care that incorporate appropriate educational, occupational and day programs for adults with ASD of all ability levels is essential. (i) Vocational system for adults. (j) Generic employment services do not meet the needs of individuals with ASD, therefore the need for more programs and research specific to individuals with ASD (e.g., supported employment models). Supportive employment programs – training related to general job skills, specific skills needed for a particular job, and ongoing support (e.g., job coach). (k) Training and support for employers. 8 Conclusions Taken together, there has been a tremendous growth in research in the ASD field. However, there remain a number of gaps that need to be addressed, with a particular focus on optimizing life course trajectories for individuals and populations with ASD. The principles of the life course health development framework describe health as an emergent set of developmental capacities that develop continuously over the lifespan in a complex, nonlinear process occurring in multiple dimensions, levels, and phases and are sensitive to timing. This is a valuable framework to direct the research agenda to continue to move the field forward and to improve the lives of individuals with ASD, their families, and their communities. References Adreon, D., & Stella, J. (2001). Transition to middle and high school: Increasing the success of students with Asperger syndrome. Intervention in School and Clinic, 36, 266–271. American Psychiatric Association. (1980). Diagnostic and statistical manual. Washington, DC: APA Press. 265 Amr, M., Raddad, D., El-Mehesh, F., Bakr, A., Sallam, K., & Amin, T. (2012). Comorbid psychiatric disorders in Arab children with autism spectrum disorders. Research in Autism Spectrum Disorders, 6, 240–248. Asperger, H. (1944). Die “autistichen Psychopathen” im Kindersalter. Archive fur psychiatrie und Nervenkrankheiten, 117, 76–136. Baker, T. B., Breslau, N., Covey, L., & Shiffman, S. (2012). DSM criteria for tobacco use disorder and tobacco withdrawal: A critique and proposed revisions for DSM-5. Addiction, 107(2), 263–275. doi:10.1111/j.1360-0443.2011.03657.x. Ballaban-Gil, K., Rapin, I., Tuchman, R., & Shinner, S. (1996). Longitudinal examination of the behavioral, language and social changes in a population of adolescents and young adults with autistic disorder. Pediatric Neurology, 15, 217–223. Baron-Cohen, S. (2011). The science of evil: On empathy and the origins of cruelty. New York: Basic Books. Beadle-Brown, J., Murphy, G., Wing, L., Gould, J., Shah, A., & Holmes, N. (2002). Changes in social impairment for people with intellectual disabilities: A follow-up of the Camberwell cohort. Journal of Autism and Developmental Disorders, 32, 195–206. Begovac, I., Begovac, B., Majic, G., & Vidovic, V. (2009). Longitudinal studies of IQ stability with childhood autism-literature survey. Psychiatria Danubina, 21(3), 310–319. Belcher, R., & Smith, M. D. (1994). Coworker attitudes towards employees with autism. Journal of Vocational Rehabilitation, 4, 29–36. Bennet, H. E., Wood, C. L., & Hare, D. J. (2005). Providing care for adults with autistic spectrum disorders in learning disability services: Needs-based or diagnosis-driven? Journal of Applied Research in Intellectual Disabilities, 18, 57–64. Billstedt, E., Gillberg, C., & Gillberg, C. (2005). Autism after adolescents: Population-based 13- to 22-year follow-up study of 120 individuals with autism diagnosed in childhood. Journal of Autism and Developmental Disorders, 35(3), 351–360. Billstedt, E., Gillberg, C., & Gillberg, C. (2007). Autism in adults: Symptom patterns and early childhood predictors. Use of the DISCO in a community sample followed from childhood. Journal of Child Psychology and Psychiatry, 48(11), 1102–1110. Billstedt, E., Gillberg, I. C., & Gillberg, C. (2011). Aspects of quality of life in adults diagnosed with autism in childhood: A population-based study. Autism, 15(1), 7–20. Bolton, P. F., Pickles, A., Murphy, M., & Rutter, M. (1998). Autism, affective, and other psychiatric disorders: Patterns of familial aggregation. Psychological Medicine, 28, 385–395. Bradley, E. A., Summers, J. A., Wood, H. L., & Bryson, S. E. (2004). Comparing rates of psychiatric and behavior disorders in adolescents and young adults with severe intellectual disability with and without autism. Journal of Autism and Developmental Disorders, 34, 151–161. 266 Brand, B. L., Lanius, R., Vermetten, E., Loewenstein, R. J., & Spiegel, D. (2012). Where are we going? An update on assessment, treatment, and neurobiological research in dissociative disorders as we move toward the DSM-5. Journal of Trauma & Dissociation, 13(1), 9–31. Brereton, A. V., Tonge, B. J., & Einfeld, S. L. (2006). Psychopathology in children and adolescents with autism compared to young people with intellectual disability. Journal of Autism and Developmental Disorders, 36, 862–870. Brown, J. R., & Rogers, S. J. (2003). Cultural issues in autism. In R. L. Hendren, S. Ozonoff, & S. Rogers (Eds.), Autism spectrum disorders (pp. 209–226). Washington, DC: American Psychiatric Press. Brugha, T. S., McManus, S., Bankart, J., Scott, F., Purdon, S., Smith, J., et al. (2011). Epidemiology of autism spectrum disorders in adults in the community in England. Archives of General Psychiatry, 68(5), 459–466. Bryson, S. A., Corrigan, S. K., McDonald, T. P., & Holmes, C. (2008). Characteristics of children with autism spectrum disorders who received services through community mental health centers. Autism, 12, 65–82. Burgess, A. F., & Gutstein, S. E. (2007). Quality of life for people with autism: Raising the standard for evaluating successful outcomes. Child and Adolescent Mental Health, 12, 80–86. Camarena, P. M., & Sarigiani, P. A. (2009). Postsecondary educational aspirations of high-functioning adolescents with autism spectrum disorders and their parents. Focus on Autism and Other Developmental Disabilities, 24(2), 115–128. Cederlund, M., Hagberg, B., Billstedt, E., Gillberg, I. C., & Gillberg, C. (2008). Asperger syndrome and autism: A comparative longitudinal follow-up study more than 5 years after original diagnosis. Journal of Autism and Developmental Disorders, 38, 72–85. Cederlund, M., Hagberg, B., & Gillberg, C. (2010). Asperger syndrome in adolescent and young adult males. Interview, self – And parent assessment of social, emotional, and cognitive problems. Research in Developmental Disabilities, 31, 287–298. Centers for Disease Control and Prevention (CDC). (2014). Prevalence of Autism Spectrum disorder among children aged 8 years — Autism and Developmental disabilities monitoring network, 11 sites, United States, 2010. Morbidity and Mortality Weekly Report, 63(SS02), 1–21. Chang, H. L., Juang, Y. Y., Wang, W. T., Huang, C. I., Chen, C. Y., & Hwang, Y. S. (2003). Screening for autism spectrum disorder in adult psychiatric outpatients in a clinic in Taiwan. General Hospital Psychiatry, 25, 284–288. Chawarska, K., Klin, A., & Volkmar, F. R. (2008). Autism spectrum disorders in infants and toddlers: diagnosis, assessment, and treatment. Autism spectrum disorders in infants and toddlers: Diagnosis, assessment, and I.E. Drmic et al. treatment (pp. 327–336, 348). New York: Guilford Press. Chiang, H.-M., Cheung, Y. K., Hickson, L., Xiang, R., & Tsai, L. Y. (2012). Predictive factors of participation in postsecondary education for high school leavers with autism. Journal of Autism and Developmental Disorders, 42, 685–696. doi:10.1007/ s10803-011-1297-7. Chiri, G., & Warfield, M. E. (2012). Unmet needs and problems accessing core health care services for children with autism spectrum disorders. Journal of Child and Maternal Health, 16, 1081–1091. Cimera, R. E., & Cowan, R. J. (2009). The costs of services and employment outcomes achieved by adults with autism in the US. Autism, 13(3), 285–302. Constantino, J. N., Zhang, Y., Frazier, T., Abbacchi, A. M., & Law, P. (2010). Sibling recurrence and the genetic epidemiology of autism. The American Journal of Psychiatry, 167(11), 1349–1356. Creak, M. (1961). Schizophrenic syndrome in childhood. Developmental Medicine and Child Neurology, 3, 501–504. Daniels, A. M., Rosenberg, R. E., Law, J. K., Lord, C., Kaufmann, W. E., & Law, P. A. (2011). Stability of initial disorder diagnoses in community settings. Journal of Autism and Developmental Disorders, 41(1), 110–121. Dawson, G., & Bernier, R. (2013). A quarter century of progress on early detection and treatment of autism spectrum disorder. Development and Psychopathology, 25, 1455–1472. Dawson, G., Jones, E. J., Merkle, K., Venema, K., Lowy, R., Faja, S., et al. (2012). Early behavioral intervention is associated with normalized brain activity in young children with autism. Journal of the American Academy of Child & Adolescent Psychiatry, 51, 1150–1159. DeMyer, M. K., Barton, S., DeMyer, W. E., Norton, J. S., Allen, J., & Steele, R. (1973). Prognosis in autism: A follow-up study. Journal of Autism and Childhood Schizophrenia, 3, 199–246. Devlin, B., & Scherer, S. W. (2012). Genetic architecture in autism spectrum disorder. Current Opinion in Genetics and Development, 22, 229–237. Devlin, B., Melhem, N., & Roeder, K. (2011). Do common variants play a role in risk for autism? Evidence and theoretical musings. Brain Research, 1380, 78–84. Drmic, I., Aljunied, M., & Reaven, J. (2017). Facing your fears in schools: Implementing CBT for teens with ASD and anxiety in Singapore. Journal of Autism and Developmental Disabilities. doi:10.1007/ s10803-016-3007-y. Drmic, I., & Szatmari, P. (2014). Emotional dysregulation and comorbidity in Autism Spectrum Disorder. Invited review for Cutting Edge Psychiatry in Practice – Autism Issue, 1, 119–131. Eaves, L. C., & Ho, H. H. (2008). Young adult outcomes of autism spectrum disorders. Journal of Autism and Developmental Disorders, 38, 739–747. Life Course Health Development in Autism Spectrum Disorders Eisenberg, L. (1956). The autistic child in adolescence. The American Journal of Psychiatry, 112, 607–612. Elsabbagh, M., Divan, G., Koh, Y.-J., Kim, Y. S., Kauchali, S., Marcin, C., et al. (2012). Global prevalence of autism and other pervasive developmental disorders. Autism Research, 5(3), 160–179. doi: http://dx.doi. org/10.1002/aur.239. Engstrom, I., Ekstrom, L., & Emilsson, B. (2003). Psychosocial functioning in a group of Swedish adults with Asperger syndrome or high-functioning autism. Autism, 7(1), 99–110. Faja, S., Webb, S. J., Jones, E., Merkle, K., Kamara, D., Bavaro, J., et al. (2012). The effects of face expertise training on the behavioral performance and brain activity of adults with high functioning autism spectrum disorders. Journal of Autism and Developmental Disorders, 42, 278–293. Farley, M. A., McMahon, W. M., Fombonne, E., Jenson, W. R., Miller, J., Gardner, M., et al. (2009). Twentyyear outcome for individuals with autism and average or near-average cognitive abilities. Autism Research, 2, 109–118. Fombonne, E. (2005a). Epidemiology of autistic disorder and other pervasive developmental disorders. Journal of Clinical Psychiatry, 66(Suppl 10), 3–8. Fombonne, E. (2005b). The changing epidemiology of autism. Journal of Applied Research in Intellectual Disabilities, 18(4), 281–294. doi: http://dx.doi. org/10.1111/j.1468-3148.2005.00266.x. Frank, A. R., & Siltington, P. L. (2000). Young adults with mental disbailities – Does transition planning make a difference? Education and Training in Mental Retardation and Developmental Disabilities, 35, 119–134. Fussell, E., & Furstenberg, F. F. (2005). The transition to adulthood during the twentieth century. In R. A. Settersten, F. F. Furstenberg, & R. G. Rumbaut (Eds.), On the frontier of adulthood: Theory, research and public policy (pp. 29–75). Chicago: University of Chicago Press. García-Villamisar, D. A., & Dattilo, J. (2010). Effects of a leisure pro-gramme on quality of life and stress of individuals with ASD. Journal of Intellectual Disability Research, 54, 611–619. Garcia-Villamisar, D., Ross, D., & Wehman, P. (2000). Clinical differential analysis of persons with autism in a work setting: A follow-up study. Journal of Vocational Rehabilitation, 14, 183–185. García-Villamisar, D., Wehman, P., & Navarro, M. D. (2002). Changes in the quality of autistic people’s life that work in supported and sheltered employment: A 5-year follow-up study. Journal of Vocational Rehabilitation, 17, 309–312. Gardener, H., Spiegelman, D., & Buka, S. L. (2009). Prenatal risk factors for autism: Comprehensive metaanalysis. British Journal of Psychiatry, 195(1), 7–14. Gerber, F., Bessero, S., Robbiani, B., et al. (2011). Comparing residential programmes for adults with autism spectrum disorders and intellectual disability: Outcomes of challenging behaviour and quality of life. Journal of Intellectual Disability Research, 55, 918–932. 267 Geschwind, D.H., & State, M.W. (2015). Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurology, Published online http:// dx.doi.org/10.1016/S1474-4422(15)00044-7. Ghaziuddin, M., & Zafar, S. (2008). Psychiatric comorbidity in adults with autism spectrum disorders. Clincial Neuropsychiatry, 5, 9–12. Ghaziuddin, M., Weidmer-Mikhail, E., & Ghaziuddin, N. (1998). Comorbidity of Asperger syndrome: A preliminary report. Journal of Intellectual Disability Research, 42, 278–283. Gillberg, C. (1991). Outcome in autism and autistic-like conditions. Journal of the American Academy of Child and Adolescent Psychiatry, 30, 375–382. Gillberg, C., & Steffenburg, S. (1987). Outcomes and prognostic factors in infantile autism and similar conditions: A population-based study of 46 cases followed through puberty. Journal of Autism and Developmental Disorders, 17, 272–288. Gillespie-Lynch, K., Sepeta, L., Wang, Y., Marshall, S., Gomez, L., Sigman, M., & Hutman, T. (2012). Early childhood predictors of the social competence of adults with autism. Journal of Autism and Developmental disorder, 42, 161–174. Gilotty, L., Kenworthy, L., Sirian, L., Black, D. O., & Wagner, A. E. (2012). Adaptive skills and executive function in autism spectrum disorders. Child Neuropsychology, 8(4), 241–248. Gjevik, E., Eldevik, S., Fjaeran-Granum, T., & Sponheim, E. (2011). Kiddie-SADS reveals high rates of DSM-IV disorders in children and adolescents with autism spectrum disorders. Journal of Autism and Developmental Disorders, 41, 761–769. Glennon, T. J. (2001). The stress of the university experience for students with Asperger syndrome. Work: Journal of Prevention, Assessment & Rehabilitation, 17(3), 183–190. Gulsrud, A. C., Hellemann, G. S., Freeman, F. N., & Kasari, C. (2014). Two to ten years: Developmental trajectories of joint attention with ASD who received targeted social communication interventions. Autism Research, 7, 207–215. Hagner, D., & Cooney, B. F. (2005). “I do that for everybody”: Supervising employees with autism. Focus on Autism and Developmental Disabilities, 20(2), 91–97. Halfon, N., & Forrest, C. B. (2017). The emerging theoretical framework of life course health development. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health-development science. Cham: Springer. Halfon, N., Larson, K., Lu, M., Tullis, E., & Russ, S. (2014). Lifecourse health development: Past, present and future. Journal of Maternal Child Health, 18, 344–365. Hallmayer, J., Cleveland, S., Torres, A., Phillips, J., Cohen, B., Torigoe, T., et al. (2011). Genetic heritability and shared environmental factors among twin pairs with autism. Archives of General Psychiatry, 68(11), 1095–1102. doi:10.1001/archgenpsychiatry. 2011.76. 268 Harris, L. J., & Handelman, J. S. (Eds.). (2000). Preschool education programs for children with autism (2nd ed.). Austin: PRO-ED. Henninger, N. A., & Taylor, J. L. (2012). Outcomes in adults with autism spectrum disorders: A historical perspective. Autism, 0(0), 1–14. doi:10.1177/1362361312441266. Hermelin, B. (2001). Bright Splinters of the mind: A personal story of research with Autistic savants. London: Jessica Kingsley. Hofvander, B., Delorme, R., Chaste, P., Nyden, A., Wentz, E., Stahlberg, O., et al. (2009). Psychiatric and psychosocial problems in adults with normal-intelligence autism spectrum disorders. BMC Psychiatry, 9, 35. doi:10.1186/1471-244X-9-35. Holden, B., & Gitlesen, J. P. (2006). A total population study of challenging behavior in the country of Hedmark, Norway: Prevalence, and risk markers. Research in Developmental Disabilities, 27(4), 456–465. Howlin, P. (1998). Children with autism and Asperger syndrome. A guide for practitioners and carers. Chichester: John Wiley & Sons. Howlin, P. (2000). Outcome in adult life for more able individuals with Autism or Asperger Syndrome. Autism, 4(1), 63–83. Howlin, P. (2003). Outcome in high-functioning adults with autism with and without early language delays: Implications for the differentiation between autism and Asperger syndrome. Journal of Autism and Developmental Disorders, 33, 3–13. Howlin, P. (2005). Outcomes in autism spectrum disorders. In P. Howlin (Ed.), Handbook od autism and pervasive developmental disorder, volume 1: Diagnosis, development, neurobiology, and behavior (3rd ed.). Hoboken: John Wiley & Sons. Howlin, P. (2013). Outcomes in adults with autism spectrum disorders. In F. Volkmar, S. Rogers, R. Paul, & K. Pelphrey (Eds.), Handbook of autism (4th ed.). Hoboken: Wiley. Howlin, P., & Goode, S. (1998). Outcomes in adult life for individuals with autism. In F. Volkmar (Ed.), Autism and developmental disorders. New York: Cambridge University Press. Howlin, P., & Moss, P. (2012). Adults with autism spectrum disorders. Canadian Journal of Psychiatry, 57(5), 275–283. Howlin, P., Alcock, J., & Burkin, C. (2005). An 8 year follow-up of a specialist supported employment service for high-ability adults with autism or Asperger syndrome. Autism, 9(5), 533–549. Howlin, P., Goode, J., Hutton, J., & Rutter, M. (2004). Adult outcomes for children with autism. Journal of Child Psychology and Psychiatry, 45, 212–229. Huerta, M., Bishop, S. L., Duncan, A., Hus, V., & Lord, C. (2012). Application of the DSM-5 criteria for autism spectrum disorder to three samples of children with DSM-IV diagnoses of pervasive developmental disorders. American Journal of Psychiatry, 169(10), 1056–1064. I.E. Drmic et al. Jobe, L., & White, S. W. (2007). Loneliness, social relationships, and a broader autism phenotype in college students. Personality and Individual Differences, 42, 1479–1489. Joshi, G., Petty, C., Wozniak, J., Henin, A., Fried, R., Galdo, M., et al. (2010). The heavy burden of psychiatric comorbidity in youth with autism spectrum disorders: A large comparative study of psychiatrically referred population. Journal of Autism and Developmental Disorders, 40, 1361–1370. Kamio, Y., Inada, N., & Koyama, T. (2012). A nationwide survey on quality of life and associated factors of adults with high-functioning autism spectrum disorders. Autism, 17, 15–26. Kamp-Becker, I., Schröder, J., Muehlan Remschmidt, H., et al. (2011). Health-related quality of life in children and adolescents with autism spectrum disorder. Zeitschrift für Kinder - und Jugendpsychiatrie und Psychotherapie, 39(2), 123–131. Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child, 2, 217–250. Kanner, L. (1971). Follow-up study of eleven autistic children originally reported in. Journal of Autism & Childhood Schizophrenia, 1(2), 119–145. Kasari, C., Gulsrud, A. C., Wong, C., Kwon, S., & Locke, J. (2010). Randomized controlled caregiver mediated joint engagement intervention for toddlers with autism. Journal of Autism Developmental Disorders, 40(9), 1045–1056. Keel, J. H., Mesibov, G. B., & Woods, A. V. (1997). TEACCH-supported employment program. Journal of Autism and Developmental Disabilities, 27(1), 3–9. Keogh, B. K., Bernheimer, L. P., & Guthrie, D. (2004). Children with developmental delays twenty years later: Where are they? How are they? American Journal on Mental Retardation, 109(3), 219–230. Kim, S.H., Macari, S., Koller, J., & Chawarska, K. (2015). Examining the phenotypic heterogeneity of early autism spectrum disorder: Subtypes and shortterm outcomes. Journal of Child Psychology and Psychiatry. doi:10.1111/jcpp.12448. King, M., & Bearman, P. (2009). Diagnostic change and the increased prevalence of autism. International Journal of Epidemiology, 38(5), 1224–1234. Epub 2009 Sep 7. PubMed PMID: 19737791. Kleinman, J., Ventola, P., Pandey, J., Verbalis, A., Barton, M., Hodgson, S., et al. (2008). Diagnostic stability in very young children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 38(4), 606–615. doi:10.1007/s10803-007-0427-8. Klin, A., Volkmar, F. R., Sparrow, S. S., Cicchetti, D. V., & et al. (1996). Validity and neuropsychological characterization of Asperger syndrome: Convergence with nonverbal learning disabilities syndrome. Annual Progress in Child Psychiatry & Child Development, 241–259. Kobayashi, R., Murata, T., & Yoshinaga, K. (1992). A follow-up study of 201 children with autism in Kyushu and Yamaguchi areas, Japan. Journal of Autism and Developmental Disorders, 22, 395–411. Life Course Health Development in Autism Spectrum Disorders Kraemer, B. R., & Blacher, J. (2001). Transition for young adults with severe mental retardation: School preparation, parent expectations, and family involvement. Mental Retardation, 39, 432–455. Kraemer, B. R., McIntyre, L. L., & Blacher, J. (2003). Quality of life for young adults with mental retardation during transition. Mental Retardation, 41, 250–262. Kim, J., Szatmari, P., Bryson, S. E., & Wilson, F. J. (2000). Prevalence of anxiety and mood problems among children with autism and Asperger syndrome. Autism, 4, 117–132. LaFramboise, T., Winckler, W., & Thomas, R. K. (2009). A flexible rank-based framework for detecting copy number aberrations from array data. Bioinformatics, 25(6), 722–728. La Malfa, G., Lassi, S., Salvini, R., Giganti, C., Bertelli, M., & Albertini, G. (2007). The relationship between autism and psychiatric disorders in intellectually disabled adults. Research in Autism Spectrum Disorders, 1, 218–228. Landa, R. J., & Kalb, L. G. (2012). Long-term outcomes of toddlers with autism spectrum disorders exposed o short-term intervention. Pediatrics, 130(2), S186– S190. doi:10.1542/peds.2012-0900Q. Landa, R. J., Holman, K. C., & Garrett-Mayer, E. (2007). Social and communication development in toddlers with early and later diagnosis of autism spectrum disorders. Archives of General Psychiatry, 64(7), 853–864. Landa, R. J., Holman, K. C., O’Neill, A. H., & Stuart, E. A. (2011). Intervention targeting development of socially synchronous engagement in toddlers with autism spectrum disorder: A randomized controlled trial. Journal of Child Psychology and Psychiatry, 52(1), 13–21. Larsen, F. W., & Mouridsen, S. E. (1997). The outcome in children with childhood autism and Asperger syndrome originally diagnosed as psychotic. A 30-year follow-up study of subjects hospitalized as children. European Child and Adolescent Psychiatry, 6(4), 181–190. Lee, G. K., & Carter, E. W. (2012). Preparing transitionage students with high-functioning autism spectrum disorders for meaningful work. Psychology in the Schools, 49(10), 988–1000. doi:10.1002/pits.21651. Leekam, S. R., Nieto, C., Libby, S. J., Wing, L., & Gould, J. (2006). Describing the sensory abnormalities of children and adults with autism. Journal of Autism and Developmental Disorders, 37, 894–910. doi:10.1007/ s10803-006-0218-7. Levy, A., & Perry, A. (2011). Outcome in adolescents and adults with autism: A review of the literature. Research in Autism Spectrum Disorders, 5, 1271–1282. Levy, S. E., Mandell, D. S., & Schultz, R. T. (2009). Autism. Lancet, 374, 1627–1638. Leyfer, O. T., Folstein, S. E., Bacalman, S., Davis, N. O., Dinh, E., Morgan, J., et al. (2006). Comorbid psychiatric disorders in children with autism: Interview development and rates of disorders. Journal of Autism and Developmental Disorders, 36, 849–861. 269 Li, X., Zou, H., & Brown, W. T. (2012). Genes associated with autism spectrum disorder. Brain Research Bulletin, 88(6), 543–552. Liptak, G. S., Benzoni, L. B., Mruzek, D. W., Nolan, K. W., Thingvoll, M. A., Wade, C. M., & Fryer, G. E. (2008). Disparities in diagnosis and access to health services for children with autism: Data from the national survey of children’s health. Journal of Developmental & Behavioral Pediatrics, 29(3), 152–160. Lockyer, L., & Rutter, M. (1969). A five- to fifteenyear follow-up study of infantile psychosis: III. Psychological aspects. The British Journal of Psychiatry, 115, 865–882. Lockyer, L., & Rutter, M. (1970). A five- to fifteen-year follow-up study of infantile psychosis: IV. Patterns of cognitive ability. British Journal of Social and Clinical Psychology, 9(2), 152–163. Loke, Y. J., Hannan, A. J., & Craig, J. M. (2015). The role of epigenetic change in autism spectrum disorders. Frontiers in Neurology, 6, 107. doi:10.3389/ fneur.2015.00107. Lord, C., & Bailey, A. (2002). Autism spectrum disorders. In M. Rutter & E. Taylor (Eds.), Child and adolescent psychiatry (4th ed., pp. 664–681). Oxford, MA: Blackwell Scientific. Lord, C., & Schopler, E. (1989). The role of age at assessment, developmental level, and test in the stability of intelligence scores in young autistic children. Journal of Autism and Developmental Disorders, 19(4), 483–499. Lotter, V. (1974). Social adjustment and placement of autistic children in Middlesex: A follow-up study. Journal of Autism and Childhood Schizophrenia, 4, 11–32. Lotter, V. (1978). Follow-up studies. In M. Rutter & E. Schopler (Eds.), Autism: A reappraisal of concepts and treatment (pp. 475–495). New York: Plenum Press. Lounds Taylor, J., Dove, D., Veenstra-VanderWeele, J., Sathe, N.A., McPheeters, M.L., Jerome, R.N., Warren, Z.. (2012). Interventions for adolescents and young Adults with Autism Spectrum disorders. Comparative effectiveness review no. 65. (Prepared by the Vanderbilt evidence-based practice Center under contract no. 290–2007-10065-I.) AHRQ publication no. 12-EHC063-EF. Rockville, MD: Agency for Healthcare Research and Quality. www.effectivehealthcare.ahrq.gov/reports/final.cfm Lovaas, I. (1987). Behavioral treatment and normal educational and intellectual functioning in young autistic children. Journal of Consulting and Clinical Psychology, 55(1), 3–9. LoVullo, S. V., & Matson, J. L. (2009). Comorbid psychopathology in adults with autism spectrum disorders and intellectual disabilities. Research in Developmental Disabilities, 30, 1288–1296. doi:10.1016/j.ridd.2009.05.004. Lugnegard, T., Hallerback, M. U., & Gillberg, C. (2011). Psychiatric comorbidity in young adults with a clinical diagnosis of Asperger syndrome. Research in Developmental Disabilities, 32, 1910–1917. 270 Lunsky, Y., Gracey, C., & Bradley, E. (2009). Adults with autism spectrum disorders using psychiatric hospitals in Ontario: Clinical profile and service needs. Research in Autism Spectrum Disorders, 3, 1006–1013. Luxford, S., Hadwin, J. A., & Kovshoff, H. (2016). Evaluating the effectiveness of a school-based cognitive behavioural therapy intervention for anxiety in adolescents diagnosed with autism spectrum disorder. Journal of Autism and Developmental Disorders, doi:http://dx.doi.org/10.1007/s10803-016-2857-7 Magiati, I., Moss, J., Charman, T., & Howlin, P. (2011). Patterns of change in children with autism spectrum disorders who received community based comprehensive interventions in their pre-school years: A seven year follow-up study. Research in Autism Spectrum Disorders, 5(3), 1016–1027. Malhotra, D., & Sebat, J. (2012). Copy number variants: Harbingers of a rare variant revolution in psychiatric genetics. Cell, 148(6), 1223–1241. Mattila, M.-L., Hurtig, T., Haapsamo, H., Jussila, K., Kuusikko-Gauffin, S., Kielinen, M., et al. (2010). Comorbid psychiatric disorders associated with Asperger syndrome/high functioning autism: A community and clinic-based study. Journal of Autism and Developmental Disorders, 40, 1080–1093. Mattilla, M.-L., Kielinen, M., Linna, S.-L., Jussilla, K., Ebeling, H., Bloigu, R., et al. (2011). Autism Spectrum disorders According to DSM-IV-TR and comparison with DSM-5 draft criteria an epidemiological study. Journal of the American Academy of Child & Adolescent Psychiatry, 50(6), 583–592. MacNeil, B. M., Lopes, V. A., & Minnes, P. M. (2009). Anxiety in children and adolescents with autism spectrum disorders. Research in Autism Spectrum Disorders, 3, 1–21. doi:10.1016/j.rasd.2008.06.001. Mandell, D. (2008). Psychiatric hospitalization among children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 38, 1059–1065. Marshall, C. R., Noor, A., Vincent, J. B., Lionel, A. C., Feuk, L., Skaug, J., et al. (2008). Structural variation of chromosomes in autism spectrum disorder. American Journal of Human Genetics, 82(2), 477–488. Matson, J. L., Wilkins, J., & Ancona, M. (2008). Autism in adults with severe intellectual disability: An empirical study of symptom presentation. Journal of Intellectual & Developmental Disability, 33, 36–42. doi:10.1080/13668250701829837. Mawhood, L., & Howlin, P. (1999). The outcome of a supported employment scheme for high functioning adults with autism or Asperger syndrome. Autism, 3, 229–254. Mawhood, L., Howlin, P., & Rutter, M. (2000). Autism and developmental receptive language disorder-a comparative follow-up in early adult life I: Cognitive and language outcomes. Journal of Child Psychology and Psychiatry, 41, 547–559. Mazefsky, C. A., Folstein, S. E., & Lainhart, J. E. (2008). Overrepresentation of mood and anxiety disorders in adults with autism and their first degree relatives: What does it mean? Autism Research, 1, 193–197. I.E. Drmic et al. McGrew, S. G., Peters, B. R., Crittendon, J. A., & Veenstra-VaderWeele, J. (2012). Diagnostic yield of chromosomal microarray analysis in autism primary care practice: Which guidelines to implement? Journal of Autism and Developmental Disorders, 42(8), 1582–1591. McDougle, C. J., Erickson, C. A., Stigler, K. A., & Posey, D. J. (2005). Neurochemistry in the pathophysiology of autism. Journal of Clinical Psychiatry, 66(Suppl 10), 9–18. Meyer, J. A., & Minshew, N. J. (2002). An update on neurocognitive profiles in Asperger syndrome and high-functioning autism. Focus on Autism and Other Developmental Disabilities, 17(3), 152–160. http:// dx.doi.org/10.1177/10883576020170030501. Moseley, D. S., Tonge, B. J., Brereton, A. V., & Einfeld, S. L. (2011). Psychiatric comorbidity in adolescents and young adults with autism. Journal of Mental Health Research in Intellectual Disabilities, 4, 229–243. Mouridsen, S. E., Rich, B., Isager, T., & Nedergaard, N. J. (2008). Psychiatric disorders in individuals diagnosed with infantile autism as children: A case control study. Journal of Psychiatric Practice, 14, 5–12. Muller, N., Myint, A., & Schwarz, M. J. (2015). Immunology and psychiatry: From basic research to therapeutic interventions. Cham: Springer International Publishing. Mundy, P., & Jarrold, W. (2010). Infant joint attention, neural networks and social cognition. Neural Networks, 23, 985–997. doi:10.1016/j.neunet.2010.08.009. Mundy, P., Sullivan, L., & Mastergeorge, A. M. (2009). A parallel and distributed processing model of joint attention, social cognition, and autism. Autism Research, 2, 2–21. Muris, P., Steerneman, P., Merckelbach, H., Holdrinet, I., & Meesters, C. (1998). Comorbid anxiety symptoms in children with pervasive developmental disorders. Journal of Anxiety Disorders, 12, 387–393. Narendorf, S. C., Shattuck, P. T., & Sterzing, P. R. (2011). Mental health services among adolescents with an autism spectrum disorder. Psychiatric Services, 62, 975–978. National Autism Center. (2015). Findings and conclusions: National standards project, phase 2. Randolph, MA: Author. National Autism Center. (2009). National Standards Report – Addressing the need for evidence-based practice guidelines for Autism Spectrum Disorders. Massachusetts: National Autism Center. National Research Council. (2001). Educating young children with autism. Washington, DC: National Academy Press. Newman, L., Wagner, M., Knokey, A. M., Marder, C., Nagle, K., Shaver, D., & Wei, X. (2011). The posyhigh school outcomes of young adults with disabilities up to 8 years after high school. Menlo Park: SRI International. Nordin, V., & Gillberg, C. (1998). The long-term course of autistic disorders: Update on follow-up studies. Acta Psychiatrica Scandinavica, 97, 99–108. Life Course Health Development in Autism Spectrum Disorders Noterdaeme, M., Wriedt, E., & Höhne, C. (2010). Asperger's syndrome and high-functioning autism: Language, motor and cognitive profiles. European Child & Adolescent Psychiatry, 19(6), 475–481. Palac, S., & Meador, K. J. (2011). Antiepileptic drugs and neurodevelopment: An update. Current Neurology and Neuroscience Reports, 11(4), 423–427. Palucka, A. M., Bradley, E., & Lunsky, Y. (2008). A case of unrecognized intellectual disability and autism misdiagnosed as schizophrenia: Are there lessons to be learned? Mental Health Aspects of Developmental Disabilities, 11(2), 55–60. Parr, J. R., Le Couteur, A., Baird, G., Rutter, M., Pickles, A., Fombonne, E., Bailey, A. J., & International Molecular Genetic Study of Autism Consortium. (2011). Early developmental regression in autism spectrum disorder: Evidence from an international multiplex sample. Journal of Autism & Developmental Disorders, 41(3), 332–340. Paul, R. (2008). Communication development and assessment. In K. Chawarska, A. Klin, & F. Volkmar (Eds.), Autism Spectrum disorders in infants and toddlers: Diagnosis, Assessment, and treatment (pp. 76–103). New York: Guilford Press. Pinto, D., Pagnamenta, A. T., Klei, L., Anney, R., Merico, D., Regan, R., et al. (2010). Functional impact of global rare copy number variation in autism spectrum disorders. Nature, 466(7304), 368–372. Piven, J., & Rabins, P. (2011). Autism spectrum disorders in older adults: Toward defining a research agenda. Journal of the American Geriatrics Society, 59(11), 2151–2155. Perkins, E. A., & Berkman, K. A. (2012). Into the unknown: Aging with autism spectrum disorders. American Journal on Intellectual and Developmental Disabilities, 117(6), 478–496. Perlman, L. (2000). Adults with Asperger disorder misdiagnosed as schizophrenic. Professional Psychology: research & Practice, 31(2), 221–225. False. Peters-Scheffer, N., Didden, R., Hubert, K., & Sturmey, P. (2011). A meta-analytic study on the effectiveness of comprehensive ABA-based early intervention programs for children with Autism Spectrum disorders. Research in Autism Spectrum Disorders, 5(1), 60–69. Polloway, E. A., Patton, J. R., & Serna, L. (2001). Strategies for teaching learners with special needs. Upper Saddle River: Merrill Prentice Hall. Potvin, M. C., Snider, L., Prelock, P. A., Wood-Dauphinee, S., & Kehayia, E. (2015). Health-related quality of life in children with high-functioning autism. Autism, 19(1), 14–19. Power, R. A., Kyaga, S., Uher, R., Maccabe, J. H., Långström, N., Landen, M., et al. (2012). Fecundity of patients with schizophrenia, Autism, bipolar disorder, depression, anorexia nervosa, or substance abuse vs their unaffected siblings. Archives of General Psychiatry, 12, 1–8. Raphael, D., Brown, I., Renwick, R., & Rootman, I. (1996). Assessing the quality of life of persons with developmental disabilities: Description of a new 271 model, measuring instruments, and initial findings. International Journal of Diability, 43(1), 25–42. Reichow, B. (2012). Overview of meta-analyses on early intensive behavioral intervention for young children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 42, 512–520. Reichow, B., & Wolery, M. (2009). Comprehensive synthesis of early intensive behavioral interventions for young children with autism based on the UCLA young Autism Project model. Journal of Autism and Developmental Disorders, 39(1), 23–41. Renty, J., & Roeyers, H. (2006). Quality of life in highfunctioning adults with autism spectrum disorder: The predictive value of disability and support characteristics. Autism, 10, 511–524. Ritvo, E. R., Jorde, L. B., Mason-Brothers, A., Freeman, B. J., Pingree, C., Jones, M. B., et al. (1989). The UCLAUniversity of Utah epidemiologic survey of autism: Recurrence risk estimates and genetic counseling. American Journal of Psychiatry, 146(8), 1032–1036. Robinson, S., Goddard, L., Dritschel, B., Wisley, M., & Howlin, P. (2009). Executive functions in children with autism spectrum disorders. Brain and Cognition, http://dx.doi.org/10.1016/j. 71(3), 362–368. bandc.2009.06.007 Rogers, S. J. (2009). What are infant siblings teaching us about autism in infancy? Autism research : Official Journal of the International Society for Autism Research, 2(3), 125–137. Rondeau, E., Klein, L. S., Masse, A., Bodeau, N., Cohen, D., & Guile, J.-M. (2011). Is pervasive developmental disorder not otherwise specified less stable than autistic disorder? A meta-analysis. Journal of Autism and Developmental Disorders, 41, 1267–1276. Ruble, L. A., & Dalrymple, N. J. (1996). An alternative view of outcome in autism. Focus on Autism and Other Developmental Disabilities, 11(1), 3–14. Rumsey, J. M., Rapoport, J. L., & Sceert, W. R. (1985). Autistic children as adults: Psychiatric, social, and behavioral outcomes. Journal of the American Academy of Child Psychiatry, 24(4), 465–473. Russ, S. A., Larson, K., Tullis, E., & Halfon, N. (2014). A lifecourse approach to health development: Implications for the maternal and child health research agenda. Maternal and Child Health Journal, 18(2), 497–510. Russell, E., & Sofronoff, K. (2005). Anxiety and social worries in children with Asperger syndrome. Australian and New Zealand Journal of Psychiatry, 39, 633–638. Rutter, M. (1967). The autistic child. Royal Institute of Public Health & Hygiene Journal, 30(4), 130–132. Rutter, M. (1973). Why are London children so disturbed? Proceedings of the Royal Society of Medicine, 66(12), 1221–1225. Rutter, M. (1978). Diagnosis and definition of childhood autism. Journal of Autism & Childhood Schizophrenia, 8(2), 139–161. Rutter, M. (1982). Prevention of children's psychosocial disorders: Myth and substance. Pediatrics, 70(6), 883–894. 272 Rutter, M. (2005). Environmentally mediated risks for psychopathology: Research strategies and findings. Journal of the American Academy of Child & Adolescent Psychiatry, 44(1), 3–18. Rutter, M., & Lockyer, L. (1967). A five to fifteen year follow-up study of infantile psychosis I. Description of sample. British Journal of Psychiatry, 113(504), 1169–1182. Rutter, M., Greenfield, D., & Lockyer, L. (1967). A five to fifteen year follow-up of infantile psychosis II social and behavioral outcome. The British Journal of Psychiatry, 113, 1183–1199. Schreibman, L., Dawson, G., Stahmer, A. C., Landa, R., Rogers, S., et al. (2015). Naturalistic developmental behavioral interventions: Empirically validated treatments for autism spectrum disorder. Journal of Autism and Developmental Disorders, 45(8), 2411–2428. Saxena, S., & Orley, J. (1997). Quality of life assessment: The world health organization perspective. European Psychiatry, 12, 263s–266s. Schumann, C. M., Bloss, C. S., Barnes, C. C., Wideman, G. M., Carper, R. A., Akshoomoff, N., et al. (2010). Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. Journal of Neuroscience, 30, 4419–4427. Seltzer, M. M., Krauss, M. W., Shattuck, P. T., Orsmond, G., Swe, A., & Lord, C. (2003). The stmptoms of autism spectrum disorders in adolescents and adulthood. Journal of Autism and Developmental Disorders, 33, 565–581. Seltzer, M. M., Shattuck, P., Abbeduto, L., & Greenberg, J. S. (2004). Trajectory of development in adolescents and adults with autism. Mental Retardation and Developmental Disabilities, 10, 234–247. Shattuck, P. T., Durkin, M., Maenner, M., Newschaffer, C., Mandell, D. S., Wiggins, L., et al. (2009). Timing of identification among children with an autism spectrum disorder: Findings from a population-based surveillance study. Journal of the American Academy of Child & Adolescent Psychiatry, 48(5), 474–483. Shattuck, P. T., Roux, A. M., Hudson, L. E., Taylor, J. L., Maenner, M. J., & Trani, J.-F. (2012). Services for adults with autism spectrum disorder. Canadian Journal of Psychiatry, 57(5), 284–291. Shattuck, P. T., Seltzer, M. M., Greenberg, J. S., Orsmond, G. I., Bolt, D., Kring, S., et al. (2007). Change in autism symptoms and maladaptive behaviors in adolescents with an autism spectrum disorder. Journal of Autism and Developmental Disorders, 37, 1735–1747. Shelton, J. F., Tancredi, D. J., & Hertz-Picciotto, I. (2010). Independent and dependent contributions of advanced maternal and paternal ages to autism risk. Autism Research, 3(1), 30–39. Sigman, M., & Kasari, C. (1995). Joint attention across contexts in normal and autistic children. (pp. 189– 203). Hillsdale: Lawrence Erlbaum Associates, Inc. Retrieved from http://search.proquest.com/docview/6 18769168?accountid=14771 Sigman, M., & McGovern, C. W. (2005). Improvement in cognitive and language skills from preschool to adoles- I.E. Drmic et al. cence in autism. Journal of Autism and Developmental Disorders, 35(1), 15–23. Sigman, M., & Ruskin, E. (1999). Continuity and change in the social competence of children with autism, down syndrome, and developmental delays. Monographs of the Society for Research in Child Development, 64(1), 1–139. Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a populationderived sample. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 921–929. Skokauskas, N., & Gallagher, L. (2010). Psychosis, affective disorders, and anxiety in autistic spectrum disorder: Prevalence and nosological considerations. Psychopathology, 43, 8–16. Smith, M., & Paulippen, L. (1999). Community integration and supported employment. In D. Zager (Ed.), Autism: Identification, education, treatment (pp. 301– 319). Mahwah: Erlbaum. State, M. W. (2010). The genetics of child psychiatric disorders: Focus on autism and Tourette syndrome. Neuron, 68(2), 254–269. Sterling, L., Dawson, G., Estes, A., & Greenson, J. (2008). Characteristics associated with presence of depressive symptoms in adults with autism spectrum disorder. Journal of Autism and Developmental Disorders, 38, 1011–1018. Stoner, J. B., Angell, M. E., House, J. J., & Bock, S. J. (2007). Transitions: Perspectives from parents of young children with autism spectrum disorders (ASD). Journal of Developmental Disabilities, 19, 23–39. Strauss, K., Mancini, F., & Fava, L. (2013). Parent inclusion in early intensive behavior interventions for young children with ASD: A synthesis of meta-analyses from 220 to 2011. Reseach in Developmental Disabilities, 34(9), 2967–2985. Sullivan, P. F., Daly, M. J., & O'Donovan, M. (2012). Genetic architectures of psychiatric disorders: The emerging picture and its implications. Nature Reviews Genetics, 13(8), 537–551. Sun F, Oristaglio J, Levy SE, Hakonarson H, Sullivan N, Fontanarosa J, Schoelles KM. 2015. Genetic testing for developmental disabilities, intellectual disability, and Autism Spectrum disorder. Technical brief no. 23. (prepared by the ECRI institute–Penn medicine evidence-based practice Center under contract no. 290–2012-00011-I.) AHRQ publication no.15EHC024-EF. Rockville, MD: Agency for Healthcare Research and Quality. www.effectivehealthcare.ahrq. gov/reports/final.cfm. Szatmari, P. (2011). New recommendations on autism spectrum disorder shifting the focus from subtypes to dimensions carriers potential costs and benefits. BMJ, 342. doi:10.1136/bmj.d2456. Szatmari, P., & McConnell, B. (2011). Anxiety and mood disorders in individuals with autism spectrum disorder. In D. G. Amaral, G. Dawson, & D. H. Geschwind Life Course Health Development in Autism Spectrum Disorders (Eds.), Autism spectrum disorders (pp. 330–338). New York: Oxford University Press, Inc. Szatmari, P., Bartolucci, G., Brenner, R., Bond, S., & Rich, S. (1989). A follow-up study of high-functioning autistic children. Journal of Autism and Developmental Disorders, 19, 213–225. Szatmari, P., Bryson, S. E., Boyle, M. H., Streiner, D. L., & Duku, E. (2003). Predictors of outcome among high functioning children with autism and Asperger syndrome. Journal of Child Psychology and Psychiatry, 44(4), 529–528. Szatmari, P., Georgiades, S., Duku, E., Bennett, T. A., Bryson, S., et al. (2015). Developmental trajectories of symptom severity and adaptive functioning in an inception cohort of preschool children with autism spectrum disorder. JAMA Psychiatry, 72(3), 276–283. Szatmari, P., Jones, M. B., Zwaigenbaum, L., & MacLean, J. E. (1998). Genetics of autism: Overview and new directions. Journal of Autism and Developmental Disorders, 28(5), 351–368. Szatmari, P., Paterson, A. D., Zwaigenbaum, L., Roberts, W., Brian, J., Autism Genome Project Consortium, et al. (2007). Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nature Genetics, 39(3), 319–328. Taylor, J. L. (2009). The transition out of high school and into adulthood for individuals with autism and for their families. International Review of Research in Mental Retardation, 38, 1–32. doi:10.1016/ S0074-7750(08)38001-X. Taylor, J. L., & Seltzer, M. M. (2010). Changes in the autism behavioral phenotype during the transition to adulthood. Journal of Autism and Developmental Disorders, 40, 1431–1446. Taylor, J. L., & Seltzer, M. M. (2011a). Changes in the mother-child relationship during the transition to adulthood for youth with autism spectrum disorders. Journal of Autism and Developmental Disorders, 41, 1397–1410. doi:10.1007/s10803-010-1166-9. Taylor, J. L., & Seltzer, M. M. (2011b). Employment and post-secondary educational activities for young adults with autism spectrum disorders during the transition to adulthood. Journal of Autism and Developmental Disorders, 41, 566–574. Taylor, J. L., McPheeters, M. L., Sathe, N. A., Dove, D., Veenstra-VanderWeele, J., & Warren, Z. (2012). A systematic review of vocational interventions for young adults with autism spectrum disorders. Pediatrics, 130(3), 531–538. doi:10.1542/peds.2012-0682. Thomas, K. C., Ellis, A. R., McLaurin, C., Daniels, J., & Morrissey, J. P. (2007). Access to care for autismrelated services. Journal of Autism and Developmental Disorders, 37, 1902–1912. Totsika, V., Flece, D., Kerr, M., & Hastings, R. P. (2010). Behavior problems, psychiatric symptoms, and quality of life for older adults with intellectual disability with and without autism. Journal of Autism and Developmental Disorders, 40, 1171–1178. 273 Tomasello, M. (1995). Joint attention as social cognition. In C. Moore, & P. J. Dunham (Eds.), Joint attention: Its origins and role in development; joint attention: Its origins and role in development (pp. 103-130, Chapter vii, 286 Pages). Hillsdale: Lawrence Erlbaum Associates, Inc. Towbin, K. E. (2005). Pervasive developmental disorder not otherwise specified. In F. R. Volkmar, A. Klin, R. Paul, & D. J. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (Vol. 1, 3rd ed., pp. 165–200). Hoboken: Wiley. Tsakanikos, E., Costello, H., Holt, G., Bouras, N., Sturmey, P., & Newton, T. (2006). Psychopathology in adults with autism and intellectual disability. Journal of Autism and Developmental Disorders, 36, 1123–1129. Tsakanikos, E., Costello, H., Holt, G., Sturmey, P., & Bouras, N. (2007a). Behavior management problems as predictors of psychotropic medication and use of psychiatric services in adults with autism. Journal of Autism and Developmental Disorders, 37, 1080–1085. Tsakanikos, E., Sturmey, P., Costello, H., Holt, G., & Bouras, N. (2007b). Referral trends in mental health services for adults with intellectual disability and autism spectrum disorders. Autism, 11, 9–17. Turner, L. M., & Stone, W. L. (2007). Variability in outcome for children with an ASD diagnosis at age 2. The Journal of Child Psychology and Psychiatry, 48(8), 793–802. Van Acker, R., Loncola, J. A., & Van Acker, E. Y. (2005). Rett syndrome: A pervasive developmental disorder. In F. R. Volkmar, R. Paul, A. Klin & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders: Diagnosis, development, neurobiology, and behavior (vol. 1) (3rd ed) (pp. 126–164, Chapter xxv, 703 Pages). Hoboken: John Wiley & Sons Inc. VanBergeik, E., Klin, A., & Volkmar, F. (2008). Supporting more able students on the autism spectrum: College and beyond. Journal of Autism and Developmental Disorders, 38, 1359–1370. doi:10.1007/s10803-007-0524-8. van Heijst, B. F. C., & Geurts, H. M. (2015). Quality of life in autism across the lifespan: A meta-analysis. Autism, 19(2), 158–167. doi:10.1177/1362361313517053. Vaughan Van Hecke, A., Mundy, P., Acra, C. F., Block, J. J., Delgado, C. E. F., Parlade, M. V., et al. (2007). Infant joint attention, temperament, and social competence in preschool children. Child Development, 78(1), 53–69. Veltman, J. A., & Brunner, H. G. (2012). De novo mutations in human genetic disease. Nature Reviews Genetics, 13(8), 565–575. Venter, A., Lord, C., & Schopler, E. (1992). A follow-up study of high-functioning autistic children. Journal of Child Psychology and Psychiatry, 33(3), 489–507. Volkmar, F. R. (2004). Adolescence and sexuality. In F. R. Volkmar & L. A. Wiesner (Eds.), Healthcare for children on the autism spectrum (pp. 245–260). New York: Harbour House. 274 Volkmar, F. R., & McPartland, J. (2014). From Kanner to DSM-5: Autism as an evolving diagnostic concept. Annual Review of Clinical Psychology, 10, 193–212. Volkmar, F. R., & Cohen, D. J. (1991). Comorbid association of autism and schizophrenia. The American Journal of Psychiatry, 148(12), 1705–1707. Volkmar, F. R., Koenig, K., & State, M. (2005). Childhood disintegrative disorder. In F. R. Volkmar, A. Klin, R. Paul, & D. J. Cohen (Eds.), Handbook of autism and pervasive developmental disorders (Vol. 1, 3rd ed., pp. 70–78). Hoboken: Wiley. Volkmar, F. R., Klin, A., & McPartland, J. (2014). Asperger's syndrome: An overview. In J. McPartland, A. Klin, & F. Volkmar (Eds.), Asperger syndrome: Assessing and treating high-functioning autism spectrum disorders (pp. 1–42). New York: Guilford Press. Watts, G. (2012). More psychiatrists attack plans for DSM-5. BMJ, 344, e3357. http://dx.doi.org/10.1136/ bmj.e3357 Weitlauf, A.S., McPheeters, M.L., Peters, B., Sathe, N., Travis, R., Aiello, R., Williamson, E., VeenstraVanderWeele, J., Krishnaswami, S., Jerome, R., Warren, Z. (2014). Therapies for Children With Autism Spectrum Disorder: Behavioral Interventions Update. Comparative Effectiveness Review No. 137. (Prepared by the Vanderbilt Evidence-based Practice Center under Contract No. 290–2012-00009-I.) AHRQ Publication No. 14-EHC036-EF. Rockville, MD: Agency for Healthcare Research and Quality. www.effectivehealthcare.ahrq.gov/reports/final.cfm I.E. Drmic et al. Wing, L. (1980). Childhood autism and social class: a question of selection? British Journal of Psychiatry, 137, 410–417. White, S., Oswald, D., Ollendick, T., & Scahill, L. (2009). Anxiety in children and adolescents with ASD. Clinical Psychology Review, 29, 216–229. Wolff, J. J., Gu, H., Elison, J. T., Styner, M., Gouttard, S., et al. (2012). Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. The American Journal of Psychiatry, 169, 589–600. Woodbury-Smith, M. R., & Volkmar, F. R. (2009). Asperger syndrome. European Child & Adolescent Psychiatry, 18(1), 2–11. Woolfenden, S., Sarkozy, V., Ridley, G., & Williams, K. (2012). A systematic review of the diagnostic stability of autism spectrum disorder. Research in Autism Spectrum Disorders, 6, 345–354. doi:10.1016/j. rasd.2011.06.008. Zachor, D. A., & Itzchak, E. B. (2010). Treatment approach, autism severity and intervention outcomes in young children. Research in Autism Spectrum Dosorders, 4(3), 425–432. Zwaigenbaum, L., Bryson, S. E., Szatmari, P., Brian, J., Smith, I. M., Roberts, W., et al. (2012). Sex differences in children with autism spectrum disorder identified within a high-risk infant cohort. Journal of Autism and Developmental Disorders, 42(12), 2585–2596. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Self-Regulation Megan McClelland, John Geldhof, Fred Morrison, Steinunn Gestsdóttir, Claire Cameron, Ed Bowers, Angela Duckworth, Todd Little, and Jennie Grammer 1 Self-Regulation Self-regulation has received enormous attention in recent years as a key predictor of a variety of outcomes, including obesity (Evans et al. 2012), school readiness (Blair and Razza 2007; McClelland et al. 2007; Morrison et al. 2010), academic achievement in adolescence (Duckworth et al. 2010b), and long-term health and educational outcomes (McClelland et al. 2013; Moffitt et al. 2011). Although researchers have focused on self-regulation from a diverse set of perspectives (Geldhof et al. 2010; M. McClelland (*) Human Development and Family Sciences, 245 Hallie E. Ford Center for Healthy Children and Families, Oregon State University, Corvallis, OR 97331, USA e-mail: megan.mcclelland@oregonstate.edu J. Geldhof Oregon State University, Human Development and Family Sciences, Corvallis, OR, USA F. Morrison University of Michigan, Department of Psychology, Ann Arbor, MI, USA S. Gestsdóttir University of Iceland, Department of Psychology, Reykjavik, Iceland C. Cameron University at Buffalo, SUNY, Learning and Instruction, Buffalo, NY, USA McClelland et al. 2010), there is consensus that self-regulation has important implications for individual trajectories of health and well-being across the life course. Indeed, over a decade ago, it was suggested that “understanding self-regulation is the single most crucial goal for advancing the understanding of development” (Posner and Rothbart, 2000, p. 427). Self-regulation is fundamental to successful accomplishment of adaptive developmental tasks at all stages of life. In the field of maternal and child health, a recent emphasis utilizing a life course health development (LCHD) perspective has shed new light on how these trajectories are shaped by dynamic mechanisms such as self-regulation. This perspective is captured by the seven LCHD principles—as described by Halfon and Forrest (2017)—which are also consistent with the relational developmental E. Bowers Clemson University, Youth Development Leadership, Clemson, SC, USA A. Duckworth University of Pennsylvania, Department of Psychology, Philadelphia, PA, USA T. Little Texas Tech University, Department of Educational Psychology and Leadership, Lubbock, TX, USA J. Grammer University of California, Los Angeles, Graduate School of Education and Information Studies, Los Angeles, CA, USA © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_12 275 M. McClelland et al. 276 systems (RDS) perspective in the field of human development. The development of self-regulation is a prime example of many of the LCHD principles in action. For example, the notion that health develops continuously over the life span would imply that individual pathways in self-regulation skills are formed partly through life course transitions and turning points or the points in a person’s life which can influence developmental pathways in either positive (protective) or negative (maladaptive) ways, and in fact this is the case. Similarly, the notion that the timing and structure of environmental exposures are important for health development applies very well to self-regulation, the development of which is significantly and adversely affected by persistent and chronic stress, especially prenatally and in the first few years of life. (Conversely, protective factors such as sensitive and engaged caregiving can be a buffer for a child’s development of these skills during this time.) Additionally, the LCHD notion that the rhythm of human development is a result of synchronized timing of molecular, physiological, behavioral, and evolutionary processes and that the synchronization of these processes contributes to the enormous individual variability in health development over time is also relevant to self-regulation. Another illustration of the degree to which the development of self-regulation serves as a powerful example of the LCHD framework and its underlying principles in action is the fact that, at a time in history when the importance of children’s self-regulation is perhaps greater than in previous decades due to an increasing academic focus in school settings, children and youth are using media to a much greater extent than ever before, a trend which could be detrimental to the development of these essential skills. This mismatch between the demands of the environment and the capacities of the developing individual is well described by the LCHD principles, which emphasize how evolution enables and constrains health development pathways and plasticity, how different aspects of development are intertwined over time (e.g., biobehavioral development is connected to sociocultural development), and how efforts to promote more optimal health development can promote survival and enhance thriving by countering the negative impact of these kinds of mismatches. Finally, the LCHD principles capture the dynamic and complex nature of health development and emphasize that development emerges as a result of person interactions at multiple levels. This speaks to the importance of integrating interventions both vertically—meaning along primary, secondary, and tertiary care continua— and horizontally, that is, across domains of function (i.e., biological, behavioral, social), as well as longitudinally (e.g., across life stages and/or generations). This is especially relevant here because the capacity for self-regulation has been shown to be highly malleable and because interventions to promote such skills have been shown to be more effective when they are integrated across different levels and contexts (Diamond and Lee 2011; Raver et al. 2011). Together, the LCHD principles will guide our discussion of self-regulation, which are also consistent with an RDS perspective. After providing a theoretical framework based on RDST, we will view the seven principles of LCHD to better understand the determinants and pathways of self-regulation, methods for studying selfregulation, and translational issues. We conclude by providing recommendations for better integrating the principles of LCHD with the study of self-regulation. 1.1 Relational Developmental Systems Theory as a Framework for Self-Regulation While many processes currently subsumed under the “self-regulation” moniker have been studied from the earliest days of psychology (e.g., James 1890), the modern study of self-regulation truly emerged as psychologists moved away from the mechanistic neopositivism that dominated their field during the middle part of the twentieth century. Work by Bandura (1969) and Mischel (1968), for instance, rejected the notion of the Self-Regulation “black box” and instead emphasized the self (and vicariously behavioral regulation by the self) as the object of valid scientific inquiry. This renewed focus on the self has made way for many of the core concepts that frame modern developmental science (e.g., that individuals are proactive agents capable of influencing their own development; Lerner 1982). Much of the recent work on selfregulation can be subsumed under the metatheoretical stance that Overton (e.g., 2010, 2013) has termed relational developmental systems ((RDS) theory. Similar to the principles of LCHD, RDS represents an approach to human development that rejects the dualistic separation of individual and context (Overton 2013). Instead, like the principles of LCHD, RDST specifies that the individual is completely embedded as a locally self-organized component of his or her larger context. Development of the individual therefore necessarily influences and is influenced by his or her surrounding environment. These mutual influences can be thought of co-regulation (i.e., action and development of the individual partially “regulate” and are partially “regulated” by the surrounding context), resulting in what Brandstädter (e.g., 2006) has called developmental regulations. Similarly, Lerner (e.g., 1985; Lerner et al. 2011) has heuristically decomposed this person-context system and has described developmental regulations as mutually influential, bidirectional person-context interactions— similar to LCHD Principle 3. Accordingly, across the life span, individuals are active agents in the mutually influential interactions among the variables from the integrated biological, social, cultural, and historical (or temporal) levels of the dynamic developmental system (as in LCHD Principles 1, 2, 7). The co-regulative nature of the person-context system described in RDST directly informs the contemporary study of self-regulation. While person and context are truly inseparable from the RDST perspective, Gestsdottir and Lerner (e.g., 2008) note that we can heuristically separate developmental regulations into those that primarily arise from the individual (i.e., the self) and those that primarily arise from the context. Using 277 this logic, they proceed to define self-regulation as comprised of “the attributes involved in and the means through which the individual contributes to developmental regulations…” (p. 203). As a broadly defined construct, self-regulation therefore entails cognitions, emotions, and actions that arise within the individual and do not differentiate between conscious and subconscious (or even automatic) action. Differentiating between consciousness and sub- or (non)conscious behavior has been a recurring issue in the study of self-regulation, and it is now widely acknowledged that all selfregulated action falls along a continuum ranging from fully intentional to fully automatic. For instance, work done by Bargh and colleagues (e.g., Bargh et al. 2001) clearly shows that subconscious goals can influence (i.e., regulate) behavior outside of the actor’s explicit awareness. Similarly, Gestsdottir and Lerner (2008) differentiate between organismic and intentional self-regulation. Here, organismic self-regulation occurs below the threshold of consciousness and includes diverse actions ranging from the cardiovascular regulation of blood oxygen levels to the regulation of outwardly directed behavior through automatized goal structures. In contrast, intentional self-regulation includes behavior that the individual is consciously aware of, representing an agent’s intentional influence over the person-context system. The remainder of this chapter focuses specifically on intentional selfregulation. In total, self-regulation may be defined as “the ability to flexibly activate, monitor, inhibit, persevere and/or adapt one's behavior, attention, emotions and cognitive strategies in response to directions from internal cues, environmental stimuli and feedback from others, in an attempt to attain personally-relevant goals” (Moilanen 2007, p. 835). 2 Definitions of Self-Regulation The study of self-regulation lacks integration across the life span. Theories that approach self-regulation within a given period of the life M. McClelland et al. 278 span are often not integrated with each other nor are they usually integrated with theories that focus on subsequent or preceding life periods. In this section, we briefly review several of the major conceptualizations of self-regulation in an attempt to highlight the complexity of self-regulated processes in children and youth. Inherent in these conceptualizations and definitions are the seven principles of LCHD, which have important implications for the concepts of turning points and transitions, how mismatches can occur in development, and the need to integrate interventions across multiple levels of influence. increase throughout adolescence and into early adulthood (e.g., Hooper et al. 2004). Finally, working memory is an aspect of executive functioning that includes the ability to actively work on and process information. In young children, it is demonstrated by children’s ability to remember and follow instructions (Gathercole et al. 2004; Kail 2003). The early years are a sensitive period of brain development, which closely parallel the development of EF. Understanding how EF develops during this developmental window has important implications for biological, cognitive, and social development. 2.1 2.2 Executive Functioning As an instantiation of self-regulation, the study of executive function (EF) emphasizes the fluid, cognitive processes that underlie self-regulated action. While the precise definition of which skills and processes constitute EF may vary across studies, researchers studying selfregulation have emphasized a few key skills. In particular, researchers have studied the importance and development of agentic control over one’s attention, inhibitory control, and working memory (McClelland et al. 2010). Research addressing the development of attentional control describes the transition from simple arousal to fully endogenous attention across the first few years of life (e.g., Colombo 2001) and the subsequent development of attentional capacities from childhood to late life (e.g., Posner and Rothbart 1998). Attentional processes play a major role in self-regulated action (e.g., Norman and Shallice’s (1986) Supervisory Attentional System) and may especially relate to emotion regulation in infants and children (Sheese et al. 2008). Children begin to display inhibitory control by approximately 3 years of age (Posner and Rothbart 1998), a time that corresponds to the onset of endogenous attention and also corresponds to the transition out of Piaget’s preoperational stage (see Geldhof et al. 2010 for a brief discussion). Inhibitory control continues to develop throughout childhood (e.g., Backen Jones et al. 2003) and continues to Self-Regulation Versus Self-Control The literature does not consistently distinguish between the concepts of self-regulation and self-control, with many authors using the terms interchangeably. Other authors consider selfregulation and self-control as distinct processes, which follow a sensitive period of development in infancy. For instance, Kopp (1982) describes self-control as developing at around 24 months of age and as including the ability to behave according to a caregiver’s requests and to adhere to social expectations in the absence of external monitors. She distinguishes this from self-regulation, which instead develops when a child is approximately 36 months old and represents an internalization of self-control that allows for a degree of flexibility, allowing children to meet the changing demands of a dynamic context. According to Kopp, the distinction between self-control and self-regulation is therefore “a difference in degree, not in kind” (Kopp 1982, p. 207). In other words, self-regulation is an outgrowth of self-control that allows for flexible adaptation to real-world demands but which develops rapidly over the infant and toddler years. As such, this progression reflects the principles of LCHD especially for our understanding of how transitions and sensitive periods influence self-regulation development. Self-Regulation 2.3 Effortful Control In addition to the terms executive functions, selfregulation, and self-control, effortful control is a related construct that stems from the temperament literature. Rothbart and colleagues have defined the effortful control dimension of childhood temperament as “the ability to inhibit a dominant response to perform a subdominant response” (Rothbart and Bates 1998, p.137). Measures of effortful control for preschool children encompass several facets, including attention focusing and inhibitory control over inappropriate impulses (Rothbart et al. 2001). Rothbart distinguishes effortful control from two temperament factors that encompass more reactive (i.e., less voluntary) tendencies: surgency/ extraversion and negative affect. Moreover, effortful control seems highly related, both conceptually and empirically, to self-control and conscientiousness in adolescents and adults (Eisenberg et al. (2012), under review). While this definition closely reflects cognitive inhibition, effortful control is instead considered an aspect of children’s temperament that develops in tandem with the development of endogenous attention. Research on infant temperament has not found a complete analogue to effortful control, for instance, with factor analyses instead uncovering a factor called orienting/regulation (e.g., Garstein and Rothbart 2003). Orienting/ regulation contains many “regulatory” components similar to effortful control (e.g., orienting, soothability) but lacks a truly effortful component. Effortful control incorporates the influence of temperament that infants are born with, along with the influence of the environment, including quality of caregiving. This dynamic coaction can be seen in the temperamental concept of “goodness of fit.” Goodness of fit refers to the match (or mismatch) between children’s temperamental states and the quality of caregiving and temperament of their parents/caregivers. When there is a positive fit or match between children and caregivers, children’s development of self-regulation is optimized. In contrast, when a mismatch occurs, there is greater potential for difficulty with self-regulation and related 279 outcomes. Thus, effortful control is especially relevant to understanding self-regulation through an LCHD framework. 2.4 Delay of Gratification Delay of gratification is another approach to selfregulation with close ties to both inhibition and attention. Mischel and colleagues (e.g., Mischel and Ebbesen 1970) originally studied delay of gratification using the now-famous delay of gratification task with children. In this task, a researcher shows a child two rewards (e.g., a single marshmallow versus several marshmallows) and asks the child which reward he or she would prefer. Subsequent research has adapted this task for adults by varying the value of the rewards—sometimes making them hypothetical—and by extending the delay time to a month or longer (e.g., Fortsmeier et al. 2011; Duckworth and Seligman 2005). Regardless of the delivery, inherent in the construct is the integration of emotion with cognition in their understanding of self-regulation. Mischel’s research especially links the ability to delay gratification to endogenous attention through what he and his colleagues have called the cognitive-affective processing system (e.g., Mischel and Ayduk 2004). This work has shown that when the rewards are visible to children during the delay period, children who distract their attention away from the reward delay longer than children that do not (Mischel et al. 1972). Similarly, children who attend only to the cool, non-motivating, features of the reward (e.g., by treating the actual reward as if it is instead a picture of the reward) delay longer than children who do not (Moore et al. 1976). Delay of gratification thus complements the principles of LCHD by assuming that self-regulated behavior includes the transactional processes of emotion and cognition. 2.5 Emotion Regulation Although the study of emotion regulation is a complete area of the literature unto itself, there is some important overlap with the study of M. McClelland et al. 280 self-regulation more generally defined. Infants’ early regulatory tasks involve regulating their reactions to stimuli, including affective, temperament-based reactions that fall under the emotion regulation umbrella (Eisenberg et al. 2004). Emotion regulation means that children can modulate their strong emotional reactions with an appropriate strategy or combination of strategies (Bridges et al. 2004). Stansbury and Zimmerman (1999) describe four types of emotion regulatory strategies: instrumental or trying to change the situation (e.g., bidding for caregiver attention), comforting or soothing oneself without changing the situation (e.g., thumb-sucking), distraction or redirecting attention elsewhere (e.g., looking away), or cognitive, which is thought to be the most sophisticated and includes reframing the situation in a positive light, bargaining, or compromising. Importantly, children use different strategies depending on their individual characteristics as well as the situational context (Zimmermann and Stansbury 2003). This line of work demonstrates that the regulation of attention and emotion is closely interrelated and also reflects the principles of LCHD. Together, the different definitions of selfregulation share many common conceptual underpinnings and are relevant to how these skills develop in individuals across the life span. They also apply to the key principles of LCHD. In the next section, we apply these principles to the developmental processes of self-regulation. 3 Developmental Processes of Self-Regulation As noted above, the principles of LCHD can help to inform our understanding of the development of self-regulation. We orient our discussion around these principles by employing three lenses through which to view the development of self-regulation: (1) the lens of transitions and turning points, (2) the lens of mismatches, and (3) the lens of intervention integration. We include important individual, contextual, and sociocultural factors that influence the development of these skills over time since such information is critical for developing effective ways to help promote strong selfregulation in individuals. 3.1 Transitions and Turning Points in the Development of Self-Regulation Because of the malleability in self-regulation evident throughout the life course, there are many transitions and turning points for the development of these skills. The early childhood years represent one important time in the life course because they constitute a sensitive period for the development of self-regulation and underlying executive function skills. This makes it especially important for children’s early biological, cognitive, and social-emotional development (Diamond 2002; Carlson et al. 2013). As noted above, children’s self-regulation undergoes rapid change during early childhood, which parallels brain development, especially of the prefrontal cortex (e.g., Diamond 2002). The translation of this development can be seen in turning points in development, one of which is the transition to formal schooling for young children. 3.1.1 The Transition to Schooling as a Turning Point for Self-Regulation Several lines of research point to relations between schooling and self-regulation as a developmental turning point for children. Evidence points to bidirectional relations between the biological and cognitive factors predicting development of self-regulation as well as the influence of context such as the schooling environment (e.g., Diamond 2002; Carlson et al. 2013; Morrison et al. 2010). Although much research focuses on how individual factors influence self-regulation (e.g., temperament, neurodevelopment of the prefrontal cortex), research has also examined how contextual factors such as schooling may influence self-regulation. For example, researchers have suggested that differences in selfregulation across cultures may be due to early instructional environments (Morrison et al. 2010) Self-Regulation as well as other factors such as temperamental variables (Hsu et al. 1981) or the prevalence of particular genes (Chang et al. 1996) that might contribute to observed advantages in selfregulation (Sabbagh et al. 2006). Research looking at the transition to formal schooling has also used a natural experiment (designated “school cutoff”) design, which examines children whose birth dates cluster closely on either side of the cutoff date for entering formal schooling (e.g., kindergarten in the United States). This method effectively equates the two groups of children on age (Morrison et al. 2010). Using this methodology, results from recent quasi-experimental and experimental investigations have provided further evidence for the importance of schooling in the development of self-regulation. For example, Burrage et al. (2008) examined the influence of experience in preschool on growth of word decoding, working memory, and inhibitory control. This quasiexperimental work suggests that schooling, and more specifically the years of prekindergarten and kindergarten, improves working memory for children who attend school compared with sameage peers who, because of arbitrary school cutoff dates, do not attend at the same time (Burrage et al. 2008). Together this research suggests that the early childhood years provide a sensitive period for the development of self-regulation, which is influenced by both individual and contextual factors. 3.1.2 Adolescence as a Turning Point for Self-Regulation In adolescence, children experience another sensitive period of development, especially for selfregulation. Adolescence, the second decade of life, is a period of ontogeny characterized by extraordinary biological, social, and ecological changes (Lerner and Steinberg 2009). Cognitive and social development means that the capacities necessary for advanced, adult-like self-regulation may for the most part emerge in adolescence. This is in large part due to the gradual maturation of the prefrontal context. In particular, as the frontal lobe develops, so does higher-order, regulation-relevant cognition, such as metacogni- 281 tion and internalized control. In turn, these skills enable adolescents to make better interpretations, choices, and decisions about how to interact with their environment, especially in accordance with long-term goals (Brandstädter 2006; Larson 2011; Steinberg 2010). In addition, the formulation of an adaptive identity, which is a major developmental task of adolescence, allows for the construction of a personal future that informs long-term decision-making and goal pursuit (Brandtstädter 2006; McClelland et al. 2010). After all, it is impossible to formulate a plan to reach a long-term goal that has not yet been determined. Finally, during adolescence, young people may, for the first time, face decreased probabilities of achieving major life goals (e.g., graduating from high school) that have long-term consequences. This fact makes self-regulation particularly pertinent during the adolescent period (McClelland et al. 2010). A growing body of research has confirmed the relation between adolescents’ self-regulation skills and positive and problematic behaviors. In the last decade, a body of research has advanced our understanding of how adolescents regulate their own learning (Zimmerman 2002; Zimmerman and Schunk 2001). Self-regulated learning involves many goal-related skills, such as the ability to set proximal learning goals, use appropriate strategies for attaining the goal, selfevaluate the method one has chosen to achieve a goal, and monitor one’s performance toward that goal. The use of self-regulated learning skills has repeatedly been related to school achievement (Miller and Byrnes 2001; Zimmerman and Schunk 2001). Similarly, the use of selfregulatory behaviors of youth is positively related to other positive outcomes, such as measures of social competence and mental well-being, and negatively related to indicators of problematic development, such as sexual risk behaviors, substance abuse, depression, and anxiety (e.g., Gestsdottir et al. 2009; Massey et al. 2008; Quinn and Fromme 2010). In addition, self-regulatory skills may have particular significance for youth living in high-risk environments. For instance, Buckner et al. (2009) found that youth from very low-income families fared better on a wide range M. McClelland et al. 282 of developmental outcomes, ranging from academic achievement to anxiety, if they had adaptive self-regulation skills. The authors emphasize that such skills help youth to cope with stressful life events, making them less likely to be overwhelmed by the difficulties that they are faced with, and as such, high levels of self-regulation are considered a key factor in supporting youth’s resiliency (Buckner et al. 2009; Quinn and Fromme 2010). In spite of the growing evidence that self-regulation has important implications for healthy functioning in adolescence, as it does in childhood, there has been limited developmental research on how such important, adult-like processes develop in adolescence. In sum, although the understanding about the nature and development of self-regulatory processes is not complete, recent research confirms the contribution of adaptive self-regulation to the healthy development of children and youth. Furthermore, some recent findings point to an emerging theme and match both the principles of LCHD and the RDS framework: complex, adultlike, self-regulatory processes appear to develop in middle adolescence and continue to grow through adolescence and early adulthood. In addition, the function of self-regulation in adolescence may differ in function from that of childhood and adulthood. As such, the structure and function of self-regulation may be specific to this age period and constitute a sensitive period in development. 3.2 Mismatches (and Matches) in the Development of Self-Regulation In addition to research pointing to the importance of examining the transaction of how selfregulation develops across multiple levels of analysis, the match (or mismatch) between different aspects of development is also important. This can be seen in the notion of goodness of fit, taken from the child temperament literature, where an individual’s characteristics and skills may not fit with those of the environment, such as the characteristics of caregivers. In the develop- ment of self-regulation, a child’s individual characteristics and skills may be adversely influenced by the aspects of their environment, such as adverse childhood experiences, stress, poor parenting, maternal depression, and the influence of the media and technology use. 3.2.1 Adverse Childhood Experiences and Cumulative Risk Recent research on adverse childhood experiences (ACEs) and toxic stress suggests that multiple and chronic environmental stressors can have significant and adverse effects on the development of a host of outcomes throughout the life span (Blair and Raver 2012; Shonkoff et al. 2012). For example, the early and chronic stress experienced by children living in poverty can have a profound influence on areas of the brain most involved in the development of selfregulation (the prefrontal cortex [PFC]; e.g., Blair 2010; Blair and Raver 2012). One study found that low-income children exhibited lower prefrontal functioning compared to higherincome children. Specifically, the PFC functioning of low-income children in the study was similar to the level of functioning of individuals with damage to the PFC (Kishiyama et al. 2009). In addition to effects on the developing brain, ACEs are related to poorer executive function and self-regulation, increased substance use, obesity, and risk-taking behaviors in adolescents and adults (see Table 1). For example, one study found that children with cumulative risk exposure (e.g., poverty, family turmoil, substandard housing) gain more weight during the transition to adolescence than their more advantaged peers, an effect mediated by lower levels of self-regulation (Evans et al. 2012). Such pernicious effects were predicted by Walter Mischel and colleagues, whose hot/cool model of self-control specified that stressful life events would potentiate impulsive (“hot”) system activity and attenuate slower, more reflective and voluntary (“cool”) system activity (Metcalfe and Mischel 1999). Research has also indicated that children from low-income families are more likely to experience family and housing instability, a lack of resources, Self-Regulation 283 Table 1 Examples of direct and/or indirect relations between self-regulation and health-related outcomes Predictor Self-regulation Health-related outcomes Obesity Weight gain and loss Addiction and substance use Risk-taking behaviors Cardiovascular disease Asthma Autoimmune diseases Depression Liver cancer Academic achievement School readiness Educational attainment Economic well-being (savings behavior, financial security, occupational prestige) Lack of criminal convictions Health behaviors Physical activity Nutritious eating ADHD and lower-quality learning environments in the home (e.g., Gershoff et al. 2007; Mistry et al. 2010; Obradovic 2010; Sektnan et al. 2010), all of which have been linked to lower levels of selfregulation. For example, children facing cumulative risk factors may experience significant difficulty with self-regulation in early childhood (Wanless et al. 2011). Partly because of this, children with chronic environmental stressors are more likely to experience school failure, unemployment, poverty, violent crime, and incarceration as adults. Moreover, and perhaps most important for the long-term implications of ACEs, these children are less likely as adults to provide supportive environments for their own children, who in turn are at significant risk of demonstrating some of these same issues. In addition to behavioral and economic effects, chronic and toxic stresses have been linked to biological changes including premature aging and death, alterations in immune functioning, and significant increases in inflammatory markers. Related to this, ACEs have been associated with a host of physical health outcomes, including cardiovascular dis- ease, liver cancer, asthma, autoimmune diseases, and depression (Committee on Psychosocial Aspects of Child and Family Health et al. 2012; Shonkoff et al. 2012). Together, this research suggests that ACEs, toxic stress, and cumulative risk can significantly impair the development of self-regulation in children. This is also an example of a potential mismatch between children’s own development and the context in which they live. For example, it is possible that children facing cumulative risk have parents who provide fewer opportunities to practice self-regulation (Fuller et al. 2010; Wachs et al. 2004). These children may also have higher levels of stress, which interfere with the development of prefrontal cortex, experience more family and housing instability, and have fewer learning and economic resources (Blair 2010; Blair and Raver 2012). Thus, there may be few opportunities for children to experience a positive match between their own developing skills and those of the environment in which they live. 3.2.2 Parenting and Caregiving As the research above indicates, poor parenting can have significant and detrimental effects on their children’s own self-regulation. For example, extensive research documents the negative effects that maternal depression can have on a range of child outcomes, including self-regulation (Center on the Developing Child 2011). In contrast to the conflicted and non-supportive parent-child relationships that undermine children’s ability to self-regulate, organized and predictable home environments and emotionally positive parent-child relationships provide a context that allows for the development of selfregulatory competencies (e.g., Bowers et al. 2011; Brody and Ge 2001; Grolnick et al. 2000; Lewin-Bizan et al. 2010; Moilanen et al. 2010). For example, parenting that includes a focus on supporting autonomy and setting limits has significantly predicted stronger self-regulation in children compared to parenting that is more controlling and focused on compliance (Bernier et al. 2010; Lengua et al. 2007). A similar line of work in early childhood classrooms has established the importance of orienting and organizing M. McClelland et al. 284 teacher behaviors for children’s self-regulation, engagement, and academic outcomes (Cameron and Morrison, 2011; Cameron Ponitz et al. 2009). Taken together, this work indicates the importance of structured and predictable environments for helping children’s emerging self-regulatory capacities. It also demonstrates the importance of matches between children’s characteristics and parenting characteristics and behaviors, which complement the principles of the LCHD perspective. 3.2.3 Media and Technology Use Another example of a possible mismatch is the increasing structure in school settings paired with the high prevalence of media and television use by children and adults. Children’s media and technology use is rapidly increasing, but there remains little evidence on the positive effects of such media on children’s development, especially for very young children (Radesky et al. 2014). Many studies have found persistent negative effects of extended television and media viewing on children’s short- and long-term development (Robertson et al. 2013), including inattention and attention deficit hyperactivity disorder (ADHD)-related behaviors (Christakis et al. 2004; Nikkelen et al. 2014). These findings indicate that media use is related to poorer selfregulation and that households with heavy media use may be a poor context for supporting children’s self-regulatory development. Thus, children’s increased media use may run counter to the increased demands for self-regulated behavior in schools and society. In addition to the issue of child media use is the high prevalence of media use by adults and parents. For example, parents who are distracted by texting and being on mobile devices may not be able to adequately respond to and parent their children. Although limited research exists, one study found that caregivers who used mobile devices at a restaurant while with their children were most often highly absorbed in the content and were less attentive to the children they were with. Those caregivers who were highly absorbed in their mobile devices were also more likely to respond harshly to child misbehavior (Radesky et al. 2014). Thus, an increased inattention and distraction on the part of parents and caregivers may provide children with fewer opportunities to learn how to self-regulate themselves. Moreover, it is possible that although children’s self-regulation is needed to successfully navigate increasing structured school settings, children and parents are using media to a much greater extent than ever before, which could be detrimental to the development of these skills. This potential mismatch may have significant long-term implications and is an area ripe for additional research. 3.3 Integrating Levels of Influence in Self-Regulation Interventions Another LCHD lens through which to view selfregulation processes is the importance of integration across multiple levels of influence, especially in the context of interventions. This integration includes lateral integration or integration across subject domains, vertical integration or integration across levels of analysis, and developmental integration or integration across time. Because of the evidence pointing to the malleability of selfregulation, there has been an explosion in recent years in interventions aiming to foster the development of these skills. Accumulating evidence suggests that interventions targeting children’s self-regulation at various levels can be effective at improving self-regulation and other outcomes. For example, at the sociocultural level, preschool curricula, such as Tools of the Mind, focus on social, emotional, and executive function skills in addition to literacy and math. Research suggests that program participation is related to significant improvement in children’s self-regulation (Blair and Raver 2014; Diamond et al. 2007), social behavior (Barnett et al. 2008), academic outcomes (Blair and Raver 2014), and neuroendocrine function (e.g., levels of salivary cortisol and alpha amylase; Blair and Raver 2014). Self-Regulation Some work, however, has not found significant intervention effects (Farran et al. 2013), suggesting that more work is needed to fully understand the key components of intervention effectiveness. Other interventions that include multiple levels of integration (e.g., at the parent, teacher, and child level) are the Promoting Alternative Thinking Strategies (PATHS) and the Head Start REDI (Research-based, Developmentally Informed) programs (Bierman et al. 2008a), which focus on social-emotional skills and self-regulation. Children receiving these interventions have demonstrated more socially competent behavior (Domitrovich et al. 2007) and significant improvements in self-regulation (Bierman et al. 2008b) compared to children in a control group. Another recent study examining a broad intervention targeting social-emotional learning and literacy development found that children in intervention schools demonstrated improvements in a variety of social behaviors and self-regulation skills (e.g., attention). Improvements were also found in children’s early math and reading achievement for those initially most at risk for behavior problems (Jones et al. 2011). Further evidence from a school-based intervention that included multiple levels of integration with teachers, mental health consultants, and children (Raver et al. 2011) reveals that preschool children participating in the Chicago School Readiness Project exhibited significantly higher performance on self-regulation tasks than did their peers in a control group. Moreover, there was a mediating role of children’s EF on pre-academic literacy and math skills. These findings complement those of Connor and colleagues (2010) who also found that an instructional intervention—which emphasized teacher planning, organization, classroom management, and opportunities for students to work independently—was most beneficial for children who started first grade with weaker self-regulation. Similarly, a recent intervention focusing on aspects of self-regulation (attentional flexibility, working memory, and inhibitory control) integrated into classroom games found that partici- 285 pation in the intervention was significantly related to gains in self-regulation skills and academic achievement compared to children in the control group (Tominey and McClelland 2011; Schmitt et al. 2015). For children with ADHD, research has also documented that interventions that focus on strengthening aspects of self-regulation and underlying executive function skills can be beneficial (Reid et al. 2005). Such interventions have been found to help children improve on task behavior, decrease inappropriate behavior, and increase academic achievement, although results have been somewhat weaker for lasting improvement in academic skills (DuPaul et al. 2011). Overall, results from a growing number of randomized control trials suggest that interventions designed to strengthen self-regulation can improve children’s self-regulation, social behavior, and academic achievement. It is not known, however, if these effects persist over time. More research is needed on the long-term effects of such interventions and how interventions may work for different subgroups of children (e.g., those most at risk). Moreover, following the principles of LCHD, interventions tend to be most effective when they include multiple levels of influence and are integrated across domains of functioning and over time (Jones and Bouffard 2012). 4 Self-Regulation and HealthRelated Outcomes Although self-regulation has been conceptualized differently in a variety of fields and at different developmental periods, accumulating evidence demonstrates the importance of selfregulation for a variety of outcomes. Moreover, our view of self-regulation reflects both the principles of LCHD and the RDS perspective. Below we review research on predictive relations between self-regulation and important outcomes such as academic achievement and educational attainment and health and wellbeing (see also Table 1). M. McClelland et al. 286 4.1 Academic Achievement, Educational Attainment, and Economic Well-Being Over a century ago, in a series of lectures for schoolteachers near his home institution of Harvard University, William James (1899) declared that much of schoolwork was necessarily “dull and unexciting” in comparison with other things children might be doing (pp. 104– 105). Consequently, James reasoned that students who could voluntary control their attention enjoyed a distinct advantage over students who regularly succumbed to the “temptation to serve aside to other subjects” (p.112). Alfred Binet, Charles Spearman, and David Wechsler all made similar observations. That three of the most important figures in the history of intelligence testing would individually highlight the importance of “will” as a necessary complement to talent is somewhat ironic, given that intellectual aptitude, rather than self-regulation, was until very recently given disproportionate emphasis in the educational psychology literature. Prospective longitudinal studies have confirmed James’s earlier intuitions. For young children, a large body of evidence now demonstrates that self-regulation sets the stage for learning in children even prior to formal schooling. For example, self-regulation in preschool and during the transition to kindergarten has uniquely predicted gains in academic achievement after controlling for child IQ and initial achievement levels (von Suchodoletz et al. 2013; Blair and Razza 2007; McClelland et al. 2007). In elementary school, strong kindergarten learning-related skills (including self-regulation and social competence) significantly predicted higher reading and mathematics achievement between kindergarten and sixth grade and growth in literacy and mathematics from kindergarten to second grade after controlling for prior achievement levels, child IQ, and a host of background variables (McClelland et al. 2006; see also Duncan et al. 2007; McClelland et al. 2006; McClelland et al. 2007; McClelland et al. 2000). Studies have also documented the long-term contributions of selfregulation to practically significant outcomes such as high school graduation and college completion (McClelland et al. 2013; Moffitt et al. 2011). In one recent study, a 4-year-old child with one standard deviation higher ratings of attention (one aspect of self-regulation) than average had 49% greater odds of completing college by age 25 (McClelland et al. 2013). In terms of economic well-being, the best evidence for the importance of self-regulation comes from a longitudinal study by Moffitt et al. (2011). Self-regulation was assessed using parent, teacher, observer, and self-report ratings at multiple time points in the first decade of life in a nationally representative sample of New Zealanders who were followed into adulthood. Childhood self-regulation predicted income, savings behavior, financial security, occupational prestige, lack of substance use, and lack of criminal convictions. These benefits were partially mediated by better decisions in adolescence, including staying in high school, not becoming a teenage parent, and not smoking. For a review of the relevance of self-regulation to academic achievement, including school readiness and lifetime educational attainment, see Duckworth and Allred (2012). 4.2 Health and Well-Being Self-regulation has been shown to be related to a variety of health behaviors, including recovery from physical illness or disabilities (e.g., exercise during and after cardiac rehabilitation (Blanchard et al. 2002), functional activity of patients undergoing surgical replacement of the hip or knee (Orbell and Sheeran 2000), physical activity for individuals in orthopedic rehabilitation (e.g., Ziegelmann et al. 2006, 2007), disease prevention (e.g., attendance for cervical cancer screenings, Sheeran and Orbell 2000; performance of breast self-examinations, Orbell et al. 1997), and general health (e.g., regulation of body weight via dieting and exercising/sport activities, Bagozzi and Edwards 1998; and increased consumption of nutritious foods and other dietary behaviors [Anderson et al. 2001; Calfas et al. 2002; Jackson et al. 2005]). Many of these stud- Self-Regulation ies are framed by Gollwitzer’s model of action phases (Gollwitzer 1990, 1996). As an action theory, Gollwitzer’s model of action phases focuses on the factors that determine how effective one is during the process of setting a goal to actual goal attainment. A key construct distinction within this model—and ultimately in predicting one’s success in behavior change or goal attainment—is between goal intentions and implementation intentions. A goal intention indicates a desired behavior or outcome and is a declaration of one’s commitment to a goal. Implementation intentions, on the other hand, specify the “when, where, and how of responses leading to goal attainment…and thus link anticipated opportunities with goal-directed responses” (Gollwitzer 1999, p. 494). As a goal intention states an individual’s commitment to a specific goal, the implementation intention states the individual’s commitment to certain actions in an effort to attain that particular goal. Gollwitzer’s model also highlights the contention that selfregulated actions fall along an intentionalautomatic continuum; forming implementation intentions allows people to “strategically switch from conscious and effortful control of their goal-directed behaviors to being automatically controlled by selected situational cues” (Gollwitzer 1999, p. 495). In turn, implementation intentions promote goal attainment by helping to initiate action, above and beyond the effects of goal intentions alone. Studies applying Gollwitzer’s model to health behavior have indicated that it is not only important for participants to have goal intentions, but it is also imperative for them to form implementation intentions and make subsequent planning strategies to work toward their goals. These strategies allow individuals to pinpoint when, where, and how they will enact specific goal-related behaviors. For example, Luszczynska (2006) examined how well patients who suffered a myocardial infarction utilized physical activity planning strategy and performed moderate physical activity after engaging in an implementation intention intervention program. The results indicated that as compared to controls, patients who participated in the implementation intention 287 intervention more frequently used their planning strategies and maintained the same levels of physical activity at 8 months after their infarction as they did at 2 weeks after rehabilitation. Furthermore, implementation intentions (as compared to goal intentions) may be more predictive of health behaviors at later time points (Orbell and Sheeran 2000; Ziegelmann et al. 2007). When participants were asked to perform breast self-examinations, those who made such planning strategies were more likely to perform the behavior in the manner in which they originally specified (i.e., time and place) and were less likely to report forgetting to perform the behavior (e.g., Orbell et al. 1997). Likewise, the formation of such plans for breast self-examinations or to attend cervical cancer screenings can lead to earlier enactment of goal intentions even among a sample of highly motivated individuals (Orbell and Sheeran 2000; Sheeran and Orbell 2000) and influence motivation and adherence (Levack et al. 2006). Another work examining the role of intentional self-regulation in health-related behaviors also focuses on specific self-regulatory cognitions and behaviors. Many studies have highlighted the importance of developing action and coping plans for successful adoption and maintenance of healthy behaviors such as physical activity and nutritious eating (e.g., Calfas et al. 2002; Sniehotta et al. 2005; Zeigelmann and Lippke 2007). Behavioral interventions aimed at initiating or increasing certain health behaviors—or aiding participants in reaching certain health goals—were often more effective when they included the creation of “action plans” (e.g., Calfas et al. 2002). The development of these plans often included having the participant explicitly identify the goals to pursue and sources for social support or resources to be utilized for achieving those goals. In some cases, the action plans also included identifying possible obstacles or barriers that might interfere with the implementation of their plans and solutions to overcome them (e.g., Calfas et al. 2002), but separate “coping plans” were also used for that purpose. For example, in a sample of 352 cardiac patients undergoing rehabilitation, Sniehotta et al. (2005) M. McClelland et al. 288 provided evidence that action planning and coping planning can be identified as distinct strategies; in addition, the combination of forming both action plans and coping plans was more effective in increasing health behaviors over time than forming action plans alone. The additive benefit of action and coping plans was replicated in experimental designs (Sniehotta et al. 2006; Sniehotta et al. 2005; Scholz et al. 2007). A large body of research also points to the importance of self-regulation for weight gain and loss (e.g., Evans et al. 2012; Francis and Susman 2009; Hofmann et al. 2014), addiction (Baumeister and Vonasch 2014), and other health-related outcomes (Moffitt et al. 2011). Several recent studies have demonstrated that poor self-regulation predicts unhealthy weight gain, particularly in adolescence, a period marked by pubertal changes that influence adiposity and greater latitude to make diet and exercise choices independent of parental control (Duckworth et al. 2010a; Tsukayama et al. 2010). In one study, children exposed to a number of risk factors were significantly more likely to gain weight during adolescence, which was mediated by having significantly lower levels of self-regulation (Evans et al. 2012). Adiposity, in turn, is a robust predictor of physical vitality later in life, suggesting one causal pathway linking childhood self-regulation to adult physical health and, ultimately, mortality. Issues with self-regulation have also been implicated in ADHD, with ADHD often characterized as a disorder of self-regulation and underlying executive function components (Barkley 1997, 2011). For example, many individuals with ADHD exhibit significant difficulties with the core executive function components of selfregulation, including attentional or cognitive flexibility, working memory, and inhibitory control. This can be seen in individuals who are inattentive, who lack behavioral inhibition, and who have difficulty with planning, organizing, and being goal-oriented. These issues can also lead to difficulty with emotion regulation. Thus, individuals with ADHD are more likely to have problems with impulse control, be more reactive, and have diminished social perspective taking abilities (Barkley 2011; Berwid et al. 2005). This means that children with ADHD may have a harder time stopping and thinking about a situation before reacting and illustrates why these children are more at risk for peer rejection and other behavior problems (Molina et al. 2009). Children with ADHD also demonstrate significant problems with academic achievement, which can also be linked back to difficulties with behavioral and emotional aspects of selfregulation (DuPaul and Kern 2011). 5 Methods for Studying Self-Regulation As demonstrated by how self-regulation relates to the principles of LCHD and RDS, selfregulation shows important transitions and sensitive periods, multiple levels of influence, and person-context fit in the form of matches or mismatches that can affect health development. Our understanding of these issues, however, hinges on how self-regulation is measured and analyzed in health-related research. In this section, we examine recent research on ways to measure and analyze self-regulation. 5.1 Measuring Self-Regulation Self-regulation is generally treated as a slowly developing phenomenon, meaning studies that target the development of self-regulation can easily take advantage of the large sample, small time point analyses that dominate research in healthrelated fields. Self-regulation research can accordingly draw on the strengths of modern statistical methods such as latent variable structural equation modeling, multilevel modeling, and mixture modeling. In this vein, researchers readily acknowledge that one size rarely fits all people. Advances in mixture modeling have allowed us to appropriately model theories that stem from the person-centered movement and systems theories. Large sample research can be Self-Regulation facilitated by utilizing advances in modern missing data procedures to incorporate planned missing data collection designs. Such designs allow researchers to collect all the data needed to utilize modern analytic methods without burdening parents, teachers, or individuals with excessively long surveys. It is also important to note, however, that challenges exist with some of these methods because self-regulation measures change over the developmental years and are often not strongly related with each other. Thus, developing self-regulation measures that are reliable and valid over a broad age range and at important points of transition is of particular importance. Some progress, however, has been made on this front. For example, the National Institutes of Health (NIH) Toolbox has developed brief assessments for a variety of skills, including aspects of self-regulation, which are appropriate to use with individuals throughout the life span (Zelazo et al. 2013). In addition to measures that span a large age range, other measures capture a broad set of children’s developmental skills, especially at school entry. Some research has focused on populationbased measures that are based on teacher or caregiver ratings. One example is the Early Developmental Instrument (EDI; Janus and Offord 2007), which measures five developmental domains: social, emotional, physical, cognitive, and communicative. Although not specifically focused on measuring self-regulation, the measure includes items tapping aspects of self-regulation mostly in the social and emotional domains. The measure has been shown to be reliable and valid and significantly related to broad measures of school readiness, although less strongly related to direct assessments of children’s skills (Hymel et al. 2011). A strength of this type of measure is the potential to capture a range of children’s skills. A weakness, however, is that there may be considerable construct overlap and variability in how teachers rate children. An example of a more targeted measure is the Head-Toes-Knees-Shoulders (HTKS) task (McClelland et al. 2014), which specifically measures behavioral aspects of self-regulation. The 289 HTKS taps children’s ability to pay attention, use working memory, and demonstrate inhibitory control by doing the opposite of what was asked. The task is most appropriate for young children during the transition to formal schooling, which is important because this time is a crucial period for the development of self-regulation. A number of studies have shown that the HTKS is reliable and valid and significantly predicts academic achievement in diverse groups of children in the US, Asian, and European countries (McClelland et al. 2007, 2014; von Suchodoletz et al. 2013; Wanless et al. 2011). In youth and adults, self-regulation is often measured either using self-report, parent-report, or teacher-report questionnaires, delay of gratification tasks, or, ideally, a multi-method battery of measures. Such measures predict report card grades and changes in report card grades over time (Duckworth and Seligman 2005), but the predictive validity of self-regulation for standardized achievement test scores, in contrast, is less dramatic (Duckworth et al. 2012). One reason that report card grades are differentially sensitive to self-regulation may be their relatively greater emphasis on effort on the part of the student, to complete homework assignments on time and with care, to come to class prepared and pay attention when present, and to study for quizzes and tests from provided materials. Notably, report card grades predict persistence through college better than standardized test scores, a testament to the continued importance of self-regulation as students move through the formal education system (Bowen et al. 2009). 5.1.1 Construct Diversity The major limitation to measure self-regulation stems from the fact that self-regulation is not a single globally measurable construct. Instead, self-regulation represents an individual’s agentic attempts to reach distal outcomes by influencing what Lerner (e.g., Gestsdottir and Lerner 2008) has called person-context relations. The extant diversity of theories and measures of selfregulation suggest that the apparently unitary domain of self-regulation actually consists of M. McClelland et al. 290 many oblique fragments that differentially influence behavior as a function of context. We therefore need refinements in the measures of and theories about context-specific self-regulation. Here, better measurement of the parts will better inform the whole. Complementing Nomothetic Analyses with Idiographic Analyses In addition, if we truly see self-regulation as part of an ongoing process that is unique to each individual, we must begin to complement our existing analyses with more idiographic examinations of self-regulation over a variety of time spans (e.g., moments, days). Idiographic analyses such as dynamic factor analysis and p-technique have a place in research, and it is important that selfregulation researchers begin to acknowledge this role. We currently have a poor understanding of self-regulation as an idiographic phenomenon. A better understanding of intraindividual differences will allow greater insight into interindividual phenomena related to self-regulation as well as its intraindividual development. tion, we suggest key issues and next steps for self-regulation research. 6.1 Integration in Conceptualizing and Measuring Self-Regulation 5.1.2 6 Issues for Future Research The previous sections demonstrate that, across a broad spectrum of disciplines, interest has steadily mounted in self-regulation and related constructs—executive function (EF), selfcontrol, and effortful control. A growing body of research has shown the importance of selfregulation for children’s success in school, as well as for subsequent health, wealth, and criminality (e.g., Moffitt et al. 2011). In addition, the study of self-regulation can be informed by a closer appreciation of the principles of LCHD and RDS, including how turning points and transitions, mismatches, and intervention integration influence self-regulation trajectories. Despite advances in many areas, our understanding of aspects of self-regulation, including the neurological underpinnings of these skills, and efforts to intervene in the development of self-regulation for children at risk remains limited. In this sec- When studied from multiple perspectives and fields, differences in how self-regulation is defined and conceptualized arise in part because its study stems from diverse research traditions that use distinct methods to examine phenomena across the life course. For example, research has burgeoned in basic investigations of selfregulation, including understanding the underlying neurological and behavioral mechanisms driving these skills in children, adolescents, and adults (Blair and Raver 2012). It is also the case that the particular domain of inquiry informs where and how phenomena and individuals are studied. Scholars sometimes refer to different levels of analysis (e.g., neurological activation, physiological responses, observed behavior, or self-report) to clarify some of these differences. More could be done, however, to provide better integration across different disciplines and contexts to study the development and measurement of these skills. For example, although the knowledge base of research on different aspects of selfregulation is deep, it lacks breadth, and most of the work in this area has been conducted in convenience samples of middle-SES North Americans. More research is needed on how selfregulation develops within different groups and populations especially as it relates to the principles of LCHD. Another critical issue is the need to move away from deficit models of self-regulation (e.g., attribution of undesirable outcomes to having “poor” self-regulation) and instead take a strength-based perspective. Each individual carries a unique set of self-regulatory strengths. By understanding how to maximize these strengths and the fit between these strengths and an individual’s contextual resources, the continued study of self-regulation will help researchers promote thriving and positive outcomes across the life course. Self-Regulation 6.2 Examining Developmental Changes in Self-Regulation Over Time In addition to issues with conceptualization, it is also not clear if constructs, as operationalized across disciplines, are all measuring the same underlying skills. In addition, longitudinal measurement of the developmental course (both behavioral and neurological) of the underlying components of self-regulation over different transitions and turning points is lacking at present. Although a number of recent investigations provide insight into the structure of self-regulation in young children (i.e., unitary vs. componential), very little of this work has involved repeated assessments over time. As a result, we know a great deal about the performance of children before and early in preschool (e.g., Carlson 2005) but much less about self-regulation as children move through formal schooling. It is also important to examine whether and how these changing abilities relate to behavior in real-world contexts. Indeed, it could be the case that children who come into school with stronger self-regulation skills—as assessed from using tasks derived from cognitive neuroscience—also exhibit stronger self-regulation on classroom-based measures (Rimm-Kaufman et al. 2009). It is also possible that the relations between these sets of skills are more limited than anticipated and that these different types of tasks tap into different abilities altogether. Finally, the malleability of selfregulation—and its components, such as working memory, inhibitory control, and attention control, and particularly the impact of different intervention efforts on these abilities—has not been extensively charted. We turn to this next. 6.3 Improving Intervention Efforts As the research reviewed suggests, there has been a sharp increase in the number of applied investigations targeting self-regulation, including a plethora of new programs for young children (Bierman et al. 2008a; Diamond and Lee 2011; 291 Jones et al. 2011; Raver et al. 2011; Schmitt et al. 2015; Tominey and McClelland 2011). Along with these changes, there has been an increase in interdisciplinary collaborations. These collaborations have led to new developments in measurements, analyses, and interventions related to understanding and promoting self-regulation skills early in the life course as a way to optimize development and prevent future difficulties. Moreover, researchers have started to examine the complex and dynamic relations among selfregulation and important variables that together influence individual health and well-being across the life course (McClelland et al. 2010). Although research has documented the stability of self-regulation trajectories over time, the malleability of these skills is also evident. Thus, although more research is needed to examine the key components of effective interventions to promote self-regulation and the long-term effects of such interventions, a few recommendations can be made. First, in accordance with the principles of LCHD, self-regulation interventions are likely most effective when administered to individuals at turning points or sensitive periods of development, such as the early childhood years (Blair and Raver 2012). In addition, interventions are most effective when they integrate multiple levels of influence across different contexts (e.g., Jones and Bouffard 2012) and involve repeated practice of skills that are relevant to behavior in everyday settings and which increase in complexity over time (e.g., Diamond and Lee 2011). There is also support for interventions to be most effective for groups of children who are at the most risk, such as those living in poverty and/or experiencing toxic stress and ACEs (Blair and Raver 2014; Schmitt et al. 2015). Finally, recent work has examined the impact of additional intervention components, such as mindfulness practices and yoga, on children’s self-regulation, with some encouraging results (Diamond and Lee 2011; Zelazo and Lyons 2012). It is also clear that more needs to be done to translate research and interventions into practice. From a public health perspective, clinicians and pediatricians need better tools for assessing children’s self-regulation especially in the early 292 childhood years. Based on the importance of developing strong self-regulation, it seems plausible that well-child visits include screening of self-regulation starting when children are 3 years of age. There are some measures available that assess aspects of self-regulation such as the EDI (Janus and Offord 2007), but more work is needed in this area. In the research realm, some progress has been made in developing ecologically valid and sensitive measures of selfregulation and in recognizing the roles of context in the development of these skills (e.g., McClelland and Cameron 2012). As noted above, however, it is unclear if self-regulation measured in one context relates to self-regulation in another context and how these relations change over time. Finally, it is critical that the results of basic and applied research get translated into policy. Some efforts are ongoing to bridge the science of selfregulation and child development with policy and between a diverse number of fields (see, e.g., Halfon 2012; Halfon and Inkelas 2003; Shonkoff 2011; Shonkoff and Bales 2011; Shonkoff et al. 2012). Thus, there is great momentum in this arena. Although more work remains, there is an increasing energy around translating the importance of self-regulation for important health and developmental outcomes into policy and practice. Framing our understanding of self-regulation within the principles of LCHD and the RDS perspective is a promising way to improve research and translational efforts and promote healthy development across the life span. References Anderson, E. S., Winett, R. A., Wojcik, J. R., Winett, S. G., & Bowden, T. (2001). A computerized social cognitive intervention for nutrition behavior: Direct and mediated effects on fat, fiber, fruits, and vegetables, self-efficacy, and outcome expectations among food shoppers. Annals of Behavioral Medicine, 23(2), 88–100. Backen Jones, L., Rothbart, M. K., & Posner, M. I. (2003). Development of executive attention in preschool children. Developmental Science, 6(5), 498–504. Bagozzi, R. P., & Edwards, E. A. (1998). Goal setting and goal pursuit in the regulation of body weight. Psychology and Health, 13(4), 593–621. M. McClelland et al. Baker, C. E., Cameron, C. E., Rimm-Kaufman, S. E., & Grissmer, D. W. (2012). Family and sociodemographic predictors of school readiness among African American boys in kindergarten. Early Education and Development, 23, 833–854. Bandura, A. (1969). Principles of behavior modification. New York, NY: Holt, Rinehart & Winston. Bargh, J. A., Gollwitzer, P. M., Lee-Chai, A., Barndollar, K., & Trötschel, R. (2001). The automated will: Nonconscious activation and pursuit of behavioral goals. Journal of Personality and Social Psychology, 81(6), 1014–1027. Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121, 65–94. Barkley, R. A. (2011). Attention-deficit/hyperactivity disorder, self-regulation, and executive functioning. In K. D. V. R. F. Baumeister (Ed.), Handbook of selfregulation: Research, theory, and applications (2nd ed., pp. 551–563). New York, NY: Guilford Press. Barnett, W. S., Jung, K., Yarosz, D. J., Thomas, J., Hornbeck, A., Stechuk, R., & Burns, S. (2008). Educational effects of the tools of the mind curriculum: A randomized trial. Early Childhood Research Quarterly, 23(3), 299–313. doi:10.1016/j. ecresq.2008.03.001. Baumeister, R. F., & Vonasch, A. J. (2014). Uses of selfregulation to facilitate and restrain addictive behavior. Addictive Behaviors, 44, 3–8. doi:10.1016/j. addbeh.2014.09.011. Bernier, A., Carlson, S. M., & Whipple, N. (2010). From external regulation to self-regulation: Early parenting precursors of young children’s executive functioning. Child Development, 81(1), 326–339. doi:10.1111/j.1467-8624.2009.01397.x. Berwid, O. G., Curko Kera, E. A., Marks, D. J., Santra, A., Bender, H. A., & Halperin, J. M. (2005). Sustained attention and response inhibition in young children at risk for attention deficit/hyperactivity disorder. Journal of Child Psychology & Psychiatry, 46(11), 1219–1229. Bierman, K. L., Domitrovich, C. E., Nix, R. L., Gest, S. D., Welsh, J. A., Greenberg, M. T., Blair, C., Nelson, K. E., & Gill, S. (2008a). Promoting academic and social-emotional school readiness: The Head Start REDI program. Child Development, 79(6), 1802–1817. Bierman, K. L., Nix, R. L., Greenberg, M. T., Blair, C., & Domitrovich, C. E. (2008b). Executive functions and school readiness intervention: Impact, moderation, and mediation in the Head Start REDI program. Development and Psychopathology, 20(03), 821–843. doi:10.1017/S0954579408000394. Blair, C. (2010). Stress and the development of selfregulation in context. Child Development Perspectives, 4(3), 181–188. doi:10.1111/j.1750-8606.2010.00145.x. Blair, C., & Raver, C. C. (2012). Child development in the context of adversity: Experiential canalization of brain and behavior. American Psychologist, 67(4), 309–318. doi:10.1037/a0027493. Self-Regulation Blair, C., & Raver, C. C. (2014). Closing the achievement gap through modification of neurocognitive and neuroendocrine function: Results from a cluster randomized controlled trial of an innovative approach to the education of children in kindergarten. PLoS One, 9(11), e112393. doi:10.1371/journal.pone.0112393. Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78(2), 647–663. doi:10.1111/j.1467-8624.2007.01019.x. Blanchard, C. M., Courneya, K. S., Rodgers, W. M., Daub, B., & Knapik, G. (2002). Determinants of exercise intention and behavior during and after phase 2 cardiac rehabilitation: An application of the theory of planned behavior. Rehabilitation Psychology, 47(3), 308–323. Bowen, W. G., Chingos, M. M., & McPherson, M. S. (2009). Test scores and high school grades as predictors. Crossing the finish line: Completing college at America’s public universities (pp. 112–133). Princeton, NJ: Princeton University Press. Bowers, E. P., Gestsdottir, S., Geldhof, G., Nikitin, J., von Eye, A., & Lerner, R. M. (2011). Developmental trajectories of intentional self-regulation in adolescence: The role of parenting and implications for positive and problematic outcomes among diverse youth. Journal of Adolescence, 34(6), 1193–1206. Brandstädter, J. (2006). Action perspectives on human development. In W. Damon (Series Ed.), & R. Lerner (Vol. Ed.), Handbook of child psychology (Theoretical models of human development). (Vol. 1, pp. 516–568). Hoboken, NJ: Wiley. Bridges, L. J., Denham, S. A., & Ganiban, J. M. (2004). Definitional issues in emotion regulation research. Child Development, 75(2), 340–345. Brody, G. H., & Ge, X. (2001). Linking parenting processes and self-regulation to psychological functioning and alcohol use during early adolescence. Journal of Family Psychology, 15(1), 82–94. doi:10.1037/0893-3200.15.1.82. Buckner, J. C., Mezzacappa, E., & Beardslee, W. R. (2009). Self-regulation and its relations to adaptive functioning in low income youths. American Journal of Orthopsychiatry, 79(1), 19–30. doi:10.1037/ a0014796. Burrage, M. J., Ponitz, C. C., McCready, E. A., Shah, P., Sims, B. C., Jewkes, A. M., & Morrison, F. J. (2008). Age- and schooling-related effects on executive functions in young children: A natural experiment. Child Neuropsychology, 14(6), 510–524. doi:10.1080/09297040701756917. Calfas, K. J., Sallis, J. F., Zabinski, M. F., Wilfley, D. E., Rupp, J., Prochaska, J. J., et al. (2002). Preliminary evaluation of a multicomponent program for nutrition and physical activity change in primary care: PACE for adults. Preventive Medicine, 34, 153–161. Cameron, C. E., & Morrison, F. J. (2011). Teacher activity orienting predicts preschoolers’ academic and selfregulatory skills. Early Education & Development, 22(4), 620–648. 293 Cameron Ponitz, C. E., Rimm-Kaufman, S. E., Grimm, K. J., & Curby, T. W. (2009). Kindergarten classroom quality, behavioral engagement, and reading achievement. School Psychology Review, 38(1), 102–120. Carlson, S. M. (2005). Developmentally sensitive measures of executive function in preschool children. Developmental Neuropsychology, 28(2), 595–616. doi:10.1207/s15326942dn2802_3. Carlson, S. M., Zelazo, P. D., & Faja, S. (2013). Executive function. In P. D. Zelazo (Ed.), The Oxford handbook of developmental psychology, Body and mind (Vol. 1, pp. 706–743). New York, NY: Oxford University Press. Center on the Developing Child. (2011). Building the brain’s “Air Traffic Control” system: How early experiences shape the development of executive function. (Working Paper No. 11). Retrieved from http://www. developingchild.harvard.edu. Chang, F., Kidd, J. R., Livak, K. J., Pakstis, A. J., & Kidd, K. K. (1996). The world-wide distribution of allele frequencies at the human dopamine D4 receptor locus. Human Genetics, 98(1), 91–101. doi:10.1007/ s004390050166. Christakis, D. A., Zimmerman, F. J., DiGiuseppe, D. L., & McCarty, C. A. (2004). Early television exposure and subsequent attentional problems in children. Pediatrics, 113(4), 708–713. Colombo, J. (2001). The development of visual attention in infancy. Annual Review of Psychology, 52, 337–367. Committee on Psychosocial Aspects of Child and Family Health, Committee on Early Childhood, Adoption, Dependent Care, Section on Developmental Behavioral Pediatrics, Garner, A. S., Shonkoff, J. P., Siegel, B. S., et al. (2012). Early childhood adversity, toxic stress, and the role of the pediatrician: Translating developmental science into lifelong health. Pediatrics, 129(1), e224–e231. doi:10.1542/peds.2011-2662. Connor, C., Ponitz, C., Phillips, B. M., Travis, Q., Glasney, S., & Morrison, F. J. (2010). First graders’ literacy and self-regulation gains: The effect of individualizing student instruction. Journal of School Psychology, 48(5), 433–455. Diamond, A. (2002). Normal development of prefrontal cortex from birth to young adulthood: Cognitive functions, anatomy, and biochemistry. In D. T. Stuss & R. T. Knight (Eds.), Principles of frontal lobe function (pp. 466–503). London, England: Oxford University Press. Diamond, A., Barnett, W. S., Thomas, J., & Munro, S. (2007). Preschool program improves cognitive control. Science, 318, 1387–1388. doi:10.1126/ science.1151148. Diamond, A., & Lee, K. (2011). Interventions shown to aid executive function development in children 4 to 12 years old. Science, 333, 959–964. doi:10.1126/ science.1204529. Domitrovich, C. E., Cortes, R. C., & Greenberg, M. T. (2007). Improving young children’s social and emotional competence: A randomized trial of the preschool ‘PATHS’ curriculum. Journal of Primary Prevention, 28(2), 67–91. 294 Duckworth, A. L., & Allred, K. M. (2012). Temperament in the classroom. In R. L. Shiner & M. Zentner (Eds.), Handbook of temperament (pp. 627–644). New York, NY: Guilford Press. Duckworth, A. L., & Seligman, M. E. P. (2005). Selfdiscipline outdoes IQ in predicting academic performance of adolescents. Psychological Science, 16(12), 939–944. Duckworth, A. L., Tsukayama, E., & Geier, A. B. (2010a). Self-controlled children stay leaner in the transition to adolescence. Appetite, 54(2), 304–308. Duckworth, A. L., Tsukayama, E., & May, H. (2010b). Establishing causality using longitudinal hierarchical linear modeling: An illustration predicting achievement from self-control. Social Psychology and Personality Science, 1(4), 311–317. doi:10.1177/1948550609359707. Duckworth, A. L., Quinn, P. D., & Tsukayama, E. (2012). What no child left behind leaves behind: A comparison of standardized achievement test score and report card grades. Journal of Educational Psychology, 104(2), 439–451. doi:10.1037/a0026280. Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., et al. (2007). School readiness and later achievement. Developmental. Psychology, 43(6), 1428–1446. doi:10.1037/0012-1649.43.6.1428. DuPaul, G. J., & Kern, L. (2011). Promotion of academic skills Young children with ADHD: Early identification and intervention (pp. 107–125). Washington, DC: American Psychological Association. DuPaul, G. J., Kern, L., Gormley, M. J., & Volpe, R. J. (2011). Early intervention for young children with ADHD: Academic outcomes for responders to behavioral treatment. School Mental Health, 3(3), 117–126. doi:10.1007/s12310-011-9053-x. Eisenberg, N., Smith, C. L., Sadovsky, A., & Spinrad, T. L. (2004). Effortful control: Relations with emotion regulation, adjustment, and socialization in childhood. In R. F. Baumeister & K. D. Vohs (Eds.), Handbook of self-regulation: Research, theory, and applications (pp. 259–282). New York, NY: Guilford. Eisenberg, N., Duckworth, A. L., Spinrad, T. L., & Valiente, C. (2014). Conscientiousness: Origins in childhood? Developmental Psychology, 50(5), 1331–1349. http://dx.doi.org/10.1037/a0030977. Evans, G. W., Fuller-Rowell, T. E., & Doan, S. N. (2012). Childhood cumulative risk and obesity: The mediating role of self-regulatory ability. Pediatrics, 129(1), e68–e73. doi:10.1542/peds.2010-3647. Farran, D., Wilson, S. J., & Lipsey, M. (2013). Effects of a curricular attempt to improve self- regulation and achievement in prekindergarten children. Paper presented at the biennial meeting for the Society for Research in Child Development in Seattle, WA. Fortsmeier, S., Drobetz, R., & Maercker, A. (2011). The delay of gratification test for adults: Validating a behavioral measure of self-motivation in a sample of older people. Motivation and Emotion, 35(2), 118–134. Francis, L. A., & Susman, E. J. (2009). Self-regulation and rapid weight gain in children from age 3 to 12 years. M. McClelland et al. Archives of Pediatrics & Adolescent Medicine, 163(4), 297–302. doi:10.1001/archpediatrics.2008.579. Fuller, B., Bein, E., Bridges, M., Halfon, N., Jung, S., Rabe-Hesketh, S., et al. (2010). Maternal practices that influence Hispanic infants’ health and cognitive growth. Pediatrics, 125(2), e324–e332. doi:10.1542/ peds.2009-0496. Garstein, M. R., & Rothbart, M. K. (2003). Studying infant temperament via the revised infant behavior questionnaire. Infant Behavior and Development, 26(1), 64–86. Gathercole, S. E., Pickering, S. J., Ambridge, B., & Wearing, H. (2004). The structure of working memory from 4 to 15 years of age. Developmental Psychology, 40(2), 177–190. doi:10.1037/0012-1649.40.2.177. Geldhof, G. J., Little, T. D., & Colombo, J. (2010). Selfregulation across the life span. In The handbook of life-span development, Social and emotional development (Vol. 2, pp. 116–157). Hoboken, NJ: Wiley. Gershoff, E. T., Aber, J. L., Raver, C. C., & Lennon, M. C. (2007). Income is not enough: Incorporating maternal hardship into models of income associations with parenting and child development. Child Development, 78(1), 70–95. Gestsdottir, S., & Lerner, R. M. (2008). Positive development in adolescence: The development and role of intentional self-regulation. Human Development, 51, 202–224. Gestsdottir, S., Lewin-Bizan, S., von Eye, A., Lerner, J. V., & Lerner, R. M. (2009). The structure and function of selection, optimization, and compensation in adolescence: Theoretical and applied implications. Journal of Applied Developmental Psychology, 30(5), 585–600. Gollwitzer, P. M. (1990). Action phases and mind-sets. In E. T. Higgins & R. M. Sorrentino (Eds.), Handbook of motivation and cognition: Foundations of social behavior (Vol. 2, pp. 53–92). New York, NY: Guilford Press. Gollwitzer, P. M. (1996). The volitional benefits of planning. In P. M. Gollwitzer & J. A. Bargh (Eds.), The psychology of action (pp. 287–312). New York, NY: Guilford. Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54(7), 493–503. Grolnick, W. S., Kurowski, C. O., Dunlap, K. G., & Hevey, C. (2000). Parental resources and the transition to junior high. Journal of Research on Adolescence, 10(4), 465–488. doi:10.1207/SJRA1004_05. Halfon, N. (2012). Addressing health inequalities in the US: A life course health development approach. Social Science & Medicine, 74(5), 671–673. doi:10.1016/j. socscimed.2011.12.016. Halfon, N., & Forrest, C. B. (2017). The emerging theoretical framework of life course health development. In N. Halfon, C. B. Forrest, R. M. Lerner, & E. Faustman (Eds.), Handbook of life course health-development science. Cham: Springer. Halfon, N., & Inkelas, M. (2003). Optimizing the health and development of children. JAMA: The Journal of Self-Regulation the American Medical Association, 290(23), 3136– 3138. doi:10.1001/jama.290.23.3136. Hofmann, W., Adriaanse, M., Vohs, K. D., & Baumeister, R. F. (2014). Dieting and the self-control of eating in everyday environments: An experience sampling study. British Journal of Health Psychology, 19(3), 523–539. doi:10.1111/bjhp.12053. Hooper, C. J., Luciana, M., Conklin, H. M., & Yarger, R. S. (2004). Adolescents’ performance on the Iowa gambling task: Implications for the development of decision making and ventromedial prefrontal cortex. Developmental Psychology, 40(6), 1148–1158. Hsu, C., Soong, W., Stigler, J. W., Hong, C., & Liange, C. (1981). The temperamental characteristics of Chinese babies. Child Development, 52(4), 1337–1340. doi:10.2307/1129528. Hymel, S., LeMare, L., & McKee, W. (2011). The early development Instrument: An examination of convergent and discriminant validity. Social Indicators Research, 103(2), 267–282. doi:10.1007/ s11205-011-9845-2. Jackson, C., Lawton, R., Knapp, P., Raynor, D. K., Conner, M., Lowe, C., & Closs, S. J. (2005). Beyond intention: Do specific plans increase health behaviours in patients in primary care? A study of fruit and vegetable consumption. Social Science & Medicine, 60(10), 2383–2391. James, W. (1890). The principles of psychology. New York, NY: Henry Holt and Company. James, W. (1899). Talks to teachers on psychology and to students on some of life’s ideals. New York, NY: Holt and Company. Janus, M., & Offord, D. R. (2007). Development and psychometric properties of the early development Instrument (EDI): A measure of children’s school readiness. Canadian Journal of Behavioural Science/ Revue canadienne des sciences du comportement, 39(1), 1–22. doi:10.1037/cjbs2007001. Jones, S. M., & Bouffard, S. M. (2012). Social and emotional learning in schools: From programs to strategies. Society for Research in Child Development Social Policy Report, 26, 1–33. http://files.eric.ed.gov/ fulltext/ED540203.pdf. Jones, S. M., Brown, J. L., & Aber, J. L. (2011). Two-year impacts of a universal school-based social- emotional and literacy intervention: An experiment in translational developmental research. Child. Development, 82(2), 533–554. doi:10.1111/j.1467-8624.2010.01560.x. Kail, R. V. (2003). Information processing and memory. In M. H. Bornstein, L. Davidson, C. L. M. Keyes, & K. A. Moore (Eds.), Crosscurrents in contemporary psychology (pp. 269–279). Mahwah, NJ: Lawrence Erlbaum. Kishiyama, M. M., Boyce, W. T., Jimenez, A. M., Perry, L. M., & Knight, R. T. (2009). Socioeconomic disparities affect prefrontal function in children. Journal of Cognitive Neuroscience, 21(6), 1106–1115. Kopp, C. (1982). Antecedents of self-regulation: A developmental perspective. Developmental Psychology, 18(2), 199–214. 295 Larson, R. W. (2011). Adolescents’ conscious processes of developing regulation: Learning to appraise challenges. New Directions for Child and Adolescent Development, 2011(133), 87–97. Lengua, L. J., Honorado, E., & Bush, N. R. (2007). Contextual risk and parenting as predictors of effortful control and social competence in preschool children. Journal of Applied Developmental Psychology, 28(1), 40–55. Lerner, R. M. (1982). Children and adolescents as producers of their own development. Developmental Review, 2(4), 342–370. Lerner, R. M. (1985). Adolescent maturational changes and psychosocial development: A dynamic interactional perspective. Journal of Youth and Adolescence, 14(4), 355–372. Lerner, R. M., & Steinberg, L. D. (2009). The scientific study of adolescent development: Historical and contemporary perspectives. In R. M. Lernerog & L. D. Steinberg (Eds.), Handbook of adolescent psychology (pp. 3–15). Hoboken, N.J: Wiley. Lerner, R. M., Lerner, J. V., von Eye, A., Bowers, E. P., & Lewin-Bizan, S. (2011). Individual and contextual bases of thriving in adolescence: A view of the issues. Journal of Adolescence, 34(6), 1107–1114. Levack, W. M. M., Taylor, K., Siegert, R. J., Dean, S. G., McPherson, K. M., & Weatherall, M. (2006). Is goal planning in rehabilitation effective? A systematic review. Clinical Rehabilitation, 20(9), 739–755. Lewin-Bizan, S., Bowers, E. P., & Lerner, R. M. (2010). One good thing leads to another: Cascades of positive youth development among American adolescents. Development and Psychopathology, 22(4), 759–770. doi:10.1017/S0954579410000441. Luszczynska, A. (2006). An implementation intentions intervention, the use of a planning strategy, and physical activity after myocardial infarction. Social Science & Medicine, 62(4), 900–908. Massey, E. K., Gebhardt, W. A., & Garnefski, N. (2008). Adolescent goal content and pursuit: A review of the literature from the past 16 years. Developmental Review, 28(4), 421–460. McClelland, M. M., Acock, A. C., & Morrison, F. J. (2006). The impact of kindergarten learning-related skills on academic trajectories at the end of elementary school. Early Child Research Quarterly, 21(4), 471–490. McClelland, M. M., & Cameron, C. E. (2012). Selfregulation in early childhood: Improving conceptual clarity and developing ecologically-valid measures. Child Development Perspectives, 6(2), 136–142. doi:10.1111/j.1750–8606.2011.00191.x. McClelland, M. M., Cameron, C. E., Connor, C. M., Farris, C. L., Jewkes, A. M., & Morrison, F. J. (2007). Links between behavioral regulation and preschoolers’ literacy, vocabulary and math skills. Developmental Psychology, 43(4), 947–959. doi:10.1037/0012-1649.43.4.947. McClelland, M. M., Cameron Ponitz, C. C., Messersmith, E., & Tominey, S. (2010). Self-regulation: The integration 296 of cognition and emotion. In R. Lerner (Series Ed.) & W. Overton (Vol. Ed.), Handbook of life-span development (Cognition, biology and methods) (Vol. 1, pp. 509–553). Hoboken, NJ: Wiley. McClelland, M. M., Acock, A. C., Piccinin, A., Rhea, S. A., & Stallings, M. C. (2013). Relations between preschool attention span-persistence and age 25 educational outcomes. Early Childhood Research Quarterly, 28, 314–324. doi:10.1016/j.ecresq.2012.07.008. McClelland, M. M., Cameron, C. E., Duncan, R., Bowles, R. P., Acock, A. C., Miao, A., & Pratt, M. E. (2014). Predictors of early growth in academic achievement: The Head-toes-knees-shoulders task. Frontiers in Psychology, 5, 1–14. doi:10.3389/fpsyg.2014.00599. Metcalfe, J., & Mischel, W. (1999). A hot/cool-system analysis of delay of gratification: Dynamics of willpower. Psychological Review, 106(1), 3–19. Miller, D. C., & Byrnes, J. P. (2001). Adolescents’ decision making in social situations. A self-regulation perspective. Journal of Applied Developmental Psychology, 22(3), 237–256. Mischel, W. (1968). Personality and assessment. Hoboken, NJ: Wiley. Mischel, W., & Ayduk, O. (2004). Willpower in a cognitive-affective processing system: The dynamics of delay of gratification. In R. F. Baumeister & K. D. Vohs (Eds.), Handbook of self-regulation: Research, theory, and applications (pp. 99–129). New York, NY: Guilford. Mischel, W., & Ebbesen, E. B. (1970). Attention in delay of gratification. Journal of Personality and Social Psychology, 16(2), 329–337. Mischel, W., Ebbesen, E. B., & Zeiss, A. R. (1972). Cognitive and attentional mechanisms in delay of gratification. Journal of Personality and Social Psychology, 21(2), 204–218. Mistry, R. S., Benner, A. D., Biesanz, J. C., Clark, S. L., & Howes, C. (2010). Family and social risk, and parental investments during the early childhood years as predictors of low-income children’s school readiness outcomes. Early Childhood Research Quarterly, 25(4), 432–449. doi:10.1016/j. ecresq.2010.01.002. Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., et al. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences, 108(7), 2693–2698. doi:10.1073/ pnas.1010076108. Moilanen, K. L. (2007). The adolescent self-regulatory inventory: The development and validation of a questionnaire of short-term and long-term self-regulation. Journal of Youth and Adolescence, 36(6), 835–848. Moilanen, K. L., Shaw, D. S., & Fitzpatrick, A. (2010). Self-regulation in early adolescence: Relations with mother-son relationship quality and maternal regulatory support and antagonism. Journal of Youth and Adolescence, 39(11), 1357–1367. doi:10.1007/ s10964-009-9485-x. M. McClelland et al. Molina, B. S. G., Hinshaw, S. P., Swanson, J. M., Arnold, L. E., Vitiello, B., Jensen, P. S., Epstein, J. N., Hoza, B., Hechtman, L., Abikoff, H. B., Elliott, G. R., Greenhill, L. L., Newcorn, J. H., Wells, K.C., Wigal, T., Gibbons, R. D., Hur, K., Houck, P. R., Houck, P. R. (2009). The MTA at 8 years: Prospective follow-up of children treated for combined-type ADHD in a multisite study. Journal of the American Academy of Child & Adolescent Psychiatry, 48(5), 484–500. doi: 10.1097/CHI.0b013e31819c23d0. Moore, B., Mischel, W., & Zeiss, A. (1976). Comparative effects of the reward stimulus and its cognitive representation in voluntary delay. Journal of Personality and Social Psychology, 34(3), 419–424. Morrison, F. J., Ponitz, C. C., & McClelland, M. M. (2010). Self-regulation and academic achievement in the transition to school. In S. D. Calkins & M. Bell (Eds.), Child development at the intersection of emotion and cognition (pp. 203–224). Washington, DC: American Psychological Association. Nikkelen, S. W. C., Valkenburg, P. M., Huizinga, M., & Bushman, B. J. (2014). Media use and ADHDrelated behaviors in children and adolescents: A meta-analysis. Developmental Psychology, 50(9), 2228–2241. doi:10.1037/a0037318. Norman, D. A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behavior. In R. J. Davidson, G. E. Schwartz, & D. Shapiro (Eds.), Consciousness and self-regulation: Advances in research and theory (Vol. 4, pp. 1–18). New York, NY: Plenum Press. Obradovic, J. (2010). Effortful control and adaptive functioning of homeless children: Variablefocused and person-focused analyses. Journal of Applied Developmental Psychology, 31(2), 109–117. doi:10.1016/j.appdev.2009.09.004. Orbell, S., & Sheeran, P. (2000). Motivational and volitional processes in action initiation: A field study of the role of implementation intentions. Journal of Applied Social Psychology, 30(4), 780–797. Orbell, S., Hodgkins, S., & Sheeran, P. (1997). Implementation intentions and the theory of planned behavior. Personality and Social Psychology Bulletin, 23(9), 945–954. Overton, W. F. (2010). Life-span development: Concepts and issues. In W. F. Overton (Ed). Cognition, biology, and methods across the lifespan. In R. M. Lerner (Ed.), Handbook of life-span development (pp. 1–29). Hoboken, NJ: Wiley. Overton, W. F. (2013). Relationism and relational developmental systems: A paradigm for developmental science in the post-Cartesian era. In R. M. Lerner & J. B. Benson (Eds.), Advances in child development and behavior, Embodiment and epigenesis: Theoretical and methodological issues in understanding the role of biology within the relational developmental system (Vol. 44, pp. 21–64). San Diego, CA: Academic Press. Posner, M. I., & Rothbart, M. K. (1998). Attention, self-regulation, and consciousness. Philosophical Self-Regulation Transactions of the Royal Society of London B, 353(1377), 1915–1927. Posner, M. I., & Rothbart, M. K. (2000). Developing mechanisms of self-regulation. Development and Psychopathology, 12(3), 427–441. Quinn, P. D., & Fromme, K. (2010). Self-regulation as a protective factor against risky drinking and sexual behavior. Psychology of Addictive Behaviors, 24(3), 376–385. doi:10.1037/A0018547. Radesky, J. S., Kistin, C. J., Zuckerman, B., Nitzberg, K., Gross, J., Kaplan-Sanoff, M., et al. (2014). Patterns of mobile device use by caregivers and children during meals in fast food restaurants. Pediatrics, 133(4), e843–e849. doi:10.1542/peds.2013-3703. Raver, C. C., Jones, S. M., Li-Grinning, C., Zhai, F., Bub, K., & Pressler, E. (2011). CSRP’s impact on low- income preschoolers’ preacademic skills: Self-regulation as a mediating mechanism. Child Development, 82(1), 362–378. doi:10.1111/j.1467-8624.2010.01561.x. Reid, R., Trout, A. L., & Schartz, M. (2005). Selfregulation interventions for children with attention deficit/hyperactivity disorder. Exceptional Children, 71, 361–377. Rimm-Kaufman, S. E., Curby, T. W., Grimm, K. J., Nathanson, L., & Brock, L. L. (2009). The contribution of children’s self-regulation and classroom quality to children’s adaptive behaviors in the kindergarten classroom. Developmental Psychology, 45(4), 958–972. Robertson, L. A., McAnally, H. M., & Hancox, R. J. (2013). Childhood and adolescent television viewing and antisocial behavior in early adulthood. Pediatrics, 131(3), 439–446. doi:10.1542/peds.2012-1582. Rothbart, M. K., & Bates, J. E. (1998). Temperament. In W. Damon (Series Ed.) & N. Eisenberg (Ed.), Handbook of child psychology (Social, emotional, and personality development) (Vol. 3). Hoboken, NJ: Wiley. Rothbart, M. K., Ahadi, S. A., Hersey, K. L., & Fisher, P. (2001). Investigations of temperament at three to seven years: The Children’s behavior questionnaire. Child Development, 72(5), 1394–1408. Sabbagh, M. A., Xu, F., Carlson, S. M., Moses, L. J., & Lee, K. (2006). The development of executive functioning and theory of mind: A comparison of Chinese and U.S. preschoolers. Psychological Science, 17(1), 74–81. Schmitt, S. A., McClelland, M. M., Tominey, S. L., & Acock, A. C. (2015). Strengthening school readiness for head start children: evaluation of a self-regulation intervention. Early Childhood Research Quarterly 30(A), 20–31.doi: http://dx.doi.org/10.1016/j. ecresq.2014.08.001. Scholz, U., Sniehotta, F. F., Burkert, S., & Schwarzer, R. (2007). Increasing physical exercise levels: Age-specific benefits of planning. Journal of Aging and Health, 19(5), 851–866. Sektnan, M., McClelland, M. M., Acock, A. C., & Morrison, F. J. (2010). Relations between early family risk, children’s behavioral regulation, and academic 297 achievement. Early Childhood Research Quarterly, 25(4), 464–479. doi:10.1016/j.ecresq.2010.02.005. Sheeran, P., & Orbell, S. (2000). Using implementation intentions to increase attendance for cervical cancer screening. Health Psychology, 19(3), 283–289. Sheese, B. E., Rothbart, M. K., Posner, M. I., White, L. K., & Fraundorf, S. H. (2008). Executive attention and self-regulation in infancy. Infant Behavior & Development, 31(1), 501–510. Shonkoff, J. P. (2011). Protecting brains, not simply stimulating minds. Science, 333(6045), 982–983. doi:10.1126/science.1206014. Shonkoff, J. P., & Bales, S. N. (2011). Science does not speak for itself: Translating child development research for the public and its policymakers. Child Development, 82(1), 17–32. doi:10.1111/j.1467-8624.2010.01538.x. Shonkoff, J. P., Garner, A. S., The Committee on Psychosocial Aspects of Child Family Health, Committee on Early Childhood, Adoption, Dependent Care, Section on Developmental Behavioral Pediatrics, Siegel, B. S., et al. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129(1), e232–e246. doi:10.1542/peds.2011-2663. Sniehotta, F. F., Schwarzer, R., Scholz, U., & Schüz, B. (2005). Action planning and coping planning for long-term lifestyle change: Theory and assessment. European Journal of Social Psychology, 35(4), 565–576. Sniehotta, F. F., Scholz, U., & Schwarzer, R. (2006). Action plans and coping plans for physical exercise: A longitudinal intervention study in cardiacrehabilitation. British Journal of Health Psychology, 11(1), 23–37. Stansbury, K., & Zimmermann, L. K. (1999). Relations among child language skills, maternal socializations of emotion regulation, and child behavior problems. Child Psychiatry & Human Development, 30(2), 121–142. Steinberg, L. (2010). A behavioral scientist looks at the science of adolescent brain development. Brain and Cognition, 72(1), 160–164. doi:10.1016/j.bandc.2009. 11.003. von Suchodoletz, A., Gestsdottir, S., Wanless, S. B., McClelland, M. M., Birgisdottir, F., Gunzenhauser, C., et al. (2013). Behavioral self-regulation and relations to emergent academic skills among children in Germany and Iceland. Early Childhood Research Quarterly, 28(1), 62–73. doi:10.1016/j.ecresq.2012.05.003. Tominey, S. L., & McClelland, M. M. (2011). Red light, purple light: Findings from a randomized trial using circle time games to improve behavioral self-regulation in preschool. Early Education & Development, 22(3), 489–519. doi:10.1080/10409289.2011.574258. Tsukayama, E., Toomey, S. L., Faith, M., & Duckworth, A. L. (2010). Self-control as a protective factor against overweight status in the transition to adolescence. Archives of Pediatrics and Adolescent Medicine, 164(7), 631–635. Wachs, T. D., Gurkas, P., & Kontos, S. (2004). Predictors of preschool children’s compliance behavior in early 298 childhood classroom settings. Journal of Applied Developmental Psychology, 25(4), 439–457. Wanless, S. B., McClelland, M. M., Tominey, S. L., & Acock, A. C. (2011). The influence of demographic risk factors on children’s behavioral regulation in prekindergarten and kindergarten. Early Education & Development, 22(3), 461–488. Zelazo, P. D., & Lyons, K. E. (2012). The potential benefits of mindfulness training in early childhood: A developmental social cognitive neuroscience perspective. Child Development Perspectives, 6(2), 154–160. doi:10.1111/j.1750–8606.2012.00241.x. Zelazo, P. D., Anderson, J. E., Richler, J., Wallner-Allen, K., Beaumont, J. L., & Weintraub, S. (2013). II. NIH toolbox cognition battery (CB): Measuring executive function and attention. Monographs of the Society for Research in Child Development, 78(4), 16–33. doi:10.1111/mono.12032. Ziegelmann, J. P., Lippke, S., & Schwarzer, R. (2006). Adoption and maintenance of physical activity: Planning interventions in young, middle-aged, and older adults. Psychology and Health, 21(2), 145–163. M. McClelland et al. Ziegelmann, J. P., Luszczynska, A., Lippke, S., & Schwarzer, R. (2007). Are goal intentions or implementation intentions better predictors of health behavior? A longitudinal study in orthopedic rehabilitation. Rehabilitation Psychology, 52(1), 97–102. Ziegelmann, J. P., & Lippke, S. (2007). Planning and strategy use in health behavior change: A life span view. International Journal of Behavioral Medicine, 14(1), 30–39. doi:10.1007/BF02999225. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. Zimmerman, B. J., & Schunk, D. H. (2001). Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed.). Mahwah, NJ: Erlbaum. Zimmermann, L. K., & Stansbury, K. (2003). The influence of temperamental reactivity and situational context on the emotion-regulatory abilities of 3-year-old children. Journal of Genetic Psychology, 164(4), 389–409. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. A Life Course Health Development Perspective on Oral Health James J. Crall and Christopher B. Forrest 1 Introduction 1.1 Life Course Health Development Concepts and Principles Life Course Health Development(LCHD) is a conceptual framework that helps to explain how health develops over an individual’s lifetime and emphasizes the need to treat health development as a long-term investment, beginning early and continuing throughout life (Halfon and Hochstein 2002). LCHD provides a powerful approach to understanding diseases and conditions and how risk factors, protective factors, critical life experiences, and environments affect long-term health and disease outcomes. LCHD also can help examine and explain how health and disease patterns, particularly health disparities, develop across populations and over time (Halfon and Hochstein 2002; Ben-Shlomo and Kuh 2002; Keating and Hertzman 1999). J.J. Crall (*) Division of Public Health and Community Dentistry, University of California Los Angeles (UCLA) School of Dentistry, Los Angeles, CA, USA e-mail: jcrall@dentistry.ucla.edu C.B. Forrest Applied Clinical Research Center, Children’s Hospital of Pennsylvania, Philadelphia, PA, USA Efforts to relate the life course perspective or life course theory to the field of maternal and child health (Fine and Kotelchuck, 2010) have emphasized the following key concepts: Pathways or Trajectories Health pathways or trajectories are constructed and modified throughout the life span. While individual trajectories vary, general patterns can be predicted for populations and communities based on social, economic, and environmental exposures and experiences. A life course does not reflect a series of discrete steps or stages, but rather an integrated, dynamic, and continuous set of exposures and experiences. The set of possible trajectories that a person can experience is constrained by evolutionary forces and is highly determined by exposure to various environmental contexts. Importance of Early Life Exposures Early experiences can markedly influence an individual’s future health development. Of particular salience are exposures that occur prenatally (i.e., exposures in utero) and intergenerationally (i.e., factors related to the health of the mother prior to conception). While adverse events and exposures can have an impact at any point in a person’s life course, the impact may be greatest at specific critical or sensitive periods when developing biological systems are most readily modified (e.g., during fetal development, in early childhood, during adolescence). © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_13 299 J.J. Crall and C.B. Forrest 300 Cumulative Impact Cumulative experiences also can influence an individual’s future health and development, even though any individual experience may not impact health development. For example, individuals may adapt with minimal impact to each episode of stress; however, the cumulative impact of multiple stresses over time (also referred to as “weathering” or “allostatic load”) may have a profound direct impact on health development by altering biological function and human behavior. Risk and Protective Factors Throughout the life span, protective factors improve health and contribute to health development, while risk factors diminish health and make it more difficult to reach full health potential. Moreover, pathways are changeable, and risk and protective factors are not limited to individual behavioral patterns or receipt of health-care and social services, but also include factors related to family, neighborhood, community, and social policy. The LCHD theoretical framework (presented more thoroughly in Chap. 2) incorporates and expands these concepts into a robust explanatory synthesis of how health in individuals and populations is produced and modified. In this chapter, we use the LCHD principles to guide our review of oral health development. The Table 1 illustrates each LCHD principle’s relevance to oral health. 1.2 Previous Efforts to Apply Life Course Concepts to Oral Health The multifactorial etiology of oral conditions, their chronic nature and occurrence over the life span, and inherent features of their expression make oral health well suited to studies which seek to apply life course concepts. For example, Nicolau and colleagues (Nicolau et al. 2007) have highlighted several features that make oral conditions amenable to life course epidemiological studies, the first of which is their observability—i.e., once these conditions develop, they are readily detectable during assessments as opposed to conditions that resolve or go unnoted if study participants fail to report them. A second feature relates to their being cumulative conditions, which allows for comparisons of the degree of disease development among individuals so that, rather than all who develop the disease being enumerated together, distinctions can be made among them with respect to the extent or severity of their disease. Third, oral conditions can be reliably measured and validly diagnosed without sophisticated or costly technology. Fourth, these conditions are moderately prevalent; thus, the required sample size for cohort studies is manageable. And, finally, oral health conditions have a public health importance that makes their study justifiable on both ethical and economic grounds. Despite growing interest in using life course concepts as the basis for various oral healthrelated scientific studies, robust applications have been rather limited. A PubMed search conducted as part of the literature review for this chapter using the terms “life course and oral health” yielded over 500 citations. However, a substantial portion of the publications examined in this and other literature searches were found to have one or more of the following limitations: studies focused on a single condition, used crosssectional data, or analyzed a relatively narrow portion of the life span, studies were based on relatively small sample sizes, and studies were predicated on retrospective analyses that frequently relied on recall of rather distant events. Furthermore, efforts to examine oral health from the perspective of LCHD concepts and principles have not been pursued. Therefore, the goals of this chapter are to (a) examine the concept of oral health and major oral diseases and conditions from a LCHD perspective using the key concepts and principles noted in the table, (b) assess what is (and what is not) known about the development of oral health and its impact on general health and well-being across the life course, and (c) offer recommendations for future research. In light of the evidence-based literature shortcomings noted in the preceding paragraph, assessments of what is known about applications of life course research approaches to studies of oral health are limited to research publications consistent with the life course epidemiology 1 Principle Health Development Brief description Health development integrates the concepts of health and developmental processes into a unified whole. Health enables adaptation to the environment, task execution, participation in desired activities, and attainment of thriving. Development refers to the mechanisms by which health changes over time. Health development, then, comprises trajectories of health assets and integrated sets of health resources that emerge as a result of a person’s continuous and relational interactions with their environments. 2 Unfolding 3 Complexity Health development unfolds continuously over the lifespan, from conception to death, and is shaped by prior experiences and environmental interactions. For human health development, there are four major functional phases: (1) generativity, preconception and prenatal period; (2) acquisition of capacity, the early years; (3) maintenance of capacity, the middle years; and (4) managing decline, the later years. Each is conceptually distinct but can be overlapping. Health development results from adaptive, multilevel, and reciprocal interactions between individuals and their physical, natural, and social environments. There is a dynamic interplay between individual and multiple physical, biochemical, psychological, social, and cultural networks which enable adaptation and individual robustness. Each level is both a part and a whole, nested and hierarchically aligned to optimize health development. Oral health illustrations Oral health is not merely the absence of oral diseases. Instead, oral health refers to the capacity to adapt to ubiquitous environmental and microbial challenges, maintaining the robustness of teeth and other structures in the mouth, and oral health development refers to the pathways and trajectories of change of these assets over the life course. Prenatal conditions can predispose developing teeth to elevated risk of early childhood caries, which is in turn a risk factor for caries later in life, which can lead to tooth loss and associated negative impacts on oral and overall health. Oral health is strongly influenced by nutritional intake and changes in the microbiome. These effects in turn impact health development of individuals as well as their energy levels, healing, behavior, and cognitive development. A Life Course Health Development Perspective on Oral Health Table 1 Life Course Health Development principles (continued) 301 302 Table 1 (continued) Principle Timing Brief description Health development is sensitive to the timing and social structuring of environmental exposures and experiences. Time-specific health development pathways refer to biological embedding that occurs during sensitive or critical periods, when developing systems are most alterable, and exogenous and endogenous influences can result in different adaptive responses. Socially structured pathways link experiences and exposures in ways that create recursive and mutually reinforcing patterns of risk, protection, and promotion. Health development phenotypes are systematically malleable and enabled and constrained by evolution to enhance adaptability to diverse environments. Heredity transmits evolutionary signals through genetic, epigenetic, behavioral, and symbolic dimensions, which establish health phenotypes that depending on environmental circumstances may or may not be selected and optimized to produce desirable outcomes. At the microlevel, a range of strategies introduce variable types and levels of plasticity to optimize adaptability from the molecular to the behavioral level. At a macrolevel, strategies organize health development phases and life stages into functional entities. 5 Plasticity 6 Thriving Optimal health development promotes survival, enhances well-being, and protects against disease. Health development can also be perturbed to create suboptimal states that are precursors to fully formed disease—so-called endophenotypes that represent intermediate phase transitions toward a fully manifest phenotypic expression of a disease or disorder. 7 Harmony Health development results from the balanced interactions of molecular, physiological, behavioral, cultural, and evolutionary processes. Genetic modulations that occur in molecular time frames measured in nanoseconds are linked to biochemical modulation measured in milliseconds, homeostatic mechanisms measured in seconds to days, social norms that evolve over years and decades, cultural processes that change from years to centuries, and ecological processes that until recently took millennia. Harmonization of these processes produces rhythms and variability that characterize health. Loss of coordination of these processes results in less robustness of the human system with negative consequences. Oral health illustrations Oral cancers result from the dynamic interaction of genetic, physical, biochemical, psychological, behavioral, social, and cultural factors. Risk for these conditions is highest for individuals who use alcohol, use tobacco, are exposed to human papilloma virus or Epstein-Barr virus, or are exposed to x-radiation before the age of 50 years The human jaw developed over millennia among our human ancestors who spent hours each day chewing their food. The interaction between chewing of tough food and bone development may have been responsible for the lack of malocclusion observed in the hominid fossil record. Today a softer diet has contributed to smaller jaws and smaller teeth in modern humans, leading to the large numbers of individuals with malocclusion and need for orthodontic treatment. Diabetes perturbs oral health development by increasing risk for periodontal disease, which when present causes pain, chewing difficulties, and tooth loss. Dry mouth, often a symptom of undetected diabetes, can cause sores, ulcers, infections and tooth decay. Smoking makes these problems worse. Conversely, reducing diabetes risk and reducing or eliminating smoking can promote oral health. Analysis of the DNA from ancient humans has demonstrated shifts in oral bacterial species ecology following dietary pattern changes and reductions in the diversity of the oral ecosystem, a possible factor in the rise of dental caries and periodontal disease in modern humans whose oral health may be better matched to circumstances in our evolutionary history than to modern environments. J.J. Crall and C.B. Forrest 4 A Life Course Health Development Perspective on Oral Health standard and LCHD framework. Furthermore, because of the diverse nature of the major oral health-related conditions noted above, assessments are largely limited to “dental conditions” (i.e., dental caries, periodontal diseases, and tooth loss), with limited additional examples pertaining to craniofacial abnormalities, oral cancers, or temporomandibular joint dysfunction. 2 Conceptualizing Oral Health and Overview of Major Conditions 2.1 Conceptualizing Oral Health Concepts and definitions of oral health have varied over time, often paralleling broader conceptualizations of health. Concepts of how oral health changes over time also often parallel more general paradigms. For example, many recent concepts of oral health have been based on the biopsychosocial model, which focuses on how the integration of various aspects of biological, psychological, and social domains influence individuals’ health in different contexts and over time (Borrell-Carrio et al. 2004). The US Surgeon General’s Report on Oral Health (SGROH), issued in 2000, and ensuing initiatives have emphasized two major themes regarding the concept of oral health—i.e., oral health means much more than healthy teeth, and oral health is integral to general health (US Department of Health and Human Services 2000). Interestingly, the SGROH embraced the World Health Organization (WHO) definition of oral health as “a state of being free from mouth and facial pain, oral and throat cancer, oral infection and sores, periodontal (gum) disease, tooth decay, tooth loss, and other diseases and disorders that limit an individual’s capacity in biting, chewing, smiling, speaking, and psychosocial wellbeing.” This definition espoused by both the WHO and SGROH is deficient in that it does not positively define what oral health is, but rather presents the concept as the absence of detrimental conditions or disorders. Conflating oral health and oral disorders is contrary to the WHO definition of overall 303 health which states that health is more than just the absence of disease and represents a state of complete physical, mental, and social well-being. Researchers working to develop measures related to oral health and its impact on quality of life also have concluded that health and disease are different domains of human experience, which vary both over time and among individuals, and are dependent on context (Locker and Slade 1994; Gregory et al. 2005). A recent development in the conceptualization and definition of oral health has emerged from the FDI World Dental Federation in late 2016. According to the new FDI definition, “Oral health is multifaceted and includes the ability to speak, smile, smell, taste, touch, chew, swallow, and convey a range of emotions through facial expressions with confidence and without pain, discomfort, and disease of the craniofacial complex. The core elements of oral health in the FDI framework are as follows: disease and condition status refers to a threshold of severity or a level of progression of disease, which also includes pain and discomfort; physiological function refers to the capacity to perform a set of actions that include, but are not limited to, the ability to speak, smile, chew, and swallow; and psychosocial function refers to the relationship between oral health and mental state that includes, but is not limited to, the capacity to speak, smile, and interact in social and work situations without feeling uncomfortable or embarrassed.” (Glick et al. 2016). LCHD theoretical principles conceptualize health as an emergent set of integrated assets that enable adaptation and pursuit of meaning and happiness, reflect broader notions of the impact of various health determinants over time, and consider health in a more positive context (see Table 1). Applying LCHD concepts, individuals with disease (e.g., diabetics or persons living with AIDS) can also be considered healthy if they have developed assets which enable adaptation to their environment, task execution, participation in desired activities, and an ability to thrive; health and disease are distinct, albeit interrelated concepts. LCHD builds and expands upon the basic tenets of the biopsychosocial and other ecologi- J.J. Crall and C.B. Forrest 304 cal models and establishes a conceptual foundation for a future era of health care that moves beyond systems focused on management of acute and chronic diseases and conditions to systems whose focus is on optimizing lifelong health for individuals and populations (Halfon et al. 2014). Using LCHD concepts and principles to conceptualize and define oral health has the potential to move practitioners, researchers, policy makers, and the public beyond their historical limited focus on the physical form and function of structures in and surrounding the oral cavity and the clinical consequences of oral/dental diseases to a new paradigm—one which fully embraces LCHD tenets and the development of more robust LCHD-based policies and systems for optimizing the oral health of individuals and populations. 2.2 Major Oral Health-Related Diseases and Conditions Clinical conditions that have major significance with respect to oral health in terms of prevalence, impact on health, development and well-being, and economic considerations include dental diseases and their consequences (dental caries, periodontal diseases, tooth loss), craniofacial developmental disorders (e.g., cleft lip, cleft palate, malocclusion), oral cancers (including pharyngeal and salivary gland cancers), and facial pain (distinct from dental pain) generally associated with temporomandibular joint and muscular disorders. Brief overviews of these conditions are provided below. Dental Conditions Despite significant progress during the latter portion of the twentieth century, dental caries (tooth decay) remains the most common chronic disease of childhood and a major cause of tooth loss in children and adults. In the United States, approximately 25% of 2–5-year-olds and over 50% of 6–8-year-old children experience dental caries in their primary teeth, and over 50% of 12–15-year-olds and 67% of 16–19-year-olds experience caries in their permanent teeth (Dye et al. 2011a). Although substantial declines in the average number of teeth and tooth surfaces affected by caries have been documented in recent decades, particularly in developed countries, according to the WHO, the prevalence of tooth decay in school-age children globally ranges from 60% to 90% (WHO, The World Oral Health Report 2008). Significant disparities in childhood caries experience also persist. On average, US children from lower-income households and children of color are three to five times more likely than their white more affluent counterparts to experience caries and exhibit more severe forms of tooth decay (Dye et al. 2011a; Vargas et al. 1998). Of interest from a LCHD perspective, many children from high-risk population groups do not exhibit severe forms of dental caries, presumably because the balance between individual’s risk and protective factors (e.g., diet, toothbrushing habits, fluoride exposure, oral microflora or other unidentified characteristics) is conducive to maintaining a healthy dentition (Feathersone 2000). Caries affects dentate individuals across the life span (Fig. 1), with over 90% of US adults with natural teeth experiencing dental caries in their permanent teeth (Dye et al. 2011a, b). According to the WHO (WHO, The World Oral Health Report, 2008) nearly 100% of dentate adults globally are affected by dental caries, making it the most prevalent chronic condition of people worldwide. From a life course perspective, early childhood caries can have a profound deleterious lifelong effect on an individual’s dentition status, as ECC often is a precursor of caries and its consequences in adults. Periodontal disease mainly results from infections and inflammation of the gingiva (gums) and bone that surround and support the teeth. In its early stage, called gingivitis, the gums can become swollen and red and may bleed. In its more serious form, called periodontitis, the gums can pull away from the teeth, bone can be lost, and teeth may loosen or even fall out. Thirty percent of US adults 30+ years of age have moderate periodontal disease, and 8.5% have severe periodontitis (Thornton-Evans 2013). Males, older adults, Black and Hispanic adults, current smokers, and those with lower incomes and less education are more likely to have moderate or severe A Life Course Health Development Perspective on Oral Health 305 94% 97% 96% 97% 50-64 65-74 75+ 82% 67% 56% 50% 29% 23% 2-5 6-8 9-11 12-15 16-19 20-34 34-49 Fig. 1 Proportion of the US population with caries experience (percentages for 2–5- and 6–8-year-olds reflect the percentage of individuals with caries experience in primary teeth; for ages 9 and above, percentages reflect the percentage with caries experience in permanent teeth) by age (years) (Data Source: National Health and Nutrition Examination Survey, 2011–2012. National Center for Health Statistics, CDC) periodontal disease (Thornton-Evans 2013). Globally, severe periodontal disease is found in 15–20% of adults aged 35–44 years and increases in prevalence with advancing age (WHO, The World Oral Health Report 2008). Dental caries and periodontal disease are the primary reasons for tooth loss (Dye et al. 2011b; Thornton-Evans 2013) which in its more severe forms (loss of multiple teeth) can have negative psychological, social, nutritional, and physical effects. A person’s quality of life is diminished as a result of tooth loss due to reductions in their ability to chew and speak and reduced social interactions and self-esteem (US Department of Health and Human Services 2000; Hollister & Weintraub 1993; Brennan et al. 2008). National survey data indicate that approximately 50% of US adults have lost at least one tooth. Approximately 5% of US adults overall and 25% of adults aged 65+ are completely edentulous (have no natural teeth) (Dye et al. 2011b). Worldwide, about 30% of people aged 65–74 have no natural teeth, with considerable disparities across countries (WHO, The World Oral Health Report 2008). Total US spending for dental services has been relatively flat at approximately $111 billion since 2010 (Wall 2013). The bulk of dental care spending is related to diagnosis, prevention and treatment of dental caries and periodontal disease, and treatment related to removal and replacement of lost teeth. Craniofacial Conditions Craniofacial defects such as cleft lip and cleft palate (CLP) are among the most common of all birth defects. The US incidence of cleft palate is 6 per 10,000 live births, and the incidence for cleft lip with or without cleft palate is 11 per 10,000 live births (Center for Disease Control and Prevention (CDC) 2014). CLP can occur as an isolated condition or may be a component of an inherited disease or syndrome. Cleft lip and cleft palate are thought to be caused by a combination of genes and other factors, such as things the mother comes in contact with in her environment, what the mother eats or drinks, or certain medications she uses during pregnancy. Maternal smoking, alcohol use, steroid use, and anticonvulsants are associated with increased risk for cleft lip and palate (Kohli and Kohli 2012). The incidence of CLP also varies by race, with Asians and Native Americans having higher rates and AfricanAmericans having lower rates. According to the Centers for Disease Control and Prevention J.J. Crall and C.B. Forrest 306 (CDC), in the United States, cleft lip and palate is the third most common birth defect, and health expenditures are approximately eight times higher in the first 10 years of life for children with clefting than for those without (Boulet et al. 2009). Malocclusion Misalignment or abnormal positioning of the teeth or an incorrect relation between the teeth in the upper and lower jaws—is neither a disease nor a life-threatening condition. Nevertheless, growing numbers of people seek and undergo orthodontic treatment, often because of esthetic concerns and other quality of life issues (Liu et al. 2009). Although children make up the majority of orthodontic patients, adults increasingly are seeking treatment for malocclusion and now comprise one-fifth of all orthodontic patients in the US. Orthodontic services account for approximately one-eighth of US dental care expenditures (Agency for Healthcare Research and Quality 2015). Oral Cancers Approximately 45,000 Americans and over 450,000 people worldwide are diagnosed each year with cancers that affect the mouth and/or pharynx. Oral cancers comprise 85% of all head and neck cancers and have relatively high mortality rates (5-year survival = 63%) (The Oral Cancer Foundation 2015; American Cancer Society 2015). Oral cancer occurrence rates are significantly higher for males than for females (except in American Indians/Alaska Natives) and higher for black males than for white males up to age 70. Oral cancer rates increase with age, increase more rapidly after age 50, and peak between ages 70 and 80 (Fig. 2) (Ram et al. 2011). Extensive case-control and longitudinal studies have implicated tobacco and alcohol as major risk factors for oral cancer. Human papillomavirus, syphilis, oro-dental factors, dietary deficiencies, chronic candidiasis, and viruses also have been shown to be significantly associated with oral cancer (Ram et al. 2011). Findings of a recent study (Jacobson et al. 2012) suggest that oral cancers may be among the most costly forms of cancer to treat in the United States. Facial Pain Associated with Temporoman dibular Joint Disorders The most common cause of facial pain is a group of conditions called temporomandibular joint and muscle disorders (TMJD). These disorders cause recurrent or chronic pain and dysfunction in the jaw joint and its associated muscles and supporting tissues. TMJD are the second most commonly occurring musculoskeletal condition resulting in pain and disability (after chronic low back pain), affecting 50 45 40 35 30 25 20 15 10 5 0 All Ages 0 to 19 20 to 29 All Races Black 30 to 39 AI/AN 40 to 49 API 50 to 59 Hispanic 60 to 69 70 to 79 80+ White Non-Hispanic Fig. 2 Incidence of oral cancer cases per 100,000 among different age and racial groups (Data Source: Oral Cancer Incidence by Age, Race and Gender, 2009. National Institute of Dental and Craniofacial Research) A Life Course Health Development Perspective on Oral Health approximately 5–12% of the population, with an annual cost estimated at $4 billion. About half to two-thirds of those with TMJD seek treatment; among those seeking treatment, approximately 15% develop chronic TMJD (NIDCR 2003). 3 Evolution of Oral Health Paradigms The evolution of concepts regarding oral health and associated analytical models designed to measure oral health in the aggregate and its various components have paralleled the evolution of general health concepts, albeit frequently with a variable time lag. Ancient theories and hypotheses have given way to more scientifically grounded concepts reflecting changing scientific paradigms, initially based on biomedical models of disease causation, followed by biopsychosocial models that attempted to incorporate a wider range of factors influencing health and, more recently, Life Course Health Development. The following section provides a broad overview of that progression, focusing primarily on concepts regarding dental conditions. 307 on Pasteur’s discovery that bacteria can ferment sugars into lactic acid, formulated the chemoparasitic theory of caries. This theory held that tooth decay is caused by acids, produced by oral bacteria following fermentation of ingested sugars, which lead to loss of mineral from teeth (demineralization) (Miller 1890). Miller’s second major contribution was the focal infection theory, which hypothesized that oral microorganisms or their products have a role in the development of a variety of diseases in sites removed from the oral cavity, including brain abscesses, pulmonary diseases, and gastric problems (Miller 1891). Later work by Keyes in the 1960s led to explanations of the mechanisms by which dental caries develops based on the interaction of cariogenic bacteria in biofilm/dental plaque and dietary substrates that lead to acid production, which in turn leads to demineralization of tooth structure (illustrated in the Venn diagram in Fig. 5). Tooth Worm Theory The concept of a tooth worm which according to prevailing popular beliefs caused caries and periodontitis existed in diverse cultures across the ages and, despite being labeled by medical doctors as superstition during the time of the Enlightenment, persisted in some cultures into the twentieth century. Numerous popular “therapies” were applied to eradicate the tooth worm, including fumigations with henbane seeds, magical formulas, and oaths (Gerabek 1999). Concept of Caries as a Specific and Transmissible Infection The concept of dental caries being infectious and transmissible is based on the biomedical model of disease causation, and grew out of well-designed rodent studies performed by Keyes (Keyes 1960) showing that caries only developed in rodents when they were caged with or ate the fecal pellets of groups of caries-active rodents. Further proof emerged when certain streptococci isolated from caries lesions in hamsters, unlike other types of streptococci, caused rampant decay in previously cariesinactive animals (Fitzgerald and Keyes 1961). The bacteria, later identified as Streptococcus mutans (SM), gave rise to the concept of caries being due to a specific infection with mutans streptococci (MS), a concept that has gained wide support within the field of caries microbiology (Fejerskov 2004). Subsequent studies conducted from the mid-1970s through the 1990s demonstrated that infants acquire MS from their mothers and that MS can colonize the mouths of infants even before teeth erupt (Berkowitz 2006). Chemo-Parasitic and Focal Infection Theories In 1890, W.D. Miller (an American dentist and the first oral microbiologist), building Conceptualizing Caries as a Complex, Chronic Disease More recently, Featherstone (Feathersone 2000) used a modification of the chronic disease 3.1 Changing Concepts of Dental Disease: From Worms to Germs to Chronic Disease Terms J.J. Crall and C.B. Forrest 308 model, referred to as the “caries balance,” to describe caries as a dynamic process that depends on the balance between constellations of risk factors (e.g., high levels of cariogenic bacteria, reduced salivary function, cariogenic dietary practices, tooth structure anomalies) and protective factors (e.g., salivary components and flow, exposure to fluorides, anti-cariogenic dietary components). Although the caries balance concept highlights the importance of risk factors and protective factors and the potential for dynamic changes over time, published examples generally depict factors related to biomedical models rather than broader biopsychosocial models. The shift from conceptualizing dental caries based on biomedical models to models based on biopsychosocial theory represents a relatively recent paradigm change. These newer models recognize that caries expression not only depends on the complex interplay between saliva, dietary habits, and many biological determinants related to biofilm composition and metabolism; it also depends on those factors acting in concert with innumerable other biological, behavioral, and social factors acting at the level of individuals and populations. In summarizing the implications of this paradigm shift, Fejerskov (Fejerskov 2004) highlighted the following: By appreciating that dental caries belongs to the group of common diseases considered as ‘complex’ or ‘mulifactorial’ such as cancer, heart diseases, diabetes, and certain psychiatric illnesses, we have to realize that there is no simple causation pathway. It is not a simplistic problem such as ‘elimination of one type of microorganism’, or a matter of improving ‘tooth resistance’. Complex diseases cannot be ascribed to mutations in a single gene or to a single environmental factor. Rather Fig. 3 Common risk factor model of oral disease and other chronic diseases (Source: Tomar 2012) they arise from the concerted action of many genes, environmental factors, and risk-conferring behaviors. … [This concept also] explains why dental caries has to be controlled lifelong if a functional dentition is to be maintained. These new concepts explain why . . . several of the ‘old’ recommended preventive programs are no longer effective. It is of course not because the agents we used in prevention are no longer efficacious. They just become ineffective because the caries incidence rate has changed as the environment has changed. This brief overview highlights changes in popular and scientific concepts regarding the etiology of common dental diseases and methods for preventing or minimizing their consequences over time. Nevertheless, the persistence of relatively high levels of dental disease in sizeable segments of the population and substantial disparities within and across populations serve to highlight the limitations of traditional concepts and approaches. 3.2 Common Risk Factors, Social Determinants, and Ecological Models Common Risk Factors As noted above, there is growing recognition that major oral healthrelated conditions are complex (multifactorial), chronic conditions whose occurrence and severity across the lifespan depend on interactions of a broad array of biological, behavioral, social, and environmental factors, which also are implicated in other chronic diseases and conditions as indicated in Fig. 3 (Fejerskov 2004; Sheiham and A Life Course Health Development Perspective on Oral Health Watt 2000; Larson et al. 2008; Tomar 2012). We now realize that different types of organisms are involved in the etiology of dental caries and periodontal diseases and that the ecology of the oral microbiome is influenced by an array of common biological and biopsychosocial factors. We recognize that craniofacial disorders are caused by developmental disturbances that occur in utero and are influenced by genetic and environmental factors. We know that oral cancers result from mutations in genes that control cell behavior and are influenced by exposure to carcinogens (most notably tobacco) and biological factors, including viruses and fungi. Evidence also indicates that risk factors implicated in TMJD include poor alignment of teeth, stress, parafunctional habits, arthritis, trauma, and structural developmental abnormalities. Cumulative Impact of Multiple Social Determinants Larson et al. (Larson et al. 2008) highlighted the cumulative impact of multiple social risk factors (limited parental education, low family income, single-parent 309 household, race, being uninsured, family conflict, poor maternal mental health, living in an unsafe neighborhood) on various aspects of children’s health, including their oral health. As shown in Fig. 4, the percentage of parents who reported that their children had suboptimal oral health and other indicators of poor health rises with increases in the number of prevailing social risk factors, with the relationship between social risk factors and poor oral health demonstrating the steepest linear gradient. The proportion reporting less than very good teeth ranged from 14% for children with no social risk factors to 64% for children with ≥6 social risk factors. Analyses that controlled for child age, gender, and number of children in the household showed an almost 11-fold increase in the odds for less than very good teeth in children with ≥6 versus no social risks, underscoring the importance of social structure to healthy development. Ecological Models of Caries and Periodontal Disease Figure 5 depicts a multidimensional Fig. 4 Percentage of children in worse health by number of social risk factors (Source: Larson et al. 2008) 310 J.J. Crall and C.B. Forrest Fig. 5 Influences on children’s oral health: a conceptual model (Source: Fisher-Owens et al. 2007) ecological model for childhood dental caries, enumerating an array of biopsychosocial influences (including six child-level, eight familylevel, and eight community-level influences identified in the literature) in addition to the Keyes classic biological triad model and the influence of environmental factors and time (Fisher-Owens et al. 2007). The bulk of published studies underlying ecological models such as this have study design limitations similar to those identified in conducting the literature review for this chapter. Accordingly, their utility with respect to LCHD studies lies in helping to identify disease correlates and generate hypotheses, not in establishing oral disease or health development pathways. Ecological models like the one shown in Fig. 5 have been used to design a variety of caries-risk assessment instruments. However, most cariesrisk assessment instruments include only a subset of the broad range of influences depicted in the Fisher-Owens model and generally have not undergone extensive validity testing (Quinonez and Crall 2009). A Life Course Health Development Perspective on Oral Health 311 Social Determinants Familial Determinants Health care system Political system Employment situation Economic system Public policies Educational system Family structure Behaviors Culture Individual Determinants Sex Smoking Diabetes Social structure Host and Genetic Factors Public health infrastructure Family health status Family norms Oral hygiene Stress Use of dental services Periodontal status Bacterial biofilm Social inequalities SES SES Physical environment Social norms Immune and inflammatory factors Fig. 6 Social determinants of oral health and disease in US men (Source: Tomar 2012) Figure 6 depicts an ecological conceptual model for periodontal disease using a framework similar to the Fisher-Owens et al. model for childhood caries (Tomar 2012). In this case, the biological model emphasizes the interactions among host and genetic factors, bacteria associated with periodontal disease (different and distinct from cariogenic bacteria), and immune and inflammatory factors—reflecting a different pathogenic mechanism for periodontal disease than for dental caries. Constellations of individual, familial, and social influences are identified within categories of social, behavioral, and contextual factors in this model. Ecological models like those shown in Figs. 5 and 6 are useful for depicting the complex array of etiological factors that potentially influence disease development, but are of limited value for explaining disease mechanisms or pathways as they do not explicitly incorporate developmental mechanisms or time into the frameworks. LCHD principles address these gaps by building on ecological models, adding time dimensions and developmental pathways to the models. 4 Research Applications of Life Course Concepts to Oral Health 4.1 Life Course Epidemiology Life course epidemiology adds the time dimension to disease causation models, moving beyond the limitations of models such as those shown in the preceding section which depict an unspecified, essentially cross-sectional interplay among nested layers of environmental and individual factors. According to Kuh and Ben-Shlomo (Ben-Shlomo and Kuh 2002), “Life course epidemiology is defined as ‘the study of long-term effects on chronic disease risk of physical and social exposures during gestation, childhood, adolescence, young adulthood and later adult life. It seeks to understand causal links between exposures and outcomes, taking into consideration the importance of time (duration) and timing in the disease development.” In a recent paper on concepts and theoretical models of life course epidemiology and its relevance to chronic oral J.J. Crall and C.B. Forrest 312 conditions, Nicolau et al. (Nicolau et al. 2007) noted that “The life course approach to studying chronic disease etiology is not merely a collection of longitudinal data or the use of a particular study design or analytical method. Rather, the unique feature of this approach is a theoretical framework which assumes and tests a temporal ordering of exposure variables and their interrelationship with a specific outcome.” The following section provides several examples of applications of life course epidemiology to studies of chronic oral diseases and conditions. 4.2 Oral Health Trajectories: Dunedin Cohort Studies A number of studies published since the turn of the twenty-first century have begun to more fully embrace life course research methodologies and examine a broader array of life course influences. Foremost among them from the standpoint of assessing changes in oral health status over time and the influence of socioeconomic influences beginning in early childhood is the Dunedin Multidisciplinary Health and Development Study—a longitudinal investigation of health and behavior in a complete birth cohort of study members born in Dunedin, New Zealand, between April, 1972 and March, 1973. Findings of several Dunedin studies are summarized below. Poulton et al. (Poulton et al. 2002) assessed a number of health outcomes, including dental caries, dental plaque scores, gingival bleeding, and periodontal disease status in a cohort of 1000 26-year-old Dunedin Study subjects whose socioeconomic status (SES) had been documented at birth and at ages, 3, 5, 7, 9, 11, 13, and 15 years. All dental health measures at age 26 years showed a graded relation with childhood SES. As SES increased, the amount of plaque and gingival bleeding and the proportion of individuals with periodontal disease and decayed surfaces decreased. The adverse influence of low childhood socioeconomic status was seen after controlling for infant health and contemporaneous adult SES. Additionally, the results showed that low adult SES had a significant effect on poor adult dental health after controlling for low childhood SES. The authors concluded that their “findings document that the social gradient in health—which has been amply documented among middle-aged and older adults—actually emerges in childhood. Whereas clinical and research interest in the social gradient has been generated mostly by studies of adults, the findings from this study suggest that the social gradient can be scrutinised in paediatric and adolescent populations as well. Further, whereas most studies of the social gradient have narrowed their attention to specific diseases, such as cardiovascular diseases, we document that the social gradient is far more ubiquitous and troubling. Low social class adversely affects many areas of people’s health, including their physical, dental and mental health” (Poulton et al. 2002). This finding concerning children’s dental health is highly noteworthy because there are few pediatric conditions that show a substantial SES gradient, which raises the possibility that body systems that develop rapidly and dramatically during childhood, such as the structures and physiological systems of the oral cavity, are more vulnerable to, and perhaps dependent on, environmental exposures. Thomson et al. (2004) analyzed data on 789 Dunedin Study subjects at ages 5 and 26 years to investigate whether adult oral health status is influenced by (a) childhood socioeconomic advantage or disadvantage (controlling for childhood oral health) or (b) oral health in childhood (controlling for childhood socioeconomic advantage or disadvantage) and whether oral health in adulthood is affected by changes in SES. With respect to the question of whether poor adult oral health is predicted by socioeconomic disadvantage in childhood, after controlling for childhood oral health, analyses revealed that oral health inequalities present at age 5 years were also apparent at age 26 years when the early childhood SES categories were used, suggesting that early socioeconomic inequalities in a number of important oral health indicators do persist well into the third decade of life. Concerning the question of whether poor adult oral health is predicted A Life Course Health Development Perspective on Oral Health interest from the standpoint of LCHD is the possibility that oral health may represent a sentinel system, with early manifestations of disease serving as a harbinger for elevated risk of other chronic diseases (e.g., obesity, diabetes). Broadbent et al. (2008) investigated longitudinal patterns of caries experience in a birth cohort of 955 Dunedin Study subjects in order to identify and describe developmental trajectories of caries experience in the permanent dentition using data collected from dental examinations at ages 5, 9, 15, 18, 26, and 32 years. Three caries experience trajectory classes were identified: “high” (15% of subjects), “medium” (43%), and “low” (42%) with respect to decayed, missing, and filled (tooth) surfaces (DMFS). All trajectories were relatively linear, although the higher trajectories were more “S-shaped” (Fig. 7). This effect disappeared following adjustment for the number of unaffected surfaces remaining at each age, suggesting that, among individuals following a similar caries trajectory, the caries rate is relatively constant across time. This finding is 40 50 by poor oral health in childhood, after controlling for childhood socioeconomic status, the evidence was unequivocal regarding dental caries: having high caries disease experience early in life predicted having greater disease experience in adulthood, other factors being equal. The pattern was not as clear with respect to periodontal disease, however. Finally, in examining the impact of individuals’ SES at ages 5 and 26, Thomson and colleagues (Thomson et al. 2004) found that, for nearly all oral health indicators, a clear gradient for disease severity and prevalence was observed across socioeconomic trajectory groups in the following ascending order: “high (at age 5)–high (at age 26),” “low–high” (upwardly mobile), “high–low” (downwardly mobile), and “low– low.” These findings led the authors to conclude that adult oral health is predicted by not only childhood socioeconomic advantage or disadvantage but also by oral health in childhood. Changes in socioeconomic advantage or disadvantage between ages 5 and 26 were associated with differing levels of oral health in adulthood. Also of 313 10 Medium trajectory N = 427 (44.7%) 0 20 DMFS 30 High trajectory N = 144 (15.1%) Low trajectory N = 384 (40.2%) 5 9 15 18 Age (years) 26 32 Bars represent inter quartile range, dotted lines represent 95% CI DMFS: Decayed, Missing, and Filled surfaces Fig. 7 Trajectory patterns of dental caries experience in the permanent dentition to the fourth decade of life (Source: Broadbent et al. 2008) 314 consistent with observations by Fejerskov (2004) that the caries incidence rate in a group of individuals appears fairly constant throughout life if no special efforts to control lesion progression are made. Broadbent et al. (2008) concluded that these results did not support the commonly held belief among dentists that childhood and adolescence are periods of special risk for dental caries or that caries “immunity” may be acquired during late adolescence or early adulthood. In fact, other studies have reported that ECC is one of the most reliable predictors of elevated risk of caries experience later in life. Crocombe et al. (2011) also analyzed data from 833 participants in the Dunedin Study to assess the impact of dental visiting trajectory patterns on clinical oral health and oral healthrelated quality of life measures. Information on the use of dental services was collected at ages 15, 18, 26, and 32. Dental visiting trajectories were significantly associated with both measures of self-reported oral health and three of the four dental clinical indicators. The regular dental attendance group had less than half the mean number of missing teeth, a lower mean DMFS score, and a lower mean decayed surfaces (DS) than those defined as “opportunists” and “decliners.” The regular group had the lowest mean Oral Health Impact Profile (OHIP-14) score and was nearly twice as likely as opportunists and decliners to report that they had better-than-average oral health. Low SES and being dentally anxious were associated with worse health outcomes for all outcome variables, while “poor plaque trajectory” and smoking were associated with worse outcomes for all variables except filled surfaces (FS). The influence of dental visiting trajectory on clinical oral health outcomes was much higher at lower SES levels than higher SES levels, leading the authors to conclude that improving dental visiting behavior among people of lower SES would improve clinical oral health outcomes more and have a greater reduction of oral health impacts than it would for people of higher SES. A number of additional studies of shorter duration and/or employing retrospective meth- J.J. Crall and C.B. Forrest ods of data collection (which may be subject to some degree of recall bias) have been conducted in other countries, most notably Brazil, Norway, the United Kingdom, and Hong Kong (Lu et al. 2011; Holst; Peres et al. 2011; Mason et al. 2006). Collectively, these studies generally affirm the findings from analyses conducted as part of the Dunedin Study, with some variation that likely reflects varying levels of SES (particularly poverty) and differences in the types of oral health-care systems and programs available across countries. Many of these studies are limited to adolescents (starting at age 15) or adults. An exception is the work of (Holst and Schuller 2011) which examined different birth cohorts in Norway and found that the health environment in childhood was important for adults’ oral health during most of the twentieth century. Attention from parents and the local environment lead to better oral health outcomes in adulthood. Social status measured by length of education also was associated with choices leading to better oral health. Regular dental visits were important especially for the eldest birth cohort. Good oral health behaviors early and during adulthood were important for oral health. The studies summarized in this section represent advances in our understanding of the impacts of various influences on oral health over the life course. Interest in using a life course perspective to study various aspects of oral health has grown steadily in recent times. Main lines of pursuit include investigations of critical periods and accumulation of risk models, which have shed light on the importance of early life experiences, oral health trajectories, and the importance of social determinants to oral and general health (Heilman et al. 2015). However, it is worth noting that life course epidemiology only reflects the principles of LCHD (see Table 1) in a very limited way, primarily with respect to LCHD Principle 3 (Complexity: multidimensional person-environmental interactions) and LCHD Principle 5 (Timing: health is highly sensitive to timing and social structuring of environmental exposures). A Life Course Health Development Perspective on Oral Health 4.3 Relating Oral Health, General Health, and Quality of Life Summarizing what is known about the influences of oral health and oral health problems on life course outcomes, general health and overall quality of life is a daunting challenge that goes beyond the scope of this chapter. In lieu of attempting such a synopsis, the reader is referred initially to the US Surgeon General’s Report on Oral Health (US Department of Health and Human Services 2000) for a broad introduction to the literature. Chapters 5 and 6 of that extensively researched and widely vetted document are summarized below. Chapter 5 of the SGROH examines multiple linkages between oral and general health, noting that the mouth and the face reflect signs and symptoms of health and disease that can serve as an adjunct for diagnosis for some conditions. Diagnostic tests using oral cells and fluids— especially saliva—are available to detect drug abuse, hormonal changes, and specific diseases, and more are being developed. The mouth also is a portal of entry for pathogens and toxins, which can affect the mouth and, if not cleared by the many defense mechanisms that have evolved to protect the oral cavity, may spread to the rest of the body. Recent epidemiologic and experimental animal research provides evidence of possible associations between oral infections—particularly periodontal disease—and diabetes, cardiovascular disease, and adverse pregnancy outcomes. The review in Chapter 5 of the SGROH also highlights the need for an aggressive research agenda to better delineate the specific nature of these associations and the underlying mechanisms of action. Chapter 6 of the SGROH looks at the impact of oral health problems on quality of life and includes examples of the kinds of questionnaires used to measure oral-health-related quality of life. Oral health generally is highly valued by society and individuals, and the chapter begins with a brief description of the reflections of those values in myth and folklore concerning facial appearance and the meaning of teeth. It then explores dimensions beyond the biological and 315 the physical to examine how oral diseases and disorders can interfere with the functions of daily living, including participation in work or school, and what is known about their psychosocial impacts and economic costs. The deleterious effects of facial disfigurement and tooth loss may be magnified in modern societies that celebrate youth and beauty. Self-reported impacts of oral conditions on social functions include limitations in communication, social interactions, and intimacy. Additional research on oral-health-related quality of life is recommended to permit further exploration of the dimensions of oral health and well-being. Sheiham (2005) also has provided a commentary on the relationships among oral health, general health, and quality of life. Many of the points highlighted in this commentary relate to LCHD Principle 1 (Health Development) that health is an emergent property and that oral health is a component of and contributes to overall health. Sheiham’s summary ultimately reiterates the SGROH’s call for cessation of “the compartmentalization involved in viewing the mouth separately from the rest of the body, noting that oral health affects general health by causing considerable pain and suffering and by altering what people eat, their speech, and their quality of life and well-being.” Additional excerpts from Sheiham’s commentary include the following. “Oral health also has an effect on other chronic diseases, and failure to tackle social and material determinants and incorporate oral health into general health promotion means that millions suffer intractable toothache and poor quality of life, and end up with few teeth. Oral diseases are the most common of the chronic diseases and are important public health problems because of their prevalence, their impact on individuals and society, and the expense of their treatment. The general determinants of oral diseases are known and include risk factors common to a number of chronic diseases: diet, inadequate hygiene practices, smoking, alcohol, risky behaviors causing injuries, and stress. Moreover, effective methods are available to prevent or reduce the impact of major oral diseases. J.J. Crall and C.B. Forrest 316 Oral health affects people physically and psychologically and influences how they grow, enjoy life, look, speak, chew, taste food and socialize, as well as their feelings of social well-being. Severe caries detracts from children’s quality of life: they experience pain, discomfort, disfigurement, acute and chronic infections, and eating and sleep disruption as well as higher risk of hospitalization, high treatment costs and loss of school days with the consequently diminished ability to learn. Caries affects nutrition, growth and weight gain. Children of three years of age with nursing caries weigh about 1 kg less than control children because toothache and infection alter eating and sleeping habits, dietary intake and metabolic processes. Disturbed sleep affects glucocorticoid production. In addition, there is suppression of hemoglobin from depressed erythrocyte production. Chronic diseases such as obesity, diabetes and caries are increasing in developed and developing countries, with the implication that quality of life related to oral health, as well as general quality of life, may deteriorate. Because oral and other chronic diseases have determinants in common, more emphasis should be on the common risk factor approach. The key concept underlying future oral health strategies is integration with this approach, a major benefit being the focus on improving health conditions in general for the whole population and for groups at high risk, thereby reducing social inequities.” 5 Relating Oral Health and LCHD Concepts 5.1 Relating Oral Health Research Findings to Key LCHD Concepts Pathways and Trajectories Analyses based on the Dunedin prospective, longitudinal birth cohort studies have distinguished three oral health trajectory groupings (high, medium, low) that emerge as early as age 5, remain relatively constant over time, and exhibit increasing divergence through the third decade of life. Authors of the Dunedin Study acknowledge that aggregating their findings into three trajectory ‘groups’ is an analytical convenience that may mask the heterogeneity of individuals within each group. To that point, LCHD suggests that a focus on intraindividual trajectories rather than on “group” means may prove more insightful because it allows for careful ascertainment of temporal exposure-outcome relationships and allows the analysis to take advantage of the diversity and complexity of these interactions rather than masking variability by aggregation into a few classes. Classes assume homogeneity of persons within a group, an assumption that is generally not supported. Little if any investigations of intraindividual change with detailed ascertainment of exposure and outcome over time have been reported in the dental/ oral health literature. Additional Dunedin cohort studies have examined the effect of SES in childhood and adulthood on adult oral health, and the effect of early childhood oral health on adult oral health, and found that the highest oral health trajectories are exhibited by those who have higher SES and better oral health in childhood; the lowest trajectories are found in those who have low SES and poor oral health in childhood; and those whose SES changed (either upwardly or downwardly) between childhood and adulthood fall between the other two trajectories. Oral health-care utilization and other personal, family, and community factors (e.g., oral hygiene, diet, dental anxiety, emphasis placed on oral health within one’s family or community) also appear to exert influences on oral health trajectories throughout the life course. Early Programming Evidence concerning the impact of early programming on oral health over the lifespan is less well defined, in part because many studies focus on adolescents or adults. Developmental disturbances that result in structural anomalies of tooth structure represent one documented example of early programming influences that can increase risk for dental disease (Targino et al. 2011). Findings from studies conducted by Holst and Schuller (2011) and Alm (Alm et al. 2008) suggest that good oral hygiene A Life Course Health Development Perspective on Oral Health 317 habits, including the use of fluoride toothpaste, established in early childhood provide a foundation for a low risk of proximal caries in adolescents. Evidence for the influence of less direct influences such as low birth weight is more equivocal, but represents an area of growing research interest (Nicolau et al. 2007). Evidence of intergenerational programming has been documented, particularly with respect to maternalchild oral health relationships (Shearer and Thomson, 2010; Shearer et al. 2011). protective factors for major dental conditions (see Figs. 5 and 6). The influence of genetic, epigenetic, family, community, social, and environmental risk factors, in individual populations or across populations, is less well documented and understood. Critical or Sensitive Periods Some evidence, including studies cited above, supports the critical or sensitive period hypothesis with respect to the impact of various influences during fetal, early childhood, and later stages on life course oral health development and the determinants of oral health disparities. The importance and etiological mechanisms of disturbances arising during the prenatal period on craniofacial developmental problems such as cleft lip and/or cleft palate have been extensively documented. However, much remains to be elucidated with respect to the effects of different influences, at different times, on other oral health-related conditions (dental caries, periodontal disease, cancer, TMJD) (Hallqvist et al. 2004). Researchers involved in studying life course influences on oral health have identified a number of gaps in the current knowledge base. Using the lens of the LCHD principles, we have compiled research needs for future studies on oral health: Cumulative Impact Substantial evidence also exists to support the hypothesis that the impact of major oral health-related conditions is a function of cumulative experiences or episodes of disease over time. Clearly, it is well established that dental caries is a chronic, cumulative disease and that the caries status of an individual develops and is subject to biological, behavioral, social, and environmental influences over time. Caries occurs at any stage in life, provided that an individual has susceptible teeth (and surfaces) remaining (Broadbent et al. 2008). The same case can be made for periodontal disease and tooth loss resulting from caries and periodontal diseases (Shearer et al. 2011; Watt 2007). Risk and Protective Factors Considerable evidence has been compiled with respect to the biological and, to a lesser extent, behavioral risk and 5.2 Gaps in Knowledge Concerning Oral Health from a LCHD Perspective • Evaluate the contribution of social isolation, social relationships, and social support to oral health. • Obtain more information on the economic, political, social, and environmental causes of individual behaviors related to oral health. • Increase the number of longitudinal studies assessing the long-term effects of early childhood caries (ECC) and treatment on the health and quality of life of preschool children. • Longitudinal studies are needed in order to obtain more knowledge about causative factors and the possible relationships between dental caries and overweight/obesity in children. • Conceptual work that articulates a definition of oral health motivated by the LCHD principles is needed. • Theoretical explanations for health inequalities are limited and biased due to the types of data collected in modern epidemiological studies, which are geared toward identifying and quantifying risk factors for disease and intended as a basis for description (of disease), not explanation (of causal pathways). This raises questions about the validity of current explanations. Current epidemiological approaches are widely criticized for neglecting broad social factors and failing to dig below the surface into issues such as how differ- J.J. Crall and C.B. Forrest 318 • • • • • • • ent social class groups live their lives and what factors influence their lifestyle decisions. Current explanations are based largely on what epidemiological researchers can see and measure; factors that are harder to assess (such as culture), but which may be critically important to advancing our understanding of social inequalities in health, are frequently neglected. Epidemiologists have largely persisted in the use of disease-based measures to assess inequalities in health and oral health. The most significant advance to our understanding of social inequalities in health may come from the creation of a data set that measures oral health as defined by the FDI or oral-healthrelated quality of life. To date there is no research exploring how members of the population understand and account for inequalities in oral health, which remains a significant omission as it could offer promising new insights. Further research is needed to clarify the apparently differing oral health beliefs, attitudes, and practices of lower and higher SES groups. Much work remains to be done to establish which biological, social, and environmental factors are determinants of oral health or disease and which are merely markers, “proxies,” or confounders. The process by which social stratification translates to poor oral health beginning in earliest childhood, especially for groups at higher risk for disease, is not well understood. There is a need for future research to move beyond traditional risk factors and more closely examine the impact of the social environment on oral health beliefs, behavior, and outcomes. A number of oral health problems have been shown to be associated with other health conditions (e.g., low birth weight, cardiovascular disease, respiratory disease); however, the demonstration of actual causal connections or pathways, such as the bidirectional relationship between diabetes and periodontal disease, has been modest. 5.3 Recommendations for Research and Policy Priorities The knowledge gaps noted above give rise to a number of recommendations for research priorities to better understand oral health life course influences and policy changes to help reduce the impact of oral health problems and reduce oral health inequalities, which can be summarized as follows: • Greater support for longer-term longitudinal oral health life course studies with detailed and frequent data collection to allow for the construction of intraindividual trajectories • Replication of true oral health life course studies in different populations to assess whether findings apply across diverse settings • Greater emphasis on research delineating relationships among oral health determinants to identify causal pathways • Greater emphasis on identifying and addressing broader influences on oral health (i.e., family, community, social, and environmental influences) 6 Summary This chapter has outlined major concepts embodied in the Life Course Health Development framework, examined evidence relating various aspects of major oral health-related conditions to this framework, and produced recommendations for advancing research and policy concerning oral health. LCHD provides a highly useful approach for understanding oral health determinants, disparities, and influences on general health and well-being and for advancing knowledge, policies, and programs to optimize health across individuals and populations. References Agency for Healthcare Research and Quality. 2015. Dental Services Mean and Median Expenses per Person With Expense and Distribution of Expenses by Source of Payment: United States, 2012. Medical Expenditure A Life Course Health Development Perspective on Oral Health Panel Survey Household Component Data. Generated interactively. (3 Mar 2015). Alm, A., et al. (2008). Oral hygiene and parent-related factors during early childhood in relation to approximal caries at 15 years of age. Caries Research, 42(1), 28–36. American Cancer Society. (2015). Cancer Facts & Figures 2015. Atlanta: American Cancer Society. Ben-Shlomo, Y., & Kuh, D. (2002). A life course approach to chronic disease epidemiology: Conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology, 31, 285–293. Berkowitz, R. J. (2006). Mutans steptococci: Acquistion and transmission. Pediatric Dentistry, 28, 106–109. Borrell-Carrio, F., Suchman, A. L., & Epstein, R. M. (2004). The biopsychosocial model 25 years later: Principles, practice, and scientific inquiry. Annals of Family Medicine, 2, 576–582. Boulet, S. L., Grosse, S. D., Honein, M. A., & CorreaVillasenor, A. (2009). Children with orofacial clefts: Health care use and costs among a privately insured population. Publ Health Reports, 124, 447–453. Brennan, D. S., Spencer, A. J., & Roberts-Thomson, K. F. (2008). Tooth loss, chewing ability and quality of life. Quality of Life Research, 17, 227–235. Broadbent, J. M., Thomson, W. M., & Poulton, R. (2008). Trajectory patterns of dental caries experience in the permanent dentition to the fourth decade of life. Journal of Dental Research, 87, 69–72. Center for Disease Control and Prevention (CDC). (2014). Prevalence (number of cases) of cleft lip and cleft palate. Available at: http://www. nidcr.nih.gov/DataStatistics/FindDataByTopic/ CraniofacialBirthDefects/PrevalenceCleft%20 LipCleftPalate.htm. Crocombe, L. A., et al. (2011). Dental visiting trajectory patterns and their antecedents. Journal of Public Health Dentistry, 71(1), 23–31. Dye, B. A., Thornton-Evans, G., Li, X., & Iafolla, T. J. (2015a). Dental caries and sealant prevalence in children and adolescents in the United States, 2011a– 2012. NCHS data brief, no 191. Hyattsville: National Center for Health Statistics. Dye, B. A., Thornton-Evans, G., Li, X., & Iafolla, T. J. (2015b). Dental caries and tooth loss in adults in the United States, 2011b–2012. NCHS data brief, no 197. Hyattsville: National Center for Health Statistics. Feathersone, J. B. D. (2000). The science and practice of caries prevention. Journal of the American Dental Association (Chicago, IL), 131, 887–899. Fejerskov, O. (2004). Changing paradigms in concepts on dental caries: Consequences for oral health care. Caries Research, 38, 182–191. Fine, A., & Kotelchuck, M. (2010). Rethinking MCH: The life course model as an organizing framework. Washington, DC: US Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Bureau. Fisher-Owens, S. A., Gansky, S. A., Platt, L. J., et al. (2007). Influences on children's oral health: A conceptual model. Pediatrics, 120, e510–e520. 319 Fitzgerald, R. J., & Keyes, P. H. (1961). Demonstration of the etiologic role of streptococci in experimental caries in the hamster. Journal of the American Dental Association (Chicago, IL), 61, 9–19. Gerabek, W. E. (1999). The tooth-worm: Historical aspects of a popular medical belief. Clin Oral Invest, 3, 1–6. Glick, M., et al. (2016). A new definition for oral health developed by the FDI World dental federation opens the door to a universal definition of oral health. Journal of the American Dental Association (Chicago, IL), 147, 915–917. Gregory, J., Gibson, B., & Robinson, P. G. (2005). Variation and change in the meaning of oral health related quality of life: A ‘grounded’ systems approach. Social Science & Medicine, 60, 1859–1868. Halfon, N., & Hochstein, M. (2002). Life course health development: An integrated framework for developing health, policy, and research. The Milbank Quarterly, 80, 433–479. Halfon, N., Larson, K., Lu, M., Tullis, E., & Russ, S. (2014). Lifecourse health development: Past, present and future. Maternal and Child Health Journal, 18, 344–365. Hallqvist, J., et al. (2004). Can we disentangle life course processes of accumulation, critical period and social mobility? An analysis of disadvantaged socioeconomic positions and myocardial infarction in the Stockholm heart epidemiology program. Social Science & Medicine, 58(8), 1555–1562. Heilman, A., et al. (2015). Oral health over the life course. In C. Burton-Jeangros, S. Cullati, & A. Sacker (Eds.), A life course perspective on health trajectories and transitions [Internet]. Cham (CH): Springer. doi:10.1007/9783319204840_3. Hollister, M. C., & Weintraub, J. A. (1993). The association of oral status with systemic health, quality of life, and economic productivity. Journal of Dental Education, 57(12), 901–912. Holst, D., & Schuller, A. A. (2011). Equality in adults’ oral health in Norway. Cohort and cross-sectional results over 33 years. Community Dent Oral Epidemiol, 39(6), 488–497. doi:10.1111/j.1600-0528.2011.00624.x. Jacobson, J. J., et al. (2012). The cost burden of oral, oral pharyngeal, and salivary gland cancers in three groups: Commercial insurance, Medicare, and Medicaid. Head & Neck Oncology, 4, 15. Keating, D. P., & Hertzman, C. (1999). Developmental health and the wealth of nations : Social, biological, and educational dynamics. New York, NY: Guilford Press. Keyes, P. H. (1960). The infectious and transmissible nature of experimental dental caries. Archives of Oral Biology, 1, 304–320. Kohli, S. S., & Kohli, V. S. (2012). A comprehensive review of the genetic basis of cleft lip and palate. J Oral Maxillofac Pathol., 16, 64–72. doi:10.4103/0973-029X.92976. Larson, K., Russ, S. A., Crall, J. J., & Halfon, N. (2008). Influence of multiple social risks on children's health. Pediatrics, 121, 337–344. 320 Liu, Z., et al. (2009). The impact of malocclusion/orthodontic treatment need on the quality of life: A systematic review. The Angle Orthodontist, 79, 585–591. Locker, D., & Slade, G. (1994). Association between clinical and subjective indicators of oral health status in an older adult population. Gerodontology, 11, 108–114. Lu, H. X., et al. (2011). Trends in oral health from childhood to early adulthood: A life course approach. Community Dentistry and Oral Epidemiology, 39(4), 352–360. Mason, J., et al. (2006). How do factors at different stages of the lifecourse contribute to oral-health-related quality of life in middle age for men and women? Journal of Dental Research, 85(3), 257–261. Miller, W. D. (1890). The micro-organisms of the human mouth. Philadelphia: S.S. White Publishing Co.. Miller, W. D. (1891). The human mouth as a focus of infection. Dental Cosmos, 33, 689–706. Nicolau, B., et al. (2007). Life-course epidemiology: Concepts and theoretical models and its relevance to chronic oral conditions. Community Dentistry and Oral Epidemiology, 35(4), 241–249. NIDCR. (2003). Prevalence of TMJD and its signs and symptoms; Available from: http://www.nidcr.nih. gov/DataStatistics/FindDataByTopic/FacialPain/ PrevalenceTMJD.htm. Peres, M. A., et al. (2011). The influence of family income trajectories from birth to adulthood on adult oral health: Findings from the 1982 Pelotas birth cohort. American Journal of Public Health, 101(4), 730–736. Poulton, R., et al. (2002). Association between children’s experience of socioeconomic disadvantage and adult health: A life-course study. Lancet, 360(9346), 1640–1645. Quinonez, R., & Crall, J. J. (2009). Caries-risk assessment in early childhood. In J. H. Berg & R. Slayton (Eds.), Early childhood oral health. Hoboken: Wiley-Blackwell. Ram, H., Kumar, H., Konwar, R., Bhatt, M. L. B., & Mohammad, S. (2011). Oral cancer: Risk factors and molecular pathogenesis. J Maxillofac Oral Surg, 10, 132–137. Shearer, D. M., & Thomson, W. M. (2010). Intergenerational continuity in oral health: A review. Community Dentistry and Oral Epidemiology, 38(6), 479–486. Shearer, D. M., et al. (2011). Maternal oral health predicts their children's caries experience in adulthood. Journal of Dental Research, 90(5), 672–677. J.J. Crall and C.B. Forrest Sheiham, A. (2005). Oral health, general health and quality of life. Bulletin of the World Health Organization, 83(9), 644–644. Sheiham, A., & Watt, R. G. (2000). The common risk factor approach: A rational basis for promoting oral health. Community Dentistry and Oral Epidemiology, 28, 399–406. Targino, A. G. R., et al. (2011). The relationship of enamel defects and caries: A cohort study. Oral Diseases, 17(4), 420–426. The Oral Cancer Foundation. (2015). Oral cancer facts. Available at: http://www.oralcancer.org/facts/. Thomson, W. M., et al. (2004). Socioeconomic inequalities in oral health in childhood and adulthood in a birth cohort. Community Dentistry and Oral Epidemiology, 32, 345–353. Thornton-Evans, et al. (2013). Periodontitis among adults aged > United States, 2009–2010. MMWR, 62(3), 129–135. Tomar, S. L. (2012). Social determinants of oral health and disease in US men. Journal of Mens Health, 9, 113–119. U.S. Department of Health and Human Services. (2000). Oral Health in America: A Report of the Surgeon General. Rockville: U.S. Department of Health and Human Services, National Institute of Dental and Craniofacial Research, National Institutes of Health. Vargas, C. M., Crall, J. J., & Schneider, D. A. (1998). Sociodemographic distribution of pediatric dental caries: NHANES III, 1988-1994. Journal of the American Dental Association (1939), 129(9), 1229–1238. Wall, T., & Vujicic M. (2014). No growth in U.S. dental spending in 2013. Health policy Institute research brief. American Dental Association. December 2014. Available from: http://www.ada.org/~/media/ ADA/Science%20and%20Research/HPI/Files/ HPIBrief_1214_4.ashx. Watt, R. G. (2007). From victim blaming to upstream action: Tackling the social determinants of oral health inequalities. Community Dentistry and Oral Epidemiology, 35(1), 1–11. WHO, The World Oral Health Report 2003. (2008). Continuous improvement of oral health in the 21st century–the approach of the WHO global oral health Programme. Community Dent Oral Epidemiol, 31(s1), 3–24. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical, and Translational Research Agenda to Optimize Health, Behavior, and Functioning Michael E. Msall, Sarah A. Sobotka, Amelia Dmowska, Dennis Hogan, and Mary Sullivan Definitions Late preterm infants (LPIs) 340/7– 366/7 week infants; moderate preterm infants (MPI) 320/7–336/7 week infants; very preterm infants (VPI) 280/7–316/7 weeks; extreme preterm infants (EPI) <280/7 weeks; cerebral palsy (CP); normal birth weight (NBW) ≥2500 g; low birth weight (LBW) 1500–2499 g; very low birth weight (VLBW) 1000–1499 g; extremely low birth weight (ELBW) <1000 g; IQ = intelligence quotient; ID = intellectual disability M.E. Msall, MD (*) Developmental and Behavioral Pediatrics University of Chicago, Comer and LaRabida Children’s Hospitals, Chicago, IL, USA JP Kennedy Research Center on Intellectual and Developmental Disabilities, University of Chicago Comer Children’s Hospital, Section of Developmental and Behavioral Pediatrics, 950 East 61St Street, SSC Room 207, Chicago, IL 60637, USA e-mail: mmsall@peds.bsd.uchicago.edu 1 Introduction National vital statistics from 2013 estimate that the rate of preterm birth (<37 weeks of gestation) involves approximately 1 in 10 infants in the United States each year (Hamilton et al. 2015). An estimated 68 per 1000 US live births, over 270,000 annually, are born at late preterm gestation (34–36 weeks). Currently, 28 per 1000 US live births, more than 100,000 infants annually, S.A. Sobotka, MD, MS • A. Dmowska, BA Section of Developmental and Behavioral Pediatrics, University of Chicago Comer Children’s Hospitals, Chicago, IL, USA D. Hogan, PhD Sociology and Demography, Population Research and Training Center, Brown University, Providence, RI, USA M. Sullivan, PhD, RN University of Rhode Island, College of Nursing, Women and Infants Hospital, Providence, RI, USA © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_14 321 322 are born <34 weeks of gestation, and 56,000 are born at very low birth weight status (<1500 g). These infants have high risks for long-term neurodevelopmental disabilities such as intellectual disability (ID), blindness, sensorineural hearing loss, and cerebral palsy (CP). There are several biomedical causes for these high rates of preterm birth, such as the role of assisted reproductive technologies increasing the rate of multiple births (twins, triplets, or quadruplets) and maternal age. Teenage pregnancy, also a risk factor for prematurity, has instead been decreasing over the past decade (Hamilton et al. 2015; The March of Dimes Data Book for Policy Makers 2012). However, it is very critical to remember that both very preterm infants (VPI) and extreme preterm infants (EPI) are heterogeneous, and our understanding of causal pathways that lead to successful interventions is at an early scientific stage (Rubens et al. 2014). For example, the same factors that create the biological risk in the mothers for prematurity may be the same factors that influence the inflammatory, hormonal, and neurochemical regulators that influence the mothers’ caregiving capacity once their premature infant is born. Therefore, ascribing causality to biomedical, behavioral, or neuro-regulatory factors is difficult and does not allow for the complex systems understanding required to advance life courseoriented prevention and intervention strategies. In addition, despite increased access to prenatal care, there still remain limitations on our ability to prevent all moderate and late preterm birth (Requejo et al. 2013). Estimates of the economic impact of prematurity typically factor in acute care medical costs, early childhood intervention expenditures, longterm special education, special health care, and disability costs. A report released by the Institute of Medicine in 2007 estimated that the economic burden associated with preterm birth was at least $26.2 billion in 2005, equivalent to $51,600 per infant born preterm. These estimates included $16.9 billion in medical care costs, $1.9 billion in maternal delivery costs, $611 million for early intervention services, $1.1 billion for special education services, and $5.7 billion in lost household and labor market productivity (Preterm M.E. Msall et al. Birth: Causes, Consequences, and Prevention 2007). Importantly, each premature/LBW baby costs employers an average of $54,149 in payments for newborn medical care during the first year of life, about twelve times that of an uncomplicated newborn (Preterm Birth: Causes, Consequences, and Prevention 2007). In addition to medical care, children born preterm are more likely to experience learning and behavior disabilities later in life, often resulting in poor test scores, grade repetition, and increased utilization of special education services. The Institute of Medicine estimates the costs of special education at $2200 per year per child (Preterm Birth: Causes, Consequences, and Prevention 2007), though for children with major neurodevelopmental disabilities, the special cost per child can exceed $50,000 per academic year. High-quality early childhood intervention programs, however, may minimize the later need for special education and drastically reduce these additional costs (Aron and Loprest 2012). Model programs like the Infant Health Development Program (IHDP) are successful in improving educational, behavioral, and life course outcomes for preterm infants experiencing poverty, thereby decreasing children’s need for special education (Aron and Loprest 2012). Economic modeling estimates the impact of comprehensive early intervention services on long-term special education costs as having a direct savings of $2.60 for every dollar invested in early intervention, early childcare services, and Early Head Start (Dmowska et al. 2016). When considering costs incurred from school dropouts, reentry to school, and additional weight on mental health-care services and the criminal justice system, the savings would likely double (Dmowska et al. 2016). Increased access to comprehensive familycentered early intervention services, therefore, may substantially reduce special education costs for children born preterm as well as long-term behavioral and social health costs. There have been dramatic improvements in long-term survival for infants born premature and low birth weight, but developmental outcomes are far from optimized. Survival has increased dramatically with the regionalization of neonatal Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… intensive care and the application of obstetrical and neonatal biomedical interventions for optimizing growth, lung maturity, cardiopulmonary functioning, and infection control. Overall, these efforts resulted in decreased rates of major neurodevelopmental disabilities in survivors in the 1980s and 1990s from 25–40% to 15–25%. For those born with extreme prematurity (<28 weeks of gestation) and extremely low birth weight status (<1000 g), survival has increased dramatically from <50% to >80%. However, there remain high rates of cognitive impairment and intellectual disability (ID), with over 50% of survivors requiring special education services. Growing inequality in the United States hits families with young children very hard. Close to 50% of children younger than 5 are growing up in households living below 200% of the poverty level. Research shows that families with this level of income do not have the economic, social, educational, and community assets they need to support optimal health development (Crouter and Booth 2014). So many of the 8% premature babies, who start off life with higher levels of inherent vulnerabilities, are increasingly facing a family, social, and community landscape that cannot support their optimal development. In addition, there is increased recognition that both moderate and late preterm births (32– 36 weeks of gestation), which account for over 8% of live births or approximately 325,000 children yearly, have increased risk for long-term health, developmental, and behavioral challenges (Table 1). In spite of these biomedical advances, environmental and social conditions that can deleteriously affect the health and well-being of these already vulnerable children have not only improved in the United States; there are some indications that they have in fact worsened. 323 Additionally, there are major gaps in accessing comprehensive family supports and quality health, early childhood, and educational and community experiences for recent cohorts of children at the highest biomedical and social risks. The purpose of this paper is to apply a life course health development perspective in order to identify factors that promote more optimal health and developmental trajectories and the mechanisms that underlie resilience for children with prematurity from a life course health development perspective. We will describe some research findings about the role of social and environmental factors among VPI and EPI survivors in aggravating or moderating neonatal risks for suboptimal developmental and behavioral outcomes. We will also discuss available evidence from longitudinal studies of preterm children at psychosocial disadvantage and what lessons can be learned from the bidirectional impacts of prematurity and poverty. Longitudinal studies will be evaluated for health, disability, and community outcome trajectories for children with prematurity across preschool, middle childhood, adolescent, and young adult epochs. Our review will highlight important opportunities on a community level for systematically optimizing population-based prevention strategies for individuals with the double jeopardy of prematurity and social adversity and the long-term impact of failing to optimize these outcomes. 1.1 Approach Two frameworks will inform our analysis. The first framework will be the International Classification of Functioning, Disability, and Health (ICF) model (World Health Organization Table 1 Life course impact of prematurity Weeks gestation <28 28–31 32–36 37+ Children special health-care needs 50% 40% 30% 20% Major neurodevelopmental disability 20% 15% 10% 5% Educational supports 50% 40% 25% 15% Behavioral disorders 20% 15% 10% 5% Morse et al. (2009), Allen et al. (2011), Stephens and Vohr (2009), B. Vohr (2013), Saigal and Doyle (2008) M.E. Msall et al. 324 2007), which we will use to comprehensively describe the diverse outcomes occurring in preterm survivors. This framework goes beyond dichotomous classification of impairments (e.g., cerebral palsy, yes or no; intellectual disability, yes or no) and instead describes a spectrum of functioning at body structure and body function levels. For example, activities in whole-person tasks include running, reading, and dancing and participation in children’s roles with peers including being on a team; participating in church, temple, or mosque; or meeting friends for a movie. The second framework will be the life course health development (LCHD) model, which holds that the trajectories of children are influenced by the dynamic interaction of multiple risk, protective, and promoting factors, especially during sensitive periods of health development. From the standpoint of fetal development, due to critical human brain development in the second and third trimesters, a focus on premature infants must consider complex maternal, placental, and fetal dynamic interactions. Likewise, infant, toddler, and childhood periods of development are indelibly influenced by multilevel, multidirectional, transactional, and long-lasting interactions and critically emphasize the importance of timing. Using a LCHD framework to analyze the origins and impact of prematurity and the opportunities to optimize health development outcomes suggest the following considerations: • Children who are born prematurely are assumed to be more developmentally vulnerable and are potentially more sensitive to a wide range and nested array of dynamic interacting influences. • Because the alterations in evolutionarily influenced and developmentally determined adaptive mechanisms are well documented, lags in developmental processes, as well as catch-up and feed-forward processes that are specific to premature infants, may influence the nature and dynamic of their health and developmental trajectories. • Understanding how the caregiving environment of premature infants interacts with emerging developmental capacities and how different types of exposures, levels of support, and adversity influence these emergent developmental trajectories is important if specific and targeted interventions are to be designed to modify developmental pathways based on specific risk profiles to shift the health and developmental curves for the entire population of premature infants. • In order to implement a broader approach to improve the health and developmental outcomes of diverse preterm populations, it is important to determine what is known about the special development vulnerabilities of premature infants, how that vulnerability manifests (timing, context, specific risks), and whether the mechanisms involved are phase or period specific, modifiable, or one of cumulative risk. 2 Framing Our Inquiry and Agenda On a population level, it is important to acknowledge the diversity of both the underlying causes and life course effects of prematurity and the gaps in proactive and comprehensive medical, developmental, and behavioral supports. This is occurring within the context of recognizing that current community systems are under-resourced to systematically audit barriers and facilitators to home visiting, medical homes, early interventions, parenting supports, and coordinated services for children with special health-care needs (CSHCN). In particular, we must go from a crisis-oriented response system for the few with the severest impairments to an optimization system for all at risk for less than optimal health development. To address these systemic needs, the following themes will be highlighted that require increased research and policy efforts: • We must better understand the role of and need to engage mothers and other caregivers in developmentally optimizing interactions from the neonatal period through school entry. This includes how they gain and utilize their knowledge about their child’s health and development, as well as how to optimally serve as their Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… • • • • child’s first teacher and advocate. This requires understanding ways that early parent involvement and developmental activities can be part of everyday childcare tasks. We must explore how social environments are best tailored to maximally support positive growth, child regulatory behaviors, and developmental competencies. This includes how all children can access comprehensive preschool services and how health, developmental, and behavioral competencies are measured, monitored, and accounted for from birth to kindergarten entry. In keeping with a two-generation model of optimizing health development, close attention to caregiver physical and behavioral health and supports that help vulnerable children access quality childcare and early child education have the potential to ensure that children’s social-emotional, communicative, and cognitive competencies are supported so that children enter kindergarten healthy and ready to learn. We must clarify the roles that preconception maternal, physical, and behavioral health play on maternal and child vulnerabilities and on epigenetic programming. This includes a better understanding of how maternal mental health stressors (depression, anxiety, isolation, and violence) increase vulnerability of children when they do not receive quality early childhood social, learning, and behavioral supports. We must determine the optimum and appropriate role and function of community outreach strategies that promote parental physical and behavioral health, child development, and social competencies. We should pursue evidence-based strategies to promote resiliency and positive adult health trajectories which include independent living, employment, and family formation while minimizing physical risk factors that increase early-onset adult chronic diseases (e.g., cardiac, pulmonary, mental illness, substance abuse) (Figs. 1 and 2). Our cases illustrate how the interaction of positive home, preschool, and educational supports can increase thriving and reduce adverse longterm adult health conditions. 3 Risk Factors for Poor Birth Outcomes 3.1 Social Risks 325 An important determinant of preterm birth is social risk. Social risk factors include suboptimal home and community environments. Poverty, domestic violence, drug addiction, crime, hunger, and poor-quality housing are some of the features of social risks (Holzmann and Jørgensen 2001). Mothers who live in adverse environments often experience multiple stressors and are prone to nutritional deficiency, suboptimal prenatal care, single parenthood, and frequent tobacco and alcohol use compared to mothers from nonpoor backgrounds (Jiang 2015). Several studies have also shown that rates of marijuana, cocaine, tobacco, and alcohol use are higher for women who are unmarried, unemployed, and have less than a college education, indicating that substance abuse and poverty are closely related (Huston 1991). It has been suggested that the prevalence of substance abuse, illicit drug use, and smoking among women from impoverished or low SES background is largely due to the sense of helplessness, low self-esteem, difficulties coping with stress, and pressure from coping with difficult financial situations in everyday living (Huston 1991; Weitzman et al. 2002). When mothers receive late prenatal care (or not at all), the opportunity to identify and intervene on maternal reproductive complications or health problems that jeopardize fetal growth is limited. Also, late or no prenatal care decreases the chances of maternal access to educational and support services (such as counseling, community health, and education services) (The March of Dimes Data Book for Policy Makers 2012). 3.2 Race and Social Disadvantage Race in the United States is closely related to SES; thus, it is not surprising to see racial discrepancies in preterm births (Table 2). AfricanAmerican infants are more than 1.5 times likely than whites to be born preterm and 2.5 times likely to be very premature than their white peers M.E. Msall et al. 326 Body Function & Structure - 3rd grade: diagnosed with verbal learning disability/ADHD - Exercise-induced asthma, controlled well with inhaler - Long-acting stimulant medication for ADHD Activities - Challenges with impulsivity in preschool - Challenges with reading and attentiveness in early grade school - Excelled in math Environmental Factors - Special education pull-out services for reading in early grade school - Tutoring and subjectspecific education supports in high school Participation - Graduated from college with mathematics degree - Will attend graduate computer science engineering program Personal Factors - At birth, parents married, mom had law degree - Parents focused on positive successes in high school Fig. 1 Case 1. James was born late preterm at 34 weeks of gestation due to preeclampsia. His parents were married and mother completed law school. She had some challenges with impulsivity in preschool, and reported significant job-related stressors during pregnancy. The immediate newborn period was complicated by immature lungs leading to respiratory distress syndrome; however, ultimately James was discharged at 38 weeks of gestational age without a need for oxygen in the home. He was enrolled in full-day daycare and preschool since the age of 2 years. James was found to have some challenges with impulsivity in preschool, which his parents addressed with occupational and behavioral therapies. He entered kindergarten without an Individualized Education Program and in early grade school was found to struggle with reading and inattentiveness. He received a formal diagnosis of verbal learning disability and ADHD in third grade. James received special education pull-out services for reading and language arts and was starting on a longacting stimulant medication for ADHD with the guidance of a Developmental and Behavioral Pediatrician. James received tutoring and subject-specific special education supports throughout high school. His parents focused on his positive successes, such as his strong performance in math. James went to college and pursued a math and engineering program. His adult health is complicated by exercise-induced asthma, well-controlled on an inhaler (The March of Dimes Data Book for Policy Makers 2012). These data on preterm birth rates correlate with disparities in wealth distribution, with African-American families experiencing the lowest 3-year average median income (2003– 2005) among US racial groups (Income, Poverty, and Health Insurance Coverage in the United States: 2005–2006). These data on higher rates of prematurity in women experiencing social disadvantage from poverty and minority status also hold across both developing and developed countries. The role of maternal health, educational, behavioral, and social competencies during her own childhood and how they impact on her child’s health and development require systematically measuring both maternal and child allostatic load. These data can help understand what factors promote positive adaptations. 4 Prematurity and Developmental Outcomes Prematurely born infants have long-term vulnerabilities on multiple outcomes, including physical and developmental health, behavioral and adaptive well-being, as well as social functioning. Over the past decade, much has been learned Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… Body Function & Structure - Ages 2-4: asthma exacerbations - Adult: obesity, asthma, and depression - Activities - 2 years: neurotypical performance on Bayley Motor and Cognitive Skills - Repeated kindergarten: poor regulatory behaviors - Continued to struggle in school Environmental Factors - Inconsistent home daycare enrollment - Exposure to secondary smoke, household mold, cockroaches: asthma complications - IEP not until 5 years old 327 Participation - Dropped out of high school junior year - Unemployed As Adult: No health insurance Self-Medicating with alcohol and street drugs. Personal Factors - At birth, mom unmarried, had not finished high school - Mom had to work 2 jobs to care for 3 kids - Child home life dominated by TV Fig. 2 Case 2. Michael was born at 28 weeks due to preeclampsia and preterm labor. His mother was unmarried and did not finish high school. His NICU course was complicated by intubation for the first 4 weeks of life, but he did not experience additional medical complications and was able to be slowly transitioned to room air and oral feedings. At 2 years of age, he tested within age appropriate range on the Bayley Scales of Toddler Development. Between ages 2 and 4, he experienced asthma exacerbations complicated by environmental exposures to secondary tobacco smoke, household mold, and cockroach infestation and was hospitalized about 2–3 times a year. Until the age of 5 years, Michael was enrolled in inconsistent home daycare programs and did not receive early intervention services because he was considered less than 30% delayed and thereby was deemed not eligible for services. He enrolled in kindergarten at age 5 years. However, he had to repeat kindergarten due to poor regulatory behaviors, which interfered with learning. Throughout his early childhood education, he continued to struggle in school. His home life was largely dominated by television watching while his mom worked two jobs to help care for him and his two siblings. In school he was supported by an Individual Education Plan, but did not access pharmacotherapies for ADHD. He dropped out of high school in his junior year. His adult health is complicated by obesity, asthma, and depression. Because of unemployment, he does not have health insurance. His state has not expanded Medicaid access in the management of preterm infants and the interventions required to stabilize their immature organ system functioning. 4.1 Table 2 Rate (%) of preterm birth by maternal race and trimester of first prenatal visit Trimester of first prenatal visit First Second Third None NonHispanic black 14.7 17.6 16.0 33.4 Lang and Iams (2009) NonHispanic white 8.3 10.2 10.0 21.7 Hispanic 9.7 10.0 10.0 19.8 Cerebral Palsy and Neurosensory Impairment Table 3 highlights rates of CP and neurodevelopmental disabilities in early childhood among extremely preterm cohorts born into the medical era, which had both prenatal maternal corticosteroid and surfactant replacement interventions available. These studies demonstrate that, despite common misconceptions, the overwhelming majority of children who survive extreme prematurity do not experience CP. These data M.E. Msall et al. 328 Table 3 Major neurodevelopmental disabilities at age 2 years among survivors of ELBW in the 1990s Study Schmidt et al. (1996–1998) Mikkola et al. (1996–1998) Vohr et al. (1993–1998) Wood et al. (1995) Doyle et al. (1997) Mestan et al. (1998–2001) Shankaren et al. (1993–1999) Wilson-Costello et al. (1990–1998) Fily et al. (1997) Sample N = 944 500–999 g N = 206 <1000 g N = 2291 22–26 weeks N = 1494 27–32 weeks GA N = 283 22–25 weeks GA N = 170 500–999 g N = 138 27.4 weeks GA N = 246; ≤750 g ≤24 weeks; APl ≤3 N = 417 500–999 g N = 545 <33 weeks GA % CP 12 % DD 27 % HI 2 % VI 2 14 9 4 2.6 19 30 2 2 11.6 26 1 0.7 16 30 2 2 11 22 1.8 2.4 9.5 27.3 0.8 1.5 30 46 5 5 14 26 7 1 9 4.7 0.8 0.2 DD developmental disability as defined as mental developmental index < 70, HI hearing impairment, VI visual impairment AP1 Apgar 1 min GA gestational age complement population registries, which dramatically highlight the increased rate of CP among preterm survivors with decreasing gestational age. In Sweden, EPI have rates of CP at 71 per 1000, VPI 40 per 1000, and LPI (32–36 weeks) 6.4 per 1000, compared to term births at 1.1 per 1000 (Himmelmann and Uvebrant 2014). Although there are many types of CP with varying degrees of difficulty with motor control and higher cortical function, the two most common types among preterm survivors are spastic diplegia and spastic hemiplegia. These CP syndromes affect the motor control of lower extremities (diplegia) or one side of the body (hemiplegia) and increase risks for communicative, perceptual, learning, and attention disorders (Allen et al. 2011). However, an examination of these broader outcomes for recent VPI and EPI cohorts at ages 5–8 years has not occurred in large United States regional or multicenter cohorts. Furthermore, functional measures of gross motor, manual ability, and communication activities offer more detailed descriptions of performance in everyday activities than topographical classifications of CP. Over the past decade, there has been an increased appreciation of a wider range of motor challenges that have been characterized as developmental coordination disorders. A developmental coordination disorder is defined by acquisition and execution of coordinated motor skills below what would be expected at a given chronologic age. Difficulties are manifested as clumsiness, slowness, and inaccuracy of performance of motor skills. These motor skill deficits persistently interfere with activities of daily living appropriate to the chronologic age (Diagnostic and Statistical Manual of Mental Disorders: DSM-5 2013). A systematic analysis of 14 studies of children <33 weeks of gestation and <1501 g demonstrated 41% had mild developmental coordination disorder (characterized by motor standard scores 1–2 standard deviations below the mean) and 19% had moderate developmental coordination disorder (≥2 standard deviations below the mean) (Williams et al. 2010). Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… The second column in Table 3 reviews rates of developmental disability. These studies demonstrated rates of cognitive disability ranging between 9 and 46% among those EPI and 5% when VPI and EPI were combined. It must be emphasized that their early cognitive disabilities do not fully capture long-term educational impact. Among ELBW survivors, there are high rates of neurosensory impairments. Severe hearing impairment ranges from 1% to 7%. This is also substantially higher than the 1 per 1000 rate in term infants. Another neurodevelopmental disability after prematurity is visual loss or blindness caused by retinopathy of prematurity (ROP). This disorder primarily affects premature infants weighing 1250 g or less and those born before 31 weeks of gestation. ROP in the current era of neonatology reaches its severest stages in those who are most immature and medically fragile. Severe visual impairment occurs in 1–2% of <27-week preemies, 20–40 times higher than in term infants. In a multicenter study on cryosurgery for ROP, increased severity of ROP was linked to motor, self-care, and communicative disability at kindergarten entry (Msall et al. 2000). Children with severe ROP, but with favorable visual acuity, had a motor disability rate of 5% compared to 43% of children with severe ROP and unfavorable visual acuity (eyesight worse than 6.4 cycles per degree on Teller Cards includes legal and total blindness). In this cohort, neonatal risk factors for severe disability involving multiple motor, self-care, and communicative domains included severe ROP, gestational age <27 weeks, birth weight <750 g, and poverty as reflected by the absence of private health insurance. A protective factor associated with a significant risk reduction for severe disability was African-American race. In middle childhood, children with severe ROP had substantial differences in cognitive and educational outcomes (Msall et al. 2004). Children with severe ROP and unfavorable visual status had a 3 in 5 chance of ID (57%) and a 3 in 5 chance of needing special education services. Children with severe ROP and favorable visual skills had a 1 in 5 chance of ID (22%) and a 1 in 4 chance of need- 329 ing special education. More than 4 in 5 children with unfavorable vision (84%) but less than half of children with favorable vision (48%) were below grade level in school performance. Lower socioeconomic status (SES) and minority status were associated with lower-grade performance and utilization of special education services across both visual outcome groups. Table 3 also highlights that over 95% of extremely preterm survivors do not experience blindness. 4.2 Cognitive Outcomes The assessment of cognitive outcomes can focus on three related domains: intelligence testing, achievement testing, and neuropsychological testing. The latter includes evaluation of specific higher cortical processes such as executive function, working memory, and information processing. Assessment of toddlers and young children combines precursors of verbal and nonverbal intelligence and cognitive processes (e.g., object permanency, symbolic play), whereas schoolage assessment and beyond require more complex testing to assess problem-solving, literacy, and numeracy. There is increased recognition that Bayley II and III scores <70 predict complex cognitive and learning challenges among schoolage children. However, among children with 2-year Bayley scores of 71–84 (1–2 standard deviations below the mean), they may be uniquely vulnerable to the lack of quality early childhood education and preschool experiences, especially in socially disadvantaged families (Patrianakos-Hoobler et al. 2009). Therefore, one needs to be cautious in using developmental assessments in the first 2 years of life to fully assess the spectrum of cognitive and learning disorders in all children, not only those preterm and growing up in vulnerable circumstances. The limitations in Bayley assessments are due to the dynamic processes of higher cortical functioning in childhood. Additionally, the assessment of developmental delay is intertwined with perceptual and sensory skills as well as early learning experiences and neuroplasticity. Learning, communication, and social challenges may not 330 be apparent until there is increased complexity of tasks for academic skills and behavioral/ attention regulatory capacity required by school environments. In order to address higher-level skills at school entry, one strategy is to examine children’s status at kindergarten entry. EPI cohorts born in the 1980s from Hamilton, Ontario, Melbourne, Australia, Buffalo, New York, and Chicago, Illinois, have demonstrated that 44–56% require special education resources and 21–29% have major neurodevelopmental impairments (Baek et al. 2002; Vohr and Msall 1997). These studies indicate that, in addition to planning for major developmental disabilities, resources are required to ensure success with peers in the classroom. Gross and colleagues followed infants born very preterm and found that 41% of preterm infants were performing at grade level versus 70% of term children. These preterm children were more likely to receive special education services and three times as likely to be diagnosed with learning disabilities. In this cohort, parental marital status and educational attainment were significantly related to educational outcomes (Gross et al. 2001). Nearly three times as many preterm children achieved grade-level performance if parents were married as compared to children from single-parent homes. Thus, preterm survivors with limited family resources are further disadvantaged and vulnerable. In Cleveland, Litt and colleagues prospectively followed 219 surviving ELBW children born between 1992 and 1995 through middle childhood and adolescence. Surviving children had a mean birth weight of 815 g, a mean GA of 26.4 weeks; and almost 1 in 5 was from multiple birth gestations. Neonatal morbidities included bronchopulmonary dysplasia in 41%, sepsis in 29%, and sonographic parenchymal brain injury in 24% (IVH3–IVH4/PVL by cranial ultrasound). Importantly, 115 term controls from the extremely preterm survivor’s community classroom were assessed at 14 years. Both the extremely preterm survivors and a prospectively recruited term control group underwent a comprehensive neuropsychological battery that included reading and mathematics and neuropsychological skills of M.E. Msall et al. processing speed, attention, visual memory, working memory, and planning. In addition, social capital as determined by maternal education and median household income as well as gender and minority status was used as covariates when analyzing predictors of high school functioning (Litt et al. 2012). Though major neurosensory disabilities occurred in approximately 1 in 6 of ELBW survivors (CP 15%, blindness in 0.5%, and hearing loss requiring amplification in 1.7%), a far greater number of children experienced ID and cognitive impairments. More than 1 in 6 (18%) had ID (IQ <70), a 4.5-fold greater risk than for term peers. More than 1 in 3 (37%) were cognitively impaired (IQ 71–84), a 2.3-fold greater risk than for term peers. Most importantly, almost 1 in 2 (49%) of ELBW survivors required an individualized educational plan, a fivefold higher rate than term peers. The educational resources required for managing these highly prevalent disorders would require an additional $50,000 per child during their high school years for tutoring, smaller class size, and specific curriculum modifications (Msall 2012). Of concern were the achievement challenges of extremely preterm survivors who experienced socioeconomic adversity. These children not only demonstrated a 6.6-point lower IQ but had standard scores that were 10 points lower in reading and 8 points lower in mathematics. Importantly, both executive function and visual memory challenges were associated with academic struggles in reading and mathematics (Johnson et al. 2009, 2011; Simms et al. 2015; Wolke et al. 2015). 4.2.1 What Does This Mean? The first lesson brings optimism: the large majority of ELBW survivors (83%) are free of neurosensory disability. Thus, unprecedented survival without major neurosensory disability can be expected. However, despite antenatal corticosteroids, surfactant replacement, improved nutrition, and infection control, the rates of CP are over 100-fold greater than for term infants. Thus, ongoing efforts in neuroprotection remain a scientific, clinical, and political priority. The second lesson is sobering: the majority of ELBW survivors experience ongoing and Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… serious challenges in learning, coordination, and executive function. These children benefit from quality and comprehensive interventions and special educational accommodations with continued review of how these services promote literacy, numeracy, and life skills. Litt and colleagues’ outcomes suggest that evidence-based psychoeducational management strategies for executive function and specific learning disorders may be key areas for maintaining children on positive developmental trajectories (Litt et al. 2012). An important challenge in the upcoming decade is to comprehensively implement, at a population level, appropriately targeted developmental and educational optimization strategies for all preterm survivors. With more developmentally appropriate individual- and population-level supports, their neurodevelopmental risks that currently result in costly and adverse long-term physical, behavioral, educational, and social health outcomes could be dramatically reduced. 4.2.2 Behavioral/Emotional Disorders Behavioral and emotional disorders are more prevalent among children born premature compared to their term birth peers. Meta-analysis of behavioral outcomes by Bhutta and colleagues revealed that children born preterm have a 2.6fold risk for developing attention-deficit hyperactivity disorder (ADHD) during school age (Bhutta et al. 2002). These children are also at high risk for externalization problems such as aggression, oppositionality, and disruptive behaviors, which also are major obstacles in establishing friendships or other social relationships (e.g., lack of patience in waiting for their turn in group play) (Farooqi et al. 2007; Saigal et al. 2003). In these studies, birth weight, family function, gender, and SES predicted the behavioral adjustment of adolescents who survived ELBW status in the 1980s or the middle childhood behavioral and childhood behavioral and social adjustment of children who survived 23–25 weeks of gestation in the 1990s. An important discovery occurred over the past decade whereby it was noted that very and 331 extremely preterm infants were at high risk for delays in social-emotional behavioral regulation that manifested as hyperactivity, impulsivity, and difficulty with social competencies in the preschool years (Jones et al. 2013). Importantly, those children receiving appropriate quality preschool education experiences were more ready for kindergarten both cognitively and behaviorally than those who did not access these services (Arpi and Ferrari 2013; Treyvaud et al. 2013). There has also been increased recognition that late preterm survivors are at risk for regulatory and social challenges. Recent comparative investigations of toddler outcomes for LPI versus other premature infants demonstrate more externalizing, oppositional, and aggressive behaviors, suggesting a unique vulnerability for LPIs (Shah et al. 2013). Infants more prone to distress who also experienced more critical parenting styles in infancy were more likely to demonstrate externalizing behaviors at age 3, suggesting that characteristics of both infants and parents influence preterm vulnerability (Poehlmann et al. 2012). There has also been an increased awareness of the vulnerability of adolescent and adult survivors of very and extremely preterm birth to increased rates of behavior and mental health disorders (Gardner et al. 2004). This is highlighted in Table 4. An important review by Johnson and Marlow emphasized a life course health development framework and highlighted the increased risk of attention, socio-communicative (including autism spectrum disorder), and emotional difficulties among extremely preterm survivors (Johnson et al. 2011). Importantly they highlighted rates of autism spectrum disorder (ASD) as high as 1 in 12 EPI survivors as they enter adolescence (Johnson et al. 2011). A New Jersey cohort found a rate of 1 in 20 for ASD in moderate LBW at school exit (Pinto-Martin et al. 2011). Most recently, in a large US cohort of 889 EPI survivors born in 2002–2004, Joseph and colleagues found a rate of ASD in males at age 10 of 9% and in females of 5%. Rates of ASD were 15% at 23–24 weeks of gestation, 6.5% at 25–26 weeks of gestation, and 3.4% at 27 weeks of gestation (Joseph et al. 2016). M.E. Msall et al. 332 Table 4 Adolescent and adult behavioral health outcomes Authors Botting et al. (1997) Cohort 1980–1983 Liverpool, England 138 VLBW and 108 matched controls Age at assessment 12 years Dahl et al. (2006) 1978–1989 Norway 99 VLBW 13–18 years Grunau et al. (2004) 1981–1986 British Columbia 79 <800 g vs. 31 term 17 years Jong et al. (2012) Meta-analysis of moderate and late preterm (32–36 weeks) 28 papers reviewed Meta-analysis of LBW and schizophrenia prevalence; 750 with schizophrenia and 29,000 control subjects Israel 90 VLBW and 90 NBW Not reported (meta-analysis) 1977–1982 Ontario, Canada 166 ELBW and 145 NBW Ontario, Canada 149 ELBW and 133 NBW 22–25 years Kunugi et al. (2001) Levy-Shiff et al. (1994) Saigal et al. (2006) Saigal et al. (2007) 4.2.3 What Does This Mean? The current understanding of the vulnerabilities of the preterm infant brain to challenges in coordination, learning, attention, and social skills offers opportunities for understanding cumulative risk and protective strategies linked to biomarkers and neurometrics. The goal of understanding what supports are needed to protect the vulnerable preterm infant brain for learning regulatory, attention, executive function, and social competencies will require increased attention to how active health Developmental outcome 1. Any psychiatric disorder: 28% VLBW vs. 9% controls had any psychiatric disorder 2. ADHD: 23% VLBW vs. 6% controls 1. VLBW adolescents report less externalizing behaviors than NBW adolescents 2. Parents of VLBW adolescents report more externalizing behaviors and emotional problems than NBW adolescents. 1. No differences for focus and attention 2. Significantly more parental reported internalizing, externalizing, and problem behaviors 30% higher psychiatric disorders Not reported (meta-analysis) LBW prevalence: 9.5% of schizophrenics and 3.9% of controls 13–14 years Significantly increased hyperactive behavior among VLBW. However paternal involvement was as predictive as birth weight for hyperactivity in childhood 1.3% of ELBW had autism vs. 0% controls 23 years 14.1% ELBW vs. 6% on antidepressants and developmental experiences shape these developmental processes and what proactive informed interventions support success academically, behaviorally, and socially. Revolutionary strategies in developmental neuroscience will help inform an evidence-based approach to early identification and intervention so that children stay on track in development. In many ways, children with prematurity can help inform a shift from categorizing children’s delays as leading to low learning expectations. We can examine how Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… attention, regulatory, and social skills can be modified and lead to pathways of resilience in academic achievement and social-emotional behavioral competencies. 5 Chronic and Acute Stress Recent studies have produced a large body of evidence that stress is significantly correlated with the occurrence of adverse birth outcomes, including prematurity and low birth weight (GraignicPhilippe et al. 2014). Prenatal stressors can include physical and psychological stress, infections, nutritional deficiencies, and drug exposures (Bock et al. 2015). In a prospective study, Killingworth-Rein et al. combined three variables into a single stress indicator comprised of state anxiety, perceived chronic stress, and life event distress. Higher scores on this aggregated factor predicted a shortened gestation and lower birth weight (Rini et al. 1999). Torche and Kleinhaus (2012) found that females who were exposed to earthquakes in early pregnancy had higher rates of preterm delivery (Torche and Kleinhaus 2012). In a study in the southern Israeli town of Sderot (4 km away from the Gaza Strip), Wainstock et al. hypothesized that the frequent exposure to rocket attacks in this community would be associated with adverse birth outcomes when compared to Kiryat Gat, a similar town without constant rocket attack exposure. Researchers found that women living in Sderot gave birth to more babies born preterm and at low birth weight, confirming the hypothesis that stress in pregnancy increases adverse birth outcomes (Wainstock et al. 2014). A number of other studies have demonstrated an association between prenatal stress and adverse pregnancy and birth outcomes, including effects on birth weight, gestational age, and preterm birth (Bock et al. 2015). Chronic stress and acute stress may exert different effects on fetal development and perinatal outcomes. Several authors have reported lower birth weight and shortened gestation in children born to mothers exposed to chronic stress compared to acute stress (Chrousos and Gold 1992; Rini et al. 1999; Sable and Wilkinson 2000; Wadhwa et al. 1996). 333 Likewise, there is increasing evidence that exposure to forms of toxic stress in early life alters stress responses in adulthood. A case-controlled longitudinal study of preterm and term infants with and without medical complications demonstrated dysregulation of the hypothalamic-pituitaryadrenal axis in adult survivors of prematurity, particularly survivors also exposed to socioeconomic disadvantage (Winchester et al. 2016). There is also a strong association between specific early pregnancy events, such as complications in the first trimester, and increased risk of preterm delivery and very preterm delivery (van Oppenraaij et al. 2009). An increased risk of low birth weight and very low birth weight was associated with first-trimester bleeding in a large prospective study (M. A. Williams et al. 1991). These adverse infant outcomes were associated with threatened miscarriage in the first trimester in another large retrospective study (Wijesiriwardana et al. 2006). 6 Epigenetic Mechanisms Although the mechanisms of prenatal stress are not precisely known, it has been proposed that the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system stress responses could lead to complications in pregnant women during pregnancy and delivery (GraignicPhilippe et al. 2014). In particular, McLean et al. (1995) suggest that high levels of neurohormones in the HPA axis as well as placental neurohormones could induce prematurity. A growing body of evidence suggests that epigenetic mechanisms induce transient and permanent alterations in gene function, resulting in altered developmental processes in the brain that result in long-term changes in emotional and cognitive behaviors (Bock et al. 2015). The main function of the epigenome is to regulate gene transcription and compaction of DNA into the cell nucleus through mechanisms such as DNA methylation and hydroxymethylation, histone modifications, ATP-dependent chromatin remodeling, and noncoding RNAs (Provencal and Binder 2015). Prenatal stress has been shown M.E. Msall et al. 334 to reduce the expression and activity of the enzyme 11-β-hydroxysteroid dehydrogenase type 2, an enzyme that converts glucocorticoids into inactive metabolites (Bock et al. 2015). An alteration in placental permeability may lead to excess exposure to maternal stress hormones, thereby promoting developmental dysregulation. Maternal intake of steroid hormones (Marciniak et al. 2011), maternal depression (O’Connor et al. 2014), and maternal anxiety (Kane et al. 2014) have been shown to alter maternal glucocorticoid levels, with a high correlation between maternal and fetal plasma glucocorticoids (Provencal and Binder 2015). In addition to affecting birth outcomes, stressful experiences in utero or during early life may also increase the risk of neurodevelopmental and behavioral disorders later in life due to alterations in epigenetic regulation (Babenko et al. 2015). Pregnant mothers who have anxiety disorders, post-traumatic stress disorder (PTSD), depressive disorders, or major psychoses are at higher risk of adverse birth outcomes including preterm birth, low birth weight, and small-for-gestationalage infants. Mothers with bipolar disorder are at twice the risk for these adverse outcomes (MacCabe et al. 2007). Importantly, pregnant mothers with both PTSD and major depressive episode have four times the risk of having a preterm birth (Yonkers et al. 2014). These risks occur because the brain and placenta are closely linked by a number of peptides and proteins, including oxytocin, somatostatin, neurotensin, encephalin, cortisol, insulin-like growth factor 1, vascular endothelial growth factor, and cyclic AMP response element binding factor. Individuals with mental illness often have a tendency toward coagulation and low activity of tissue plasminogen activator, potentially contributing to placental insufficiency that may lead to increased exposure of the fetus to maternal hormones. Hyperemesis gravidarum, a condition more common in women with eating disorders and anxiety than in controls, is another risk factor for pregnancy complications which may increase the risk of miscarriage, low birth weight infants, and preterm infants (Hoirisch-Clapauch et al. 2015). 7 Prenatal Stress Reduction Strategies Intervention programs aimed at reducing stress should be considered to potentially lower the rates of unfavorable pregnancy outcomes (Wainstock et al. 2014). Feinberg et al. examined the effects of family foundation (FF), a transition to parenthood program, through a randomized control study of 148 expectant mothers. Researchers showed that the intervention significantly reduced the negative impact of maternal cortisol on birth weight, gestational age, and days in hospital in infants, thereby decreasing the risk for adverse birth outcomes (Feinberg et al. 2015). Follow-up at age 5–7 years demonstrated improved outcomes in behavior regulation and children’s school adjustment (Feinberg et al. 2014). It is important to recognize the extent to which a child is affected by a mother’s stress, and mental health heavily depends on moderating factors that include quality of parenting, social support, and the length and severity of the parental disorder (Howard et al. 2014). In this respect, early identification and interventions are essential (Stein et al. 2014). A recent report estimated that the long-term economic costs of perinatal mental disorders for each annual cohort exceed 8 billion euros in the UK alone (Bauer et al. 2014). Early interventions for perinatal mental disorders could produce great economic benefits and improve maternal and child physical and mental health (Howard et al. 2014) via impacting on child’s regulatory behaviors and learning opportunities. 8 The Impact of Fetal Environment and Prematurity on Adult Health Outcomes Exposure to an unfavorable environment in early life is closely associated with an increased tendency to develop adult disease. In 1992, David Barker hypothesized that the period of pregnancy and the intrauterine environment have a profound impact on risk of developing diseases Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… like hypertension; diabetes; cardiac, pulmonary, and renal diseases; and mental illness (Capra et al. 2013). Intrauterine environment and early postnatal life are now generally accepted as important factors that may lead to increased risk for disease in adulthood (Hofman et al. 2004). In particular, low birth weight, a marker of poor fetal growth, is linked to vascular disease, hypertension, obesity, and insulin resistance (Calkins and Devaskar 2011). Although several studies have reported an association between low birth weight and poor adult health outcomes, it remains unclear whether this association exists for children with poor fetal growth born small for gestational age as well as premature babies whose weight is appropriate for gestational age. In a retrospective study, Kaijser et al. identified subjects born preterm or with a low birth weight at four major delivery units in Sweden from 1925 to 1945. Researchers found that future risk for ischemic heart disease was most closely mediated by fetal growth restriction instead of preterm birth without growth restriction. Of note, these results came from a 1925 to 1949 birth cohort, a period when infants did not benefit from modern neonatal care. During more recent decades, infant survival has improved substantially for children born very preterm. It may not be appropriate, therefore, to generalize these results to preterm and growth-restricted infants being born today (Kaijser et al. 2008). Babies born prematurely, whether or not they have intrauterine growth restriction (IUGR), are also at risk as adults for poor cardiovascular health outcomes (Irving et al. 2000). In a study of mothers of 72 LBW and 54 NBW infants born in Edinburgh between November 1973 and February 1975, LBW premature babies had higher adult blood pressure and fasting plasma glucose than NBW controls born at term. Babies born prematurely also had trends for an adverse metabolic profile and were at risk for hypertension and hyperglycemia as adults. Infants with IUGR were not measurably more disadvantaged than preterm infants with birth weight appropriate for gestational age (Irving et al. 2000). A prospective follow-up study of 458 adults at age 30 in New Zealand also aimed to distinguish 335 the relative contributions of gestation length and fetal growth to cardiovascular risk factors in adulthood. Preterm birth, rather than poor fetal growth, was shown to be the major determinant of the association between early environment and adult health outcomes; adults born preterm had increased systolic blood pressure and insulin resistance at age 30. Birth weight, independent of gestational age, was not associated with increased adult systolic blood pressure and insulin resistance, suggesting that length of gestation may be the major contribution to cardiovascular risk in adulthood (Dalziel et al. 2007). Adult-onset insulin resistance, an early marker for type 2 diabetes, is also associated with premature birth (Hofman et al. 2004). Exposure to an adverse environment may be responsible for this reduction to insulin sensitivity, whether during intrauterine life in infants small for gestational age or a primarily adverse postnatal environment in premature infants (Hofman et al. 2004). For the most part, mechanisms to explain the associations between intrauterine and postnatal environments and adult health outcomes are unknown. It is hypothesized that alterations to the HPA axis may explain the link between low birth weight and later increased blood pressure (Dalziel et al. 2007). Furthermore, it is theorized that earlier maturation of organ system exposure to nutritional, metabolic, hormonal, sensory, and respiratory environments at earlier time points in premature babies may also lead to abnormal development of organ systems involved in cardiovascular health (Curhan et al. 1996). Preterm infants and small-for-gestational-age infants often experience a period of “catchup growth” in the postnatal period, which may influence changes in their metabolism that are related to increased risk for these diseases (Smith and Ryckman 2015). Increasing preterm infant survival has critical implications on the burden of adult diseases such as diabetes and cardiovascular disease. In fact, the historic trends in preterm survival and birth may be playing a critical, yet underappreciated, role in the population trend toward increasing prevalence of diabetes and cardiovascular disease (Dalziel et al. 2007). M.E. Msall et al. 336 9 Postnatal and Social Risk Despite knowledge that supports that intervention in early childhood can positively impact disability trajectories, large gaps in services exist and disproportionally impact disadvantaged families. A 1997–2000 study looked at access to early intervention services among a population of infants aged birth to 3 years. Even in the highest risk neonates, access to community and early intervention supports was problematic and fragmented at best (>40% not enrolled in early intervention). Those toddlers with higher rates of disability were more likely to receive more services, yet a significant unmet need for services was documented among milder cases (Hintz et al. 2008). Second, a Chicago-based cohort was followed after NICU discharge and demonstrated that less than 60% of VLBW infants living in extreme poverty (<50% federal poverty level) were receiving early intervention (EI) services despite having access to a medical home and legal advocacy. Of the 415 infants deemed not automatically eligible by EI, 95% had child and family impairments (e.g., gastrostomy, feeding delays, income <$10 K per year, parental mental or developmental disorder) that met EI inclusion criteria. This data confirms that a substantial number of infants with multiple medical and social risks do not receive ongoing developmental surveillance or early intervention services (Weiss et al. 2007). Third, in a recent analysis of the Early Childhood Longitudinal Survey-Birth Cohort, it was found that eligibility for Part C varied widely between states (2–78%), yet the proportion of children receiving services remained consistently low (1.5–7%). There is significant state-by-state variability between eligibility criteria, yet there is also a clear national trend of insufficient enrollment for children with qualifying delays (Rosenberg et al. 2008). Given that in elementary school years 10–20% of children nationally have Individual Educational Plans, early intervention rates less than 5% reflect significant missed opportunities for secondary and tertiary prevention. Among both VLBW and ELBW survivors, disproportionate numbers live in communities with high rates of school dropouts and poorly per- forming schools. This will result in barriers to accessing quality early and middle childhood educational experiences. The combination of developmental vulnerability due to LBW and preterm birth with social and family distress has shown to have cumulative impacts (Hille et al. 1994). In an 8–10-year follow-up study of infants who were the sickest and tiniest and had the most medical complications in the newborn period, Msall and colleagues found that not only were favorable vision and functional motor status at kindergarten entry associated with significantly lower rates of special education and below-gradelevel educational achievement, but higher SES was also associated with positive academic and developmental outcomes. Factors strongly associated with increased risk for special education services included minority status, poverty, lack of access to a car, and Supplemental Social Security Income because of disability and poverty (Msall et al. 2004). 9.1 Adolescent and Adult Outcomes Clinical research to date on the outcomes for preterm infants during adolescence and adulthood reveals a spectrum of strengths and challenges in physical and behavioral health, educational achievement and supports, and community participation. However, the social, educational/vocational, and independent adaptive life skills required for adulthood are inherently more quantitatively and qualitatively complex than basic developmental milestones in an infant or school achievement in middle childhood. A few crude measures of adult success have been used to examine adolescent and adult outcomes, but much work remains to unveil more meaningful multidimensional measures of adult physical and behavioral health, daily functioning, social participation, family formation, and economic well-being. Educational attainment is a frequently examined adult outcome in the literature. The studies highlighted in Table 5 demonstrate lower rates of academic achievement, high school completion, and postsecondary education among preterm survivors. Premature survivors show high rates of Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… 337 Table 5 Cognitive, executive function and academic achievement in adolescence and adulthood Authors Botting et al. (1998) Saigal et al. (2000) Cohort 1980–1983 Liverpool, England 138 VLBW 108 matched controls 1977–1982 Ontario, Canada 141 ELBW 124 matched controls Assessment age 12 years Developmental outcome 1. VLBW lower IQ 2. Lower math and reading comprehension 12–16 years 1. 28% reported neurosensory impairments 2. 25% of ELBW vs. 6% repeated a grade 3. 49% of ELBW vs. 10% required special education services 4. 22% ELBW required full-time educational assistance (vs. 0%) 5. Lower mean WISC-R DQ 6. Lower mean WRAT-R 1. No differences for tests of executive function, verbal memory, attention 2. Preterm group had impaired verbal fluency 1. 56.1% ELBW vs. 84.6% controls completed HS 2. 33% vs. 9% required special education 3. Significant differences in low IQ (<85) 1. 74% VLBW graduated HS vs. 83% NBW 2. 30% pursued secondary education vs. 53% NBW 3. 40% repeated grade vs. 27% NBW 4. Scored 1/3 SD lower on WAIS-R 71% vs. 78.6% completed 12 or more years of school 1979–1980 London, England <33 weeks; 75 premature and 53 FT 1976–1981 Montreal, Canada 57 ELBW and 44 NBW 14–15 years Hack et al. (2002) 1977–1979 Cleveland, Ohio 242 VLBW and 233 controls 20 years Lindstrom et al. (2009) 1973–1979 Sweden 24–28 weeks vs. FT 1976–1983 Norway 325 preterm (23– 27 weeks) vs. 828,227 FT 1960–1965 Baltimore 226 near-term and 1393 FT 1987–2002 national registry Rushe et al. (2001) Lefebvre et al. (2005) Moster et al. (2008) Nomura et al. (2009) Johns Hopkins Collaborative Perinatal Study Saigal et al. (2006) 1977–1982 Ontario, Canada 166 ELBW and 145 controls 18 years 20–36 years 1. 67.7 preterm vs. 75.4% completed HS 2. 4.4% preterm vs. 0.1% fullterm with ID 27–33 years 1. Near-term birth associated with lower adult educational attainment only for those living below poverty line 2. SGA had no association with educational attainment No significant difference in: 1. % graduation from high school 2. Pursuit of postsecondary education 22–25 years HS high school, SD standard deviation, WAIS-R Wechsler Adult Intelligence Scale-Revised, WISC-R Wechsler Intelligence Scale for Children-Revised, DQ deviation quotient, WRAT-R Wide Range Achievement Test-Revised, FT full term need for special education supports, both in general special education services and with individual assistants, and have higher rates of grade repetition than their normal birth weight peers (Saigal et al. 2003). Furthermore, preterm survivors are less likely to graduate high school and pursue secondary education (Hack et al. 2002; Moster et al. 2008). 338 However, this outcome is also influenced by contextual factors. When high school graduation rates are adjusted for parental SES, there is evidence of mitigation of the difference in educational attainment. In a study by Nomura et al., it was found that in near-term survivors, only those living at incomes close to poverty line differed from full-term peers on educational attainment. The relationship between cognitive testing in adulthood and gestational age has also been significantly mitigated by socioeconomic factors (Ekeus et al. 2010). There is compelling evidence that the same risk factors disproportionately distributing premature birth to poorer, less educated mothers are also at play with the long-term outcomes of low birth weight progeny, compounding the disadvantage of the disadvantaged. IQ outcomes have been repeatedly tested and show consistently lower scores for ELBW adults compared to matched peers. However, many studies are unable to match for socioeconomic status or parental IQ which are highly influential mediating factors. Maternal education and income have been shown consistently to predict IQ within these cohorts, indeed often with stronger correlation coefficients than LBW status (Drillien 1961; Drillien et al. 1980). There is evidence that IQ in middle childhood is strongly predictive of IQ at adolescence, suggesting that, on this measure, there is neither increasing gap nor improved catch-up to normal birth weight peers. Alternatively, one study highlighted a widening IQ gap in later childhood (Botting et al. 1998), which may be due to the complexity of the involved intellectual task in testing at older ages or the additional components of social/emotional intelligence, which are not entirely captured by younger measures of IQ. Furthermore, studies that evaluate both educational and professional attainment do not consistently demonstrate strong correlation; 50% of preterm adults in the 1983 Dutch cohort who reported poor educational attainment during the school years held full-time employment as young adults (Hille et al. 2007). How this translates to US cohorts in the ongoing economic crisis for those without college degrees is yet unknown. Other educational outcomes, such as reading and math M.E. Msall et al. achievement, have shown significant gaps in core educational outcomes between VLBW and agematched peers. However given that many term children in low-income urban or rural communities in the United States also are struggling with reading, mathematics, science, and social studies performance, it is difficult to make international comparisons. Additional evidence demonstrates the power of socioeconomic status in modifying the effect of prematurity; Lefebvre et al. assessed the cognitive and academic achievement outcomes in early adulthood of a cohort of 82 ELBW subjects in Montreal (Lefebvre et al. 2005). There were significant differences between ELBW and NBW groups in full-scale IQ (94 versus 108), verbal IQ (93 versus 106), and performance IQ (97 versus 109). However, father’s low socioeconomic status contributed significantly to the prevalence of IQ <85 (19% versus 2%, p = 0.012), schooling in mainstream education with a regular curriculum for age (36% versus 68%), requirement for special classes or schools (33% versus 9%), and high school graduation for those 18 years or older (56% versus 85%). Adolescent/adult self-perceptions of health, quality of life, and overall functioning are important outcomes, which may guide our clinical understanding and facilitate improved anticipatory guidance to families of premature neonates. A socioeconomically diverse cohort of adolescents born in 1983–1984 at <801 g (Brown et al. 2003) reported themselves and was reported by their parents as having lower functional status than normal birth weight peers; however, overall these health concerns did not significantly interfere with tasks of daily living. On rating scales of externalizing and internalizing problems, however, (The Behavioral Assessment System for Children- BASC), there were no differences between preterm and comparison groups. Additionally, using a self-perception profile, which included domains of scholastic competence, social acceptance, athletic competence, and global self-worth, responses did not significantly differ. In a similar cohort of adults (born at weight <1250 g in Zurich), preterm and term controls did not differ on overall physical health Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… 339 Table 6 Adult outcomes after very preterm birth Authors Baumgardt et al. (2012) Hack et al. (2007) Hille et al. (2008) Moster et al. (2008) Saigal et al. (2006) Cohort 1983–1985 Zurich, Switzerland 52 preterm (<1250 g) 75 controls 1977–1979 Cleveland, Ohio 241 VLBW 232 NBW 1983 Netherlands 959 adult survivors of prematurity (<32 weeks or VLBW) 1976–1983; Norway 325- 23–27 weeks 1608- 28–30 weeks 6363- 31–33 weeks 31,169- 34–36 weeks 828,227- full term 1977–1982 Ontario, Canada 166 ELBW and 145 controls Age at assessment 23 years Developmental outcome No difference in overall self-reported quality of life 20 years 1. No difference on self-reported health satisfaction 2. No difference on self-reported comfort (physical or emotional) 3. Decreased self-reported resiliency 4. Increased self-reported risk avoidance 1. 11.4% had moderate/severe problem with profession 2. ½ of individuals with moderate/ severe problems in education had full-time employment 1. 10.6% vs. 1.7% receiving disability pension 2. Lower gestation less likely to have found life partner 3. Lower gestation less likely to have children No significant difference in rates of: 1. Employment 2. Independent living 3. Married/cohabitation 4. Parenthood 19 years 20–36 years 22–25 years VLBW very low birth weight (<1500 g), ELBW extremely low birth weight (<1000 g), NBW normal birth weight (>2500 g), CPT Conners’ continuous performance task or mental health scores on the Short Form 36 Health Survey (Baumgardt et al. 2012) (Table 6). Adult males who survived prematurity rated themselves significantly lower than male term adults on the “physical functioning domain,” but females did not differ from matched controls in any subset. Patterns of high risk and protective health habits varied: the use of marijuana was significantly lower among adults who survived prematurity, yet significantly more control adults practiced sports and more males exercised. The overall trend appears that adolescents and adult survivors of prematurity, by and large, consider themselves similar to normal term survivors on self-ratings of physiologic and psychological functioning. Some studies suggest that females may be more similar to full-term peers than males, suggesting that males may be somewhat more vulnerable to the long-term effects of prematurity and LBW. It deserves mentioning that the discrepancy between self-reported health and external measures of health has been reported in other health conditions; overall persons with a health condition rate the impact of that condition more positively than health professionals may surmise (Groot 2000). Research on long-term consequences of low birth weight has focused on adolescent and early adult life – especially the negative impact of low birth weight on academic achievement, high school graduation and years of school completion, and college attendance. Low birth weight has been linked to these outcomes through the effects of low birth weight on kindergarten preparedness and weak performance in the first few years of schooling. Poor educational performance in subsequent years is negatively linked to both poor school readiness and accomplishment on subsequent school performance and the lingering health effects of low birth weight. The 340 latter often includes not only growth and respiratory sequelae but increased vulnerability to a spectrum of neurobehavioral disorders that impact attention and mood. Importantly, buffers for these disorders include attention to caregiver mental health and child behavior health in the context of increasing school and community successes. While longerterm effects of low birth weight, mediated by poor educational performance and continued weak health, are hypothesized, little research has examined the midlife consequences of low birth weight and what factors contribute to thriving. An additional complexity of measuring adolescent and adult outcomes is the shift of health status reporting from the parent of a premature survivor to the adolescent or adult himself/herself. In an adolescent follow-up of 99 very low birth weight babies born in Norway, there was found to be a significant discrepancy between adolescent and parental reporters, with the overall trend being parents reporting significantly more emotional and behavior problems than the adolescents reported in themselves (Dahl et al. 2006). In these later stages of follow-up, investigators must consider the inherent bias in asking parents vs. adolescents to report on health and behavior. This research limitation can be remedied through analysis of data from two population datasets that include information on weight at birth and current health and well-being – the Wisconsin Longitudinal Study (WLS) and the Midlife in the United States: A National Study of Health and Well-Being (MIDUS). Additionally, utilizing these cohorts from the United States may help remedy the current data gap between well-tracked international cohorts and the dearth of longitudinal US data. International cohorts, which have lower rates of single parenthood and more homogenous racial ethnic and socioeconomic composition as compared to the United States, dominate the current knowledge base of long-term outcomes for premature babies. Extrapolating from their long-term outcomes likely vastly underestimates the need for social support throughout the life course in the United States. Additionally, international countries with single-payer systems tend to diagnose and provide more consistent social support for persons M.E. Msall et al. with disabilities when compared to the fragmented nature of health care in the United States. The Wisconsin Longitudinal Study (WLS) is a long-term study of a random sample of 10,317 men and women who graduated from Wisconsin high school in 1957. The WLS provides an opportunity to study the life course, intergenerational transfers and relationships, family functioning, physical and mental health and well-being, and morbidity and mortality from late adolescence (in 1957) through early geriatric (in 2008). WLS data also cover social background, youthful aspirations, schooling, military service, labor market experiences, family characteristics and events, social participation, psychological characteristics, and retirement. Survey data were collected from the original respondents or their parents in 1957, 1964, 1975, 1992, and 2004 (when they were approximately at age 64 or 65). The WLS includes endof-life data for members of the cohort who have died. Sibling and twin data for the original respondents, as well as gene-environment interactions from late adolescent to the retirement years, will permit models that separate the effects of family origins from the impact of low birth weight at retirement age. The first national representative survey of Midlife Development in the United States (MIDUS) was conducted in 1995–1996. MIDUS is a 1994 national sample survey of 7189 randomly chosen adults ages 25–74 years, as well as 1914 respondents to a separate nationally representative sample of twin pairs. The wide age range of the sample was intended to permit comparisons on persons in their early adult life to those in midlife and old age. The survey permits the assessment of many psychological factors such as personality traits, sense of control, and goal commitments and their linkages to marital status, family structure, socioeconomic standing, social participation, social support, employment status, health status, and health-care utilization. MIDUS respondent follow-ups were conducted from 2002 to 2006, and there were a third round of interviews from 2006 to 2011. This dataset, including sibling data, will permit models that separate the effects of family origins and early Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… life experiences from the impact of low birth weight at retirement age and beyond. 10 Conclusion The interdependent challenges of prematurity and socioeconomic risk create numerous hurdles for achieving optimal physical, developmental, and emotional health. With over 50,000 surviving extremely preterm and very preterm infants per year in the United States, we have the equivalent of an ongoing annual polio crisis in the prevaccination era. Not only are many of these children growing up in families that are strapped for material resources, services and time, but our current early intervention and preschool systems are not delivering child health, developmental, and behavioral supports on a population basis. Therefore, there is decreased likelihood that the 325,000 late preterm survivors who annually enter kindergarten will be healthy and ready to learn. Nor will they stay on track during the first three grades, achieving adequate levels of literacy, numeracy, information handling, and social participation; they will need to succeed in school and life. As their development lags and their academic performance falters, there is too often a response of educational systems, especially during the middle school years, to divert children by using grade repetition, expulsions, and nonevidence-based remediation that only increase the likelihood that the child will leave school or be classified with behavioral problems. Importantly, unlike their middle-class and affluent counterparts, low-income children with a spectrum of neurodevelopmental dysfunction will have less access to high-quality medical care, appropriately targeted and responsive parent-child interactions, quality educational accommodations and explicit strategies for promoting basic competencies, and extracurricular activities. Although potentially detrimental to a child’s future, his/her gestational age and birth weight are rarely the major determining factors when long-term educational, social, and behavioral outcomes are considered. In this chapter we have 341 examined several research studies that emphasized the importance of family, social, and economic environmental factors, which can either aggravate or moderate neonatal risks caused by premature birth. Much is to be gained by attention to how adversity impacts allostatic load, and through epigenetic mechanisms and complex protective buffers of parenting, health promotion, and social supports, leads to long-term outcomes of thriving across physical, behavioral, and social health outcomes. With so much to lose while living under suboptimal conditions, it is of critical importance that both health and educational professionals create systems for enhancing access to early childhood learning experiences, parenting supports, quality preschool education services, and biopsychosocial strategies in middle childhood to help children achieve basic educational, behavioral, and social competencies and adolescent supports of mentoring, preparation for independent living, and proactive behavioral health strategies for stress, time management, mood disorders, and self-efficacy. We need more research about how to spread and scale successful biopsychosocial health development strategies at a community level to insure that VLBW and ELBW survivors do not miss out on the critical experiences, which have potential to compensate for birth disadvantage, and are critical for longterm adult success. A summary of the gaps and recommendations for increasing research in life course health development after prematurity follows. Research in life course health development for children who survive prematurity has made the following advances: 1. Medical advances in pregnancy: Over the past decades, there has been improved management of high-risk pregnancy disorders such as hypertension, diabetes, multiple gestation, intrauterine growth restriction, prenatal infections, and fetal malformations. There has been growing awareness that women’s health involving the use of tobacco, alcohol, illicit drugs, anticonvulsants, and antidepressants has subtle neurobehavioral consequences on child development. Additionally, society has M.E. Msall et al. 342 had greater recognition that poverty, social adversity involving basic resources as well as lack of partner/family supports, previous preterm birth, minority status, limited education, and low health literacy increase the risk of low birth weight, prematurity, and early childhood health and developmental impairments. 2. Medical advances in the neonatal period: Over the last decades, there has been remarkable improvement in the management of premature labor and delivery. In particular, the widespread screening for and treatment of Group B Streptococcus colonization and the use of maternal corticosteroids when preterm labor presents have had marked improvements in premature survival and sequelae of neonatal infections. We have had improved resuscitation and neonatal transport to tertiary care centers. Throughout the vulnerable neonatal course, improved respiratory support (the use of positive pressure and noninvasive ventilator techniques after surfactant), evidence-based nutritional interventions, and infection control management have improved neonatal survival. Although there is increased recognition that very and extremely preterm survivors experience a variety of health (e.g., growth, pulmonary), neurological (e.g., sensory impairments, seizures), neurodevelopmental (e.g., CP, ID, ASD), and learning and behavior impairment, the population-level risks have not been systematically measured. In order to improve life course health development, we propose seven theme research agenda. 11 Recommendations for an After Prematurity Life Course Health Disparities Research Agenda 1. Establish national registries. It is imperative to create national registries for children from birth to 5 years old who are at highest risk of CP and neurodevelopmental disabilities such as intellectual disability, neu- rosensory disability, and autism spectrum disorders. These registries would not only include preterm infants but also include term infants with history of critical illness from cardiac, pulmonary, infectious, neurological, or genetic disorders. Such registries would allow for the investigation of diverse interventions and link health, early intervention, special education, and rehabilitation services to long-term outcomes across the life course. Most importantly attention must be paid to maternal health, adversity, and mental health. Registries ought to include placental samples as well as cord blood and newborn screening blood spots, in order to allow for the evaluation of early biomarkers of toxic stress during early childhood. 2. Build population-based datasets. To understand health across the life course, it is imperative to create population-based datasets which originate at delivery and during newborn primary care visits. We must be able to link information on birth certificates with early childhood health, developmental, and educational outcomes through collaborative arrangements between public health, regional neonatal follow-up, early intervention, and district-wide educational testing. We should use these population databases to engage in ongoing health and developmental surveillance of all children with all degrees of prematurity requiring neonatal intensive care, especially those with seizures, neonatal encephalopathy, congenital heart disease, malformation requiring surgery, and abnormalities in newborn genetic and hearing screening. We should model our US efforts on the Longitudinal Study of Australian Children to understand diverse trajectories of health, development, and behavior for those with and without special health-care needs. Preterm infants need the establishment of longitudinal data collection registries that can collect information that is similar to the National Survey of Children’s Health and NHIS disability follow-up studies. We must determine the risk factors for trajectories of positive and negative outcomes across cohorts Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… of middle childhood, adolescence, and adulthood for sequential preterm survivors who were <28 weeks of gestation, 29–31 weeks of gestation, 32–36 weeks of gestation, and term gestation. Only with informed, collaborative, and comprehensive life course health development registries will we determine ways to support resiliency among preterm offspring of high-risk mothers with respect to health, developmental, educational, and social outcomes over the life course. 3. Expansion and evaluation of comprehensive health and developmental surveillance interventions on a population level. We need to improve population surveillance for children exposed to high-risk maternal conditions such as hypertension, diabetes, obesity, asthma, seizure disorders, depression, ADHD, anxiety, and maternal use of tobacco, alcohol, and illegal substances. In the setting of these risk factors, we must evaluate what factors promote maternal and child resiliency and promote positive newborn and developmental outcomes. We must understand which factors mitigate the negative effect of these exposures on growth, obesity, developmental delays, and regulatory disorders. We should seek to determine the impact of positive parenting, quality early child education (early intervention, head start preschool programming), and pediatric medical homes especially using neonatal and epigenetic biomarkers. Surveillance should extend beyond the neonatal period into preschool (ages 2–4 years) to identify young children who lack the basic numeracy, literacy, and social and emotional maturity needed to enter kindergarten ready to learn. Kindergarten is also a key transition for measuring health and developmental status, yet there are no systematic screening modalities in place at kindergarten entry across diverse biomedical and social risks. 4. Integration of twenty-first-century technologies to improve care coordination and integrated health and engage families in time-efficient interventions. 343 Diagnostic tools such as fidgety movements are shown to have high reliability in detection of future motor delays; texting has been shown to improve parental engagement with early childhood development; and telemedicine has enabled the delivery of highly specialized care to remote or mobility-challenged populations. These growing technologies, increasingly available across socioeconomic populations, must be fully embraced and leveraged for population surveillance and therapeutic interventions. 5. Shift research methodology from antiquated methods studying individuals and parent-child dyads to novel techniques examining clusters of families and communities. Increasingly we need to better understand the power of parent-child interactions on determining developmental trajectories in conjunction with genetic and epigenetic biomarkers. Furthermore, siblings certainly impact health and development of one another, yet most research fails to consider the contribution of sibling factors. We must seek to develop and improve our research modalities, which look beyond a single child as the unit of measure and examine interrelatedness among family and community factors. 6. Research on impacting parent-child interventions. A critical need is to formally evaluate the caregiving environment of premature infants and determine how cumulative parent-child interactions impact the child’s developmental capacities and parental health and well-being. Examine developmental activities as a part of everyday childcare tasks to promote early parent involvement and positive health and regulatory trajectories. 7. Determine what types of interventions effectively impact maternal mental health in the setting of adversities. It is incompletely known how maternal mental health stressors (depression, anxiety, isolation, and violence) increase vulnerability of children when they do not receive quality early 344 childhood social learning and behavioral supports. Strategies for integrating maternal and child physical and behavioral health have the potential to increase resilience for high-risk populations. By combining themes from this research agenda to populations involving life course health development and the ICF model for promoting health, functioning, and participation, we have the opportunities to improve thriving after prematurity and measure our impact on longterm health, education, and community costs. Acknowledgments This work was supported in part by NIH NINR03095 (Sullivan, Msall). Dr. Msall was also supported in part by NIH/NICHD Grant P30 HD0544275 JP Kennedy Intellectual and Developmental Disabilities Research Center and HRSA-DHHS T73 MC 11047 Leadership Education in Neurodevelopmental and Related Disorders Training Program (LEND). References Allen, M. C., Cristofalo, E. A., & Kim, C. (2011). Outcomes of preterm infants: Morbidity replaces mortality. Clinics in Perinatology, 38(3), 441–454. doi:10.1016/j.clp.2011.06.011. Aron, L., & Loprest, P. (2012). Disability and the education system. The Future of Children, 22(1), 97–122. Arpi, E., & Ferrari, F. (2013). Preterm birth and behaviour problems in infants and preschool-age children: A review of the recent literature. Developmental Medicine and Child Neurology, 55(9), 788–796. doi:10.1111/dmcn.12142. Babenko, O., Kovalchuk, I., & Metz, G. A. (2015). Stressinduced perinatal and transgenerational epigenetic programming of brain development and mental health. Neuroscience and Biobehavioral Reviews, 48, 70–91. doi:10.1016/j.neubiorev.2014.11.013. Baek, Y., Vohr, B. R., Alksninis, B., Cashore, W. J., Hogan, D. P., & Msall, M. E. (2002). Kindergarten readiness in very low birth weight (VLBW) children. Pediatric Research, 51(4), 293A. Bauer, A., Parsonage, M., Knapp, M., Iemmi, V., & Adelaja, B. (2014). The costs of perinatal mental health problems. London: Cemtre for Mental Health and London School of Economics. Baumgardt, M., Bucher, H. U., Mieth, R. A., & Fauchere, J. C. (2012). Health-related quality of life of former very preterm infants in adulthood. Acta Paediatrica, 101(2), e59–e63. doi:10.1111/j.1651-2227.2011.02422.x. Bhutta, A. T., Cleves, M. A., Casey, P. H., Cradock, M. M., & Anand, K. J. (2002). Cognitive and behav- M.E. Msall et al. ioral outcomes of school-aged children who were born preterm: A meta-analysis. JAMA, 288(6), 728–737. Bock, J., Wainstock, T., Braun, K., & Segal, M. (2015). Stress in utero: Prenatal programming of brain plasticity and cognition. Biological Psychiatry. doi:10.1016/j.biopsych.2015.02.036. Botting, N., Powls, A., Cooke, R. W., & Marlow, N. (1998). Cognitive and educational outcome of verylow-birthweight children in early adolescence. Developmental Medicine and Child Neurology, 40(10), 652–660. Brown, K. J., Kilbride, H. W., Turnbull, W., & Lemanek, K. (2003). Functional outcome at adolescence for infants less than 801 g birth weight: Perceptions of children and parents. Journal of Perinatology, 23(1), 41–47. doi:10.1038/sj.jp.7210850. Calkins, K., & Devaskar, S. U. (2011). Fetal origins of adult disease. Current Problems in Pediatric and Adolescent Health Care, 41(6), 158–176. doi:10.1016/j.cppeds.2011.01.001. Capra, L., Tezza, G., Mazzei, F., & Boner, A. L. (2013). The origins of health and disease: The influence of maternal diseases and lifestyle during gestation. Italian Journal of Pediatrics, 39, 7. doi:10.1186/1824-7288-39-7. Chrousos, G. P., & Gold, P. W. (1992). The concepts of stress and stress system disorders. Overview of physical and behavioral homeostasis. JAMA, 267(9), 1244–1252. Crouter, A. C., & Booth, A. (Eds). (2014). Work-family challenges for low-income parents and their children. Routledge. Abingdon, UK. Curhan, G. C., Willett, W. C., Rimm, E. B., Spiegelman, D., Ascherio, A. L., & Stampfer, M. J. (1996). Birth weight and adult hypertension, diabetes mellitus, and obesity in US men. Circulation, 94(12), 3246–3250. Dahl, L. B., Kaaresen, P. I., Tunby, J., Handegard, B. H., Kvernmo, S., & Ronning, J. A. (2006). Emotional, behavioral, social, and academic outcomes in adolescents born with very low birth weight. Pediatrics, 118(2), e449–e459. doi:10.1542/peds.2005-3024. Dalziel, S. R., Parag, V., Rodgers, A., & Harding, J. E. (2007). Cardiovascular risk factors at age 30 following pre-term birth. International Journal of Epidemiology, 36(4), 907–915. doi:10.1093/ije/dym067. Diagnostic and Statistical Manual of Mental Disorders: DSM-5. (2013). (5th ed.). Washington, DC: American Psychiatric Association. Dmowska, A., Andrews, B., Schreiber, M., & Msall, M. E. (2016). Preterm survivors: Community support to lessen health disparities. In J. Merrick & L. Rubin (Eds.), Environmental health disparities: Costs and benefits of breaking the cycle. Hauppauge, NY: Nova Publishers. Drillien, C. M. (1961). A longitudinal study of the growth and development of prematurely and maturely born children. VIII. Morbidity in the age period 2-5 years. Archives of Disease in Childhood, 36, 515–525. Drillien, C. M., Thomson, A. J., & Burgoyne, K. (1980). Low-birthweight children at early school-age: A lon- Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… gitudinal study. Developmental Medicine and Child Neurology, 22(1), 26–47. Ekeus, C., Lindstrom, K., Lindblad, F., Rasmussen, F., & Hjern, A. (2010). Preterm birth, social disadvantage, and cognitive competence in Swedish 18- to 19-yearold men. Pediatrics, 125(1), e67–e73. doi:10.1542/ peds.2008-3329. Farooqi, A., Hagglof, B., Sedin, G., Gothefors, L., & Serenius, F. (2007). Mental health and social competencies of 10- to 12-year-old children born at 23 to 25 weeks of gestation in the 1990s: A Swedish national prospective follow-up study. Pediatrics, 120(1), 118– 133. doi:10.1542/peds.2006-2988. Feinberg, M. E., Jones, D. E., Roettger, M. E., Solmeyer, A., & Hostetler, M. L. (2014). Long-term follow-up of a randomized trial of family foundations: Effects on children's emotional, behavioral, and school adjustment. Journal of Family Psychology, 28(6), 821–831. doi:10.1037/fam0000037. Feinberg, M. E., Roettger, M. E., Jones, D. E., Paul, I. M., & Kan, M. L. (2015). Effects of a psychosocial couplebased prevention program on adverse birth outcomes. Maternal and Child Health Journal, 19(1), 102–111. doi:10.1007/s10995-014-1500-5. Gardner, F., Johnson, A., Yudkin, P., Bowler, U., Hockley, C., Mutch, L., et al. (2004). Behavioral and emotional adjustment of teenagers in mainstream school who were born before 29 weeks’ gestation. Pediatrics, 114(3), 676–682. doi:10.1542/peds.2003-0763-L. Graignic-Philippe, R., Dayan, J., Chokron, S., Jacquet, A. Y., & Tordjman, S. (2014). Effects of prenatal stress on fetal and child development: A critical literature review. Neuroscience and Biobehavioral Reviews, 43, 137–162. doi:10.1016/j.neubiorev.2014.03.022. Groot, W. (2000). Adaptation and scale of reference bias in self-assessments of quality of life. Journal of Health Economics, 19(3), 403–420. Gross, S. J., Mettelman, B. B., Dye, T. D., & Slagle, T. A. (2001). Impact of family structure and stability on academic outcome in preterm children at 10 years of age. The Journal of Pediatrics, 138(2), 169–175. doi:10.1067/mpd.2001.111945. Hack, M., Flannery, D. J., Schluchter, M., Cartar, L., Borawski, E., & Klein, N. (2002). Outcomes in young adulthood for very-low-birth-weight infants. The New England Journal of Medicine, 346(3), 149–157. doi:10.1056/NEJMoa010856. Hamilton, B. E., Martin, J. A., Osterman, M. J., Curtin, S. C., & Matthews, T. J. (2015). Births: Final data for 2014. National Vital Statistics Reports, 64(12), 1–64. Retrieved from http://www.ncbi.nlm.nih.gov/ pubmed/26727629. Hille, E. T., den Ouden, A. L., Bauer, L., van den Oudenrijn, C., Brand, R., & Verloove-Vanhorick, S. P. (1994). School performance at nine years of age in very premature and very low birth weight infants: Perinatal risk factors and predictors at five years of age. Collaborative project on preterm and small for 345 gestational age (POPS) infants in The Netherlands. The Journal of Pediatrics, 125(3), 426–434. Hille, E. T., Weisglas-Kuperus, N., van Goudoever, J. B., Jacobusse, G. W., Ens-Dokkum, M. H., de Groot, L., et al. (2007). Functional outcomes and participation in young adulthood for very preterm and very low birth weight infants: The Dutch project on preterm and small for gestational age infants at 19 years of age. Pediatrics, 120(3), e587–e595. doi:10.1542/ peds.2006-2407. Himmelmann, K., & Uvebrant, P. (2014). The panorama of cerebral palsy in Sweden. XI. Changing patterns in the birth-year period 2003-2006. Acta Paediatrica, 103(6), 618–624. doi:10.1111/apa.12614. Hintz, S. R., Kendrick, D. E., Vohr, B. R., Poole, W. K., & Higgins, R. D. (2008). Community supports after surviving extremely low-birth-weight, extremely preterm birth: Special outpatient services in early childhood. Archives of Pediatrics & Adolescent Medicine, 162(8), 748–755. doi:10.1001/ archpedi.162.8.748. Hofman, P. L., Regan, F., Jackson, W. E., Jefferies, C., Knight, D. B., Robinson, E. M., et al. (2004). Premature birth and later insulin resistance. The New England Journal of Medicine, 351(21), 2179–2186. doi:10.1056/NEJMoa042275. Hoirisch-Clapauch, S., Brenner, B., & Nardi, A. E. (2015). Adverse obstetric and neonatal outcomes in women with mental disorders. Thrombosis Research, 135(Suppl 1), S60–S63. doi:10.1016/ s0049-3848(15)50446-5. Holzmann, R., & Jørgensen, S. (2001). Social risk management: A new conceptual framework for social protection, and beyond. International Tax and Public Finance, 8(4), 529–556. doi:10.102 3/A:1011247814590. Howard, L. M., Piot, P., & Stein, A. (2014). No health without perinatal mental health. The Lancet, 384(9956), 1723–1724. doi:10.1016/S0140-6736(14)62040-7. Huston, A. C. (1991). Children in poverty : Child development and public policy. Cambridge, NY: Cambridge University Press. Income, Poverty, and Health Insurance Coverage in the United States: 2005 (2006). In U. S. C. Bureau (Ed.). Irving, R. J., Belton, N. R., Elton, R. A., & Walker, B. R. (2000). Adult cardiovascular risk factors in premature babies. Lancet, 355(9221), 2135–2136. doi:10.1016/ s0140-6736(00)02384-9. Jiang Y. E. M., & Skinner C. (2015). Basic facts about low-income children: Children under 3 Years, 2013 (trans: D. o. H. P. Management). Columbia University: National Center for Children in Poverty. Johnson, S., Hennessy, E., Smith, R., Trikic, R., Wolke, D., & Marlow, N. (2009). Academic attainment and special educational needs in extremely preterm children at 11 years of age: The EPICure study. Archives of Disease in Childhood. Fetal and Neonatal Edition, 94(4), F283–F289. doi:10.1136/ adc.2008.152793. 346 Johnson, S., Wolke, D., Hennessy, E., & Marlow, N. (2011). Educational outcomes in extremely preterm children: Neuropsychological correlates and predictors of attainment. Developmental Neuropsychology, 36(1), 74–95. doi:10.1080/87565641.2011.540541. Jones, K. M., Champion, P. R., & Woodward, L. J. (2013). Social competence of preschool children born very preterm. Early Human Development, 89(10), 795– 802. doi:10.1016/j.earlhumdev.2013.06.008. Joseph, R. M., O'Shea, T. M., Allred, E. N., Heeren, T., Hirtz, D., Paneth, N., Leviton, A., & Kuban, K. C. (2016). Prevalence and associated features of autism spectrum disorder in extremely low gestational age newborns at age 10 years. Autism Research, 10(2), 224–232. doi:10.1002/aur.1644. Kaijser, M., Bonamy, A. K., Akre, O., Cnattingius, S., Granath, F., Norman, M., et al. (2008). Perinatal risk factors for ischemic heart disease: Disentangling the roles of birth weight and preterm birth. Circulation, 117(3), 405–410. doi:10.1161/circulationaha.107.710715. Kane, H. S., Dunkel Schetter, C., Glynn, L. M., Hobel, C. J., & Sandman, C. A. (2014). Pregnancy anxiety and prenatal cortisol trajectories. Biological Psychology, 100, 13–19. doi:10.1016/j.biopsycho.2014.04.003. Lang, C. T., & Iams, J. D. (2009). Goals and strategies for prevention of preterm birth: An obstetric perspective. Pediatric Clinics of North America, 56(3), 537–563. Lefebvre, F., Mazurier, E., & Tessier, R. (2005). Cognitive and educational outcomes in early adulthood for infants weighing 1000 grams or less at birth. Acta Paediatrica, 94(6), 733–740. doi:10.1080/08035250510025987. Litt, J. S., Gerry Taylor, H., Margevicius, S., Schluchter, M., Andreias, L., & Hack, M. (2012). Academic achievement of adolescents born with extremely low birth weight. Acta Paediatrica, 101(12), 1240–1245. doi:10.1111/j.1651-2227.2012.02790.x. MacCabe, J. H., Martinsson, L., Lichtenstein, P., Nilsson, E., Cnattingius, S., Murray, R. M., et al. (2007). Adverse pregnancy outcomes in mothers with affective psychosis. Bipolar Disorders, 9(3), 305–309. doi:10.1111/j.1399-5618.2007.00382.x. Marciniak, B., Patro-Malysza, J., PoniedzialekCzajkowska, E., Kimber-Trojnar, Z., LeszczynskaGorzelak, B., & Oleszczuk, J. (2011). Glucocorticoids in pregnancy. Current Pharmaceutical Biotechnology, 12(5), 750–757. McLean, M., Bisits, A., Davies, J., Woods, R., Lowry, P., & Smith, R. (1995). A placental clock controlling the length of human pregnancy. Nature Medicine, 1(5), 460–463. Morse, S. B., Zheng, H., Tang, Y., & Roth, J. (2009). Early school-age outcomes of late preterm infants. Pediatrics, 123(4), e622–e629. doi:10.1542/peds.2008-1405. Moster, D., Lie, R. T., & Markestad, T. (2008). Long-term medical and social consequences of preterm birth. The M.E. Msall et al. New England Journal of Medicine, 359(3), 262–273. doi:10.1056/NEJMoa0706475. Msall, M. E. (2012). Academic achievement after extreme prematurity: Optimizing outcomes for vulnerable children in times of uncertainty. Acta Paediatrica, 101(12), 1196–1197. doi:10.1111/apa.12038. Msall, M. E., Phelps, D. L., DiGaudio, K. M., Dobson, V., Tung, B., McClead, R. E., et al. (2000). Severity of neonatal retinopathy of prematurity is predictive of neurodevelopmental functional outcome at age 5.5 years. Behalf of the Cryotherapy for retinopathy of prematurity cooperative group. Pediatrics, 106(5), 998–1005. Msall, M. E., Phelps, D. L., Hardy, R. J., Dobson, V., Quinn, G. E., Summers, C. G., et al. (2004). Educational and social competencies at 8 years in children with threshold retinopathy of prematurity in the CRYO-ROP multicenter study. Pediatrics, 113(4), 790–799. O'Connor, T. G., Monk, C., & Fitelson, E. M. (2014). Practitioner review: Maternal mood in pregnancy and child development--implications for child psychology and psychiatry. Journal of Child Psychology and Psychiatry, 55(2), 99–111. doi:10.1111/jcpp.12153. van Oppenraaij, R. H., Jauniaux, E., Christiansen, O. B., Horcajadas, J. A., Farquharson, R. G., & Exalto, N. (2009). Predicting adverse obstetric outcome after early pregnancy events and complications: A review. Human Reproduction Update, 15(4), 409–421. doi:10.1093/humupd/dmp009. Patrianakos-Hoobler, A. I., Msall, M. E., Marks, J. D., Huo, D., & Schreiber, M. D. (2009). Risk factors affecting school readiness in premature infants with respiratory distress syndrome. Pediatrics, 124(1), 258–267. doi:10.1542/peds.2008-1771. Pinto-Martin, J. A., Levy, S. E., Feldman, J. F., Lorenz, J. M., Paneth, N., & Whitaker, A. H. (2011). Prevalence of autism spectrum disorder in adolescents born weighing <2000 grams. Pediatrics, 128(5), 883– 891. doi:10.1542/peds.2010-2846. Poehlmann, J., Hane, A., Burnson, C., Maleck, S., Hamburger, E., & Shah, P. E. (2012). Preterm infants who are prone to distress: Differential effects of parenting on 36-month behavioral and cognitive outcomes. Journal of Child Psychology and Psychiatry, 53(10), 1018–1025. doi:10.1111/j.1469-7610.2012.02564.x. Preterm Birth: Causes, Consequences, and Prevention (2007). In R. E. Behrman, & A. S. Butler (Eds.). Washington, DC: Committee on Understanding Premature Birth and Assuring Healthy Outcomes, Institute of Medicine of the Academies. Provencal, N., & Binder, E. B. (2015). The effects of early life stress on the epigenome: From the womb to adulthood and even before. Experimental Neurology, 268, 10–20. doi:10.1016/j.expneurol.2014.09.001. Life Course Health Development Outcomes After Prematurity: Developing a Community, Clinical… Requejo, J., Merialdi, M., Althabe, F., Keller, M., Katz, J., & Menon, R. (2013). Born too soon: Care during pregnancy and childbirth to reduce preterm deliveries and improve health outcomes of the preterm baby. Reproductive Health, 10(Suppl 1), S4. doi:10.1186/1742-4755-10-s1-s4. Rini, C. K., Dunkel-Schetter, C., Wadhwa, P. D., & Sandman, C. A. (1999). Psychological adaptation and birth outcomes: The role of personal resources, stress, and sociocultural context in pregnancy. Health Psychology, 18(4), 333–345. Rosenberg, S. A., Zhang, D., & Robinson, C. C. (2008). Prevalence of developmental delays and participation in early intervention services for young children. Pediatrics, 121(6), e1503–e1509. doi:10.1542/ peds.2007-1680. Rubens, C. E., Sadovsky, Y., Muglia, L., Gravett, M. G., Lackritz, E., & Gravett, C. (2014). Prevention of preterm birth: Harnessing science to address the global epidemic. Science Translational Medicine, 6(262), 262sr265. doi:10.1126/scitranslmed.3009871. Sable, M. R., & Wilkinson, D. S. (2000). Impact of perceived stress, major life events and pregnancy attitudes on low birth weight. Family Planning Perspectives, 32(6), 288–294. Saigal, S., & Doyle, L. W. (2008). An overview of mortality and sequelae of preterm birth from infancy to adulthood. Lancet, 371(9608), 261–269. doi:10.1016/ s0140-6736(08)60136-1. Saigal, S., Pinelli, J., Hoult, L., Kim, M. M., & Boyle, M. (2003). Psychopathology and social competencies of adolescents who were extremely low birth weight. Pediatrics, 111(5 Pt 1), 969–975. Shah, P. E., Robbins, N., Coelho, R. B., & Poehlmann, J. (2013). The paradox of prematurity: The behavioral vulnerability of late preterm infants and the cognitive susceptibility of very preterm infants at 36 months post-term. Infant Behavior & Development, 36(1), 50–62. doi:10.1016/j.infbeh.2012.11.003. Simms, V., Gilmore, C., Cragg, L., Clayton, S., Marlow, N., & Johnson, S. (2015). Nature and origins of mathematics difficulties in very preterm children: A different etiology than developmental dyscalculia. Pediatric Research, 77(2), 389–395. doi:10.1038/pr.2014.184. Smith, C. J., & Ryckman, K. K. (2015). Epigenetic and developmental influences on the risk of obesity, diabetes, and metabolic syndrome. Diabetes Metab Syndr Obes, 8, 295–302. doi:10.2147/dmso.s61296. Stein, A., Pearson, R. M., Goodman, S. H., Rapa, E., Rahman, A., McCallum, M., et al. (2014). Effects of perinatal mental disorders on the fetus and child. Lancet, 384(9956), 1800–1819. doi:10.1016/ s0140-6736(14)61277-0. Stephens, B. E., & Vohr, B. R. (2009). Neurodevelopmental outcome of the premature infant. Pediatric Clinics of North America, 56(3), 631–646., Table of Contents. doi:10.1016/j.pcl.2009.03.005. 347 The March of Dimes data book for policy makers: Maternal, infant, and child health in the United States (2012). Washington, DC: March of Dimes. Torche, F., & Kleinhaus, K. (2012). Prenatal stress, gestational age and secondary sex ratio: The sex-specific effects of exposure to a natural disaster in early pregnancy. Human Reproduction, 27(2), 558–567. doi:10.1093/humrep/der390. Treyvaud, K., Ure, A., Doyle, L. W., Lee, K. J., Rogers, C. E., Kidokoro, H., et al. (2013). Psychiatric outcomes at age seven for very preterm children: Rates and predictors. Journal of Child Psychology and Psychiatry, 54(7), 772–779. doi:10.1111/jcpp.12040. Vohr, B. (2013). Long-term outcomes of moderately preterm, late preterm, and early term infants. Clinics in Perinatology, 40(4), 739–751. doi:10.1016/j. clp.2013.07.006. Vohr, B. R., & Msall, M. E. (1997). Neuropsychological and functional outcomes of very low birth weight infants. Seminars in Perinatology, 21(3), 202–220. Wadhwa, P. D., Dunkel-Schetter, C., Chicz-DeMet, A., Porto, M., & Sandman, C. A. (1996). Prenatal psychosocial factors and the neuroendocrine axis in human pregnancy. Psychosomatic Medicine, 58(5), 432–446. Wainstock, T., Anteby, E. Y., Glasser, S., Lerner-Geva, L., & Shoham-Vardi, I. (2014). Exposure to lifethreatening stressful situations and the risk of preterm birth and low birth weight. International Journal of Gynaecology and Obstetrics, 125(1), 28–32. doi:10.1016/j.ijgo.2013.09.035. Weiss, E. L., Msall, M.E., & Muhammad, C. (2007). Barriers to receipt of early intervention. Passages. doi:E-PAS2007:616311.10. Weitzman, M., Byrd, R. S., Aligne, C. A., & Moss, M. (2002). The effects of tobacco exposure on children's behavioral and cognitive functioning: Implications for clinical and public health policy and future research. Neurotoxicology and Teratology, 24(3), 397–406. Wijesiriwardana, A., Bhattacharya, S., Shetty, A., Smith, N., & Bhattacharya, S. (2006). Obstetric outcome in women with threatened miscarriage in the first trimester. Obstetrics and Gynecology, 107(3), 557–562. doi:10.1097/01.AOG.0000199952.82151.de. Williams, M. A., Mittendorf, R., Lieberman, E., & Monson, R. R. (1991). Adverse infant outcomes associated with first-trimester vaginal bleeding. Obstetrics and Gynecology, 78(1), 14–18. Williams, J., Lee, K. J., & Anderson, P. J. (2010). Prevalence of motor-skill impairment in preterm children who do not develop cerebral palsy: A systematic review. Developmental Medicine and Child Neurology, 52(3), 232–237. doi:10.1111/j.1469-8749.2009.03544.x. Winchester, S. B., Sullivan, M. C., Roberts, M. B., & Granger, D. A. (2016). Prematurity, birth weight, and socioeconomic status are linked to atypical diurnal hypothalamic-pituitary-adrenal Axis activity in young adults. Research in Nursing & Health, 39(1), 15–29. 348 Wolke, D., Strauss, V. Y., Johnson, S., Gilmore, C., Marlow, N., & Jaekel, J. (2015). Universal gestational age effects on cognitive and basic mathematic processing: 2 cohorts in 2 countries. J Pediatr, 166(6), 1410–1416. doi:10.1016/j.jpeds.2015.02.065. World Health Organization. (2007). ICF-CY, international classification of functioning, disability, and M.E. Msall et al. health: Children & Youth version. Geneva: World Health Organization. Yonkers, K. A., Smith, M. V., Forray, A., Epperson, C. N., Costello, D., Lin, H., et al. (2014). Pregnant women with posttraumatic stress disorder and risk of preterm birth. JAMA Psychiatry, 71(8), 897–904. doi:10.1001/ jamapsychiatry.2014.558. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. A Life Course Approach to Hearing Health Shirley A. Russ, Kelly Tremblay, Neal Halfon, and Adrian Davis 1 Introduction Challenges to hearing health are a significant public health problem. At least ten million Americans have a hearing loss that interferes with the understanding of normal speech (Mitchell 2005). If lesser degrees of loss are included, the number rises to 28 million (US DHHS 2010). Sensorineural hearing loss S.A. Russ, MD, MPH (*) UCLA Center for Healthier Children, Families and Communities, Department of Pediatrics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA e-mail: shirlyruss@cox.net K. Tremblay Speech & Hearing Sciences College of Arts & Sciences, University of Washington, Seattle, WA, USA N. Halfon Department of Pediatrics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA Department of Health Policy and Management, Fielding School of Public Health, UCLA, Los Angeles, CA, USA Department of Public Policy, Luskin School of Public Affairs, UCLA, Los Angeles, CA, USA Center for Healthier Children, Families, and Communities, UCLA, Los Angeles, CA, USA A. Davis University College London, NHS Newborn Hearing Screening Program, London, UK (SNHL) is the commonest sensory deficit in more developed societies (Davis 1989). The term “deaf” is usually reserved for individuals with severe (>60–90 dBHL) or profound (>90 dBHL) losses, representing half a million Americans, while those with mild (<40 dBHL) or moderate (40–60 dBHL) losses are referred to as “hard of hearing” (Smith et al. 2005; Blanchfield et al. 2001). Congenital losses sufficient to adversely affect speech and language development are seen in at least one per thousand newborns (Fortnum et al. 2001; Russ et al. 2003; Van Naarden et al. 1999), and if lesser degrees of loss and unilateral losses are included, this number rises to up to five per thousand. Only 4% cases of hearing loss nationwide are accounted for by children under the age of 18 years, while 50% cases occur in persons 65 years of age or older (Russ 2001). The cumulative prevalence of hearing loss within the US population rises with age, as has been demonstrated in other countries (Russ 2001; Parving and Christensen 1996), with the sharpest rise in prevalence occurring in old age, when 50–80% are ultimately affected (see Table 1). The relative contributions of delayed diagnosis of congenital losses, progression of existing losses, and acquisition of new losses to the rising prevalence of hearing loss with age are uncertain. Improvements to the prevention, diagnosis, and management of hearing loss across all age groups are public health priorities (Reavis et al. 2016; Davis et al. 2016). © The Author(s) 2018 N. Halfon et al. (eds.), Handbook of Life Course Health Development, DOI 10.1007/978-3-319-47143-3_15 349 S.A. Russ et al. 350 Table 1 Prevalence of hearing loss in the USA across the life span Age Newborn Newborn Newborn Newborn 3 years 10 years 8 years 6–19 years 18–34 years Estimated prevalence of hearing loss % (95% CI) Case definition 0.14% Bilateral loss >35 dBHL requiring amplification 0.19% Loss ≥35 dBHL in one or both ears 0.31% Unilateral and bilateral sensorineural and conductive losses 0.33% Unilateral and bilateral sensorineural hearing loss Bilateral PTA loss 0.067% at 0.5, 1, and 2 (0.053– KHz ≥40dBHL in 0.085) better unaided ear 0.14% (0.12–0.16) 0.14% Bilateral PTA loss 0.12% at 0.5, 1, and 2 KHz ≥40 dBHL in better unaided ear 0.4% Bilateral loss at low (0.5, 1, and 2 kHZ) frequencies PTA ≥26 dBHL 0.7% Bilateral loss at high (3, 4, and 6 kHZ) frequencies PTA ≥26 dBHL 14.9% Unilateral or bilateral loss PTA >16 dBHL at low or high frequencies 3.4% Self-reported hearing trouble 35–44 years 6.3% Self-reported hearing trouble 45–54 years 10.3% Self-reported hearing trouble 48–59 years 70–79 years 80 years 21% 66% 90% PTA 0.5, 1, 2, and 4 KHz >25 dBHL in the worst ear Author and year of study Mason, Herrman 1992–1997 Place of study and sample size Honolulu, HI, 10,372 Mehl, Thompson 1992–1999 Finitzo, Albright, O’Neal 1994–1997 Colorado 63,590 Texas 54,228 Barsky-Firsker, Sun Livingston, New 1993–1995 Jersey, 15,749 Metropolitan Van Naarden, Decoufle, Caldwell Atlanta 255,742 1991–1993 Data source Kaiser Permanente Honolulu, HI, NHSP Colorado NHSP Texas Newborn Hearing Screening Data St. Barnabas Medical Center Newborn Hearing Screening Data Metropolitan Atlanta Developmental Disabilities Surveillance Program (MADDSP) Karapurkar Bhasin, Brocksen, Avchen, Van Naarden Braun 1996 and 2000 Niskar, Kieszak, Holmes, et al. 1988–1994 Metropolitan Atlanta 36,749 (1996) 43,593 (2000) USA 5249 MADDSP National Center for Health Statistics (1990 and 1991) National Center for Health Statistics (1990 and 1991) National Center for Health Statistics (1990 and 1991) Cruickshanks, Wiley, Tweed, et al. USA 239,663 National Health Interview Survey (NHIS) National Health Interview Survey (NHIS) National Health Interview Survey (NHIS) Epidemiology of Hearing Loss Study (EHLS) USA 239,663 USA 239,663 Beaver Dam, WI, (EHLS) 4541 NHANES III (continued) A Life Course Approach to Hearing Health 351 Table 1 (continued) Age 60–90 years 73–84 years Estimated prevalence of hearing loss % (95% CI) Case definition 29% PTA 0.5, 1, and 2 KHz >26 dBHL in the better ear 59.9% Hearing loss: two averages of thresholds 0.5, 1, and 2 KHz >25 dBHL 76.9% High-frequency hearing loss >40 dBHL 2, 4, and 8 KHz Author and year of study Gates, Cooper, Kannel, et al. 1983–1985 Helzner, Cauley, Pratt, et al. 1997–1998 Although there have been considerable advances in understanding the etiology of hearing loss, with genetic causes now thought to account for up to 50% of congenital losses (Mitchell 2005), in many individual cases, the cause of hearing loss remains unknown. Even where genetic causes have been identified, discovery of the abnormal gene does not necessarily lead to an understanding of the mechanism whereby the gene’s product exerts its effect. Similarly, genetic and environmental causes of hearing losses that have their onset later in life have not been well defined. This lack of knowledge of the basic pathophysiology of hearing difficulties hampers prevention and treatment efforts. Hearing health has important implications for general health and well-being. Both children and adults with hearing loss face significant educational and social challenges. For children who are profoundly deaf, language and academic levels at high school graduation have been reported historically to correspond to those of fourth grade students with normal hearing (Holt 1993). Adults with hearing loss are reported to have higher levels of unemployment (Parving and Christensen 1993) and lower quality of life than their hearing peers (Appollonio et al. 1996). For older individuals, hearing disability is associated with accelerated cognitive decline, depression, increased risk of dementia, poorer balance, falls, hospitaliza- Place of study and sample size Framingham 1662 Pittsburgh, Pennsylvania, and Memphis, Tennessee, 2052 Data source Framingham Heart Study Health, Aging, and Body Composition (ABC) Study tions, and early mortality (for a review, see Davis et al. 2016). In addition to these medical consequences, there are also social functioning implications including social isolation due to reduced communication, loss of autonomy, and financial decline. Traditionally, hearing losses in childhood and in adult life have been considered as separate issues. Growing interest in life course theory has led to suggestions that it could prove useful to apply a life course lens to the study of hearing loss, and of hearing health, throughout the life span. The early years of life, especially the period from conception through to 3 years of age, are now understood to impact lifelong health. Childhood conditions and early experiences can become “embedded” into emerging biological systems, altering health trajectories. The Life Course Health Development (LCHD) model posits that health is an emergent capacity of human beings that dynamically develops over time in response to multiple-nested, ever-changing genetic, biological, behavioral, social, and economic contexts. Multiple risk and protective factors influence development of key biological systems, including the anatomic and biochemical determinants of hearing ability, during critical and sensitive periods of development (see Table 2). Health, at individual and population levels, is also influenced by the timing and sequence of biological, S.A. Russ et al. 352 Table 2 Risk and protective factors for hearing loss across the life span Life stage Prenatal Perinatal Early childhood Middle childhood, adolescence, and adulthood Risk factor 1. Syndrome association with HL 2. Family history of permanent childhood SNHL 3. Craniofacial anomalies 4. In utero TORCH 1. NICU >48 h 2. Jaundice-exchange Tx 3. Ototoxic medications 4. Meningitis Protective factor 1. Maternal rubella immunization Pre- and Perinatal risk factors, plus: 1. Parent/caregiver concern 2. Persistent pulmonary hypertension with ventilation. 3. Conditions requiring ECMO 4. Syndromes associated with progressive hearing loss (e.g., neurofibromatosis, osteopetrosis, Usher’s) 5. Neurodegenerative disorder (e.g., Hunter’s, sensorimotor neuropathies, Friedreich’s ataxia, Charcot-Marie-Tooth) 6. Head trauma 7. Recurrent/persistent OME for ≥ 3 months Early childhood risk factors plus: Noise Drug/chemical exposure Head trauma Otosclerosis cultural, and historic events and experiences. Application of the LCHD model to hearing health challenges predominantly biomedical models and suggests that there are multiple potential avenues for improving hearing health. As hearing losses in childhood and in adult life have been considered as separate issues, investigations into adult hearing loss largely ignore early-life exposures. However, the LCHD model highlights the importance of studying these links. In this paper we consider the implications of the LCHD model for understanding the mechanisms, pathways, and determinants of hearing ability. We consider the implications of early hearing loss for health development over the life course and the factors through the life course that contribute to hearing ability in adult life. We consider the concept not just of hearing loss but of “hearing 1. Prompt treatment for neonatal jaundice. 2. Avoid/monitor ototoxic medications. 3. Prompt antibiotic treatment for meningitis 1. Immunization 2. Avoid/monitor ototoxic medications. 3. Prompt antibiotic treatment for meningitis 4.Head injury prevention 5.Noise avoidance/protection 1. Noise avoidance/protection 2. Avoid/monitor ototoxic drugs 3. Head injury prevention 4. Immunizations 5. Higher family income 6. Education health” and how to achieve it, the research priorities that are suggested by this review, and the implications for policy and practice. 2 The Life Course Health Development Model and the Mechanisms, Pathways, and Determinants of Hearing Ability According to Halfon and Forrest (2017), the LCHD model is grounded in the following seven principles: 1. Health development: Health development integrates the concepts of health and developmental processes into a unified whole. A Life Course Approach to Hearing Health 2. Unfolding: Health development unfolds continuously over the life span, from conception to death, and is shaped by prior experiences and environmental interactions. 3. Complexity: Health development results from adaptive, multilevel, and reciprocal interactions between individuals and their physical, natural, and social environments. 4. Timing: Health development is sensitive to the timing and social structuring of environmental exposures and experiences. 5. Plasticity: Health development phenotypes are malleable and enabled and constrained by evolution to enhance adaptability to diverse environments. 6. Thriving: Optimal health development promotes survival, enhances well-being, and protects against disease. 7. Harmony: Health development results from the balanced interactions of molecular, physiological, behavioral, cultural, and evolutionary processes. Taken together, these seven principles suggest that in order to understand the mechanisms underlying hearing loss and hearing health from a life course standpoint, it is essential to explore the role of multiple risk and protective factors operating at multiple levels to influence hearingrelated outcomes. In addition, scientists must study the emergence and development of hearing health trajectories over extended time frames, including the pivotal role of social relationships in the development of functional hearing capacity. Researchers must consider the critical importance of timing in relation to sensitive periods and turning points in the development of hearing abilities. All of these principles point to the importance of adopting a developmental perspective on hearing health. The following section takes each of these issues in turn, providing a review of the evidence pertaining to each. 353 single or principal cause for clinic