Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 Oct 1.
Published in final edited form as: Cancer Causes Control. 2016 Sep 9;27(10):1273–1285. doi: 10.1007/s10552-016-0807-5

Maternal pre-pregnancy and gestational diabetes, obesity, gestational weight gain, and risk of cancer in young children: a population-based study in California

Zuelma A Contreras 1, Beate Ritz 1, Jasveer Virk 1, Myles Cockburn 2, Julia E Heck 1
PMCID: PMC5066566  NIHMSID: NIHMS815903  PMID: 27613707

Abstract

Purpose

We aimed to examine the influence of pre-pregnancy diabetes, pre-pregnancy body mass index (BMI), gestational diabetes, and gestational weight gain on childhood cancer risk in offspring.

Methods

We identified cancer cases (n=11,149) younger than age 6 years at diagnosis from the California Cancer Registry registered between 1988–2013. Controls (n=270,147) were randomly sampled from California birth records, and frequency-matched by year of birth to all childhood cancers during the study period. Exposure and covariate information was extracted from birth records. Unconditional logistic regression models were generated to assess the importance of pre-pregnancy diabetes, pre-pregnancy BMI, gestational diabetes, and gestational weight gain on childhood cancer risk.

Results

We observed increased risks of acute lymphoblastic leukemia (ALL) and Wilms’ tumor in children of mothers with pre-pregnancy diabetes [odds ratio (OR) =1.37, 95% confidence interval (CI): (1.11, 1.69), OR=1.45, 95% CI: (0.97, 2.18), respectively]. When born to mothers who were overweight prior to pregnancy (BMI 25–<30), children were at increased risk of leukemia [OR=1.27, 95% CI: (1.01, 1.59)]. Insufficient gestational weight gain increased the risk of acute myeloid leukemia (AML) [OR=1.50 (95% CI: 0.92, 2.42)] while excessive gestational weight gain increased the risk of astrocytomas [OR=1.56, 95% CI: (0.97, 2.50)]. No associations were found between gestational diabetes and childhood cancer risk in offspring.

Conclusions

We estimated elevated risks of several childhood cancers in the offspring of mothers who had diabetes and were overweight prior to pregnancy, as well as mothers who gained insufficient or excessive weight. Since few studies have focused on these factors in relation to childhood cancer, replication of our findings in future studies is warranted.

Keywords: diabetes, gestational weight gain, BMI, childhood cancer epidemiology, risk factors

Introduction

It is estimated that 10,380 new childhood cancer cases and 1,250 deaths will occur in the US alone in 2016 [1]. The incidence of pediatric cancer in the United States has increased at an annual rate of 0.6% between 1975 and 2010, most notably for ALL, AML, non-Hodgkin lymphoma, and testicular germ cell tumors. In large part, the factors contributing to these increasing trends are largely unknown, as few risk factors for childhood cancer have been established [2]. Known risk factors include ionizing radiation, prior chemotherapy, and congenital genetic syndromes such as Down syndrome, neurofibromatosis, Fanconi anemia, and Bloom Syndrome, though these are only suspected to contribute to 5% to 10% of childhood cancers [3].

Many studies have consistently reported higher birthweights with an increased risk of leukemia, particularly for ALL [4–6]. Several population-based studies have reported that increasing birthweight may also increase the risk of other childhood cancers, such as Wilms’ tumor, central nervous system (CNS) tumors, soft tissue sarcomas, neuroblastomas, lymphomas, germ cell tumors, and malignant melanomas [6–8]. Non-linear relationships with birthweight have been noted for some cancer types, as hepatoblastoma has been shown to decrease in risk with increasing birthweight, and a U-shaped association has been observed for AML with birthweight [4, 5, 8].

Biological mechanisms potentially linking higher birthweight to childhood cancers are not yet fully understood, but it has been hypothesized that insulin-like growth factor-1 (IGF-1) may play a role since IGF-1 is positively associated with birthweight and has also been implicated in several forms of childhood cancer [9–11]. The IGFs stimulate cell proliferation, inhibit apoptosis, and are also important in blood cell formation and regulation since receptors for IGF-1 are found on cells of hematopoietic origin, and IGF-1 stimulates red blood cell production and regulates normal B-lymphocyte development [10, 12]. In the case of hepatoblastoma, which has consistently been related to low birthweight, it has been suggested that the relation may be explained by parental smoking or medical interventions in early life [13]. If myeloid cells are also particularly susceptible to these factors, this could explain the association between low birthweight and AML. Also IGF levels or particular gene variations and alterations that result in low birthweight may be selectively harmful for developing myeloid cells [14].

The impact of metabolic factors on childhood cancer risk has not been extensively studied and to date these studies have produced inconclusive results, with some suggestive evidence for a positive association between maternal diabetes and childhood leukemias and lymphomas, but inconsistent results for maternal BMI and gestational weight gain [6, 15–26]. We hypothesize that since maternal diabetes, obesity, and excess weight gain during pregnancy have been shown to promote fetal growth, these conditions will increase the risk of childhood cancers that have been associated with higher birthweight [27–31]. Whereas pre-pregnancy underweight and insufficient gestational weight gain, which have been linked to restricted fetal growth, will result in an increased risk of childhood cancers that have been associated with lower birthweight [27, 28, 32]. Given the increasing prevalence of obesity and overweight status among women of childbearing age and increasing rates of pre-pregnancy and gestational diabetes deliveries in the US [33–35], we aim to assess the association between pre-pregnancy diabetes, gestational diabetes, pre-pregnancy BMI, and gestational weight gain on the risk of all childhood cancers before age 6 in a very large, diverse and population-based sample of children born in California, in which Hispanics are the dominant ethnicity.

Methods

Study population

This study includes children from the Air Pollution and Childhood Cancers (APCC) study previously described [36]. Childhood cancer cases aged 5 years or younger at diagnosis were identified from the California Cancer Registry from 1988–2013. This analysis was restricted to young children as we hypothesized that pregnancy exposures are likely to be more relevant to the etiology of cancers diagnosed in early childhood. Approximately 89% of cases were successfully matched to their birth certificate by first and last name, date of birth, and when available, social security number. It is likely that children we were unable to match were those who moved to California after birth but before the age of 6 years [37]. Controls were frequency-matched by year of birth to all childhood cancer cases during the study period (20:1 matching rate) and randomly selected from all California birth certificates. The rationale for choosing a 20:1 ratio was to ensure that in the APCC study, a study of environmental exposures, there would be sufficient controls selected who resided in rural areas. Selection criteria for controls consisted of absence of a cancer diagnosis before 6 years of age in California. Also, potential control children were excluded if they died of any cause prior to age 6 (n=1,792). We also excluded children that were missing sex (n=3), births that were likely not viable (gestational age <20 weeks and/or birthweight <500g) (n=169), and children diagnosed with Down syndrome (n=151). The latter was done because Down syndrome is a strong risk factor for childhood cancer [3] and potentially related to pregnancy-related characteristics, including maternal obesity [38]. Additionally, mothers who had extreme or implausible BMI values (<17 kg/m2 or > 45 kg/m2) and gestational weight gain values (< −2 kg or >32 kg) were excluded. Only cancer types with at least 5 exposed cases with respect to pre-pregnancy diabetes were considered for inclusion in our study. AML was also included since ALL and AML are thought to have distinct etiologies. The final sample included 11,149 cases and 270,147 controls. We examined the childhood cancer types classified according to their respective International Classification of Childhood Cancer, 3rd edition (ICCC-3) codes [39]: 5,034 leukemias (codes 011–015) of which 4,101 were ALL (code 011) and 706 were AML (code 012), 990 astrocytomas (code 032), 709 intracranial and intraspinal embryonal brain tumors (code 033), 445 germ cell tumors (code 101–105), 337 hepatoblastomas (code 071), 1,378 neuroblastomas (code 041), 741 retinoblastomas (code 050), 463 rhabdomyosarcomas (code 091), and 1,052 Wilms’ tumors (code 061). Our study used de-identified records so we were not required to obtain informed consent. The institutional review boards of University of California Los Angeles and the Committee for the Protection of Human Subjects approved this study.

Study variables

California birth records were our source of covariate data, which among other factors include information on birthweight, child sex, parental age at child birth, parental race/ethnicity, parental education, method of payment for prenatal care (private insurance/Medi-Cal/self-pay, which we previously found to be related to family income [40]) and gestational age, based on date of last menses. Size for gestational age was defined as small if birthweight was less than the 10th percentile and as large if it was greater than the 90th percentile of the birthweight standards for a given gestational age, using the method of Alexander and colleagues [41]. The 10th and 90th percentile values were obtained for each gestational week (20–45 weeks) by maternal race/ethnicity (non-Hispanic white, Hispanic of any race, Black, Asian/Pacific Islander, and other) and child’s sex based on the total singleton live births in California between 1988 and 2006. We also categorized birthweight as low (<2500 grams), normal (2500–3999 grams), and high (>4000 grams). Presence of pre-pregnancy and gestational diabetes (Yes/No) was ascertained using birth records, detailed information on blood glucose level or other diabetes markers was unavailable. Gestational diabetes was only collected on birth certificates starting in 2006. Pre-pregnancy BMI was derived using pre-pregnancy weight in kilograms divided by the square of height in meters, and was only collected on California birth certificates starting in 2007. Pre-pregnancy BMI was categorized according to the World Health Organization criteria: <18.5 kg/m2 (underweight), 18.5–24.9 kg/m2 (normal), 25–29.9 kg/m2 (overweight), ≥30 kg/m2 (obese) [42]. Gestational weight gain was defined as the difference in kilograms between maternal weight at delivery and pre-pregnancy weight, and was also recorded on birth certificates from 2007 onwards. Gestational weight gain was further categorized according to the Institute of Medicine (IOM) 2009 guidelines on optimal weight gain during pregnancy: 12.5–18 kg in underweight women (<18.5 kg/m2), 11.5–16 kg in normal weight women (18.5–24.9 kg/m2), 7–11.5 in overweight women (25–29.9 kg/m2), and 5–9 kg in obese women (≥30 kg/m2) [43]. Socioeconomic status was assessed with a census-based index that has been previously described [44] and combines seven census-level indicators: education, median household income, percent living 200% below the poverty level, percent blue-collar workers, percent older than 16 years in workforce without job, median rent, and median house value.

Statistical analysis

Unconditional logistic regression was used to examine the associations between pre-pregnancy diabetes, gestational diabetes, pre-pregnancy BMI, gestational weight gain, and childhood cancer types. We report crude and adjusted odds ratios (OR) and 95% confidence intervals (CI). Selection of covariates was based upon our own exploration of the data in terms of change-in-estimate-criteria (included covariates that changed estimates by 10% or more), the confounding structure explored in directed acyclic graphs (DAGs), as well as the literature [45–48]. The change-in-estimate criteria was used for each model and each cancer type. Covariates that met our change-in-estimate criteria for at least one cancer type were included in our final models. Our final adjusted models included the matching variable, year of birth, as well as maternal and paternal race/ethnicity, and maternal age (<20, 20–29, 30–34, 35+). Further adjustment for paternal age was considered, but after adjusting for maternal age it did not change effect estimates more than minimally. The socioeconomic variables (parental education, method of payment for prenatal care, and census-based SES) and race/ethnicity using finer 4-level (White non-Hispanic/Hispanic of any race/Black/other) and 5-level race categorizations (White non-Hispanic/Hispanic of any race/Black/Asian/Pacific Islander/other) were considered for adjustment, but not included in final models as they impacted point estimates by <10%.

We conducted sensitivity analyses to assess the impact of missing values for pre-pregnancy BMI and gestational weight gain using multiple imputation methods (PROC MI and PROC MIANALYZE). Most point estimates and confidence intervals changed minimally (<10%) thus here we report results without imputations; we present multiple imputation results in Supplementary Table 3. We additionally tested the sensitivity of associations to the potential inter-relatedness of all exposure variables through mutual adjustment. We also examined the relation between gestational weight gain and gliomas using the Central Brain Tumor Registry of the United States (CBTRUS) definition of gliomas in order to compare our results to other studies [49]. Thus, these glioma cases overlap with astrocytoma cases. Finally, we investigated leukemia types other than AML and ALL in relation to pre-pregnancy BMI, and the effect of pre-pregnancy diabetes on leukemia stratified by birthweight group. Since our study was underpowered to examine all exposures for all cancer types, we relied on strength of the association and confidence interval width (whether it was almost entirely above or below the null) rather than on traditional statistical significance testing to identify exposures as either elevating or decreasing risk. All analyses were conducted using SAS 9.3 software (Cary, NC).

Results

Sociodemographic characteristics for cases and controls for the entire study period are shown in Table 1, along with their distribution for specific cancer types in Supplementary Table 1. We also report sociodemographic characteristics for cases and controls born 2006 and onwards in Supplementary Table 2 since many exposures were only collected after 2006. We observed a similar distribution of characteristics in both time periods. Cancer was more common among males than females (55% vs. 45%). Leukemia was the most common cancer type followed by CNS tumors. More than 40% of children had a Hispanic mother or father. A higher proportion of cases than controls had private payment for prenatal care. The distribution of parental age and census-based SES appeared similar between cases and controls, but differed more by specific cancer types.

Table 1.

Sociodemographic characteristics of cases and controls, birth years 1988–2011

Controls (n=270147)
n (%)		 Cases (n=11149)
n (%)
Child sex
Male 137903 (51.1) 6135 (55.0)
Female 132244 (49.0) 5014 (45.0)
Maternal age at birth
<20 28520 (10.6) 1083 (9.7)
20–29 140146 (51.9) 5577 (50.0)
30–34 62952 (23.3) 2743 (24.6)
35+ 38479 (14.2) 1744 (15.6)
Missing 50 (0.02) 2 (0.02)
Father’s age
<20 10331 (3.8) 358 (3.2)
20–29 111645 (41.3) 4491 (40.3)
30–34 64765 (24.0) 2793 (25.1)
35+ 65683 (24.3) 2927 (26.3)
Missing 17723 (6.6) 580 (5.2)
Mother race/ethnicity
White non-Hispanic 94660 (35.0) 4364 (39.1)
Hispanic of any race 123975 (45.9) 4938 (44.3)
Other/not specified 51512 (19.1) 1847 (16.6)
Father’s race/ethnicity
White non-Hispanic 83101 (30.8) 3978 (35.7)
Hispanic of any race 117684 (43.6) 4687 (42.0)
Other/not specified 69362 (25.7) 2484 (22.3)
Maternal Educationa
8 or less years 29283 (12.4) 1098 (11.3)
9–11 years 42758 (18.1) 1613 (16.6)
12 years 65640 (27.8) 2834 (29.1)
13 to 15 years 47105 (19.9) 1920 (19.7)
16 or more years 47137 (20.0) 2093 (21.5)
Missing 4411 (1.9) 169 (1.7)
Paternal Educationa
8 or less years 29845 (12.6) 1123 (11.6)
9–11 years 33292 (14.1) 1267 (13.0)
12 years 65536 (27.7) 2767 (28.5)
13 to 15 years 39220 (16.6) 1684 (17.3)
16 or more years 48540 (20.5) 2184 (22.5)
Missing 19901 (8.4) 702 (7.2)
Source of payment for prenatal carea
Private 116717 (49.4) 5332 (54.8)
Medi-Cal/other governmental/self-pay 116935 (49.5) 4323 (44.4)
Missing 2682 (1.1) 72 (0.7)
Census-based SES
1 (lowest) 67375 (24.9) 2571 (23.1)
2 65424 (24.2) 2741 (24.6)
3 59729 (22.1) 2495 (22.4)
4 42568 (15.8) 1806 (16.2)
5 (highest) 34279 (12.7) 1520 (13.6)
Missing 772 (0.3) 16 (0.1)
Open in a new tab
a

Collected starting 1989

Compared to controls, a higher proportion of ALL and Wilms’ tumor cases had high birthweight whereas a higher proportion of germ cell tumor and hepatoblastoma cases had low birthweight. We also noted some differences in gestational age, with a higher proportion of ALL, germ cell, and Wilms’ tumor cases born large for gestational age (LGA) and a higher proportion of small for gestational age (SGA) births for hepatoblastoma than controls. More preterm births occurred in AML, germ cell, and hepatoblastoma cases than controls. Birth Certificates had a higher proportion of data missing for pre-pregnancy BMI and gestational weight gain compared to all other variables (9–10%). Missing values did not differ by disease status for most of our variables except for intraspinal and intracranial embryonal brain tumors, which had a much higher proportion of missing pre-pregnancy BMI and gestational weight gain values (Table 2).

Table 2.

Maternal and perinatal characteristics of cases and controls, birth years 1988–2011

Controls
n (%)		 Leukemia
n (%)		 ALL
n
(%)		 AML
n
(%)		 Astrocytomas
n (%)		 Intracranial
and
intraspinal
embryonal
tumors
n (%)		 Germ
cell
tumors
n (%)		 Hepatoblastoma
n (%)		 Neuroblastoma
n (%)		 Retinoblastoma
n (%)		 Rhabdomyosarcoma
n (%)		 Wilms’
tumor
n (%)
n 270147 5034 4101 706 990 709 445 337 1378 741 463 1052
Birthweight
<2500 g 15932
(5.9)		 246 (4.9) 183
(4.5)		 48
(6.8)		 55 (5.6) 55 (7.8) 40
(9.0)		 79 (23.4) 91 (6.6) 50 (6.8) 28 (6.1) 56
(5.3)
2500–3999 g 226158
(83.7)		 4160
(82.6)		 3390
(82.7)		 587
(83.1)		 818 (82.6) 575 (81.1) 356
(80.0)		 230 (68.3) 1124 (81.6) 616 (83.1) 380 (82.1) 832
(79.1)
4000+ g 27824
(10.3)		 625 (12.4) 526
(12.8)		 70
(9.9)		 116 (11.7) 79 (11.1) 49
(11.0)		 28 (8.3) 160 (11.6) 75 (10.1) 55 (11.9) 159
(15.1)
Missing 233 (0.1) 3 (0.1) 2
(0.1)		 1
(0.1)		 1 (0.1) 0 (0.0) 0 (0.0) 0 (0.0) 3 (0.2) 0 (0.0) 0 (0.0) 5 (0.5)
Size for
gestational
age
Small for
gestational
age		 27258
(10.1)		 429 (8.5) 327
(8.0)		 77
(10.9)		 92 (9.3) 63 (8.9) 34
(7.6)		 54 (16.0) 129 (9.4) 76 (10.3) 50 (10.8) 90
(8.6)
Normal for
gestational
age		 204806
(75.8)		 3787
(75.2)		 3095
(75.5)		 532
(75.4)		 749 (75.7) 541 (76.3) 326
(73.3)		 237 (70.3) 1047 (76.0) 566 (76.4) 344 (74.3) 766
(72.8)
Large for
gestational
age		 37850
(14.0)		 815 (16.2) 677
(16.5)		 96
(13.6)		 148 (15.0) 105 (14.8) 85
(19.1)		 46 (13.7) 199 (14.4) 99 (13.4) 69 (14.9) 191
(18.2)
Missing 233 (0.1) 3 (0.1) 2
(0.1)		 1
(0.1)		 1 (0.1) 0 (0.0) 0 (0.0) 0 (0.0) 3 (0.2) 0 (0.0) 0 (0.0) 5 (0.5)
Gestational
age
37 or less
weeks
(Preterm)		 26226
(9.7)		 532 (10.6) 404
(9.9)		 104
(14.7)		 96 (9.7) 82 (11.6) 85
(19.1)		 80 (23.7) 149 (10.8) 75 (10.1) 40 (8.6) 120
(11.4)
38–42 weeks
(Term)		 221370
(81.9)		 4067
(80.8)		 3333
(81.3)		 548
(77.6)		 817 (82.5) 560 (79.0) 320
(71.9)		 236 (70.0) 1113 (80.8) 614 (82.9) 381 (82.3) 857
(81.5)
43 or more
weeks (Post
term)		 9810
(3.6)		 190 (3.8) 159
(3.9)		 24
(3.4)		 30 (3.0) 28 (4.0) 17
(3.8)		 7 (2.1) 61 (4.4) 25 (3.4) 20 (4.3) 30
(2.9)
Missing 12741
(4.7)		 245 (4.9) 205
(5.0)		 30
(4.3)		 47 (4.8) 39 (5.5) 23
(5.2)		 14 (4.2) 55 (4.0) 27 (3.6) 22 (4.8) 45
(4.3)
Pre-
pregnancy
diabetes
Yes 4289
(1.6)		 104 (2.1) 94
(2.3)		 4
(0.6)		 11 (1.1) 6 (0.9) 7 (1.6) 7 (2.1) 18 (1.3) 11 (1.5) 5 (1.1) 25
(2.4)
No 265858
(98.4)		 4930
(97.9)		 4007
(97.7)		 702
(99.4)		 979 (98.9) 703 (99.2) 438
(98.4)		 330 (97.9) 1360 (98.7) 730 (98.5) 458 (98.9) 1027
(97.6)
Gestational
diabetesa
Yes 1667
(3.2)		 24 (3.7) 17
(4.0)		 4
(2.4)		 6 (4.1) 5 (4.0) 2 (2.2) 5 (5.3) 12 (4.1) 7 (4.4) 1 (1.2) 7 (3.6)
No 50133
(96.8)		 628 (96.3) 412
(96.0)		 163
(97.6)		 141 (95.9) 119 (96.0) 88
(97.8)		 89 (94.7) 282 (95.9) 152 (95.6) 84 (98.8) 186
(96.4)
Mother’s
height (m)b
<1.57 m 8350
(21.2)		 96 (21.5) 49
(18.1)		 30
(23.4)		 17 (15.0) 18 (20.2) 16
(22.5)		 22 (28.6) 45 (19.1) 20 (16.3) 9 (15.8) 25
(17.1)
1.57–<1.65 m 16000
(40.6)		 177 (39.7) 120
(44.3)		 45
(35.2)		 38 (33.6) 39 (43.8) 27
(38.0)		 32 (41.6) 73 (30.9) 47 (38.2) 21 (36.8) 53
(36.3)
≥1.65 m 13136
(33.3)		 155 (34.8) 92
(34.0)		 46
(35.9)		 56 (49.6) 24 (27.0) 26
(36.6)		 23 (29.9) 102 (43.2) 48 (39.0) 26 (45.6) 62
(42.5)
Missing 1973
(5.0)		 18 (4.0) 10
(3.7)		 7
(5.5)		 2 (1.8) 8 (9.0) 2 (2.8) 0 (0.0) 16 (6.8) 8 (6.5) 1 (1.8) 6 (4.1)
Pre-
pregnancy
weight (kg)b
<56 kg 8945
(22.7)		 94 (21.1) 54
(19.9)		 31
(24.2)		 18 (15.9) 24 (27.0) 17
(23.9)		 25 (32.5) 47 (19.9) 25 (20.3) 12 (21.1) 24
(16.4)
56–<68 kg 13148
(33.3)		 139 (31.2) 89
(32.8)		 39
(30.5)		 37 (32.7) 31 (34.8) 25
(35.2)		 22 (28.6) 85 (36.0) 38 (30.9) 16 (28.1) 64
(43.8)
68–<80 kg 8108
(20.6)		 105 (23.5) 58
(21.4)		 30
(23.4)		 30 (26.6) 18 (20.2) 15
(21.1)		 12 (15.6) 44 (18.6) 37 (30.1) 13 (22.8) 24
(16.4)
≥80 kg 6167
(15.6)		 73 (16.4) 48
(17.7)		 19
(14.8)		 22 (19.5) 5 (5.6) 8
(11.3)		 12 (15.6) 44 (18.6) 15 (12.2) 12 (21.1) 24
(16.4)
Missing 3091
(7.8)		 35 (7.9) 22
(8.1)		 9
(7.0)		 6 (5.3) 11 (12.4) 6 (8.5) 6 (7.8) 16 (6.8) 8 (6.5) 4 (7.0) 10
(6.9)
Pre-
pregnancy
BMIb
<18.5 1249
(3.2)		 8 (1.8) 5
(1.9)		 3
(2.3)		 4 (3.5) 4 (4.5) 5 (7.0) 3 (3.9) 7 (3.0) 2 (1.6) 2 (3.5) 1 (0.7)
18.5–<25 18484
(46.8)		 195 (43.7) 123
(45.4)		 57
(44.5)		 51 (45.1) 44 (49.4) 34
(47.9)		 38 (49.4) 112 (47.5) 52 (42.3) 27 (47.4) 78
(53.4)
25–<30 9352
(23.7)		 125 (28.0) 74
(27.3)		 34
(26.6)		 30 (26.6) 21 (23.6) 16
(22.5)		 14 (18.2) 55 (23.3) 40 (32.5) 13 (22.8) 33
(22.6)
30+ 6758
(17.1)		 80 (17.9) 47
(17.3)		 23
(18.0)		 20 (17.7) 8 (9.0) 10
(14.1)		 16 (20.8) 43 (18.2) 19 (15.5) 11 (19.3) 22
(15.1)
Missing 3616
(9.2)		 38 (8.5) 22
(8.1)		 11
(8.6)		 8 (7.1) 12 (13.5) 6 (8.5) 6 (7.8) 19 (8.1) 10 (8.1) 4 (7.0) 12
(8.2)
Gestational
weight gain
(kg)b
<0 161 (0.4) 1 (0.2) 0
(0.0)		 1
(0.8)		 0 (0.0) 0 (0.0) 1 (1.4) 0 (0.0) 1 (0.4) 1 (0.8) 0 (0.0) 1 (0.7)
0–<10 9801
(24.8)		 126 (28.3) 70
(25.8)		 38
(29.7)		 24 (21.2) 27 (30.3) 19
(26.8)		 16 (20.8) 51 (21.6) 27 (22.0) 11 (19.3) 39
(26.7)
10–<15 12183
(30.9)		 125 (28.0) 74
(27.3)		 36
(28.1)		 32 (28.3) 23 (25.8) 21
(29.6)		 24 (31.2) 80 (33.9) 46 (37.4) 17 (29.8) 47
(32.2)
15–<20 8601
(21.8)		 93 (20.9) 59
(21.8)		 28
(21.9)		 34 (30.1) 19 (21.4) 15
(21.1)		 20 (26.0) 49 (20.8) 27 (22.0) 14 (24.6) 32
(21.9)
≥20 5022
(12.7)		 60 (13.5) 42
(15.5)		 15
(11.7)		 17 (15.0) 8 (9.0) 9
(12.7)		 10 (13.0) 35 (14.8) 13 (10.6) 11 (19.3) 17
(11.6)
Missing 3691
(9.4)		 41 (9.2) 26
(9.6)		 10
(7.8)		 6 (5.3) 12 (13.5) 6 (8.5) 7 (9.1) 20 (8.5) 9 (7.3) 4 (7.0) 10
(6.9)
Gestational
weight gain
(IOM 2009
guidelines)b
Not enough
weight		 8089
(20.5)		 94 (21.1) 50
(18.5)		 35
(27.3)		 22 (19.5) 21 (23.6) 18
(25.4)		 13 (16.9) 39 (16.5) 22 (17.9) 10 (17.5) 31
(21.2)
IOM recommended 10698
(27.1)		 132 (29.6) 83
(30.6)		 31
(24.2)		 24 (21.2) 27 (30.3) 18
(25.4)		 20 (26.0) 68 (28.8) 33 (26.8) 16 (28.1) 45
(30.8)
Too much 16550
(41.9)		 177 (39.7) 112
(41.3)		 50
(39.1)		 59 (52.2) 28 (31.5) 29
(40.9)		 37 (48.1) 106 (44.9) 57 (46.3) 27 (47.4) 58
(39.7)
Missing 4122
(10.5)		 43 (9.6) 26
(9.6)		 12
(9.4)		 8 (7.1) 13 (14.6) 6 (8.5) 7 (9.1) 23 (9.8) 11 (8.9) 4 (7.0) 12
(8.2)
Open in a new tab
a

Collected starting 2006

b

Collected starting 2007

Pre-pregnancy diabetes increased the risk of all leukemias combined and ALL [OR (95% CI): 1.23 (1.01, 1.49), 1.37 (1.11, 1.69), respectively]. We estimated an elevated risk to develop Wilms’ tumor when mothers had a diagnosis of diabetes prior to pregnancy [OR (95% CI): 1.45 (0.97, 2.18)] (Table 3).

Table 3.

Odds ratios and 95% CIs from logistic regression models for childhood cancers in relation to pre-pregnancy diabetes, gestational diabetes, pre-pregnancy BMI, and gestational weight gain

Leukemia ALL AML Astrocytomas Intracranial
& intraspinal
embryonal
brain tumors		 Germ cell
tumors		 Hepatoblastoma Neuroblastoma Retinoblastoma Rhabdomyosarcoma Wilms’
tumor
Crude
ORa 		OR
(95%
CI)b, c 		Crude
ORa 		OR
(95%
CI)b, c 		Crude
ORa 		OR
(95%
CI)b, c 		Crude
ORa 		OR
(95%
CI)b, c 		Crude
ORa 		OR
(95%
CI)b, c 		Crude
ORa 		OR
(95%
CI)b, c 		Crude
ORa 		OR
(95%
CI)b, c 		Crude
ORa 		OR
(95%
CI)b, c 		Crude
ORa 		OR
(95%
CI)b, c 		Crude
ORa 		OR
(95%
CI)b, c 		Crude
ORa 		OR
(95%
CI)b, c
Birth years 1983–2011
Pre-pregnancy diabetes
Yes 1.31 1.23
(1.01,
1.49)		 1.46 1.37
(1.11,
1.69)		 0.36 --- 0.70 0.71
(0.39,
1.30)		 0.53 0.54
(0.24,
1.20)		 0.99 0.97
(0.46,
2.06)		 1.34 1.22
(0.58,
2.60)		 0.82 0.85
(0.53,
1.35)		 0.94 0.93
(0.51,
1.69)		 0.68 0.66
(0.27,
1.60)		 1.51 1.45
(0.97,
2.18)
Birth years 2006–2011
Gestational diabetes
Yes 1.17 1.14
(0.76,
1.72)		 1.29 1.26
(0.77,
2.05)		 0.74 --- 1.29 1.32
(0.58,
3.02)		 1.27 1.25
(0.51,
3.07)		 0.68 --- 1.65 1.49
(0.60,
3.70)		 1.26 1.31
(0.73,
2.34)		 1.36 1.34
(0.63,
2.88)		 0.37 --- 1.14 1.23
(0.57,
2.63)
Birth years 2007–2011
Pre-pregnancy BMI
<18.5 0.61 0.62
(0.31,
1.27)		 0.60 0.62
(0.25,
1.52)		 0.78 --- 1.16 --- 1.35 --- 2.18 2.14
(0.83,
5.51)		 1.17 --- 0.93 0.90
(0.42,
1.94)		 0.57 --- 1.09 --- 0.19 ---
18.5–<25 ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref
25–<30 1.28 1.27
(1.01,
1.59)		 1.21 1.17
(0.88,
1.57)		 1.18 1.22
(0.80,
1.88)		 1.17 1.28
(0.81,
2.03)		 0.94 0.91
(0.54,
1.53)		 0.92 0.94
(0.52,
1.72)		 0.72 0.71
(0.38,
1.31)		 0.97 1.05
(0.76,
1.45)		 1.51 1.40
(0.92,
2.14)		 0.96 0.96
(0.49,
1.88)		 0.84 0.85
(0.56,
1.28)
30+ 1.14 1.13
(0.86,
1.47)		 1.08 1.03
(0.73,
1.45)		 1.10 1.17
(0.71,
1.91)		 1.08 1.23
(0.73,
2.09)		 0.50 0.47
(0.22,
1.00)		 0.80 0.82
(0.40,
1.68)		 1.13 1.10
(0.60,
1.99)		 1.04 1.16
(0.81,
1.66)		 0.98 0.88
(0.52,
1.51)		 1.13 1.13
(0.55,
2.30)		 0.78 0.79
(0.49,
1.28)
Gestational weight gain (IOM 2009 guidelines)
Not enough
weight		 0.93 0.93
(0.71,
1.21)		 0.78 0.78
(0.55,
1.11)		 1.50 1.50
(0.92,
2.43)		 1.20 1.28
(0.72,
2.28)		 1.03 1.05
(0.59,
1.86)		 1.33 1.31
(0.68,
2.52)		 0.87 0.85
(0.42,
1.72)		 0.76 0.80
(0.54,
1.19)		 0.89 0.88
(0.51,
1.52)		 0.82 0.85
(0.38,
1.87)		 0.91 0.93
(0.59,
1.47)
IOM
recommended		 ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref ref
Too much 0.86 0.86
(0.69,
1.08)		 0.66 0.86
(0.65,
1.14)		 1.04 1.05
(0.67,
1.64)		 1.58 1.56
(0.97,
2.50)		 0.67 0.66
(0.39,
1.13)		 1.05 1.07
(0.59,
1.94)		 1.20 1.25
(0.72,
2.16)		 1.01 1.00
(0.74,
1.36)		 1.12 1.15
(0.74,
1.76)		 1.08 1.06
(0.57,
1.97)		 0.83 0.80
(0.54,
1.19)
Open in a new tab
a

Adjusted for the matching variable, year of birth

b

Adjusted for year of birth, maternal/paternal race/ethnicity, maternal age

c

Adjusted OR estimates were not calculated for categories with <5 exposed cases

We observed an increased risk of all leukemias combined in unconditional logistic regression without [OR (95% CI): 1.27 (1.01, 1.59)] as well as with multiple imputations [OR (95% CI): 1.26 (1.01, 1.58) for those born to mothers with an overweight pre-pregnancy BMI, but the point estimates for ALL and AML were weaker and included the null value. We found that other leukemia subtypes (ICCC-3 codes 013–015) were strongly related to an overweight pre-pregnancy BMI in mothers [adjusted OR (95% CI): 2.18 (1.08, 4.41)] and largely responsible for the increased risk we saw with leukemia, however this result was based on a small sample size (42 cases). Also, an overweight pre-pregnancy BMI was associated with an increased risk of retinoblastoma [OR (95% CI): 1.40 (0.92, 2.14). In contrast an underweight pre-pregnancy BMI seemed to contribute to germ cell tumor risk [OR (95% CI): 2.14 (0.83, 5.51)]. Intracranial and intraspinal embryonal brain tumors showed a decreased risk with a BMI considered as being obese [OR (95% CI): 0.47 (0.22, 1.00)]. However, after multiple imputations to handle missing values, these associations between pre-pregnancy BMI and retinoblastoma, germ cell tumors, and intracranial and intraspinal embryonal brain tumors were attenuated (Table 3, Supplementary Table 3).

For gestational weight gain grouped according to the IOM 2009 guidelines, we observed a suggestive positive association between insufficient weight gain and AML [OR (95% CI): 1.50 (0.92, 2.43)]. We also found an elevated risk of astrocytoma with excessive gestational weight gain [OR (95% CI): 1.56 (0.97, 2.50)] (Table 3). When examining the effect of gestational weight gain on gliomas and low-grade gliomas (105 of our 165 gliomas were astrocytomas), we found a similarly elevated risk of gliomas with excessive gestational weight gain (insufficient weight gain: adjusted OR (95% CI): 1.32 (0.84 2.06), excessive weight gain: adjusted OR (95% CI): 1.37 (0.94, 2.00)), but no associations with low-grade gliomas (insufficient weight gain: adjusted OR (95% CI): 0.57 (0.24, 1.35), excessive weight gain: adjusted OR (95% CI): 0.67 (0.36, 1.30)). After multiple imputations, associations between gestational weight gain, and AML and astrocytoma were attenuated [OR (95% CI): 1.43 (0.86, 2.41), 1.49 (0.92, 2.43), respectively] (Supplementary Table 3).

In sensitivity analyses, when mutually adjusting for exposure variables, our estimates for the associations we found did not change or changed minimally (<10%) (data not shown). We also found that the effect of pre-pregnancy diabetes on leukemia risk was similar in those born in the range of normal and high birthweight, with a slightly larger point estimate for the high birthweight group (low birthweight: adjusted OR (95% CI): 0.63 (0.20, 1.98), normal birthweight: adjusted OR (95% CI): 1.20 (0.95, 1.51), high birthweight: adjusted OR (95% CI): 1.40 (0.92, 2.12).

Discussion

In this population-based study of California children, we found several positive associations between maternal conditions in the pre-gestational and gestational period, and risk of cancer in offspring. Most notably, we observed an increased risk of leukemia and Wilms’ tumor in children of mothers with pre-pregnancy diabetes and an increased risk of leukemia in children of overweight mothers. In relation to gestational weight gain, we found an elevated risk of astrocytoma in children of mothers with excessive weight gain and of AML in children of mothers with inadequate weight gain.

The positive associations seen between pre-pregnancy diabetes and risk of leukemia, particularly ALL, have been reported in other population-based studies. These studies reported associations of similar magnitude between maternal diabetes and ALL (OR=1.44) and leukemia (OR=1.40)[17, 18], except for one study that failed to find any association (OR=1.00) [19]. However, these studies did not differentiate between pre-pregnancy and gestational diabetes. No studies to date have published on Wilms’ tumor in relation to pre-pregnancy diabetes. Of the few studies that have specifically reported on gestational diabetes, several have found positive associations [ORs ranging from 2 to 3] with leukemia [18, 20, 21], and others have found no associations with hepatoblastoma and retinoblastoma [23, 24]. We found that several point estimates were elevated for gestational diabetes, but confidence intervals were too wide to draw conclusions from our results. This may be due to potential misclassification of gestational diabetes because women with pre-pregnancy diabetes who do not undergo early screening may be incorrectly diagnosed with gestational diabetes [50]. Thus, gestational diabetes comprises of a heterogeneous risk group of women with controlled diabetes and uncontrolled diabetes at the start of pregnancy. Consequently, though pre-pregnancy and gestational diabetes both result in maternal hyperglycemia, their impact on fetal development is dependent on the management of these conditions [51]. Maternal hyperglycemia increases fetal growth, alters fetal metabolism, and induces oxidative stress and epigenetic changes [28, 52, 53]. The pathways linking maternal diabetes to childhood cancer risk are not fully understood, but the associations we observed are likely explained by a combination of these factors, which may explain why we did not find a consistently higher risk for all childhood cancers that have been associated with accelerated fetal growth.

In children of overweight mothers, we found an increased risk of leukemia and retinoblastoma. In contrast, underweight appeared to increase the risk of germ cell tumors. The few studies that have assessed the relation between pre-pregnancy BMI and childhood cancer risk have produced conflicting and inconclusive results [6, 15, 16, 23–26]. Given the unexpected pattern of a greater risk of these cancers with overweight but a drop in risk with obesity, it is notable that two studies of leukemia and retinoblastoma also observed an attenuation of the size of the estimate in the obese group [24, 26]. This may be explained by an increased risk of competing outcomes that cause selective survival of affected fetuses specifically fetal death, stillbirth, and neonatal, perinatal, and infant deaths that have been consistently observed to be associated with higher BMI. A recent meta-analysis suggested a 2 to 3-fold increased risk of fetal loss with maternal obesity [54]. No studies have been published specifically on maternal BMI and germ cell tumors to our knowledge. In our study we found that intracranial and intraspinal embryonal brain tumors seemed to show a decreased risk with obesity. In light of the mixed and limited findings in the literature between BMI and childhood cancers, it is possible that our findings with BMI are spurious in nature. However, it is plausible that maternal obesity could increase the risk of childhood cancers since it results in maternal hyperglycemia [28].

We found astrocytoma to be associated with excessive weight gain while inadequate weight gain was positively associated with AML. Most studies on gestational weight gain and childhood cancer have not defined pregnancy weight gain in terms of the IOM guidelines. The use of arbitrary weight gain cutoffs that fail to take pre-pregnancy BMI into account may not accurately reflect risk and are likely to have produced some of the inconsistent findings across studies [15, 18, 19, 23, 25, 26]. A study that used the IOM 2009 guidelines found that both inadequate and excessive weight gain were associated with an increased risk of childhood brain tumors and low-grade gliomas [16]. We found an elevated risk of overall gliomas with excessive gestational weight gain, but none with low-grade gliomas. Birth certificates do not collect information on trimester-specific weight gain, which may be relevant since studies have shown that birthweight is dependent on the timing of weight gain during pregnancy [55]. This may explain why our observations were only partially explained by our hypotheses.

This study has several limitations. The use of birth certificate data avoids recall bias in this study since exposure information is ascertained prior to disease status, however the possibility of exposure misclassification bias exists. Validity of birthweight, race/ethnicity, and other demographic characteristics reported on birth certificate is typically high [56], while pre-pregnancy diabetes and gestational diabetes typically have low sensitivity and high specificity [57]. Thus, nondifferential underreporting of pre-pregnancy diabetes and gestational diabetes is likely and would have biased our estimates towards the null.

The validity of birth certificate-derived pre-pregnancy BMI and gestational weight gain is also of concern. In a Florida study, pre-pregnancy BMI based on weight and height reported on the birth certificate was shown to have an overall high specificity of 97% for underweight, 82% for normal weight, 88% for overweight, and 98% for obesity. Sensitivity was generally lower with a sensitivity of 77% for underweight, 86% for normal weight, 61% for overweight, and 76% for obesity [58]. A Pennsylvania study showed that agreement between pregnancy weight gain on birth records and medical records tends to be poorest for very low and very high weight gain. Errors in pre-pregnancy weight seem to be the main source of misclassification of pre-pregnancy BMI and gestational weight gain, which is plausible since pre-pregnancy weight recorded on the birth certificate is typically ascertained by maternal recall at delivery [59]. Although our multiple imputation analyses for missing values for pre-pregnancy BMI and gestational weight gain changed results minimally, this method relies on the assumption that the data are missing at random.

Another limitation of our study was our small sample size for gestational diabetes, pre-pregnancy BMI, and gestational weight gain since this information was only provided on birth certificates for a few years. Thus, our analyses were underpowered. As with all records-based studies, we lacked detailed information on our exposures of interest such as type, duration, and treatment of maternal diabetes. We also lacked information on cytogenetic characteristics of cancer types, so we were unable to explore differential risk for cytogenetic abnormalities. Since our study sample only included children under 6 years of age, the generalizability of our findings to cancers in older children is limited. Lastly, it is possible that some of our findings could be explained by chance, particularly for those without prior supporting evidence in the literature, due to the many associations we examined and the multiple comparisons we did not adjust for.

Strengths of the study include the prospective population-based design and the inclusion of various childhood cancer types. It is one of few studies to date that focused on assessing the impact of maternal weight and diabetes in pregnancy on childhood cancer risk, which is highly relevant for the US population given the current epidemic of obesity and its link with diabetes. This is particularly important in this predominantly Hispanic population as Hispanics in California have one of the highest incidence rates of childhood cancer worldwide [60]. Few studies have differentiated between pre-pregnancy and gestational diabetes and assessed weight gain according to IOM 2009 guidelines. We hope that this study underscores the importance of drawing these distinctions so that results across studies can be readily compared.

In conclusion, in our sample of California children, pre-pregnancy diabetes in mothers increased the risk of leukemia and particularly ALL in California children, and we estimated elevated risks for several childhood cancers in relation to pre-pregnancy BMI and gestational weight gain. Our study supports a potential role for these maternal conditions in affecting childhood cancer risk in offspring. These factors should be further investigated by pooling data in order to increase statistical power for these rare childhood cancers.

Supplementary Material

10552_2016_807_MOESM1_ESM

Acknowledgments

This work was supported by the grants R21ES018960 and R21ES019986 from the US National Institutes of Health. Ms. Contreras was supported by the predoctoral fellowship T32CA009142 from the US National Institutes of Health and received the Cornelius Hopper Diversity Award Supplement (grant # 24RT-0033H) from the California Tobacco-Related Disease Research Program. Dr. Cockburn was supported in part by the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California, and contract HHSN261201000034C awarded to the Public Health Institute, as well as the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement U58DP003862-01 awarded to the California Department of Public Health.

References

  • 1.Siegel RL, Miller KD, Jemal A. Cancer statistics. 2016;66:7–30. doi: 10.3322/caac.21332. [DOI] [PubMed] [Google Scholar]
  • 2.Ward E, DeSantis C, Robbins A, et al. Childhood and adolescent cancer statistics, 2014. CA Cancer J Clin. 2014;64:83–103. doi: 10.3322/caac.21219. [DOI] [PubMed] [Google Scholar]
  • 3.Spector LG, Pankratz N, Marcotte EL. Genetic and Nongenetic Risk Factors for Childhood Cancer. Pediatr Clin North Am. 2015;62:11–25. doi: 10.1016/j.pcl.2014.09.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Caughey RW, Michels KB. Birth weight and childhood leukemia: a meta-analysis and review of the current evidence. Int J Cancer. 2009;124:2658–2670. doi: 10.1002/ijc.24225. [DOI] [PubMed] [Google Scholar]
  • 5.Hjalgrim LL, Rostgaard K, Hjalgrim H, et al. Birth weight and risk for childhood leukemia in Denmark, Sweden, Norway, and Iceland. J Natl Cancer Inst. 2004;96:1549–1556. doi: 10.1093/jnci/djh287. [DOI] [PubMed] [Google Scholar]
  • 6.Paltiel O, Tikellis G, Linet M, et al. Birthweight and Childhood Cancer: Preliminary Findings from the International Childhood Cancer Cohort Consortium (I4C) Paediatr Perinat Epidemiol. 2015;29:335–345. doi: 10.1111/ppe.12193. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Bjørge T, Toft Sørensen H, Grotmol T, et al. Fetal Growth and Childhood Cancer: A Population-Based Study Fetal Growth and Childhood Cancer: A Population-Based Study. Pediatrics. 2013;132:e1265–e1275. doi: 10.1542/peds.2013-1317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.O’Neill Ka, Murphy MF, Bunch KJ, et al. Infant birthweight and risk of childhood cancer: international population-based case control studies of 40 000 cases. Int J Epidemiol. 2015;44:153–168. doi: 10.1093/ije/dyu265. [DOI] [PubMed] [Google Scholar]
  • 9.Badr M, Hassan T, Tarhony SEl, Metwally W. Insulin-like growth factor-1 and childhood cancer risk. Oncol Lett. 2010;1:1055–1059. doi: 10.3892/ol.2010.169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Callan AC, Milne E. Involvement of the IGF system in fetal growth and childhood cancer: An overview of potential mechanisms. Cancer Causes Control. 2009;20:1783–1798. doi: 10.1007/s10552-009-9378-z. [DOI] [PubMed] [Google Scholar]
  • 11.Chokkalingam AP, Metayer C, Scelo G, et al. Fetal growth and body size genes and risk of childhood acute lymphoblastic leukemia. Cancer Causes Control. 2012;23:1577–1585. doi: 10.1007/s10552-012-0035-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Petridou E, Skalkidou A, Dessypris N, et al. Endogenous risk factors for childhood leukemia in relation to the IGF system (Greece) Cancer Causes Control. 2000;11:765–771. doi: 10.1023/a:1008988819494. [DOI] [PubMed] [Google Scholar]
  • 13.Spector LG, Birch J. The epidemiology of hepatoblastoma. Pediatr Blood Cancer. 2012;59:776–779. doi: 10.1002/pbc.24215. [DOI] [PubMed] [Google Scholar]
  • 14.O’Neill Ka, Bunch KJ, Murphy MFG. Intrauterine growth and childhood leukemia and lymphoma risk. Expert Rev Hematol. 2012;5:559–576. doi: 10.1586/ehm.12.39. [DOI] [PubMed] [Google Scholar]
  • 15.Westbom L, Aberg A, Källén B. Childhood malignancy and maternal diabetes or other auto-immune disease during pregnancy. Br J Cancer. 2002;86:1078–1080. doi: 10.1038/sj.bjc.6600192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Greenop KR, Blair EM, Bower C, et al. Factors Relating to Pregnancy and Birth and the Risk of Childhood Brain Tumors: results from An Australian Case-Control Study. Pediatr Blood Cancer. 2014;61:493–498. doi: 10.1002/pbc.24751. [DOI] [PubMed] [Google Scholar]
  • 17.McLaughlin CC, Baptiste MS, Schymura MJ, et al. Birth weight, maternal weight and childhood leukaemia. Br J Cancer. 2006;94:1738–1744. doi: 10.1038/sj.bjc.6603173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Podvin D, Kuehn CM, Mueller BA, Williams M. Maternal and birth characteristics in relation to childhood leukaemia. Paediatr Perinat Epidemiol. 2006;20:312–322. doi: 10.1111/j.1365-3016.2006.00731.x. [DOI] [PubMed] [Google Scholar]
  • 19.Johnson KJ, Soler JT, Puumala SE, et al. Parental and infant characteristics and childhood leukemia in Minnesota. BMC Pediatr. 2008;8:7. doi: 10.1186/1471-2431-8-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Milne E, Laurvick CL, Blair E, et al. Fetal growth and acute childhood leukemia: Looking beyond birth weight. Am J Epidemiol. 2007;166:151–159. doi: 10.1093/aje/kwm065. [DOI] [PubMed] [Google Scholar]
  • 21.Petridou E, Trichopoulos D, Kalapothaki V, et al. The risk profile of childhood leukaemia in Greece: a nationwide case-control study. Br J Cancer. 1997;76:1241–1247. doi: 10.1038/bjc.1997.541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Petridou ET, Sergentanis TN, Skalkidou A, et al. Maternal and birth anthropometric characteristics in relation to the risk of childhood lymphomas: A Swedish nationwide cohort study. Eur J Cancer Prev. 2015;24:535–541. doi: 10.1097/CEJ.0000000000000122. [DOI] [PubMed] [Google Scholar]
  • 23.Musselman JRB, Georgieff MK, Ross JA, et al. Maternal pregnancy events and exposures and risk of hepatoblastoma: A Children’s Oncology Group (COG) study. Cancer Epidemiol. 2013;37:318–320. doi: 10.1016/j.canep.2012.12.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Heck JE, Omidakhsh N, Azary S, et al. A case-control study of sporadic retinoblastoma in relation to maternal health conditions and reproductive factors: a report from the Children’s Oncology group. BMC Cancer. 2015;15:735. doi: 10.1186/s12885-015-1773-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.McLaughlin CC, Baptiste MS, Schymura MJ, et al. Maternal and infant birth characteristics and hepatoblastoma. Am J Epidemiol. 2006;163:818–828. doi: 10.1093/aje/kwj104. [DOI] [PubMed] [Google Scholar]
  • 26.Spector LG, Davies SM, Robison LL, et al. Birth characteristics, maternal reproductive history, and the risk of infant leukemia: A report from the children’s oncology group. Cancer Epidemiol Biomarkers Prev. 2007;16:128–134. doi: 10.1158/1055-9965.EPI-06-0322. [DOI] [PubMed] [Google Scholar]
  • 27.Zhang CH, Liu XY, Zhan YW, et al. Effects of Prepregnancy Body Mass Index and Gestational Weight Gain on Pregnancy Outcomes. Asia-Pacific J Public Heal. 2015;27:620–630. doi: 10.1177/1010539515589810. [DOI] [PubMed] [Google Scholar]
  • 28.Dimasuay KG, Boeuf P, Powell TL, Jansson T. Placental responses to changes in the maternal environment determine fetal growth. Front Physiol. 2016;7:1–9. doi: 10.3389/fphys.2016.00012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Marshall NE, Guild C, Cheng YW, et al. The Effect of Maternal Body Mass Index on Perinatal Outcomes in Women with Diabetes. Am J Perinatol. 2014;31:249–256. doi: 10.1055/s-0033-1347363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Ross JA. High Birthweight and Cancer: Evidence and Implications. Cancer Epidemiol Biomarkers Prev. 2006;15(1):1–2. doi: 10.1158/1055-9965.EPI-05-0923. [DOI] [PubMed] [Google Scholar]
  • 31.Bowers K, Laughon SK, Kiely M, et al. Gestational diabetes, pre-pregnancy obesity and pregnancy weight gain in relation to excess fetal growth: Variations by race/ethnicity. Diabetologia. 2013;56:1263–1271. doi: 10.1007/s00125-013-2881-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Jeric M, Roje D, Medic N, et al. Maternal pre-pregnancy underweight and fetal growth in relation to institute of medicine recommendations for gestational weight gain. Early Hum Dev. 2013 doi: 10.1016/j.earlhumdev.2012.10.004. [DOI] [PubMed] [Google Scholar]
  • 33.Robbins CL, Zapata LB, Farr SL, et al. Core state preconception health indicators - pregnancy risk assessment monitoring system and behavioral risk factor surveillance system, 2009. MMWR Surveill Summ. 2014;63:1–62. [PubMed] [Google Scholar]
  • 34.Bardenheier BH, Imperatore G, Devlin HM, et al. Trends in Pre-Pregnancy Diabetes Among Deliveries in 19 U.S. States, 2000–2010. Am J Prev Med. 2015;48:154–161. doi: 10.1016/j.amepre.2014.08.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Bardenheier BH, Imperatore G, Gilboa SM, et al. Trends in Gestational Diabetes Among Hospital Deliveries in 19 U.S. States, 2000–2010. Am J Prev Med. 2015;49:12–19. doi: 10.1016/j.amepre.2015.01.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Heck JE, Lombardi Ca, Cockburn M, et al. Epidemiology of rhabdoid tumors of early childhood. Pediatr Blood Cancer. 2013;60:77–81. doi: 10.1002/pbc.24141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Urayama KY, Von Behren J, Reynolds P, et al. Factors associated with residential mobility in children with leukemia: implications for assigning exposures. Ann Epidemiol. 2009;19:834–840. doi: 10.1016/j.annepidem.2009.03.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Hildebrand E, Källén B, Josefsson A, et al. Maternal obesity and risk of Down syndrome in the offspring. Prenat Diagn. 2014;34:310–315. doi: 10.1002/pd.4294. [DOI] [PubMed] [Google Scholar]
  • 39.Steliarova-Foucher E, Stiller C, Lacour B, Kaatsch P. International classification of childhood cancer, third edition. Cancer. 2005;103:1457–1467. doi: 10.1002/cncr.20910. [DOI] [PubMed] [Google Scholar]
  • 40.Ritz B, Wilhelm M, Hoggatt KJ, Ghosh JK. Ambient air pollution and preterm birth in the environment and pregnancy outcomes study at the University of California, Los Angeles. Am J Epidemiol. 2007;166:1045–1052. doi: 10.1093/aje/kwm181. [DOI] [PubMed] [Google Scholar]
  • 41.Alexander GR, Himes JH, Kaufman RB, et al. A United States national reference for fetal growth. Obstet Gynecol. 1996;87:163–168. doi: 10.1016/0029-7844(95)00386-X. [DOI] [PubMed] [Google Scholar]
  • 42.World Health Organization (WHO) Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i–xii. 1–253. [PubMed] [Google Scholar]
  • 43.Institute of Medicine. Weight gain during pregnancy: reexamining the guidelines. Washington, DC: National Academies Press; 2009. [PubMed] [Google Scholar]
  • 44.Yost K, Perkins C, Cohen R, et al. Socioeconomic status and breast cancer incidence in California for different race / ethnic groups. Cancer causes Control. 2001;12:703–711. doi: 10.1023/a:1011240019516. [DOI] [PubMed] [Google Scholar]
  • 45.Howlader N, Noone AM, Krapcho M, Garshell J, Miller D, Altekruse SF, Kosary CL, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA, editors. SEER Cancer Statistics Review, 1975–2012. Bethesda, MD: National Cancer Institute; 2015. [Accessed September 2015]. http://seer.cancer.gov/csr/1975_2012/ [Google Scholar]
  • 46.Oksuzyan S, Crespi CM, Cockburn M, et al. Race/ethnicity and the risk of childhood leukaemia: a case–control study in California. J Epidemiol Community Heal. 2015;69:795–802. doi: 10.1136/jech-2014-204975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Chow EJ, Puumala SE, Mueller Ba, et al. Childhood cancer in relation to parental race and ethnicity a 5-state pooled analysis. Cancer. 2010;116:3045–3053. doi: 10.1002/cncr.25099. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Johnson KJ, Carozza SE, Chow EJ, et al. Parental age and risk of childhood cancer: A pooled analysis. Epidemiology. 2009;20:475–483. doi: 10.1097/EDE.0b013e3181a5a332. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Ostrom QT, Gittleman H, Farah P, et al. CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2006–2010. Neuro Oncol. 2013;15:ii1–ii56. doi: 10.1093/neuonc/not151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Reece EA, Leguizamón G, Wiznitzer A. Gestational diabetes: the need for a common ground. Lancet. 2009;373:1789–1797. doi: 10.1016/S0140-6736(09)60515-8. [DOI] [PubMed] [Google Scholar]
  • 51.Mitanchez D, Yzydorczyk C, Siddeek B, et al. The offspring of the diabetic mother - Short- and long-term implications. Best Pract Res Clin Obstet Gynaecol. 2015;29:256–269. doi: 10.1016/j.bpobgyn.2014.08.004. [DOI] [PubMed] [Google Scholar]
  • 52.Simmen FA, Simmen RCM. The maternal womb: a novel target for cancer prevention in the era of the obesity pandemic? Eur J Cancer Prev. 2011;20:539–548. doi: 10.1097/CEJ.0b013e328348fc21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Vambergue A, Nuttens MC, Verier-Mine O, et al. Is mild gestational hyperglycaemia associated with maternal and neonatal complications? The Diagest Study. Diabet Med. 2000;17:203–208. doi: 10.1046/j.1464-5491.2000.00237.x. [DOI] [PubMed] [Google Scholar]
  • 54.Aune D, Saugstad OD, Henriksen T, Tonstad S. Maternal Body Mass Index and the Risk of Fetal Death, Stillbirth, and Infant Death: A Systematic Review and Meta-analysis. JAMA J Am Med Assoc. 2014;311:1536–1546. doi: 10.1001/jama.2014.2269. [DOI] [PubMed] [Google Scholar]
  • 55.Brown JE, Murtaugh MA, Jacobs DR, Margellos HC. Variation in newborn size according to pregnancy weight change by trimester. Am J Clin Nutr. 2002;76:205–209. doi: 10.1093/ajcn/76.1.205. [DOI] [PubMed] [Google Scholar]
  • 56.Reichman NE, Hade EM. Validation of birth certificate data: A study of women in New Jersey’s healthstart program. Ann Epidemiol. 2001;11:186–193. doi: 10.1016/s1047-2797(00)00209-x. [DOI] [PubMed] [Google Scholar]
  • 57.Devlin HM, Desai J, Walaszek A. Reviewing performance of birth certificate and hospital discharge data to identify births complicated by maternal diabetes. Matern Child Health J. 2009;13:660–666. doi: 10.1007/s10995-008-0390-9. [DOI] [PubMed] [Google Scholar]
  • 58.Park S, Sappenfield WM, Bish C, et al. Reliability and validity of birth certificate prepregnancy weight and height among women enrolled in prenatal WIC program: Florida, 2005. Matern Child Health J. 2011;15:851–859. doi: 10.1007/s10995-009-0544-4. [DOI] [PubMed] [Google Scholar]
  • 59.Bodnar LM, Abrams B, Bertolet M, et al. Validity of birth certificate-derived maternal weight data. Paediatr Perinat Epidemiol. 2014;28:203–212. doi: 10.1111/ppe.12120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Kaatsch P. Epidemiology of childhood cancer. Cancer Treat Rev. 2010;36:277–285. doi: 10.1016/j.ctrv.2010.02.003. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

10552_2016_807_MOESM1_ESM
NIHMS815903-supplement-10552_2016_807_MOESM1_ESM.docx (68.7KB, docx)

RESOURCES