Additional file 5. Median value of key biomarkers, sample subset membership and sample similarity... more Additional file 5. Median value of key biomarkers, sample subset membership and sample similarity score for the cardiometabolic module and the microbiome richness module.
The diffusion-weighted magnetic resonance imaging (DWI) technique enables quantification of water... more The diffusion-weighted magnetic resonance imaging (DWI) technique enables quantification of water mobility for probing microstructural properties of biological tissue and has become an effective tool for collecting information about the underlying pathology of cancerous tissue. Measurements using multiple b-values have indicated biexponential signal attenuation, ascribed to "fast" (high ADC) and "slow" (low ADC) diffusion components. In this empirical study, we investigate the properties of the diffusion time (Δ)-dependent components of the diffusion-weighted (DW) signal in a constant b-value experiment. A xenograft gliobastoma mouse was imaged using Δ = 11 ms, 20 ms, 40 ms, 60 ms, and b = 500-4000 s/mm(2) in intervals of 500 s/mm(2). Data were corrected for EPI distortions, and the Δ-dependence on the DW-signal was measured within three regions of interest [intermediate- and high-density tumor regions and normal-appearing brain (NAB) tissue regions]. In this stu...
Clinical cancer research : an official journal of the American Association for Cancer Research, Jun 1, 2016
Restriction spectrum imaging (RSI-MRI), an advanced diffusion imaging technique, can potentially ... more Restriction spectrum imaging (RSI-MRI), an advanced diffusion imaging technique, can potentially circumvent current limitations in tumor conspicuity, in vivo characterization, and location demonstrated by multiparametric magnetic resonance imaging (MP-MRI) techniques in prostate cancer detection. Prior reports show that the quantitative signal derived from RSI-MRI, the cellularity index, is associated with aggressive prostate cancer as measured by Gleason grade (GG). We evaluated the reliability of RSI-MRI to predict variance with GG at the voxel-level within clinically demarcated prostate cancer regions. Ten cases were processed using whole mount sectioning after radical prostatectomy. Regions of tumor were identified by an uropathologist. Stained prostate sections were scanned at high resolution (75 μm/pixel). A grid of tiles corresponding to voxel dimensions was graded using the GG system. RSI-MRI cellularity index was calculated from presurgical prostate MR scans and presented a...
ABSTRACT The heavily convoluted human cerebral cortex comprises layers of cells that run parallel... more ABSTRACT The heavily convoluted human cerebral cortex comprises layers of cells that run parallel to the pial surface. Cortical architectonic studies evaluate the topography of changes among areas in the composition of these layers based on cell size, cell distribution, and axonal orientation. Because of the curvature of the cortical mantle, local areas of interest need to be sampled perpendicularly to the surface in histological sections in order to represent the geometry of cortical layers correctly. A cross section that is tangential to the cortical surface will yield a biased assessment of layer thickness and axonal orientation. The cortex is also organized into mini-columns oriented perpendicular to the pial surface. Therefore, the spacing between mini-columns in a histological slice must be measured orthogonal to the pial surface. T1-weighted magnetic resonance images were acquired for each fixed whole brain specimen. Each specimen was subsequently cryoprotected using a series of sucrose solutions of increasing concentrations, embedded in gelatin, and cut frozen using a sliding microtome. Color blockface images with 50µm/pixel resolution were taken for every 70µm thick section. We used 3-D volume reconstruction and surface triangulation of the MRI to measure curvature and surface orientation relative to cortical layering at every point on the surface. The resulting surfaces were elastically co-registered to 3-D volumes reconstructed from the blockface images. Using these data, we identify which areas, given the unique plane of histological sectioning in each brain specimen, are suitable for analysis of cyto- and myelo- architectonic parameters. We show how this method can be used to validate the acquisition of architectonic data from the histological sections obtained from each specimen. The angle of the cross section relative to the pial surface, as well as the amount of curvature, informs the inclusion/exclusion of regions of interests from analysis. Furthermore, we predict, for specific regions of interest, the ‘best’ angle at which to section a given specimen, based on the percentage of the region that is predicted to fall within 10 degrees of perpendicular for a given plane of section (coronal, sagittal, or axial). This allows us to target specific ROIs in future studies, by analyzing the MRI prior to sectioning the specimen. By systematically targeting specific ROIs, a complete map of the human cortex may be compiled from data acquired from hundreds of brain specimens. Through the careful planning, the angle of section chosen in different brains will enable us to acquire unbiased architectonic data for all regions of cortex.
Diffusion-weighted imaging (DWI) has been at the forefront of cancer imaging since the early 2000... more Diffusion-weighted imaging (DWI) has been at the forefront of cancer imaging since the early 2000s. Before its application in clinical oncology, this powerful technique had already achieved widespread recognition due to its utility in the diagnosis of cerebral infarction. Following this initial success, the ability of DWI to detect inherent tissue contrast began to be exploited in the field of oncology. Although the initial oncologic applications for tumor detection and characterization, assessing treatment response, and predicting survival were primarily in the field of neurooncology, the scope of DWI has since broadened to include oncologic imaging of the prostate gland, breast, and liver. Despite its growing success and application, misconceptions about the underlying physical basis of the DWI signal exist among researchers and clinicians alike. In this review, we provide a detailed explanation of the biophysical basis of diffusion contrast, emphasizing the difference between hin...
Background Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensiv... more Background Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures. Methods We collected 1385 data features from diverse modalities, including metabolome, microbiome, genetics, and advanced imaging, from 1253 individuals and from a longitudinal validation cohort of 1083 individuals. We utilized a combination of unsupervised machine learning methods to identify multimodal biomarker signatures of health and disease risk. Results Our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers. Stratification of individuals based on the signatures of these biomarkers identified distinct subsets of individuals with similar health statuses. Subset membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistanc...
Genome sequencing has established clinical utility for rare disease diagnosis. While increasing n... more Genome sequencing has established clinical utility for rare disease diagnosis. While increasing numbers of individuals have undergone elective genome sequencing, a comprehensive study surveying genome-wide disease-associated genes in adults with deep phenotyping has not been reported. Here we report the results of a 3-y precision medicine study with a goal to integrate whole-genome sequencing with deep phenotyping. A cohort of 1,190 adult participants (402 female [33.8%]; mean age, 54 y [range 20 to 89+]; 70.6% European) had whole-genome sequencing, and were deeply phenotyped using metabolomics, advanced imaging, and clinical laboratory tests in addition to family/medical history. Of 1,190 adults, 206 (17.3%) had at least 1 genetic variant with pathogenic (P) or likely pathogenic (LP) assessment that suggests a predisposition of genetic risk. A multidisciplinary clinical team reviewed all reportable findings for the assessment of genotype and phenotype associations, and 137 (11.5%) ...
Background: Alzheimer's disease is a major health problem, affecting ~4.5% of people aged 60 ... more Background: Alzheimer's disease is a major health problem, affecting ~4.5% of people aged 60 and older in 2016 with over 43 million affected globally. The traditional approach for detection evaluates an individual in the presence of symptoms. However, it has been established that amyloid deposits begin to accumulate years before symptoms begin to appear. With improved technology, there is increased focus on risk reduction, timely diagnosis, and early intervention. Early identification of at-risk individuals may enable patients and their families to better prepare for and reduce the impact of this condition. Methods: We obtained data for patients from two longitudinal retrospective cohorts (Alzheimer's Disease Neuroimaging Initiative: ADNI and National Alzheimer's Coordinating Center: NACC), including T1-weighted MRI and genetics data. The polygenic risk score (PRS) used in this study was built based on a published Genome Wide Association Study (GWAS) that identified vari...
Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimoda... more Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. We collected 1,385 data features from diverse modalities, including metabolome, microbiome, genetics and advanced imaging, from 1,253 individuals and from a longitudinal validation cohort of 1,083 individuals. We utilized an ensemble of unsupervised machine learning techniques to identify multimodal biomarker signatures of health and disease risk. In particular, our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers, which were used to cluster individuals into distinct health profiles. Cluster membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and BMI. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential bi...
Additional file 5. Median value of key biomarkers, sample subset membership and sample similarity... more Additional file 5. Median value of key biomarkers, sample subset membership and sample similarity score for the cardiometabolic module and the microbiome richness module.
The diffusion-weighted magnetic resonance imaging (DWI) technique enables quantification of water... more The diffusion-weighted magnetic resonance imaging (DWI) technique enables quantification of water mobility for probing microstructural properties of biological tissue and has become an effective tool for collecting information about the underlying pathology of cancerous tissue. Measurements using multiple b-values have indicated biexponential signal attenuation, ascribed to "fast" (high ADC) and "slow" (low ADC) diffusion components. In this empirical study, we investigate the properties of the diffusion time (Δ)-dependent components of the diffusion-weighted (DW) signal in a constant b-value experiment. A xenograft gliobastoma mouse was imaged using Δ = 11 ms, 20 ms, 40 ms, 60 ms, and b = 500-4000 s/mm(2) in intervals of 500 s/mm(2). Data were corrected for EPI distortions, and the Δ-dependence on the DW-signal was measured within three regions of interest [intermediate- and high-density tumor regions and normal-appearing brain (NAB) tissue regions]. In this stu...
Clinical cancer research : an official journal of the American Association for Cancer Research, Jun 1, 2016
Restriction spectrum imaging (RSI-MRI), an advanced diffusion imaging technique, can potentially ... more Restriction spectrum imaging (RSI-MRI), an advanced diffusion imaging technique, can potentially circumvent current limitations in tumor conspicuity, in vivo characterization, and location demonstrated by multiparametric magnetic resonance imaging (MP-MRI) techniques in prostate cancer detection. Prior reports show that the quantitative signal derived from RSI-MRI, the cellularity index, is associated with aggressive prostate cancer as measured by Gleason grade (GG). We evaluated the reliability of RSI-MRI to predict variance with GG at the voxel-level within clinically demarcated prostate cancer regions. Ten cases were processed using whole mount sectioning after radical prostatectomy. Regions of tumor were identified by an uropathologist. Stained prostate sections were scanned at high resolution (75 μm/pixel). A grid of tiles corresponding to voxel dimensions was graded using the GG system. RSI-MRI cellularity index was calculated from presurgical prostate MR scans and presented a...
ABSTRACT The heavily convoluted human cerebral cortex comprises layers of cells that run parallel... more ABSTRACT The heavily convoluted human cerebral cortex comprises layers of cells that run parallel to the pial surface. Cortical architectonic studies evaluate the topography of changes among areas in the composition of these layers based on cell size, cell distribution, and axonal orientation. Because of the curvature of the cortical mantle, local areas of interest need to be sampled perpendicularly to the surface in histological sections in order to represent the geometry of cortical layers correctly. A cross section that is tangential to the cortical surface will yield a biased assessment of layer thickness and axonal orientation. The cortex is also organized into mini-columns oriented perpendicular to the pial surface. Therefore, the spacing between mini-columns in a histological slice must be measured orthogonal to the pial surface. T1-weighted magnetic resonance images were acquired for each fixed whole brain specimen. Each specimen was subsequently cryoprotected using a series of sucrose solutions of increasing concentrations, embedded in gelatin, and cut frozen using a sliding microtome. Color blockface images with 50µm/pixel resolution were taken for every 70µm thick section. We used 3-D volume reconstruction and surface triangulation of the MRI to measure curvature and surface orientation relative to cortical layering at every point on the surface. The resulting surfaces were elastically co-registered to 3-D volumes reconstructed from the blockface images. Using these data, we identify which areas, given the unique plane of histological sectioning in each brain specimen, are suitable for analysis of cyto- and myelo- architectonic parameters. We show how this method can be used to validate the acquisition of architectonic data from the histological sections obtained from each specimen. The angle of the cross section relative to the pial surface, as well as the amount of curvature, informs the inclusion/exclusion of regions of interests from analysis. Furthermore, we predict, for specific regions of interest, the ‘best’ angle at which to section a given specimen, based on the percentage of the region that is predicted to fall within 10 degrees of perpendicular for a given plane of section (coronal, sagittal, or axial). This allows us to target specific ROIs in future studies, by analyzing the MRI prior to sectioning the specimen. By systematically targeting specific ROIs, a complete map of the human cortex may be compiled from data acquired from hundreds of brain specimens. Through the careful planning, the angle of section chosen in different brains will enable us to acquire unbiased architectonic data for all regions of cortex.
Diffusion-weighted imaging (DWI) has been at the forefront of cancer imaging since the early 2000... more Diffusion-weighted imaging (DWI) has been at the forefront of cancer imaging since the early 2000s. Before its application in clinical oncology, this powerful technique had already achieved widespread recognition due to its utility in the diagnosis of cerebral infarction. Following this initial success, the ability of DWI to detect inherent tissue contrast began to be exploited in the field of oncology. Although the initial oncologic applications for tumor detection and characterization, assessing treatment response, and predicting survival were primarily in the field of neurooncology, the scope of DWI has since broadened to include oncologic imaging of the prostate gland, breast, and liver. Despite its growing success and application, misconceptions about the underlying physical basis of the DWI signal exist among researchers and clinicians alike. In this review, we provide a detailed explanation of the biophysical basis of diffusion contrast, emphasizing the difference between hin...
Background Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensiv... more Background Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures. Methods We collected 1385 data features from diverse modalities, including metabolome, microbiome, genetics, and advanced imaging, from 1253 individuals and from a longitudinal validation cohort of 1083 individuals. We utilized a combination of unsupervised machine learning methods to identify multimodal biomarker signatures of health and disease risk. Results Our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers. Stratification of individuals based on the signatures of these biomarkers identified distinct subsets of individuals with similar health statuses. Subset membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistanc...
Genome sequencing has established clinical utility for rare disease diagnosis. While increasing n... more Genome sequencing has established clinical utility for rare disease diagnosis. While increasing numbers of individuals have undergone elective genome sequencing, a comprehensive study surveying genome-wide disease-associated genes in adults with deep phenotyping has not been reported. Here we report the results of a 3-y precision medicine study with a goal to integrate whole-genome sequencing with deep phenotyping. A cohort of 1,190 adult participants (402 female [33.8%]; mean age, 54 y [range 20 to 89+]; 70.6% European) had whole-genome sequencing, and were deeply phenotyped using metabolomics, advanced imaging, and clinical laboratory tests in addition to family/medical history. Of 1,190 adults, 206 (17.3%) had at least 1 genetic variant with pathogenic (P) or likely pathogenic (LP) assessment that suggests a predisposition of genetic risk. A multidisciplinary clinical team reviewed all reportable findings for the assessment of genotype and phenotype associations, and 137 (11.5%) ...
Background: Alzheimer's disease is a major health problem, affecting ~4.5% of people aged 60 ... more Background: Alzheimer's disease is a major health problem, affecting ~4.5% of people aged 60 and older in 2016 with over 43 million affected globally. The traditional approach for detection evaluates an individual in the presence of symptoms. However, it has been established that amyloid deposits begin to accumulate years before symptoms begin to appear. With improved technology, there is increased focus on risk reduction, timely diagnosis, and early intervention. Early identification of at-risk individuals may enable patients and their families to better prepare for and reduce the impact of this condition. Methods: We obtained data for patients from two longitudinal retrospective cohorts (Alzheimer's Disease Neuroimaging Initiative: ADNI and National Alzheimer's Coordinating Center: NACC), including T1-weighted MRI and genetics data. The polygenic risk score (PRS) used in this study was built based on a published Genome Wide Association Study (GWAS) that identified vari...
Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimoda... more Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. We collected 1,385 data features from diverse modalities, including metabolome, microbiome, genetics and advanced imaging, from 1,253 individuals and from a longitudinal validation cohort of 1,083 individuals. We utilized an ensemble of unsupervised machine learning techniques to identify multimodal biomarker signatures of health and disease risk. In particular, our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers, which were used to cluster individuals into distinct health profiles. Cluster membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and BMI. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential bi...
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