bs_bs_banner
doi: 10.1111/ppe.12193
1
Birthweight and Childhood Cancer: Preliminary Findings from the
International Childhood Cancer Cohort Consortium (I4C)
Ora Paltiel,a Gabriella Tikellis,b Martha Linet,c Jean Golding,d Stanley Lemeshow,e Gary Phillips,f Karen Lamb,g
Camilla Stoltenberg,h,i Siri E. Håberg,h Marin Strøm,j Charlotta Granstrøm,j Kate Northstone,k Mark Klebanoff,e,l
Anne-Louise Ponsonby,b,m Elizabeth Milne,n Marie Pedersen,o–s Manolis Kogevinas,o,p,r,t Eunhee Ha,u Terence Dwyer,b,m,v
on behalf of the International Childhood Cancer Cohort Consortium
a
Department of Hematology and Braun School of Public Health, Hadassah-Hebrew University, Jerusalem, Israel,
b
Department of Environmental and Genetic Epidemiology, Murdoch Children’s Research Institute, Royal Childrens Hospital, University of
Melbourne, Melbourne,
g
Centre for Physical Activity and Nutrition, Deakin University, Burwood,
m
Menzies Research Institute, University of Tasmania, Hobart, Tasmania,
n
Telethon Kids Institute, University of Western Australia, Perth, Western Australia, Australia,
c
Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health,
Bethesda, MD,
e
Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio,
f
l
Division of Biostatistics, The Ohio State University Center for Biostatistics, Columbus, Ohio,
The Research Institute at Nationwide Children’s Hospital, The Ohio State University College of Medicine, Columbus, OH,
d
k
Centre for Child & Adolescent Health, School of Social & Community Medicine, University of Bristol,
ALSPAC (Children of the 90s), School of Social and Community Medicine, University of Bristol, Bristol, UK,
h
Norwegian Institute of Public Health, Oslo,
i
Department of Global Public Health and Community Care, University of Bergen, Bergen, Norway,
j
Department of Epidemiology Research, Center for Fetal Programming, Statenserum Institute, Copenhagen, Denmark,
o
Centre for Research in Environmental Epidemiology (CREAL),
q
Universitat Pompeu Fabra, Barcelona, Spain,
r
IMIM (Hospital del Mar Research Institute), Barcelona,
p
s
CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain,
U823, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Institute Albert Bonniot, INSERM (National
Institute of Health and Medical Research), Grenoble,
v
International Agency for Research on Cancer, Lyon, France,
t
Department of Nutrition, National School of Public Health, Athens, Greece, and
u
School of Medicine, Ewha Medical Research Center, Department of Preventive Medicine, Ewha Womans University, Seoul, Korea
Abstract
Background: Evidence relating childhood cancer to high birthweight is derived primarily from registry and case–
control studies. We aimed to investigate this association, exploring the potential modifying roles of age at diagnosis and maternal anthropometrics, using prospectively collected data from the International Childhood Cancer
Cohort Consortium.
Methods: We pooled data on infant and parental characteristics and cancer incidence from six geographically and
temporally diverse member cohorts [the Avon Longitudinal Study of Parents and Children (UK), the Collaborative
Perinatal Project (USA), the Danish National Birth Cohort (Denmark), the Jerusalem Perinatal Study (Israel), the
Norwegian Mother and Child Cohort Study (Norway), and the Tasmanian Infant Health Survey (Australia)].
Birthweight metrics included a continuous measure, deciles, and categories (≥4.0 vs. <4.0 kilogram). Childhood
cancer (377 cases diagnosed prior to age 15 years) risk was analysed by type (all sites, leukaemia, acute
Correspondence: Ora Paltiel, Department of Hematology and Braun School of Public Health, Hadassah-Hebrew University, POB 12000,
91120 Jerusalem, Israel.
E-mail: orap@hadassah.org.il
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd.
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
2
O. Paltiel et al.
lymphoblastic leukaemia, and non-leukaemia) and age at diagnosis. We estimated hazard ratios (HR) and 95%
confidence intervals (CI) from Cox proportional hazards models stratified by cohort.
Results: A linear relationship was noted for each kilogram increment in birthweight adjusted for gender and
gestational age for all cancers [HR = 1.26; 95% CI 1.02, 1.54]. Similar trends were observed for leukaemia. There
were no significant interactions with maternal pre-pregnancy overweight or pregnancy weight gain. Birthweight
≥4.0 kg was associated with non-leukaemia cancer among children diagnosed at age ≥3 years [HR = 1.62; 95% CI
1.06, 2.46], but not at younger ages [HR = 0.7; 95% CI 0.45, 1.24, P for difference = 0.02].
Conclusion: Childhood cancer incidence rises with increasing birthweight. In older children, cancers other than
leukaemia are particularly related to high birthweight. Maternal adiposity, currently widespread, was not demonstrated to substantially modify these associations. Common factors underlying foetal growth and carcinogenesis
need to be further explored.
Keywords: Childhood cancer, leukemia, cohort studies, pooled analysis.
Over 50 years ago, MacMahon and Newill1 suggested
that birthweight may be linked to childhood cancer
risk. This putative association was subsequently examined in diverse geographical settings, mainly in case–
control studies. Early studies focused on childhood
cancer mortality1–3 while later investigations, summarised in two meta-analyses,4,5 primarily addressed the
association between birthweight and the incidence
of acute leukaemia, or its main subtypes, acute
lymphoblastic (ALL), and acute myeloid leukaemia
(AML). Evidence from these studies supports an
overall weakly to moderately increased risk of ALL
among children with high birthweight (generally
defined as ≥4.0 kg), or a linear association with each
kilogram birthweight increment,4,5 although some
studies have had null or negative findings.,6–8 and the
influence of birthweight on AML is less consistent.
Evidence regarding non-leukaemia cancers points
to higher risks of renal (notably Wilms), embryonal
and specific Central Nervous System (CNS)
tumours9–11 with high birthweight. For some cancers,
non-linear models best describe the association with
birthweight and, for hepatic tumours (notably
hepatoblastoma), a negative association has been
observed.11
More recent research has emphasised the role of
accelerated foetal growth (taking into account factors
such as gestational age (GA)), rather than birthweight
per se, as a determinant of childhood cancer.12–16
Among these studies are recent pooled analyses of
case–control studies.11,17,18 Adjustment for GA may
change both the magnitude and the precision of relative risk estimates.11
Fetal growth is determined by both environmental
and genetic factors19 and is influenced by maternal
attributes, notably height, parity, diabetes and other
metabolic factors, smoking, socioeconomic status, and
ethnicity.20,21 Moreover, maternal pre-pregnancy overweight22,23 and excess pregnancy weight gain23 are
increasingly recognised determinants of large-for-GA
babies. Current maternal obesity trends portend an
increased proportion of these infants,24 and a possible
concomitant rise in metabolic and cardiovascular morbidity for the offspring.25 However, the potential consequences of maternal adiposity for childhood cancer
have rarely been considered.26 In contrast to a wealth
of information regarding determinants of foetal
growth, risk factors for childhood cancer are largely
unknown. Controversy remains as to whether the
association between birthweight and childhood
cancer varies, for example, by age at diagnosis.
The International Childhood Cancer Cohort Consortium (I4C)27 provides a platform to examine cancer
risk factors using pooled data collected prior to
disease onset. This, combined with the prospect of
evaluating the contribution of a rich set of covariates,
affords an opportunity to obtain a deeper and less
biased understanding of the association between
birthweight and childhood cancer. Our aims were to
re-examine this association, taking into account GA
and other covariates, and to explore the potential
modifying effects of age at diagnosis and maternal
anthropometric measures.
Methods
The I4C
I4C was established in 2005 to address the lack of prospective, adequately powered studies investigating
the aetiology of childhood cancer. The initial collaboration involved 11 international birth/infant cohorts
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
Birthweight and childhood cancer: I4C Cohort Consortium
ranging from ∼11 000 to 100 000 participants at various
stages of recruitment or follow-up.27 Additional
cohorts have since joined. This report involves the
pooling of data from (alphabetically): the Avon Longitudinal Study of Parents and Children (ALSPAC, UK),
the Collaborative Perinatal Project (CPP, USA), the
Danish National Birth Cohort (DNBC, Denmark), the
Jerusalem Perinatal Study (JPS, Israel), the Norwegian
Mother and Child Cohort Study (MoBa, Norway), and
the Tasmanian Infant Health Survey (TIHS, Australia).
Data from all cohorts were transferred to the I4C
International Data Coordinating Center (IDCC) at the
Murdoch Childrens Research Institute (Australia).
Harmonisation and pooling of data from the six
cohorts was undertaken at the I4C IDCC and involved
creating variables that would allow for amalgamation
of the available data across all six cohorts (Please see
supporting information Appendix S1 for description
of participating cohorts, references, ethical issues, and
harmonisation strategies).
Study design and population
We performed a pooled cohort study, identifying and
including all cancer cases from 380 000 livebirths in
the six participating cohorts. The dataset includes all
livebirths for ALSPAC, CPP, and TIHS. As per Consortium agreements with the I4C, a random 10%
sample of non-cases from MoBa and DNBC rather
than the entire cohorts were included. Offspring from
the JPS cohort were included if their GA was recorded
(from mothers’ pre- or postpartum interviews), comprising all those born 1974–1976, and a subset born
1964–1973 (total n = 20 944). The pooled dataset thus
comprises 112 781 livebirths, after excluding multiple
births (due to their high rate of low birthweight)28 and
children with Down syndrome (due to their particularly high risk of childhood leukaemia)29 (Table 1).
Cancer ascertainment
Childhood cancer (diagnosed <15 years of age) was
ascertained by linkage to national registries for
ALSPAC, DNBC, JPS, and MoBa. For TIHS, linkage
was with the Tasmanian Cancer Registry. Cancer cases
for CPP were identified via examination of diagnostic
summaries and other indirect methods such as identifying children reported in previous investigations of
cancer and x-ray exposure, and manually reviewing
death records for children with birthweight ≥1500 g
who survived the first week of life. Each potential
cancer diagnosis was reviewed by two board-certified
paediatricians.
Tumours were classified into four main groups
based on the International Classification of Diseases
(ICD)-0 Third Edition:30 all cancers (C-code 42), leukaemia (morphology codes 9800–9941), ALL (codes
9820–9827, 9850), and non-leukaemia cancer (C-code
42, excluding 9800–9991). Small numbers of AML and
specific solid tumours across the six cohorts precluded
analysis of individual cancer subtypes besides ALL.
Birthweight metrics
Birthweight was analysed using three approaches:
first, dichotomised as ≥4.0 kg vs. <4.0 kg. The second
approach took into account differing birthweight distributions across populations and time. For example
the 90th percentile of birthweight was as follows:
ALSPAC: 4129, CPP: 3827, DNBC: 4320, JPS: 3880,
MoBa: 4260, and TIHS: 4030 g. To explore whether the
heaviest newborns in each cohort, regardless of absolute weight, were at higher risk of cancer, we chose
membership in the top decile as the ‘exposed’ group
while the lower 90% of children comprised the reference group. Finally, birthweight was assessed as a continuous variable in 0.5 and 1.0 kg increments.
Covariates and potential confounders
A number of variables previously shown to be associated with birthweight or childhood cancer were
assessed as potential confounders or effect modifiers.
These included:
1. Maternal factors: age at time of index child’s birth
(years); married/cohabitating at time of enrolment
(yes/no); at least 12 years of education completed
(yes/no); any smoking during pregnancy (yes/no);
exposure to any smoking at home during pregnancy (yes/no); parity – defined as the number of
previous livebirths (for all cohorts except ALSPAC
and DNBC that includes number of previous pregnancies and stillbirths), grouped as 0/1–2/≥ 3;
pre-existing or gestational diabetes (yes/no); prepregnancy body mass index [BMI = weight (kg)/
height (m2)]; and total pregnancy weight gain (kg).
2 Factors relating to the index child: GA (weeks),
determined by date of last menstrual period (or
ultrasound in a subgroup from MoBa and ALSPAC);
sex; first born (yes/no), birth length (cm); placental
weight (g); and age at diagnosis of primary cancer
(years).
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
3
4
O. Paltiel et al.
Table 1. Descriptive maternal, paternal, and offspring characteristics of the six I4C member cohorts included in the pooled dataset
Recruitment years
Singleton livebirths with no DS
Years of follow-up
Mean (range)
Maternal age (years)
Mean ± SD
Missing (%)
Married or cohabitating, n (%)
Missing (%)
Mother completed 12 or more years of
education, n (%)
Missing (%)
Maternal prenatal smoking, n (%)
Missing (%)
Passive smoking at home, prenatal, n (%)b
Missing (%)
Parity, n (%)
No prior livebirth
No prior pregnancy
1–2
≥3
Missing (%)
Maternal pre-pregnancy BMI, kg/m2
Mean ± SD
Missing (%)
Maternal pregnancy weight gain, kg
Mean ± SD
Missing (%)
Maternal height, cm
Mean ± SD
Missing (%)
Maternal DM, n (%)
Pre-existing
Gestational
Missing (%)
Any previous miscarriage, n (%)
Missing (%)
Paternal age (years)
Mean ± SD
Missing (%)
Father completed at least 12 years of
education, n (%)
Missing (%)
Gestational age, weeks
Mean ± SD
Missing (%)
Gender, male n (%)
Missing (%)
Birthweight, grams
Mean ± SD
Missing (%)
Placental weight, grams
Mean ± SD
Missing (%)
First born, n (%)
Missing (%)
Length at birth, cm
Mean ± SD
Missing (%)
ALSPAC
CPP
DNBC
JPS
MoBa
TIHS
Total
1991–1992
13 664
14.9
(0.5–15)
1959–1965
50 342
5.6
(0.0–8.0)
1996–2002
8603
11
(8.3–14.0)
1964–1976
20 313
15
(15.0–15.0)
1999–2009
10 497
4.4
(0.5–10.1)
1987–1995
9 362
14.7
(12.7–15.0)
1959–2007
112 781
9.9
(0.0–15.0)
28.0 ± 5.0
0 (0.0)
9 588 (70.2)a
861 (6.3)
4 286 (31.4)d
24.1 ± 5.9
0 (0.0)
38 658 (76.8)
2 (0.01)
20 767 (41.3)
30.5 ± 4.2
2 (0.02)
8094 (94.1)
359 (4.2)
4097 (47.6)
27.3 ± 5.4
77 (3.8)
20 142 (99.2)
114 (0.6)
9 866 (48.6)
30.2 ± 4.6
10 (0.1)
9 591 (91.4)
591 (5.6)
6 246 (59.5)
23.6 ± 4.4
0 (0.0)
7 318 (78.2)
32 (0.3)
1 690 (18.0)
26.2 ± 5.9
89 (0.08)
93 389 (82.8)
1 590 (1.7)
46 952 (41.6)
1 536 (11.2)
3 530 (25.8)
1 639 (12.0)
5 362 (39.2)
1 859 (13.6)
122 (0.24)
23 269 (46.2)
263 (0.5)
n/a
2349 (27.3)
2 193 (25.5)
9 (0.1)
5584 (64.9)
3005 (34.9)
358 (1.8)
2 568 (12.6)
193 (1.0)
7 438 (36.6)
3 828 (18.8)
595 (5.7)
925 (8.8)
2 295 (21.9)
770 (7.3)
1 397 (13.3)
21 (0.2)
5 023 (53.6)
16 (0.2)
5 242 (56.0)
20 (0.2)
4 981 (4.4)
37 508 (33.3)
4 415 (3.9)
24 396 (39.1)
10 109 (16.2)
1 377 (10.1)
4 263 (31.2)
6 090 (44.6)
733 (5.4)
1 201 (8.8)
1 142 (2.3)
14 187 (28.2)
19 768 (39.3)
15 191 (30.2)
54 (0.1)
n/a
3861 (44.9)
4117 (47.9)
261 (3.0)
364 (4.2)
n/a
6 249 (30.8)
8 713 (42.9)
5 279 (26.0)
76 (0.4)
1 239 (11.8)
3 229 (30.8)
4 960 (47.2)
292 (2.8)
777 (7.4)
n/a
3 758 (3.6)
31 789 (30.7)
43 648 (42.2)
21 753 (21.0)
2 472 (2.4)
22.9 ± 3.8
2 396 (17.5)
22.7 ± 4.3
4 372 (8.7)
23.6 ± 4.4
493 (5.7)
22.1 ± 3.1
6 010 (29.6)
24.0 ± 4.2
835 (7.9)
23.2 ± 4.8
3 113 (33.2)
22.9 ± 4.1
17 219 (15.3)
12.5 ± 4.7
1 573 (11.5)
9.8 ± 4.8
3 816 (7.6)
15.1 ± 5.8
1947 (22.6)
11.2 ± 4.3
5 679 (27.6)
14.9 ± 5.8
2 550 (24.3)
13.9 ± 6.4
1 704 (18.2)
11.5 ± 5.4
17 269 (15.3)
163.9 ± 6.7
1 717 (12.6)
160.9 ± 6.9
3 740 (7.4)
168.8 ± 6.0
361 (4.2)
162.0 ± 6.0
4 678 (23.0)
168.0 ± 5.9
660 (6.3)
162.2 ± 7.3
2 452 (26.2)
n/a
162.9 ± 7.2
13 608 (12.1)
442 (3.2)c
55 (0.4)
1 585 (11.6)
2 719 (19.9)
961 (7.0)
264 (0.5)
127 (0.2)
129 (0.3)
9 031 (17.9)
0 (0)
25 (0.3)
139 (1.6)
0 (0.0)
1563 (18.2)
365 (4.2)
36 (0.2)
110 (0.5)
90 (0.4)
4 416 (21.7)
104 (0.5)
55 (0.5)
85 (0.8)
0 (0.0)
1 925 (18.3)
777 (7.4)
30.7 ± 5.8
2 269 (16.6)
5 151 (37.7)d
28.2 ± 7.0
12 795 (25.4)
19 209 (38.2)
32.8 ± 5.1
123 (1.4)
2700 (31.4)
30.9 ± 6.5
654 (3.2)
10 828 (53.3)
32.7 ± 5.3
47 (0.4)
4 782 (45.6)
26.5 ± 5.6
215 (2.3)
1 624 (17.3)
29.8 ± 6.6
16 103 (14.3)
44 294 (39.3)
2 012 (14.7)
9 651 (19.2)
2541 (29.5)
883 (8.4)
865 (9.2)
16 370 (14.5)
39.5 ± 1.9
0 (0.0)
7 052 (51.6)
2 (0.01)
39.4 ± 3.1
329 (0.6)
25 461 (50.6)
81 (0.2)
40.1 ± 1.7
0 (0.0)
4367 (50.8)
0 (0.0)
39.7 ± 2.2
0 (0.0)
10 485 (51.6)
0 (0.0)
39.5 ± 1.8
47 (0.4)
5 274 (50.2)
0 (0.0)
38.8 ± 2.6
29 (0.3)
6 673 (71.3)
0 (0.0)
39.5 ± 2.6
405 (0.4)
59 312 (52.6)
93 (0.1)
3 410 ± 551
172 (1.3)
3 177 ± 531
192 (0.4)
3599 ± 549
32 (0.4)
3 260 ± 508
52 (0.3)
3 604 ± 562
20 (0.2)
3 195 ± 751
0 (0.0)
3 293 ± 577
468 (0.4)
652 ± 138
8 103 (59.3)
5 500 (40.2)
1 090 (8.0)
437 ± 94
7 864 (15.6)
14 187 (28.2)
54 (0.1)
663 ± 148
288 (3.3)
3861 (44.9)
364 (4.2)
n/a
676 ± 149
325 (3.1)
4 468 (42.6)
777 (7.4)
613 ± 161
149 (1.6)
44 387 (46.9)
11 (0.1)
531 ± 163
16 729 (18.1)
38 651 (34.3)
2 372 (2.1)
50.7 ± 2.4
3 399 (24.9)
49.9 ± 2.7
1 515 (3.0)
52.3 ± 2.5
81 (0.9)
418 (2.1)
6 248 (30.8)
76 (0.4)
n/a
50.4 ± 2.4
387 (3.7)
n/a
48.8 ± 3.4
221 (2.4)
882 (0.8)
516 (0.5)
1 804 (1.7)
19 654 (19.0)
2 207 (2.1)
50.2 ± 2.9
5 603 (6.1)
ALSPAC = the Avon Longitudinal Study of Parents and Children (UK); CPP = the Collaborative Perinatal Project (USA); DNBC = the Danish National Birth
Cohort (Denmark); DS = Down syndrome; JPS = the Jerusalem Perinatal Study (Israel); MoBa = the Norwegian Mother and Child Cohort Study (Norway);
n/a = data not collected/provided; TIHS = the Tasmanian Infant Health Survey (Australia).
a
Concerned marriage only.
b
Passive smoking defined as any exposure to smoke at home by partner or others living in the home.
c
Includes glycosuria.
d
Educational qualifications obtained were used as a proxy – but by law the school leaving age was 16 at the earliest.
Note that if a subject characteristic was n/a for a particular cohort, then the percentage in the ‘Total’ column is based on total number of observations
without including that cohort in the summary statistic.
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
Birthweight and childhood cancer: I4C Cohort Consortium
3 Paternal factors: age at time of index child’s birth
(years), and completion of at least 12 years of education (yes/no).
Follow-up time
Children in the ALSPAC and JPS cohorts were followed to at least 15 years of age, or censored at date
of death. Follow-up of children in DNBC and MoBa
is ongoing. Children within these cohorts without
cancer are assumed to have been followed to the point
of last linkage to their national registries: 1 September
2011 and 31 December 2009, respectively. For TIHS,
in the absence of systematic follow-up of cohort
members, non-cases were deemed to be followed to
the last date of diagnosis of a case in the Tasmanian
Cancer Registry (28 September 2008), when the
youngest child was aged 12.73 years. Follow-up time
for the CPP was calculated as the number of months
from date of birth (or age 1 week) to the last recorded
visit, for a maximum of 8 years.
Missing data
Missing covariate data among the cohorts ranged from
0% to nearly 40% (see Table 1). In order to construct
multivariable models with maximal sets of covariates,
we used chained multiple imputation to impute 20
complete datasets.31 Cox regression was performed
separately on each imputed dataset and the results
pooled into a single multiple imputation result. We
used truncated linear regression to impute missing
continuous variables (paternal age, maternal height,
pregnancy weight gain, and pre-pregnancy BMI)
where the imputations are limited to lower and upper
boundaries set at the minimum and maximum values
of non-missing observations. Logistic regression was
used to impute missing dichotomous variables (first
born and maternal smoking). Variables used to
impute missing data were maternal age, GA,
birthweight, sex of child, and cohort.
Statistical analysis
We report hazard ratios (HR) and 95% confidence
intervals (CI) from Cox proportional hazards regression models. All models were stratified by cohort.
Model 1 was unadjusted (birthweight was the only
independent variable). Model 2 adjusted for GA and
child’s sex. Model 3 was a parsimonious multivariable
model adjusted for GA, child’s sex, as well as
different combinations of covariates for each cancer
outcome, chosen as follows:
Starting with all confounders in the model, we
removed variables one at a time (beginning with the
variable with the largest P-value, so long as that variable no longer changed the coefficients for
birthweight or the other covariates in the model by
>15% in either direction) using the multiple imputation dataset. Once removed, a variable could not
re-enter the model.
Schoenfeld residuals were used to assess the proportional hazard assumption with all covariates
entered into the model, first, on the original data containing missing observations and, second, after imputing the missing data. Proportionality assumptions
were met in both.32 We assessed the linearity of continuous variables in the log-hazard using the method
of fractional polynomials.33 Paternal age, determined
to be non-linear, was transformed to a quadratic
expression.
To assess the possibility of effect modification by
maternal anthropometric measurements, we introduced interaction terms of birthweight × maternal
pre-pregnancy BMI using a cut-off of normal or
underweight (<25 kg/m2) vs. overweight (≥25 kg/
m2).34 In separate models, we introduced an interaction term of birthweight × pregnancy weight gain,
dichotomised according to the Institute of Medicine
recommendation (based on a healthy BMI) of ≤16 kg
vs. >16 kg.35
To determine whether the birthweight–cancer relation varied by age at diagnosis, we used a time-varying
coefficient approach, allowing for the estimation of
two HRs, one before a particular age at diagnosis and
one after. This time indicator variable is zero before the
relevant age and one afterwards. To test the sensitivity
of these results to changes in the indicator time variable, we ran the analyses for 3, 4, 5, 6, and 7 years. The
results indicated that the HRs for birthweight were
significantly different before and after age 3 years,
then fairly stable for years 4 to 7 (data not shown). We
thus retained the cut-off at age 3 for our analysis.
To assess heterogeneity effects by cohort, we generated random-effects (shared frailty) Cox models. The
results were similar to those obtained using a stratified analysis with each cohort serving as a stratum
that we report herein.
Analyses were conducted using Stata Statistical
Software, Version 12.1 (StataCorp, College Station, TX,
USA).
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
5
6
O. Paltiel et al.
Results
Table 1 presents the cohort-specific characteristics of
mothers, fathers, and index children. Mean maternal
age ranged from 23.6 [standard deviation (SD) 4.4]
years (TIHS) to 30.5 (SD 4.3) years (DNBC), with
paternal age showing similar variation. Scandinavian
mothers were the tallest, on average, yet maternal BMI
was fairly consistent across studies. Mean pregnancy
weight gain varied from 9.8 kg (4.80) (CPP) to 14.9
(5.8) kg (MoBa). Active maternal smoking during
pregnancy ranged from <10% (MoBa) to just over 50%
(TIHS). Mean birthweights were higher in the Scandinavian cohorts and lower in CPP.
Table S1 shows the distribution of cancer cases by
age and sex and the absolute risks of cancer in each
cohort. In total, the pooled analysis included 377
children with cancer, of whom 115 were diagnosed
with leukaemia, 98 with ALL, and 262 with nonleukaemia-type cancers, with 54% of cancers occurring among males. Ranges and mean ages at diagnosis
varied according to the length of cohort follow-up.
For each cohort, the HRs for all cancers, considering
birthweight as a continuous variable (per kilogram)
after controlling for GA and child’s sex consistently
exceeded 1.0 (Figure 1). Table 2 presents the pooled
analysis for birthweight and childhood cancer,
leukaemia, ALL, and non-leukaemia cancers. When
birthweight was considered as a continuous variable,
a significant increased risk of 26% for every kilogram
increment in birthweight was observed for all cancers,
after adjustment for GA and sex [HR 1.26 (95% CI
ALSPAC
1 kg increase, HR = 1.27 (0.31–2.23)
CPP
1 kg increase, HR = 1.51 (0.71–2.31)
DNBC
1 kg increase, HR = 1.03 (0.72–1.34)
JPS
1 kg increase, HR = 1.75 (0.64–2.85)
MoBa
1 kg increase, HR = 1.41 (0.91–1.92)
1.02, 1.54), P = 0.031]. Further adjustment for other
covariates (model 3) resulted in similar effect sizes. A
42% increase in risk was also observed for leukaemia,
adjusting for GA and child’s sex, with borderline statistical significance. HRs were elevated for children
born with birthweight ≥4.0 kg, compared with those
with lower birthweight for all cancer outcomes,
although the findings were not statistically significant.
The pattern was similar when comparing the highest
birthweight decile to the lower 90%, per cohort.
Figure 2 shows a monotonic increased risk of all
cancers with increasing birthweight [Spearman rank
correlation (rho) = 0.943, P = 0.005], as well as for leukaemia and ALL, but not for non-leukaemia cancers,
in the pooled analysis.
The association between birthweight and childhood
cancer differed according to age at diagnosis (Table 3).
In models adjusted for GA and sex, a significant association between birthweight, using all metrics, was
observed for cancers occurring at or after age 3 years,
while HRs were reduced and not statistically significant for younger children. This finding appeared to be
driven by non-leukaemia cancers. Although HRs were
higher for children diagnosed with leukaemia at or
after age 3 years, there was no statistical evidence of
time dependency.
Maternal pre-pregnancy BMI (unadjusted HR 1.01,
95% CI 0.99, 1.04) and pregnancy weight change
(unadjusted HR 1.0, 95% CI 0.99, 1.02) were not in
themselves associated with childhood cancer risks. We
explored potential effect modification by these
anthropometric measures on the association between
birthweight and the various cancer outcomes, and
found no significant interactions. Specifically, HRs, in
general, did not substantially differ when we examined the association between birthweight and cancer,
leukaemia or non-leukaemic tumours in two strata of
maternal pre-pregnancy BMI (top half of Table S2), or
in the high and low strata of gestational weight gain.
It should be noted that case numbers in each stratum
were relatively limited.
TIHS
1 kg increase, HR = 1.20 (0.56–1.84)
Comment
Pooled
1 kg increase, HR = 1.26 (1.02–1.54)
0.5
1.0
1.5
2.0
Hazard ratio
2.5
3.0
Figure 1. Hazard ratios for any cancer in each cohort and
pooled overall for birthweight continuous (per kilogram
increase) in a Cox proportional hazards model adjusted for gestational age and sex of child (model 2).
In this pooled analysis, we provide evidence from
prospectively collected data that birthweight, adjusted
for GA and sex, is positively associated with increased
cancer and leukaemia risks in children. In addition,
higher birthweight is particularly associated with nonleukaemia cancer diagnosed at or after age 3 years.
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
Birthweight and childhood cancer: I4C Cohort Consortium
7
Table 2. The association between birthweight and childhood cancers, leukaemia, ALL, and non-leukaemia cancers in the pooled
dataset
Model 1b
Birthweight metric (n
cases)
HRa
Birthweight ≥4.0 kge
Cancer (377)
1.14
Leukaemia (115)
1.25
ALL (98)
1.21
Non-leukaemia (262)
1.09
Top 10% of birthweights in each cohortf
Cancer (377)
1.17
Leukaemia (115)
1.25
ALL (98)
1.14
Non-leukaemia (262)
1.14
Continuous birthweight, kgg
Cancer (377)
1.10
Leukaemia (115)
1.25
ALL (98)
1.16
Non-leukaemia (262)
1.04
Model 2c
Model 3d
95% CI
HRa
95% CI
HRa
95% CI
0.88, 1.48
0.80, 1.96
0.74, 1.96
0.79, 1.50
1.19
1.31
1.25
1.14
0.91, 1.55
0.83, 2.08
0.76, 2.06
0.82, 1.58
1.17
1.21
1.21
1.11
0.89, 1.54
0.74, 1.96
0.72, 2.04
0.79, 1.56
0.85, 1.61
0.72, 2.19
0.61, 2.13
0.77, 1.68
1.22
1.31
1.17
1.18
0.88, 1.69
0.74, 2.31
0.62, 2.23
0.80, 1.75
1.18
1.16
1.08
1.14
0.84, 1.65
0.63, 2.12
0.55, 2.14
0.75, 1.71
0.91, 1.31
0.89, 1.75
0.81, 1.67
0.83, 1.28
1.26
1.42
1.29
1.19
1.02, 1.54
0.98, 2.06
0.85, 1.93
0.93, 1.52
1.26
1.35
1.29
1.18
1.02, 1.56
0.90, 2.02
0.83, 1.99
0.91, 1.54
a
Hazard ratios (95% CI) from a stratified Cox proportional hazard regression using all observations in the pooled dataset. In models 2
and 3 missing observations are imputed using a chained multiple imputation method.
b
Model 1 is an unadjusted Cox proportional hazard regression model stratified by cohort in which birthweight is the only independent
variable.
c
Model 2 is an adjusted Cox proportional hazard regression model stratified by cohort in which birthweight, gestational age, and sex of
the child are the independent variables.
d
Model 3 is an adjusted Cox proportional hazard regression stratified by cohort in which, for:
• Cancer: birthweight hazard ratio is adjusted for gestational age, child’s sex, maternal age, paternal age (rescaled as quadratic), first
born, and maternal pre-pregnancy BMI.
• Leukaemia: birthweight hazard ratio is adjusted for gestational age, child’s sex, maternal age, total pregnancy weight gain, maternal
pre-pregnancy BMI, first born, and any maternal smoking during pregnancy.
• ALL: birthweight hazard ratio is adjusted for gestational age, child’s sex, paternal age (rescaled as quadratic), total pregnancy weight
gain, and any maternal smoking during pregnancy.
• Non-leukaemia cancers: birthweight hazard ratio is adjusted for gestational age, child’s sex, paternal age (rescaled as quadratic), total
pregnancy weight gain, first born, and maternal pre-pregnancy BMI.
e
Reference group for birthweight ≥4.0 kg is birthweight <4.0 kg.
f
The reference group for the top 10% of each cohort is the bottom 90% of each cohort.
g
For continuous birthweight, the hazard ratio represents a 1 kg increase in birthweight.
Although maternal obesity is associated with high
birthweight,22,23 heightened cancer risks in high
birthweight offspring of overweight women or those
with excessive pregnancy weight gain were not
observed in our exploratory analyses.
For every kilogram increment in birthweight, the
HR for cancer was 1.26, similar to that reported in
large registry-based studies. The Norwegian Medical
Birth and Cancer Registries reported a HR of 1.23
(1.14–1.32)/kg birthweight increase adjusting for
GA,14 with no modification by age at diagnosis.
Recently, a case–control study9 (17 698 cases, 172 422
controls) based on registries in four Nordic countries
reported odds ratios (OR) of 1.2 and 1.4 for
birthweight 4000–4500 and 4500–6000 g, respectively,
for all cancers; OR estimates for ALL were also
similar to our pooled analysis, with little variation
among age groups.9 Comparable findings were
reported in a large cohort of ethnic Chinese in Singapore.36 Among nearly 2 000 000 children identified
through the Danish Birth Registry, Westergaard
showed a Relative Risk (RR) for ALL of 1.46/kg
birthweight increase.37 In contrast, a recent large
study reporting on a total of 40 326 cases and 86 922
controls from the UK and US showed more modestly
elevated ORs of 1.06 per 500 g increment for all
cancer. For ALL, reported ORs were 1.08 (UK) and
1.11 (US), the latter adjusted for GA.11
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
8
Birthweight, kg
(a)
O. Paltiel et al.
≥ 4.5
4.0 to < 4.5
3.5 to < 4.0
3.0 to < 3.5
2.5 to < 3.0
< 2.5
Birthweight, kg
(b)
≥ 4.5
4.0 to < 4.5
3.5 to < 4.0
3.0 to < 3.5
2.5 to < 3.0
< 2.5
Birthweight, kg
(c)
≥ 4.5
4.0 to < 4.5
3.5 to < 4.0
3.0 to < 3.5
2.5 to < 3.0
< 2.5
Birthweight, kg
(d)
≥ 4.5
4.0 to < 4.5
3.5 to < 4.0
3.0 to < 3.5
2.5 to < 3.0
< 2.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Hazard ratio
Figure 2. Hazard ratios in the pooled dataset for birthweight* in
500 g increments in Cox proportional hazards models adjusted
for gestational age and sex of child (model 2) by cancer type.
(a) Cancer; (b) leukaemia; (c) acute lymphoblastic leukemia; (d)
non-leukaemia.
Spearman rank correlation for all cancers (rho) = 0.943, P =
0.005; for all leukaemia rho = 0.886, P = 0.019; for ALL rho =
0.943, P = 0.005, and for non-leukaemia cancers rho = 0.486,
P = 0.329.
*Birthweight 3.5–<4 kg is the reference category.
In our dataset, high birthweight was strongly associated with non-leukaemia cancers diagnosed at or after
the age of 3 years. However, leukaemia risks were
not modified by age at diagnosis, echoing findings
from a large meta-analysis.5 The time-varying pattern
of birthweight effects may be due to the fact that
many cancer subtypes vary by age of onset and their
relation with birthweight may vary. As the I4C cohorts
mature, more detailed analyses on specific solid
tumours and lymphomas, including those which are
usually diagnosed in older children will be possible.
Few investigators have explored the effects of
maternal anthropometrics on the birthweight–
leukaemia
or
birthweight–cancer
association.
McLaughlin and colleagues,26 in a case–cohort study,
noted an association between birthweight and leukaemia only among infants whose mothers weighed
<80 kg. That study lacked data on maternal height,
thus overweight per se, as measured by BMI, was not
addressed. They observed an effect of pregnancy
weight gain on ALL risk (RR 1.31), using a cut-off of
14.1 kg; however, no interaction with birthweight was
noted. Most women in the I4C cohorts were nonobese, with pregnancy weight gain within the recommended range. However, given worldwide trends in
maternal adiposity,38 this relation deserves further
scrutiny, particularly as our analysis was limited by
small numbers.
Moving beyond the established association between
accelerated foetal growth and childhood cancer to
explanatory mechanisms presents a considerable challenge. The complex contributions of both genetics and
the intrauterine environment are illustrated by early
observations, even among twins, that the heavier
sibling was more likely to develop leukaemia.3 Proposed biological explanations include increased risks
of somatic mutations related to higher stem cell
number in large babies, and growth factor effects (e.g.
IGF) on both foetal growth and leukaemogenesis.
Early clues suggest that haplotypes in IGF1 and IGF2
are related to both high birthweight and ALL risk.39
Furthermore, overgrowth syndromes related to abnormal methylation patterns of IGF genes have been associated with particular cancers.40
Our study’s strengths include prospectively collected data from a wide variety of geographic and
temporal settings. All birth and maternal characteristics were ascertained at birth or during pregnancy,
minimising recall bias. Most of the contributing
cohorts were representative of their respective source
populations and cancer cases were derived from the
same populations as non-cases. This contrasts with
case–control studies, in which (because of low
response rates and consequent selection bias) controls
may differ substantially from the case population,
including in their birthweight distribution.41
Given that the correlation between birthweight and
birthweight-adjusted-for-GA is not high (kappa =
0.45),16 accounting for GA, as we did, is important. In
our analysis, adjustment for GA generally resulted in
improved precision of the HR estimates.
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
Birthweight and childhood cancer: I4C Cohort Consortium
9
Table 3. Cancer, leukaemia, ALL, and non-leukaemia cancer hazard ratios in the pooled dataset using a time-varying coefficient for
birthweight across two time periods (age at diagnosis <3 vs. ≥3 years old) adjusting for sex and gestational age
Diagnosis
Birthweight
Cases
HR
95% CI
Cases
HR
95% CI
Comparison of HRs
between time periods,
P-value
Cancer
≥4.0 kg
Top 10%
Continuous
≥4.0 kg
Top 10%
Continuous
≥4.0 kg
Top 10%
Continuous
≥4.0 kg
Top 10%
Continuous
182
0.84
0.80
1.08
1.08
1.08
1.29
1.02
1.07
1.23
0.75
0.67
0.99
0.56 ,1.27
0.46, 1.39
0.82, 1.42
0.55, 2.13
0.46, 2.54
0.79, 2.11
0.48, 2.15
0.42, 2.73
0.72, 2.11
0.45, 1.24
0.33, 1.38
0.71 ,1.38
195
1.60
1.64
1.44
1.56
1.55
1.57
1.49
1.28
1.34
1.62
1.68
1.39
1.13, 2.26
1.10, 2.44
1.11, 1.88
0.84, 2.88
0.72, 3.30
0.96, 2.57
0.77, 2.88
0.54, 3.03
0.78, 2.30
1.06, 2.46
1.05, 2.68
1.02 ,1.91
0.018
0.037
0.099
0.43
0.54
0.56
0.45
0.79
0.81
0.020
0.035
0.10
Diagnosed <3 years old
Leukaemia
ALL
Non-leukaemia
59
49
123
Diagnosed ≥3 years old
56
49
139
Model 2: Stratified Cox proportional hazard regression with a time-varying coefficient for birthweight based on an indicator function
for time defined at the age of diagnosis cut-point adjusted for gestational age and sex of the child.
The I4C platform enabled us to simultaneously
examine a wide range of potential confounders (e.g.
maternal adiposity, parity, diabetes, and maternal
active and passive smoking) unavailable in many previous record-linkage studies. However, the maximally
adjusted models did not differ substantially from
those which adjusted for child’s sex and GA.
Our study’s limitations include the modest number
of cases available for analysis – despite the pooling of
six cohorts. This restricted our ability to study subtypes such as AML, as well as specific solid tumours,
and provided limited power to study interactions.
Missing covariate data necessitated imputation. Subjects in some of the cohorts had not yet reached
15 years of age, so the entire childhood cancer experience of the cohort cannot be fully summarised.
Furthermore, methods of cancer ascertainment and
follow-up were inconsistent among the cohorts, and
for one cohort (TIHS), enrolment was selective.
Pooling data from different cohorts may be problematic due to heterogeneity of observed effects. For
instance, variation by ethnicity may occur in the association between IGF haplotypes, leukaemia, and
birthweight.41 Recent pooled analyses have shown
substantial heterogeneity in the association between
birthweight and cancer across countries.11,18 In an
attempt to diminish the effects of differential
birthweight distributions across cohorts, we stratified
all models by cohort and performed an analysis
taking into account the highest birthweight decile
within each cohort. The analysis using birthweight
(adjusted for GA and child sex) as a continuous
variable showed consistent results across cohorts
(Figure 1), and can serve as a simple measure facilitating comparison between large registry-based studies,
meta-analyses, and pooled analyses of case–control
studies.
In conclusion, evidence has now been added from
pooled prospectively collected data spanning six
countries on four continents over 50 years, strengthening the observation that increasing birthweight is a
risk factor for childhood cancer and leukaemia. Notwithstanding the known association of maternal
obesity with high birthweight and potential metabolic
and cardiovascular morbidity, our preliminary findings do not support a substantial main effect of maternal adiposity on childhood cancer nor an interaction
with birthweight. With accumulating person-years of
follow-up, the addition of cancer cases from newer
cohorts, and the availability of biological samples,
I4C’s future pooled projects will enable further exploration of the roles of pre- and postnatal events, genetics and epigenetics, as well as providing power to
discern which cancer subtypes are associated with
high birthweight in older children. Further investigations should continue to focus on mechanisms and
exposures that jointly influence both foetal growth
and malignant transformation.
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
10
O. Paltiel et al.
Acknowledgements
This work was supported by: NIH (NCI, NICHD) –
USA; National Children’s Study – USA; the Childrens
Cancer Centre Foundation – Australia; Bluey Day
Foundation – Australia; Baxter Family Foundation –
Australia; The Rotary Club of North Brighton –
Australia; Tour de Cure – Australia; Private
philanthropic donations – Australia; and Murdoch
Children’s Research Institution (M1300049), Australia.
The UK Medical Research Council and the Wellcome
Trust (Grant ref: 092731) and the University of Bristol
provide core support for ALSPAC. The Maria Ascoli
Foundation, Jerusalem, Israel, provided support for
data pooling of the JPS.
11
12
13
14
15
References
1 MacMahon B, Newill VA. Birth characteristics of children
dying of malignant neoplasms. Journal of the National Cancer
Institute 1962; 28:231–244.
2 Iverson T. Leukemia in infancy and childhood. A material
of 570 Danish cases. Acta Paediatrica Scandinavica 1966; 167
(Suppl.):1+.
3 Jackson EW, Norris FD, Klauber MR. Childhood leukemia
in California-born twins. Cancer 1969; 23:913–919.
4 Hjalgrim LL, Rostgaard K, Hjalgrim H, Westergaard T,
Thomassen H, Forestier E, et al. Birth weight and risk for
childhood leukemia in Denmark, Sweden, Norway, and
Iceland. Journal of National Cancer Institute 2004;
96:1549–1556.
5 Caughey RW, Michels KB. Birth weight and childhood
leukemia: a meta-analysis and review of the current
evidence. International Journal of Cancer 2009; 124:2658–2670.
6 Glinianaia SV, Pearce MS, Rankin J, Pless-Mulloli T,
Parker L, McNally RJ. Birth weight by gestational age
and risk of childhood acute leukemia: a population-based
study 1961–2002. Leukemia Lymphoma 2011; 52:709–712.
7 Shaw G, Lavey R, Jackson R, Austin D. Association of
childhood leukemia with maternal age, birth order, and
paternal occupation. A case-control study. American Journal
of Epidemiology 1984; 119:788–795.
8 McKinney PA, Cartwright RA, Saiu JM, Mann JR,
Stiller CA, Draper GJ, et al. The inter-regional
epidemiological study of childhood cancer (IRESCC):
a case control study of aetiological factors in leukaemia
and lymphoma. Archives of Diseases in Childhood 1987;
62:279–287.
9 Bjorge T, Sorensen HT, Grotmol T, Engeland A,
Stephansson O, Gissler M, et al. Fetal growth and childhood
cancer: a population-based study. Pediatrics 2013;
132:e1265–e1275.
10 Schmidt LS, Schuz J, Lahteenmaki P, Trager C, Stokland T,
Gustafson G, et al. Fetal growth, preterm birth, neonatal
stress and risk for CNS tumors in children: a Nordic
16
17
18
19
20
21
22
23
24
25
population- and register-based case-control study. Cancer
Epidemiology Biomarkers and Prevention 2010; 19:1042–1052.
O’Neill KA, Murphy MFG, Bunch KJ, Puumala SE,
Carozza SE, Chow EJ, et al. Infant birthweight and risk
of childhood cancer: international population-based case
control studies of 40000 cases. International Journal of
Epidemiology 2015; 44:153–168.
Milne E, Laurvick CL, Blair E, Bower C, de Klerk N. Fetal
growth and acute childhood leukemia: looking beyond birth
weight. American Journal of Epidemiology 2007; 166:151–159.
Sprehe MR, Barahmani N, Cao Y, Wang T, Forman MR,
Bondy M, et al. Comparison of birth weight corrected for
gestational age and birth weight alone in prediction of
development of childhood leukemia and central nervous
system tumors. Pediatric Blood and Cancer 2010; 54:242–249.
Samuelsen SO, Bakketeig LS, Tretli S, Johannesen TB,
Magnus P. Birth weight and childhood cancer. Epidemiology
(Cambridge, Mass.) 2009; 20:484–487.
Rangel M, Cypriano M, de Martino Lee ML, Luisi FA,
Petrilli AS, Strufaldi MW, et al. Leukemia, non-Hodgkin’s
lymphoma, and Wilms tumor in childhood: the role of birth
weight. European Journal of Pediatrics 2010; 169:875–881.
Schuz J, Forman MR. Birthweight by gestational age and
childhood cancer. Cancer Causes and Control 2007;
18:655–663.
Milne E, Greenop KR, Metayer C, Schuz J, Petridou E,
Pombo-de-Oliveira MS, et al. Fetal growth and childhood
acute lymphoblastic leukemia: findings from the childhood
leukemia international consortium. International Journal of
Cancer 2013; 133:2968–2979.
Roman E, Lightfoot T, Smith AG, Forman MR, Linet MS,
Robison L, et al. Childhood acute lymphoblastic leukaemia
and birthweight: insights from a pooled analysis of casecontrol data from Germany, the United Kingdom and the
United States. European Journal of Cancer 2013; 49:1437–1447.
Johnston LB, Clark AJ, Savage MO. Genetic factors
contributing to birth weight. ADC Fetal Neonatal Edition
2002; 86:F2–F3.
Amini SB, Catalano PM, Hirsch V, Mann LI. An analysis of
birth weight by gestational age using a computerized
perinatal data base, 1975–1992. Obstetrics and Gynecology
1994; 83:342–352.
Dubois L, Girard M. Determinants of birthweight
inequalities: population-based study. Pediatrics International
2006; 48:470–478.
Bhattacharya S, Campbell DM, Liston WA. Effect of body
mass index on pregnancy outcomes in nulliparous women
delivering singleton babies. BMC Public Health 2007; 7:168.
Nohr EA, Vaeth M, Baker JL, Sorensen T, Olsen J,
Rasmussen KM. Combined associations of prepregnancy
body mass index and gestational weight gain with the
outcome of pregnancy. American Journal of Clinical Nutrition
2008; 87:1750–1759.
Lu GC, Rouse DJ, DuBard M, Cliver S, Kimberlin D,
Hauth JC. The effect of the increasing prevalence of
maternal obesity on perinatal morbidity. American
Journal of Obstetrics and Gynecology 2001; 185:845–849.
Hochner H, Friedlander Y, Calderon-Margalit R, Meiner V,
Sagy Y, Avgil-Tsadok M, et al. Associations of maternal
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
Birthweight and childhood cancer: I4C Cohort Consortium
26
27
28
29
30
31
32
33
34
35
36
37
prepregnancy body mass index and gestational weight gain
with adult offspring cardiometabolic risk factors: the
Jerusalem Perinatal Family Follow-up Study. Circulation
2012; 125:1381–1389.
McLaughlin CC, Baptiste MS, Schymura MJ, Nasca PC,
Zdeb MS. Birth weight, maternal weight and childhood
leukaemia. British Journal of Cancer 2006; 94:1738–1744.
Brown RC, Dwyer T, Kasten C, Krotoski D, Li Z, Linet MS,
et al. Cohort profile: the International Childhood Cancer
Cohort Consortium (I4C). International Journal of
Epidemiology 2007; 36:724–730.
Vogel JP, Torloni MR, Seuc A, Betran AP, Widmer M,
Souza JP, et al. Maternal and perinatal outcomes of twin
pregnancy in 23 low- and middle-income countries.
PLoS ONE 2013; 8:e70549.
Seewald L, Taub JW, Maloney KW, McCabe ER. Acute
leukemias in children with Down syndrome. Molecular
Genetics and Metabolism 2012; 107:25–30.
Fritz A, Percy C, Jack A, Shanmugaratnam K, Sobin L,
Parkin M, et al. International Classification of Diseases for
Oncology, 3rd edn. Geneva: World Health Organization,
2000.
White IR, Royston P, Wood AM. Multiple imputation using
chained equations: Issues and guidance for practice.
Statistics in Medicine 2011; 30:377–399.
Grambsch PM, Therneau TM. Proportional hazards tests
and diagnostics based on weighted residuals. Biometrika
1994; 81:515–526.
Royston P, Altman DG. Regression using fractional
polynomials of continuous covariates – parsimonious
parametric modeling. Applied Statistics 1994; 43:429–467.
Defining Overweight and Obesity. http://www.cdc.gov/
obesity/adult/defining.html [last accessed April 2015].
Rasmussen KM, Catalano PM, Yatkine AL. New guidelines
for weight gain during pregnancy: what obstetrician/
gynecologists should know. Curr Opin Obstet Gynecol 2009;
21:521–526.
Lee J, Chia KS, Cheung KH, Chia SE, Lee HP. Birthweight
and the risk of early childhood cancer among Chinese in
Singapore. International Journal of Cancer 2004; 110:465–467.
Westergaard T, Andersen PK, Pedersen JB, Olsen JH,
Frisch M, Sorensen HT, et al. Birth characteristics,
sibling patterns, and acute leukemia risk in childhood:
38
39
40
41
a population-based cohort study. Journal of the National
Cancer Institute 1997; 89:939–947.
Rees M, Karoshi MA, Keith L. Obesity and Pregnancy. Boca
Raton, FL: CRC Press, 2008.
Chokkalingam AP, Metayer C, Scelo G, Chang JS,
Schiffman J, Urayama KY, et al. Fetal growth and body
size genes and risk of childhood acute lymphoblastic
leukemia. Cancer Causes and Control 2012; 23:1577–1585.
Chu A, Heck JE, Ribeiro KB, Brennan P, Boffetta P, Buffler P,
et al. Wilms’ tumour: a systematic review of risk factors
and meta-analysis. Paediatric Perinatal Epidemiology 2010;
24:449–469.
Puumala SE, Spector LG, Robison LL, Bunin GR,
Olshan AF, Linabery AM, et al. Comparability and
representativeness of control groups in a case-control
study of infant leukemia: a report from the Children’s
Oncology Group. American Journal of Epidemiology 2009;
170:379–387.
Supporting Information
Additional Supporting Information may be found in
the online version of this article at the publisher’s
web-site:
Table S1. Distribution and absolute risks of all
cancers, leukaemia, acute lymphoblastic leukaemia
(ALL), and non-leukaemia cancer by cohort, gender,
and age of diagnosis, in the pooled dataset based on
singleton births and excluding children with Down
syndrome.
Table S2. Assessment of maternal pre-pregnancy
body mass index (BMI) and pregnancy weight gain as
possible effect modifiers of the association between
childhood cancers, leukaemia, acute lymphoblastic
leukaemia (ALL), and non-leukaemia cancers and
birthweight adjusting for sex and gestational age in
the pooled dataset.
Appendix S1. Participating I4C-member cohorts, data
harmonization and ethics.
© 2015 The Authors. Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd
Paediatric and Perinatal Epidemiology, 2015, ••, ••–••
11