Consistent Cord Blood DNA Methylation Signatures of Gestational Age between South Asian and white European Cohorts

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Deng, Marie Pigeyre, Sandi M. Azab, Samantha L Wilson, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3997065/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Epigenetic modifications, particularly DNA methylation (DNAm) in cord blood, are an important biological marker of how external exposures during gestation can influence the in-utero environment and subsequent offspring development. Despite the recognized importance of DNAm during gestation, comparative studies to determine the consistency of these epigenetic signals across different ethnic groups are largely absent. To address this gap, we first performed epigenome-wide association studies (EWAS) of gestational age (GA) using newborn cord blood DNAm comparatively in a white European ( n = 342) and a South Asian ( n = 490) birth cohort living in Canada. Then, we capitalized on established cord blood epigenetic GA clocks to examine the associations between maternal exposures, offspring characteristics and epigenetic GA, as well as GA acceleration, defined as the residual difference between epigenetic and chronological GA at birth. Results Individual EWASs identified 1,211 and 1,543 differentially methylated CpGs associated with GA in white European and South Asian cohorts, respectively, with a similar distribution of effects. We confirmed that Bohlin’s cord blood GA clock was robustly correlated with GA in white Europeans ( r = 0.71; p = 6.0×10 − 54 ) and South Asians ( r = 0.66; p = 6.9×10 − 64 ). In both cohorts, Bohlin’s clock was positively associated with newborn weight and length, and negatively associated with parity, newborn female sex, and gestational diabetes. Exclusive to South Asians, the GA clock was positively associated with the newborn ponderal index, while pre-pregnancy weight and gestational weight gain were strongly predictive of increased epigenetic GA in white Europeans. Important predictors of GA acceleration included gestational diabetes mellitus, newborn sex, and parity in both cohorts. Conclusions These results demonstrate the consistent DNAm signatures of GA and the utility of Bohlin’s GA clock across the two populations. Although the overall pattern of DNAm is similar, its connections with the mother's environment and the baby's anthropometrics can differ between the two groups. Further research is needed to understand these unique relationships. cord blood DNA methylation gestational age at birth epigenetic gestational age accelerated gestational age Figures Figure 1 Figure 2 Background Epigenome-wide DNA methylation (DNAm) patterns across a variety of tissues and cells have been shown to accurately capture the "biological clock" in both adult ( 1 – 3 ) and pediatric populations ( 4 – 6 ), providing insights into an individual's biological age, which can differ from their chronological age. These epigenetic clocks and their deviations from chronological age, otherwise known as accelerated aging, have been linked to a range of health outcomes and age-related diseases, suggesting that DNAm changes serve not just as markers of age, but also play a role in the mechanisms of aging that overlap with many chronic diseases ( 7 , 8 ). Chronological gestational age (GA) at birth – the actual time elapsed since the last menstrual period of a pregnant woman – is a fundamental component of neonatal care. Babies born preterm (before 37 weeks) or post-term (after 42 weeks of gestation) may have increased risks for various health conditions later in life ( 9 – 11 ), such as cardiovascular diseases, respiratory issues, and metabolic syndrome. In turn, maternal exposures, such as the mother’s health and lifestyle choices, many of which are modifiable, can also influence GA ( 12 , 13 ). On the other hand, biological GA at birth has been conceptualized as a measurement of fetal development that can deviate from the chronological gestational age. It is typically measured using biomarkers, such as epigenetic ( 5 ) or metabolic markers ( 14 ), which allow for a more nuanced understanding of fetal development and the impact of maternal exposures. In particular, epigenetic GA, which can be determined by DNAm GA clocks, has emerged as a more precise approach to measuring biological maturation in response to in-utero environmental exposures with potential predictive value for children’s future development and health. Considering that DNAm patterns are tissue-specific and can reflect gene expression changes, maternal exposures can act as potential triggers to induce tissue-dependent DNAm alterations. For example, nutrition, cigarette smoking, alcohol use, parity, education, and physical health during gestation have been linked to DNAm shifts measured in the placenta ( 15 – 18 ) and newborn cord blood ( 19 – 37 ). These exposures can reshape the epigenetic landscape of the fetus and placenta, and influence health in later life ( 38 – 42 ). Further, a pronounced acceleration in GA (GAA), measured by how much the predicted epigenetic age is higher than the chronological age at birth, has also been associated with maternal exposures and offspring characteristics, including older maternal age, a higher pre-pregnancy body mass index (BMI), maternal smoking, gestational diabetes mellitus (GDM), pre-eclampsia, mode of delivery, higher birth weight and length, as well as male sex in newborns ( 28 , 43 – 50 ). Meanwhile, placental and cord blood DNAm can provide complementary but distinct information, as placental DNAm reflects the interface between mother and fetus and its unique adaptations, while cord blood DNAm is more representative of the systemic fetal epigenetic profile at birth. Consequently, epigenetic clocks based on placental DNAm data do correlate with those derived for cord blood, but differ in terms of the CpGs included, with very little overlap ( 18 ), possibly due to the transient nature of placenta tissue and how specific tissues respond to environmental exposures. The majority of studies on epigenetic GA, GAA, and the characterization of their associations focused on white Europeans. In particular, epigenetic GA clocks were derived almost exclusively in white European populations ( 5 , 6 , 51 – 53 ) and knowledge about their performance in non-white populations is limited. The general lack of ethnic and racial diversity in omics research restricts the generalizability of these findings. Certain racial groups, such as Black and Asian, are at a disadvantage as they have on average a shorter gestation duration ( 54 ) and are at a higher risk for pregnancy complications ( 55 , 56 ). South Asian women, owing to their distinct genetic backgrounds ( 57 ), environmental exposures ( 58 ), and socio-cultural practices ( 59 ), are nearly two times more likely to develop GDM as compared to white European women ( 60 , 61 ). These genetic and environmental differences become especially pronounced in the length of gestation and its relationship with birth weight. Non-white populations, with their distinct set of genetic and environmental exposures, may present a different profile of epigenetic GA and GAA, which warrants a comparative study of the associated characteristics among diverse populations. Here we propose to comparatively examine EWAS of GA and association studies of epigenetic GA and GAA between white European and South Asian birth cohorts with all participants living in Canada. First, to understand whether differences exist between the two populations, we contrasted characteristics of DNAm signatures at individual CpG levels using EWASs, and then at an aggregated level using epigenetic GA and GAA between the two populations. Second, to gain insights on whether these epigenetic markers are differentially or similarly influenced by prenatal factors in the two populations, we comprehensively examined associations of epigenetic GA, and GAA with an extensive collection of maternal exposures and offspring outcomes. Methods Study designs CHILD is a prospective longitudinal birth cohort ( 62 ) that enrolled > 3,600 pregnant women who gave birth between 2009 and 2012, in Vancouver, Edmonton, Winnipeg, and Toronto, Canada. The SouTh Asian biRth cohorT (START) study is a prospective longitudinal birth cohort ( 63 ) that focused exclusively on people who originated from the Indian subcontinents known as South Asians. These cohorts were not enriched for any clinical conditions and only singleton mothers were recruited. Our analyses focused on a subset of white European-origin mother-offspring pairs from CHILD and South Asian mother-offspring pairs from START, all of whom provided cord blood samples for genome-wide DNA methylation profiling. Cord blood DNA methylation data in CHILD and START Data quality processing has been described previously ( 64 ). Briefly, 997 cord blood samples were hybridized to the Illumina Human-Methylation450K BeadChip (HM450K) array, covering CpG or 5'—C—phosphate—G—3' sites in the entire genome ( 65 ). We followed standard quality control procedures designed for HM450K using the R “ sesame ” package ( 66 ) and generated the β-matrix for further processing. Suppl. Table 1 summarizes the sample and probe inclusion/exclusion criteria. Briefly, duplicated probes, probes with detection threshold p -value < 0.05, more than 10% missing, or known to be cross-reactive or overlap with single nucleotide polymorphisms were removed ( 67 , 68 ). Further, for CpG probes with a missing rate < 10%, mean imputation was used to fill in the missing values. At each CpG site, the β-value reflects the ratio of methylated signals relative to total signals and is a continuous measure between 0 and 1. The cleaned datasets contain 504 START samples with 361,234 CpGs and 352 CHILD samples with 358,113 CpGs, covering CpG sites on autosomes and X chromosomes. Finally, we estimated cell-type proportions (CD8T, CD4T, Natural Killer cells, B cells, monocytes, granulocytes, and nucleated red blood cells) following a reference-based approach developed for cord blood ( 69 ) using the R package “ FlowSorted.CordBloodCombined.450k ”. Maternal variables Three previously validated maternal diet patterns ( 70 ) were derived using ethnic-specific food frequency questionnaires: 1) the plant-based pattern was characterized by vegetables, legumes, fermented dairy, whole grains, nonmeat dishes, and a lack of red meat; 2) the Western pattern had a high loading of sweets, red and processed meats, French fries, starchy vegetables, condiments, and sweet drinks; and 3) the health-conscious pattern was characterized by seafood, poultry, and red meats; eggs; cruciferous vegetables; leafy greens; fruit; refined grains; stir-fried dishes; and condiments. Maternal smoking history (current, quit during pregnancy, quit before pregnancy, never-smoker) and household smoking exposure (hours per week) were self-reported. Maternal education questions were harmonized between CHILD and START to produce a single variable indicating the number of years in school. Socioeconomic status was captured by the validated social disadvantage index, a composite measure of household income, marital status, and maternal employment ( 71 ). Clinical variables of interest included maternal age at delivery, pre-pregnancy BMI (kg/m 2 ), pre-pregnancy weight (kg), gestational weight gain (kg), GDM status, gestational hypertension, pre-eclampsia, and parity (defined as the number of births before current pregnancy). GDM was determined by self-report only in CHILD, while in START, it was determined by a combination of oral glucose tolerance test (OGTT), self-report, and reported diabetic treatments, including insulin, pills, and restricted diet. The OGTT threshold ( > = 5.2 at baseline or > = 7.2 at 2 hours) for South Asian women proposed in Born in Bradford ( 72 , 73 ) was used in START. Offspring characteristics Chronological GA at birth, reported in weeks and days, was collected from participants’ birth charts. As an additional quality control step, we excluded 10 newborns from CHILD and 16 from START who either lacked GA data or were born before 36 weeks, such that the remaining newborns had gestation at birth between 36 and 42 weeks. Offspring anthropometrics are available at birth from medical charts or measured at < 2 days old, and subsequently measured at the 1, 2, 3, and 5-year follow-up visits. These included height (cm), weight (g), ponderal index (PI; kg/m 3 ), and BMI (kg/m 2 ), at all visits; and the sum of skinfolds (triceps and subscapular skinfolds in mm), waist circumference (cm), hip circumference (cm), and waist-hip ratio (WHR) at 3- and 5-year visits. EWAS of GA The final datasets with cleaned DNAm and chronological GA at birth, consisted of 342 and 490 mother-newborn pairs from CHILD and START, respectively. We performed an EWAS separately in each cohort, testing the association between DNAm β-values at each CpG and GA using a linear regression model. Though neither the plate or the row number, the two main sources of batch effects ( 74 ), was associated with GA using an omnibus test, there was more variability in the pairwise differences of GA between plate numbers ( p = 0.005–0.94 in CHILD and p = 0.01–0.94 in START). Thus, the final model adjusted for the plate number to correct for potential batch effects, the estimated compositions of each cell type, maternal age, maternal education, maternal smoking history, social disadvantage index, parity, and newborn sex (coded as 0 for male and 1 for female). For association testing of X chromosome CpGs, we adopted a robust 2 degrees of freedom test proposed for X chromosome genetic associations to mitigate the influence of unknown X-inactivation ( 75 ). We then meta-analyzed association results from CHILD and START using an inverse variance-weighted fixed-effect model, and CpGs that showed heterogeneity in effects were retained for further clarification. CpG sites located within genes have been labelled using the UCSC reference gene names provided by the annotation file from Illumina (‘HumanMethylation450_15017482_v.1.2.bpm’). For each EWAS or meta-analysis, the false discovery rate (FDR) adjustment was used to control for multiple testing and an FDR-adjusted p -value < 0.05 was considered statistically significant. We further contrasted our primary findings with CpGs that were previously linked to GA using cord blood DNAm as reported in Bohlin ( 51 ). The concordance between signals from Bohlin and each EWAS, and those between START and CHILD, was tested using a two-sample proportion test. To further investigate whether significant CpGs were enriched in genes associated with type 2 diabetes (T2D) and GDM, we conducted a search in the GWAS catalog( 76 ) for genetic variants associated with T2D or GDM at genome-wide significance ( p 100,000 and published in the last five years, using the keywords “type 2 diabetes mellitus” (EFO ID: MONDO_0005148) and “gestational diabetes” (EFO ID: EFO_0004593). Epigenetic GA and GAA Epigenetic GA was estimated using Bohlin’s cord blood ( 51 ), Knight’s cord blood ( 5 ), and Mayne’s placenta ( 50 ) GA clocks developed for HM450K data, as well as Lee’s placenta ( 6 ) and Haftorn’s cord blood ( 52 ) clock for EPIC data. These algorithms are implemented in the R package “methylclock” ( 77 ). GAA was calculated as the residual from a linear regression using the estimated epigenetic GA as the outcome and the chronological GA as the predictor such that GAA would be uncorrelated with epigenetic GA. A positive GAA indicates acceleration whereby the newborn’s epigenetic GA is older than the chronological GA, and a negative value implies deceleration. We empirically assessed whether the GA clocks and the corresponding GAA were transferrable to the START cohort by examining the distribution of these scores between START and CHILD using a t -test. Statistical analysis The performance of various GA clocks to predict chronological GA was quantified using Pearson’s correlation coefficient in both cohorts. The GA clock that most strongly correlates with the chronological GA was used as the estimated epigenetic GA in all subsequent analyses. We then explored maternal characteristics that were predictive of GA, epigenetic GA, and GAA using a stepwise model selection method. The search would yield a final deterministic model with a subset of the variables as evaluated by the Akaike Information Criterion. For completeness, we further tested the univariate association between GA, epigenetic GA, and GAA and individual traits in the following categories: 1) newborn sex, and birth anthropometrics (4 variables); 2) offspring anthropometrics at each follow-up visit (20 variables); and 3) maternal exposures (14 variables), using simple linear regression. A FDR adjustment was applied to univariate results and q -values were reported whereby we claimed suggestive evidence of association when q < 0.1. For offspring anthropometrics, whenever appropriate, the model adjusted for children’s age and sex at each visit. The reported effect sizes were based on the original scale of each variable for ease of interpretation. Missing data were imputed using a random forest imputation algorithm implemented in the R package “missforest” ( 78 ). All data processing and analyses were conducted in R v.4.1.0 ( 79 ). Results Study sample characteristics Demographic characteristics and relevant covariates of the epigenetic subsamples (Table 1 ) were not statistically different ( p > 0.001) from their respective full cohort after controlling for multiple hypothesis ( Suppl. Table 2 ). Maternal characteristics, including parity, maternal age, pre-pregnancy weight, GDM, smoking history, education, social disadvantage index, and diet patterns, were significantly different between South Asian and white European women. Specifically, there was a much higher rate of GDM (36% in START vs. 4% in CHILD), and pregnant South Asian women in START were exclusively non-smoking during pregnancy. There was also a noticeable difference in the pattern of phenotypic correlations between the two cohorts, both in size of correlation and directions ( Suppl. Figure 1 ). In terms of newborn characteristics, the mean GA at birth in START (39.3 ± 1.1) was lower than in CHILD (39.6 ± 1.3) when restricted to gestation superior or equal to 36 weeks. As expected, South Asians had a lower birth weight (3.3 ± 0.45 kg vs. 3.5 ± 0.48 kg), but were of similar length as compared to white Europeans. Even after accounting for chronological GA at birth, newborn sex, social disadvantage index, and GDM, the difference in birth weight persisted ( p < 0.001). Table 1 Sample characteristics of CHILD and START cohorts. CHILD START P-value (N = 342) (N = 490) Maternal Mother's Age Mean (SD) 32.7 (± 4.4) 30.1 (± 3.9) < 0.001 Missing 4 (1.2%) 0 (0%) Parity Mean (SD) 0.72 (± 0.89) 0.80 (± 0.81) 0.191 Missing 0 (0%) 13 (2.6%) Pre-pregnancy BMI Mean (SD) 24.7 (± 5.2) 23.7 (± 4.4) 0.0236 Missing 131 (38.3%) 2 (0.4%) Pre-pregnancy weight Mean (SD) 68.8 (± 15.2) 62.6 (± 11.8) < 0.001 Missing 126 (36.8%) 0 (0%) Gestational weight gain Mean (SD) 15.4 (± 6.2) 14.3 (± 7.5) 0.0413 Missing 120 (35.1%) 9 (1.8%) Gestational Diabetes Mellitus No 328 (95.9%) 313 (63.9%) < 0.001 Yes 14 (4.1%) 176 (35.9%) Missing 0 (0%) 1 (0.2%) Gestational Hypertension No 326 (95.3%) 479 (97.8%) 0.0801 Yes 16 (4.7%) 11 (2.2%) Pre-eclampsia No 331 (96.8%) 486 (99.2%) 0.0217 Yes 11 (3.2%) 4 (0.8%) Smoking History never smoked 241 (70.5%) 487 (99.4%) < 0.001 quit before this pregnancy 68 (19.9%) 1 (0.2%) quit during this pregnancy 17 (5.0%) 1 (0.2%) currently smoking 11 (3.2%) 0 (0%) Missing 5 (1.5%) 1 (0.2%) Smoking Exposure (hr/week) Mean (SD) 1.0 (± 7.8) 0.33 (± 2.7) 0.13 Missing 12 (3.5%) 39 (8.0%) Years of Education Mean (SD) 17.0 (± 3.1) 15.8 (± 2.4) < 0.001 Missing 9 (2.6%) 0 (0%) Social Disadvantage Index Mean (SD) 0.41 (± 0.95) 1.8 (± 1.4) < 0.001 Missing 37 (10.8%) 69 (14.1%) Plant Based Diet Mean (SD) -0.48 (± 0.46) 1.6 (± 1.1) < 0.001 Missing 21 (6.1%) 16 (3.3%) Health Conscious Diet Mean (SD) 0.21 (± 0.81) -0.42 (± 0.79) < 0.001 Missing 21 (6.1%) 16 (3.3%) Western Diet Mean (SD) -0.16 (± 0.64) -0.51 (± 0.65) < 0.001 Missing 21 (6.1%) 16 (3.3%) Newborn Gestational Age (weeks) Mean (SD) 39.6 (± 1.3) 39.3 (± 1.1) < 0.001 Newborn Sex Male 187 (54.7%) 234 (47.8%) 0.0581 Female 155 (45.3%) 256 (52.2%) Birth Length (cm) Mean (SD) 51.7 (± 2.5) 51.5 (± 2.6) 0.381 Missing 65 (19.0%) 7 (1.4%) Birth Weight (kg) Mean (SD) 3.5 (± 0.48) 3.3 (± 0.45) < 0.001 Missing 4 (1.2%) 1 (0.2%) Newborn BMI (kg/m2) Mean (SD) 13.1 (± 1.4) 12.3 (± 1.4) < 0.001 Missing 66 (19.3%) 7 (1.4%) Newborn Ponderal Index (kg/m3) Mean (SD) 25.4 (± 3.1) 24.0 (± 3.2) < 0.001 Missing 66 (19.3%) 7 (1.4%) EWAS of GA Figure 1 highlighted the 1,652 and 2,136 differentially methylated CpGs associated with GA after FDR correction (above the red dashed line and in Suppl. Table 3 ) in CHILD and START, respectively. The number of significant CpGs overlapping between the two cohorts was 599 ( Suppl. Figure 2 ). The meta-analysis of all CpGs with heterogeneity p -value > 0.01 identified an additional 4,664 CpGs (7,492 CpGs total) that were significantly associated with GA. Only 108 CpGs that were significant in the meta-analysis showed marginal evidence for heterogeneity of effects (heterogeneity p -value < 0.01), however, the estimated effects of these associations were always in the same direction ( Suppl. Figure 3 ). Approximately 78% of the CpGs identified as significant in Bohlin's EWAS (11,337 of the 14,501 reported) were also significant in both CHILD and START. To benchmark our results, we highlighted these CpGs in Fig. 1 in purple. In general, there was good agreement in signals identified between CHILD and START, as well as those between CHILD or START and Bolin’s ( Suppl. Figure 4 ). We retained 1509 unique gene regions from the GWAS catalog, of which 1508 were linked to T2D and 7 were associated with GDM. Of these, 130 were represented in genes mapped onto by significant CpGs identified in either CHILD, START, or both ( Suppl. Table 4 ). In both CHILD and START, we found the number of genes containing at least one significant CpGs to be overrepresented among known T2D genes ( Suppl. Figure 5 ). Specifically, 71 out of 921 genes identified in CHILD (binomial test for proportion p = 9.9×10 − 5 ) and 97 out of 1158 genes identified in START (binomial test for proportion p = 1.3×10 − 7 ) were represented in genes reported in T2D or GDM GWASs ( Suppl. Table 5 ). Epigenetic GA and GAA Out of the six epigenetic GA clocks tested, the Knight and Bohlin clocks designed for the HM450 array had better overlap with CpGs in our data as compared to the EPIC clocks with high missingness (Table 2 ). In terms of performance, the cord blood-based Knight and Bohlin clocks had better correlation with the chronological GA, with the Bohlin clock being consistently the best in START ( r = 0.66; p = 6.9×10 − 64 ) and CHILD ( r = 0.71; p = 6.0×10 − 54 ), producing the highest correlation coefficient and the smallest median absolute difference (Table 2 ). Thus, all subsequent results were based on epigenetic GA estimated using Bohlin’s cord blood clock. The estimated epigenetic age using Bohlin’s clock and the chronological GA roughly followed a linear relationship, while the residual approach ensured that GAA was uncorrelated with the chronological age (Fig. 2 ). Finally, there was no statistically significant difference between the mean estimated epigenetic GA in white European (40.6 ± 0.81) and South Asian (40.6 ± 0.74) newborns, respectively. Similarly, there was no difference in the GAA derived using the residual approach ( Suppl. Table 6 ). Table 2 Performance of cord blood DNA methylation gestational age clocks in CHILD and START. Dataset Clock Tissue CpGs used (total) Pearson's r P-value MAD (days) Knight Cord blood 132 (148) 0.60 5.74E-35 9 CHILD Bohlin Cord blood 79 ( 96 ) 0.71 5.98E-54 4 n = 342 Mayne Placenta 54 ( 62 ) 0.12 1.18E-02 6 Lee.RPC Placenta 950 (1125) 0.20 9.50E-05 6 Lee.CPC Placenta 950 (1125) 0.33 1.81E-10 7 Haftorn-EPIC Cord blood 71 (176) 0.13 7.73E-03 6 START Knight Cord blood 132 (148) 0.44 4.09E-25 14 n = 490 Bohlin Cord blood 79 ( 96 ) 0.66 6.90E-64 4 Mayne Placenta 54 ( 62 ) 0.13 0.0030 11 Lee.RPC Placenta 958 (1125) 0.024 0.59 10 Lee.CPC Placenta 958 (1125) 0.15 7.40E-04 9 Haftorn-EPIC Cord blood 74 (176) 0.028 0.53 8 Maternal characteristics that are predictive of GA, epigenetic GA, and GAA The final models for each outcome were summarized in Table 3 . Between CHILD and START, the common set of predictors for GA included pre-eclampsia and GDM, but maternal age and dietary patterns were specific to START, while pre-pregnancy weight, BMI, and gestational wight gain were unique predictors for GA in CHILD alone. These models explained 8.03% and 7.90% of the GA variance in CHILD and START, respectively. Meanwhile, parity, GDM, and newborn sex were important predictors in both CHILD and START for epigenetic GA, with gestational weight gain, pre-pregnancy weight and BMI being additionally implicated in CHILD. In particular, GDM was negatively associated with epigenetic GA, but more severely in CHILD with a reduction of 0.66 ± 0.19 weeks as compared to the 0.16 ± 0.07 weeks in START. Together, these predictors explained 8.52% and 4.50% of the epigenetic GA variance, which was higher than that for GA in CHILD but lower than that in START. Both parity and newborn female sex were also negatively associated with GAA in CHILD and START, but GDM was only associated with GAA in CHILD while dietary patterns, gestational weight gain, and maternal smoking were uniquely associated with GAA in START (Table 3 ). Table 3 Multivariate models using maternal characteristics to predict gestational age, epigenetic gestational age, acceleration of gestational age in CHILD and START. Univariate association of maternal and offspring characteristics with epigenetic GA and GAA Newborns from mothers with GDM on average had a lower epigenetic GA in both CHILD (0.6 weeks, p = 2.98×10 − 3 , q = 0.02; Suppl. Table 7 ) and START (0.18 weeks, p = 6.58×10 − 3 , q = 0.039; Suppl. Table 8 ), but more severely in CHILD as compared to START. Parity was significantly associated with epigenetic GA in both CHILD ( p = 0.0016, q = 0.013) and START ( p = 1.0×10 − 4 , q = 0.0014), indicating that the number of previous births reduces epigenetic GA. Additionally, in START, the age of the mother was negatively associated with both GA and epigenetic GA ( Suppl. Table 8 ). However, a subsequent subset analysis revealed that maternal age was not associated with epigenetic age when adjusted for parity ( p = 0.49) and that it was not associated with epigenetic GA when stratified based on the number of previous pregnancies ( Suppl. Figure 6 ). Further, male sex was only associated with GAA in CHILD ( p = 0.0044; FDR adjusted p = 0.036; Suppl. Table 7 ) but not in START ( p = 0.14; Suppl. Table 8 ). As expected, the associations between epigenetic GA and newborn weight and length were significant in both South Asian and white European cohorts ( q < 0.0025; Suppl. Tables 7 and 8 ). The most interesting contrast was the association with newborn PI, whereby a positive association with GA (0.063 weeks; q = 2.89 ×10 − 4 ) and epigenetic GA (0.034 weeks; q = 0.005) was observed in START, but it was negatively associated with GAA in CHILD (0.032 weeks; p = 0.0009; q = 0.019), despite newborn weight and height being similarly positively associated with GA and epigenetic GA in both cohorts. Both epigenetic GA and GAA were positively associated with 1-year length in CHILD ( q < 0.02), and additionally GAA with 1-year PI and 2-year length ( q < 0.05; Suppl. Table 7 ). We did not observe any association between anthropometrics at subsequent follow-up visits and GA, epigenetic GA, or GAA in START ( Suppl. Table 8 ). Discussion This is the first study to comparatively examine DNAm signatures of GA in two ethnic populations (living in the same country) using cord blood. We found consistent DNAm signatures of gestational age at individual CpG levels and confirmed that the Bohlin's epigenetic GA clock correlated well with chronological GA in white Europeans and can be generalized to South Asians. In both populations, the GA clock was positively linked to newborn weight and length, and negatively to gestational diabetes, newborn female sex, and parity. Unique to South Asians, the GA clock was also associated with a higher newborn PI. We confirmed the associations of parity and newborn sex with GAA in both populations, and discovered that white European newborn males exhibited twice as much accelerated GA as compared to South Asians. Previous EWASs of GA have found overlapping signals with other outcomes, such as birth weight, while EWASs of birthweight were enriched for CpGs previously associated with prenatal smoking, folic acid intake, and maternal hypertension or pre-eclampsia ( 26 , 53 , 80 ). Since the strongest association we found with epigenetic GA clock was GDM, we were interested in whether GDM or T2D genetics could be implicated in the epigenetics of GA. We found two CpGs significantly associated with GA in START that were present in the TCF7L2 gene, whose risk alleles increase the risk of T2D by reducing insulin secretion and are also associated with a lower BMI ( 81 , 82 ). This lends support to the hypothesis that GDM in South Asians is predominantly due to insulin deficiency, which is consistent with their lower birth weight ( 83 ), whereby comparatively white Europeans may have a stronger insulin resistance component. Meanwhile, GLIS3 is another recognized gene for diabetes that is associated with the development of beta cells ( 84 ) and neonatal diabetes ( 85 ). Further, ADA gene, which has been linked to T2D ( 86 ) and several serum metabolites ( 87 ), was also mapped to CpGs significantly associated in START but was just below statistical significance in CHILD. Overall, there was an overrepresentation of genes that were known to associate with T2D in both START and CHILD, but with a more pronounced enrichment in START. The shared genes could be part of the mechanisms through which metabolic risks are transmitted from mothers to offspring, contributing to a transgenerational cycle of metabolic disorders ( 88 – 90 ). Despite widespread differences in DNAm patterns between South Asians and white Europeans in an adult population ( 91 ), at an aggregate level, the Bohlin clock consisting of 132 CpGs produced a robust measure of biological GA with comparable performance in both cohorts. While the epigenetic GA clocks can adequately capture biological GA in both populations, they displayed subtle yet differential associations with maternal exposures and effects on offspring. The multitude of maternal exposures, ranging from nutrition, substance use, and metabolic factors to chronic health conditions, presented a complex tapestry of influences on GA, epigenetic GA and GAA. In general, South Asian women had low to no smoking and a diet that was mostly plant-based, and their newborns often exhibited a lower birthweight and a lower GA as compared to white Europeans. In this particular population, we found evidence of Western dietary pattern to influence GA and GAA. But this should be interpreted in the context of differences in the correlation between two populations: a plant-based diet score was positively correlated with Western and Health Conscious diets in South Asian women ( r = 0.35 and 0.32), but a choice of plant-based diet implied a reduced Western diet ( r = -0.21) and a strong preference for Health Conscious diet ( r = 0.64) in white European women ( Suppl. Figure 1 ). In both cohorts, parity and newborn female sex were found to be negatively associated with epigenetic GA and GAA. While both maternal age and parity have been shown to associate with GAA ( 18 , 49 ), our univariate and multivariate analyses pointed to parity being independently associated with epigenetic GA and GAA, but not maternal age. Consistent with this claim, a previous study showed a negative association between parity and maternal telomere length, which is an indicator of cellular aging. Advanced maternal age (e.g. >35) is typically considered a risk factor for pregnancy-related outcomes ( 92 , 93 ). Here we showed that maternal age was negatively associated with GA and epigenetic GA in the univariate analysis, but only in the South Asian cohort, suggesting it could be capturing the effect of parity. This was supported by the observation that parity was more strongly correlated with maternal age in START (Pearson’s r = 0.45) than CHILD ( r = 0.27), reflecting a higher proportion of older mothers being multiparous among South Asians. This could also explain why maternal age has the most significant impact on negative outcomes in South Asian women ( 94 ). On the other hand, our finding that newborn males have accelerated GA in both CHILD and START is consistent with established literature that male sex was associated with accelerated aging in newborns ( 43 ) and in adult populations ( 95 ). In maternal health outcomes, the observed prevalence of GDM was consistent with those reported for the two populations in general ( 96 , 97 ). We observed a negative relationship between GDM and epigenetic GA in both cohorts, as well as GDM and GAA in CHILD. The direction of associations was in line with a previous report ( 48 ). However, there was no evidence to support the role of GDM on GAA in the South Asian cohort despite much higher prevalence and sample size. We were able to replicate previous findings of pre-pregnancy weight and BMI having an impact on epigenetic GA in CHILD ( 43 ), but there was a coherent indication that gestational weight gain might be the more relevant exposure to GA and epigenetic GA in both cohorts. The findings of this study should be interpreted in the light of several limitations. Cord blood DNAm in newborns reflects the regulation of gene activity during embryonic development, cellular differentiation, and response to environmental factors, and is thus a valuable source of information for assessing maternal risk factors and predicting future health outcomes. However, depending on the tissue types, resolution of the epigenetic data (e.g. single cell vs. tissue), technology (HM450K vs. EPIC), and method of GA clock construction, the performance of existing GA clocks varied substantially. The main observation is that the barriers to trans-ethnic EWAS analysis lie in various choices of technical and study design. For example, different tissue types might reveal tissue-specific responses, a placental clock could produce an elevated association with maternal exposures vs. offspring outcomes ( 6 ), which is complementary to existing studies of GA using cord blood. We have shown that DNAm clock, while universal in its foundational concept, can exhibit specific patterns and variations when analyzed across different ethnic groups. Factors contributing to these disparities might range from genetic backgrounds to varied environmental and socio-cultural practices that might not be readily available, such as alcohol consumption ( 34 , 98 ) and air pollution ( 99 , 100 ), both are known to alter epigenetics. These discrepancies, while enriching our understanding of epigenetic variability between ethnic groups, also serve as a clarion call for tailored healthcare and research approaches across populations. In summary, this paper comparatively examined the transferability and health implications of epigenetic GA and GAA in white European and South Asian birth cohorts living in the same country. Two key findings are the consistent associations of parity, GDM, and newborn sex with epigenetic GA in both South Asian and white European cohorts, but with weaker effects observed in the South Asian cohort; and the positive association between epigenetic GA and newborn ponderal index that is exclusive to South Asian newborns. The epigenetic GA clock presents a promising avenue to bridge our understanding of maternal exposures and offspring health outcomes, and these differentiating characteristics of associations across the cohorts, enrich this narrative. Further research in the South Asian population, both adult and pediatric, is needed to strengthen these findings. Abbreviations BMI: body mass index CpGs: 5'—C—phosphate—G—3' DNAm: DNA methylation EWAS: epigenome-wide association study GA: gestational age GAA: gestational age acceleration GDM: gestational diabetes mellitus GWAS: genome-wide association study OGTT: oral glucose tolerance test PI: ponderal index T2D: type 2 diabetes WHR: waist-to-hip ratio Declarations Ethics Statement Ethical approval was obtained independently from the Hamilton Integrated Research Ethics Board: CHILD (REB 07–2929) and START (REB 10–640). CHILD was additionally approved by the respective Human Research Ethics Boards at McMaster University, the Universities of Manitoba, Alberta, and British Columbia, and the Hospital for Sick Children. Legal guardians of each participant provided written informed consent. Written informed consent was obtained from the parent/guardian (participating mother) for each study separately. We also have now obtained additional ethics board approval from HiREB (REB 16592) for using the data from the two cohorts together without additional consent from the participants. Consent for publication Not applicable Availability of data and materials The EWAS summary statistics are available from the EWAS catalog at https://www.ewascatalog.org/?study=27717397_gestational_age_ultrasoundestimated_gestational_age. Summary statistics generated in the current study, including a total of 2 primary association studies and the meta-analyzed results are available upon request. All scripts to reproduce the results and figures can be found at https://github.com/WeiAkaneDeng/EpigeneticResearch/tree/main/EpigenticGA. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the Canadian Institutes of Health Research Metabolomics Team Grant: MWG-146332. The Canadian Institutes of Health Research (CIHR), Debbie and Don Morrison and the Allergy, Genes, and Environment Network of Centres of Excellence (AllerGen NCE), Women's and Children's Health Research Institute provided core support for establishing the CHILD Study. Dr. Anand is supported by a Tier 1 Canada Research Chair in Ethnicity and CVD and Heart, Stroke Foundation Chair in Population Health, a grant from the Canadian Partnership Against Cancer, Heart and Stroke Foundation of Canada and Canadian Institutes of Health Research. Dr. Azad is supported by a Tier 2 Canada Research Chair in the Developmental Origins of Chronic Disease. Dr. Subbarao is supported by a Tier 1 Canada Research Chair in Pediatric Asthma and Lung Health. Dr. Turvey is supported by a Tier 1 Canada Research Chair in Pediatric Precision Health. Authors' contributions WQD conceptualized the study, designed the methodology, performed data analyses and visualization, and prepared the first draft and subsequent revisions. MP, SMA, SLW, KMM, GP, SAA contributed to the interpretation of data and results and substantively revised the manuscript. NCawte contributed to methylation data curation, pre-processing, and quality control methodology. NC was responsible for phenotypic data curation and pre-processing. PS, SET, TJM, PM, MBA, and ES contributed to data curation, the interpretation of data, and revision. SMA, NC, SSA contributed to project administration. GP and SSA contributed to study conceptualization and data curation. SSA was responsible for funding acquisition and resources. All authors edited and critically reviewed the manuscript. Acknowledgements We express our sincere gratitude to all the participating families and the START and CHILD study teams, including interviewers, nurses, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, and receptionists. We thank the CHILD Cohort Study (CHILD) participant families for their dedication and commitment to advancing health research. CHILD was initially funded by CIHR and AllerGen NCE. Visit CHILD at childstudy.ca. We would like to acknowledge the Genetic and Molecular Epidemiology Laboratory (GMEL), an associate of Hamilton Health Sciences and McMaster University, for their indispensable contributions to this work. The technical staff of GMEL conducted all epigenetic profiling, including sample processing and other technical operations. We thank the members of the Nutrigen Alliance for providing the data: Sonia S. Anand; Stephanie A. Atkinson; Meghan Azad; Allan B. 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Epigenome-wide meta-analysis of methylation in children related to prenatal NO2 air pollution exposure. Environ Health Perspect. 2017;125(1). Table 3 Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Suppl.FigureandTablelegends.docx SupplTables2024.xlsx SupplFig1.png SupplFig2.png SupplFig3.png SupplFig4.png SupplFig5.pdf SupplFig6.png Table3.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Mar, 2024 Reviews received at journal 17 Mar, 2024 Reviewers agreed at journal 08 Mar, 2024 Reviewers invited by journal 08 Mar, 2024 Editor assigned by journal 04 Mar, 2024 Submission checks completed at journal 04 Mar, 2024 First submitted to journal 28 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3997065","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276371242,"identity":"19ced93d-78b9-462b-a227-73bf32b48784","order_by":0,"name":"Wei Q. Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDCCA2DShnQtacgc4rQcJkEL3/Hew695ys5H8087/Pjzh4p7DPztBDRKnjmXZs1z7nbujNtpZhIHzhQzSJxJwK/F4EaOmXFu2+3cDdIJZgwH2xIYDBgIabn/BqTlHFBL+ucPB/8BtfA/IGQLj/Hj3LYDQC05BhIHG4BaJAjYInkmx4z5z7lkoF9yyiTOHEvgkbhBwBa+42eMP84os8vtn52++UNFTYIcfz8BW4CATYKBDcHjIageCJg/IGsZBaNgFIyCUYABAODUSu2/MH5DAAAAAElFTkSuQmCC","orcid":"","institution":"St. Joseph’s Healthcare Hamilton","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"Q.","lastName":"Deng","suffix":""},{"id":276371243,"identity":"51a98c43-a4ac-4f83-9439-fae2a1e265c8","order_by":1,"name":"Marie Pigeyre","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Marie","middleName":"","lastName":"Pigeyre","suffix":""},{"id":276371244,"identity":"81d8dc6f-18ab-47b2-b234-5bdf04102798","order_by":2,"name":"Sandi M. Azab","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Sandi","middleName":"M.","lastName":"Azab","suffix":""},{"id":276371245,"identity":"6e338cc4-6c5a-4800-b772-d8125a6b98e0","order_by":3,"name":"Samantha L Wilson","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"L","lastName":"Wilson","suffix":""},{"id":276371246,"identity":"e80e7819-f936-4f04-a41e-35abd7e7f9d4","order_by":4,"name":"Natalie Campbell","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Natalie","middleName":"","lastName":"Campbell","suffix":""},{"id":276371247,"identity":"7f8b40d2-a25b-436a-9fad-70f17fb87f08","order_by":5,"name":"Nathan Cawte","email":"","orcid":"","institution":"Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Cawte","suffix":""},{"id":276371248,"identity":"b6df7b5e-2372-4d9a-9051-617cd13a0aa5","order_by":6,"name":"Katherine M. Morrison","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"M.","lastName":"Morrison","suffix":""},{"id":276371249,"identity":"1afe0129-fe16-47cb-97e7-e7a88e8ed976","order_by":7,"name":"Stephanie A. Atkinson","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"A.","lastName":"Atkinson","suffix":""},{"id":276371250,"identity":"a08e9d0f-3783-4eee-8c0d-07e12ae2a1f3","order_by":8,"name":"Padmaja Subbarao","email":"","orcid":"","institution":"SickKids Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Padmaja","middleName":"","lastName":"Subbarao","suffix":""},{"id":276371251,"identity":"0957947e-8679-41b6-8234-712a4db4141e","order_by":9,"name":"Stuart E. Turvey","email":"","orcid":"","institution":"The University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Stuart","middleName":"E.","lastName":"Turvey","suffix":""},{"id":276371252,"identity":"5befc460-09e1-430e-a649-17fd649a1c71","order_by":10,"name":"Theo J. Moraes","email":"","orcid":"","institution":"Hospital for Sick Children, University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Theo","middleName":"J.","lastName":"Moraes","suffix":""},{"id":276371253,"identity":"0be3f1d9-4d16-440d-8362-bdb654774bbb","order_by":11,"name":"Piush Mandhane","email":"","orcid":"","institution":"University of Alberta","correspondingAuthor":false,"prefix":"","firstName":"Piush","middleName":"","lastName":"Mandhane","suffix":""},{"id":276371254,"identity":"045cb68e-f713-4360-ae83-2f079b4a2160","order_by":12,"name":"Meghan B. Azad","email":"","orcid":"","institution":"Children’s Hospital Research Institute of Manitoba, University of Manitoba","correspondingAuthor":false,"prefix":"","firstName":"Meghan","middleName":"B.","lastName":"Azad","suffix":""},{"id":276371255,"identity":"2ac8fcb1-ae33-4cb0-839d-fd687e9713a2","order_by":13,"name":"Elinor Simons","email":"","orcid":"","institution":"University of Manitoba","correspondingAuthor":false,"prefix":"","firstName":"Elinor","middleName":"","lastName":"Simons","suffix":""},{"id":276371256,"identity":"13abb5bb-11ee-45b9-b2c5-ebf18dcbaee8","order_by":14,"name":"Guillaume Pare","email":"","orcid":"","institution":"Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"Pare","suffix":""},{"id":276371257,"identity":"32f2102f-ef27-43fc-aff1-7f541445b420","order_by":15,"name":"Sonia S. Anand","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"S.","lastName":"Anand","suffix":""}],"badges":[],"createdAt":"2024-02-28 15:21:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3997065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3997065/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52194296,"identity":"0491a7df-3eca-46b2-8e7a-3d2398bce6c7","added_by":"auto","created_at":"2024-03-07 19:50:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2292722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan plots of the EWAS results of gestational age in CHILD (top) and START (bottom).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eManhattan plots summarized the association \u003cem\u003ep\u003c/em\u003e-values between cord blood DNA methylation levels and gestational age in CHILD (top) and START (bottom). The red line denotes the smallest -log10(\u003cem\u003ep\u003c/em\u003e-value) that is below the FDR correction threshold of 0.05. The red dots represent established associations with gestational age based on ultrasound (51)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/479d254718deca911904e76c.png"},{"id":52196430,"identity":"2d44c953-be00-4a10-b8ab-c80b7d11b7a7","added_by":"auto","created_at":"2024-03-07 19:58:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":255134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between newborn epigenetic gestational age at birth based on cord blood DNA methylation data, chronological gestational age at birth, and gestational age acceleration.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanels A-B) show the scatterplot of chronological gestational age at birth and Bohlin’s gestational age clock in CHILD and START, respectively. Panels C-D) show the scatterplot of chronological gestational age at birth and gestational age acceleration in CHILD and START, respectively. The black lines are the best fitted line using ordinary least square, with equation displayed in Panels A) and B). The red lines indicate the reference line of y=x.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/be03de2adeb4a85f3a72cfff.png"},{"id":52197142,"identity":"0ff55a33-681e-43f1-9d13-7a1f0c672a12","added_by":"auto","created_at":"2024-03-07 20:06:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1458440,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/a48e88bb-bdfa-4ee9-8a55-7850ecfd9ae3.pdf"},{"id":52194290,"identity":"6494fa83-209c-4ca1-951c-0e42ccb72fb2","added_by":"auto","created_at":"2024-03-07 19:50:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":519933,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.FigureandTablelegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/8164bb7c019128ca02ae5ff6.docx"},{"id":52196432,"identity":"afd9d5c0-2221-4a0b-9951-9df4e735aeaa","added_by":"auto","created_at":"2024-03-07 19:58:21","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":621955,"visible":true,"origin":"","legend":"","description":"","filename":"SupplTables2024.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/ed9918c081326567e75a8fc0.xlsx"},{"id":52196431,"identity":"68b7ec89-c624-40c2-96fb-5c4cc5926be0","added_by":"auto","created_at":"2024-03-07 19:58:21","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":672899,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/147b01742efc878c9c6bdaeb.png"},{"id":52194292,"identity":"f2d9e25f-dbbc-48a1-b016-93d88c29e413","added_by":"auto","created_at":"2024-03-07 19:50:21","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":59706,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/3fa448166617fa66d387bba5.png"},{"id":52194297,"identity":"40803186-7c96-4506-bf63-4b6fb74a074d","added_by":"auto","created_at":"2024-03-07 19:50:21","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":794594,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/66e934c5a6e1f0ac46528120.png"},{"id":52194294,"identity":"d3dfffd0-2abe-49ca-8632-88bb7b1e8c79","added_by":"auto","created_at":"2024-03-07 19:50:21","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":945121,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/a187bf54639f3afe24f0a19e.png"},{"id":52194299,"identity":"70e553b7-d40f-4efb-96bc-12955ebf6b6f","added_by":"auto","created_at":"2024-03-07 19:50:21","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16593,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/1b1129d8a5b81043b7f656ec.pdf"},{"id":52194301,"identity":"36eb4609-c1ba-4548-a0d4-1b3e0d0e9083","added_by":"auto","created_at":"2024-03-07 19:50:21","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":468479,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig6.png","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/99c6ab6328f00d1110443f53.png"},{"id":52194298,"identity":"855d32b0-60ee-4c20-b961-61206d600a8b","added_by":"auto","created_at":"2024-03-07 19:50:21","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":648421,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3997065/v1/eba9279e07c63a07da588f89.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Consistent Cord Blood DNA Methylation Signatures of Gestational Age between South Asian and white European Cohorts","fulltext":[{"header":"Background","content":"\u003cp\u003eEpigenome-wide DNA methylation (DNAm) patterns across a variety of tissues and cells have been shown to accurately capture the \"biological clock\" in both adult (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and pediatric populations (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), providing insights into an individual's biological age, which can differ from their chronological age. These epigenetic clocks and their deviations from chronological age, otherwise known as accelerated aging, have been linked to a range of health outcomes and age-related diseases, suggesting that DNAm changes serve not just as markers of age, but also play a role in the mechanisms of aging that overlap with many chronic diseases (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChronological gestational age (GA) at birth \u0026ndash; the actual time elapsed since the last menstrual period of a pregnant woman \u0026ndash; is a fundamental component of neonatal care. Babies born preterm (before 37 weeks) or post-term (after 42 weeks of gestation) may have increased risks for various health conditions later in life (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), such as cardiovascular diseases, respiratory issues, and metabolic syndrome. In turn, maternal exposures, such as the mother\u0026rsquo;s health and lifestyle choices, many of which are modifiable, can also influence GA (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). On the other hand, biological GA at birth has been conceptualized as a measurement of fetal development that can deviate from the chronological gestational age. It is typically measured using biomarkers, such as epigenetic (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) or metabolic markers (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), which allow for a more nuanced understanding of fetal development and the impact of maternal exposures. In particular, epigenetic GA, which can be determined by DNAm GA clocks, has emerged as a more precise approach to measuring biological maturation in response to \u003cem\u003ein-utero\u003c/em\u003e environmental exposures with potential predictive value for children\u0026rsquo;s future development and health.\u003c/p\u003e \u003cp\u003eConsidering that DNAm patterns are tissue-specific and can reflect gene expression changes, maternal exposures can act as potential triggers to induce tissue-dependent DNAm alterations. For example, nutrition, cigarette smoking, alcohol use, parity, education, and physical health during gestation have been linked to DNAm shifts measured in the placenta (\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and newborn cord blood (\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). These exposures can reshape the epigenetic landscape of the fetus and placenta, and influence health in later life (\u003cspan additionalcitationids=\"CR39 CR40 CR41\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Further, a pronounced acceleration in GA (GAA), measured by how much the predicted epigenetic age is higher than the chronological age at birth, has also been associated with maternal exposures and offspring characteristics, including older maternal age, a higher pre-pregnancy body mass index (BMI), maternal smoking, gestational diabetes mellitus (GDM), pre-eclampsia, mode of delivery, higher birth weight and length, as well as male sex in newborns (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44 CR45 CR46 CR47 CR48 CR49\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Meanwhile, placental and cord blood DNAm can provide complementary but distinct information, as placental DNAm reflects the interface between mother and fetus and its unique adaptations, while cord blood DNAm is more representative of the systemic fetal epigenetic profile at birth. Consequently, epigenetic clocks based on placental DNAm data do correlate with those derived for cord blood, but differ in terms of the CpGs included, with very little overlap (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), possibly due to the transient nature of placenta tissue and how specific tissues respond to environmental exposures.\u003c/p\u003e \u003cp\u003eThe majority of studies on epigenetic GA, GAA, and the characterization of their associations focused on white Europeans. In particular, epigenetic GA clocks were derived almost exclusively in white European populations (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) and knowledge about their performance in non-white populations is limited. The general lack of ethnic and racial diversity in omics research restricts the generalizability of these findings. Certain racial groups, such as Black and Asian, are at a disadvantage as they have on average a shorter gestation duration (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) and are at a higher risk for pregnancy complications (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). South Asian women, owing to their distinct genetic backgrounds (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e), environmental exposures (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), and socio-cultural practices (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), are nearly two times more likely to develop GDM as compared to white European women (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). These genetic and environmental differences become especially pronounced in the length of gestation and its relationship with birth weight. Non-white populations, with their distinct set of genetic and environmental exposures, may present a different profile of epigenetic GA and GAA, which warrants a comparative study of the associated characteristics among diverse populations.\u003c/p\u003e \u003cp\u003eHere we propose to comparatively examine EWAS of GA and association studies of epigenetic GA and GAA between white European and South Asian birth cohorts with all participants living in Canada. First, to understand whether differences exist between the two populations, we contrasted characteristics of DNAm signatures at individual CpG levels using EWASs, and then at an aggregated level using epigenetic GA and GAA between the two populations. Second, to gain insights on whether these epigenetic markers are differentially or similarly influenced by prenatal factors in the two populations, we comprehensively examined associations of epigenetic GA, and GAA with an extensive collection of maternal exposures and offspring outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy designs\u003c/h2\u003e \u003cp\u003eCHILD is a prospective longitudinal birth cohort (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e) that enrolled\u0026thinsp;\u0026gt;\u0026thinsp;3,600 pregnant women who gave birth between 2009 and 2012, in Vancouver, Edmonton, Winnipeg, and Toronto, Canada. The SouTh Asian biRth cohorT (START) study is a prospective longitudinal birth cohort (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) that focused exclusively on people who originated from the Indian subcontinents known as South Asians. These cohorts were not enriched for any clinical conditions and only singleton mothers were recruited. Our analyses focused on a subset of white European-origin mother-offspring pairs from CHILD and South Asian mother-offspring pairs from START, all of whom provided cord blood samples for genome-wide DNA methylation profiling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCord blood DNA methylation data in CHILD and START\u003c/h2\u003e \u003cp\u003eData quality processing has been described previously (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). Briefly, 997 cord blood samples were hybridized to the Illumina Human-Methylation450K BeadChip (HM450K) array, covering CpG or 5'\u0026mdash;C\u0026mdash;phosphate\u0026mdash;G\u0026mdash;3' sites in the entire genome (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). We followed standard quality control procedures designed for HM450K using the R \u0026ldquo;\u003cem\u003esesame\u003c/em\u003e\u0026rdquo; package (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) and generated the β-matrix for further processing. \u003cb\u003eSuppl. Table\u0026nbsp;1\u003c/b\u003e summarizes the sample and probe inclusion/exclusion criteria. Briefly, duplicated probes, probes with detection threshold \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, more than 10% missing, or known to be cross-reactive or overlap with single nucleotide polymorphisms were removed (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Further, for CpG probes with a missing rate\u0026thinsp;\u0026lt;\u0026thinsp;10%, mean imputation was used to fill in the missing values. At each CpG site, the β-value reflects the ratio of methylated signals relative to total signals and is a continuous measure between 0 and 1. The cleaned datasets contain 504 START samples with 361,234 CpGs and 352 CHILD samples with 358,113 CpGs, covering CpG sites on autosomes and X chromosomes. Finally, we estimated cell-type proportions (CD8T, CD4T, Natural Killer cells, B cells, monocytes, granulocytes, and nucleated red blood cells) following a reference-based approach developed for cord blood (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e) using the R package \u0026ldquo;\u003cem\u003eFlowSorted.CordBloodCombined.450k\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMaternal variables\u003c/h2\u003e \u003cp\u003eThree previously validated maternal diet patterns (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e) were derived using ethnic-specific food frequency questionnaires: 1) the plant-based pattern was characterized by vegetables, legumes, fermented dairy, whole grains, nonmeat dishes, and a lack of red meat; 2) the Western pattern had a high loading of sweets, red and processed meats, French fries, starchy vegetables, condiments, and sweet drinks; and 3) the health-conscious pattern was characterized by seafood, poultry, and red meats; eggs; cruciferous vegetables; leafy greens; fruit; refined grains; stir-fried dishes; and condiments. Maternal smoking history (current, quit during pregnancy, quit before pregnancy, never-smoker) and household smoking exposure (hours per week) were self-reported. Maternal education questions were harmonized between CHILD and START to produce a single variable indicating the number of years in school. Socioeconomic status was captured by the validated social disadvantage index, a composite measure of household income, marital status, and maternal employment (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Clinical variables of interest included maternal age at delivery, pre-pregnancy BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), pre-pregnancy weight (kg), gestational weight gain (kg), GDM status, gestational hypertension, pre-eclampsia, and parity (defined as the number of births before current pregnancy). GDM was determined by self-report only in CHILD, while in START, it was determined by a combination of oral glucose tolerance test (OGTT), self-report, and reported diabetic treatments, including insulin, pills, and restricted diet. The OGTT threshold (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;5.2 at baseline or \u0026gt;\u0026thinsp;=\u0026thinsp;7.2 at 2 hours) for South Asian women proposed in Born in Bradford (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e) was used in START.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOffspring characteristics\u003c/h2\u003e \u003cp\u003eChronological GA at birth, reported in weeks and days, was collected from participants\u0026rsquo; birth charts. As an additional quality control step, we excluded 10 newborns from CHILD and 16 from START who either lacked GA data or were born before 36 weeks, such that the remaining newborns had gestation at birth between 36 and 42 weeks. Offspring anthropometrics are available at birth from medical charts or measured at \u0026lt;\u0026thinsp;2 days old, and subsequently measured at the 1, 2, 3, and 5-year follow-up visits. These included height (cm), weight (g), ponderal index (PI; kg/m\u003csup\u003e3\u003c/sup\u003e), and BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), at all visits; and the sum of skinfolds (triceps and subscapular skinfolds in mm), waist circumference (cm), hip circumference (cm), and waist-hip ratio (WHR) at 3- and 5-year visits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEWAS of GA\u003c/h2\u003e \u003cp\u003eThe final datasets with cleaned DNAm and chronological GA at birth, consisted of 342 and 490 mother-newborn pairs from CHILD and START, respectively. We performed an EWAS separately in each cohort, testing the association between DNAm β-values at each CpG and GA using a linear regression model. Though neither the plate or the row number, the two main sources of batch effects (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e), was associated with GA using an omnibus test, there was more variability in the pairwise differences of GA between plate numbers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005\u0026ndash;0.94 in CHILD and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.94 in START). Thus, the final model adjusted for the plate number to correct for potential batch effects, the estimated compositions of each cell type, maternal age, maternal education, maternal smoking history, social disadvantage index, parity, and newborn sex (coded as 0 for male and 1 for female). For association testing of X chromosome CpGs, we adopted a robust 2 degrees of freedom test proposed for X chromosome genetic associations to mitigate the influence of unknown X-inactivation (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). We then meta-analyzed association results from CHILD and START using an inverse variance-weighted fixed-effect model, and CpGs that showed heterogeneity in effects were retained for further clarification. CpG sites located within genes have been labelled using the UCSC reference gene names provided by the annotation file from Illumina (\u0026lsquo;HumanMethylation450_15017482_v.1.2.bpm\u0026rsquo;). For each EWAS or meta-analysis, the false discovery rate (FDR) adjustment was used to control for multiple testing and an FDR-adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. We further contrasted our primary findings with CpGs that were previously linked to GA using cord blood DNAm as reported in Bohlin (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). The concordance between signals from Bohlin and each EWAS, and those between START and CHILD, was tested using a two-sample proportion test. To further investigate whether significant CpGs were enriched in genes associated with type 2 diabetes (T2D) and GDM, we conducted a search in the GWAS catalog(\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e) for genetic variants associated with T2D or GDM at genome-wide significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). We only selected genetic variants reported in studies with a discovery sample size\u0026thinsp;\u0026gt;\u0026thinsp;100,000 and published in the last five years, using the keywords \u0026ldquo;type 2 diabetes mellitus\u0026rdquo; (EFO ID: MONDO_0005148) and \u0026ldquo;gestational diabetes\u0026rdquo; (EFO ID: EFO_0004593).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEpigenetic GA and GAA\u003c/h2\u003e \u003cp\u003eEpigenetic GA was estimated using Bohlin\u0026rsquo;s cord blood (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), Knight\u0026rsquo;s cord blood (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and Mayne\u0026rsquo;s placenta (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) GA clocks developed for HM450K data, as well as Lee\u0026rsquo;s placenta (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) and Haftorn\u0026rsquo;s cord blood (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) clock for EPIC data. These algorithms are implemented in the R package \u0026ldquo;methylclock\u0026rdquo; (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). GAA was calculated as the residual from a linear regression using the estimated epigenetic GA as the outcome and the chronological GA as the predictor such that GAA would be uncorrelated with epigenetic GA. A positive GAA indicates acceleration whereby the newborn\u0026rsquo;s epigenetic GA is older than the chronological GA, and a negative value implies deceleration. We empirically assessed whether the GA clocks and the corresponding GAA were transferrable to the START cohort by examining the distribution of these scores between START and CHILD using a \u003cem\u003et\u003c/em\u003e-test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe performance of various GA clocks to predict chronological GA was quantified using Pearson\u0026rsquo;s correlation coefficient in both cohorts. The GA clock that most strongly correlates with the chronological GA was used as the estimated epigenetic GA in all subsequent analyses. We then explored maternal characteristics that were predictive of GA, epigenetic GA, and GAA using a stepwise model selection method. The search would yield a final deterministic model with a subset of the variables as evaluated by the Akaike Information Criterion. For completeness, we further tested the univariate association between GA, epigenetic GA, and GAA and individual traits in the following categories: 1) newborn sex, and birth anthropometrics (4 variables); 2) offspring anthropometrics at each follow-up visit (20 variables); and 3) maternal exposures (14 variables), using simple linear regression. A FDR adjustment was applied to univariate results and \u003cem\u003eq\u003c/em\u003e-values were reported whereby we claimed suggestive evidence of association when \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1. For offspring anthropometrics, whenever appropriate, the model adjusted for children\u0026rsquo;s age and sex at each visit. The reported effect sizes were based on the original scale of each variable for ease of interpretation. Missing data were imputed using a random forest imputation algorithm implemented in the R package \u0026ldquo;missforest\u0026rdquo; (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). All data processing and analyses were conducted in R v.4.1.0 (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy sample characteristics\u003c/h2\u003e \u003cp\u003eDemographic characteristics and relevant covariates of the epigenetic subsamples (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were not statistically different (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.001) from their respective full cohort after controlling for multiple hypothesis (\u003cb\u003eSuppl. Table\u0026nbsp;2\u003c/b\u003e). Maternal characteristics, including parity, maternal age, pre-pregnancy weight, GDM, smoking history, education, social disadvantage index, and diet patterns, were significantly different between South Asian and white European women. Specifically, there was a much higher rate of GDM (36% in START vs. 4% in CHILD), and pregnant South Asian women in START were exclusively non-smoking during pregnancy. There was also a noticeable difference in the pattern of phenotypic correlations between the two cohorts, both in size of correlation and directions (\u003cb\u003eSuppl. Figure\u0026nbsp;1\u003c/b\u003e). In terms of newborn characteristics, the mean GA at birth in START (39.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1) was lower than in CHILD (39.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3) when restricted to gestation superior or equal to 36 weeks. As expected, South Asians had a lower birth weight (3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45 kg vs. 3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 kg), but were of similar length as compared to white Europeans. Even after accounting for chronological GA at birth, newborn sex, social disadvantage index, and GDM, the difference in birth weight persisted (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics of CHILD and START cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHILD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTART\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;342)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;490)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMother's Age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.7 (\u0026plusmn;\u0026thinsp;4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1 (\u0026plusmn;\u0026thinsp;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (\u0026plusmn;\u0026thinsp;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80 (\u0026plusmn;\u0026thinsp;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePre-pregnancy BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7 (\u0026plusmn;\u0026thinsp;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.7 (\u0026plusmn;\u0026thinsp;4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePre-pregnancy weight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.8 (\u0026plusmn;\u0026thinsp;15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.6 (\u0026plusmn;\u0026thinsp;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGestational weight gain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4 (\u0026plusmn;\u0026thinsp;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.3 (\u0026plusmn;\u0026thinsp;7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGestational Diabetes Mellitus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328 (95.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e313 (63.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGestational Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326 (95.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e479 (97.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePre-eclampsia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e331 (96.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e486 (99.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSmoking History\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enever smoked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241 (70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e487 (99.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003equit before this pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003equit during this pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecurrently smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSmoking Exposure (hr/week)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (\u0026plusmn;\u0026thinsp;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33 (\u0026plusmn;\u0026thinsp;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYears of Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0 (\u0026plusmn;\u0026thinsp;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.8 (\u0026plusmn;\u0026thinsp;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSocial Disadvantage Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41 (\u0026plusmn;\u0026thinsp;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8 (\u0026plusmn;\u0026thinsp;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePlant Based Diet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.48 (\u0026plusmn;\u0026thinsp;0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6 (\u0026plusmn;\u0026thinsp;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHealth Conscious Diet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (\u0026plusmn;\u0026thinsp;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.42 (\u0026plusmn;\u0026thinsp;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWestern Diet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.16 (\u0026plusmn;\u0026thinsp;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.51 (\u0026plusmn;\u0026thinsp;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNewborn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGestational Age (weeks)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.6 (\u0026plusmn;\u0026thinsp;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.3 (\u0026plusmn;\u0026thinsp;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNewborn Sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187 (54.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBirth Length (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.7 (\u0026plusmn;\u0026thinsp;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.5 (\u0026plusmn;\u0026thinsp;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBirth Weight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (\u0026plusmn;\u0026thinsp;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3 (\u0026plusmn;\u0026thinsp;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNewborn BMI (kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1 (\u0026plusmn;\u0026thinsp;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.3 (\u0026plusmn;\u0026thinsp;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNewborn Ponderal Index (kg/m3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4 (\u0026plusmn;\u0026thinsp;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0 (\u0026plusmn;\u0026thinsp;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEWAS of GA\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e highlighted the 1,652 and 2,136 differentially methylated CpGs associated with GA after FDR correction (above the red dashed line and in \u003cb\u003eSuppl. Table\u0026nbsp;3\u003c/b\u003e) in CHILD and START, respectively. The number of significant CpGs overlapping between the two cohorts was 599 (\u003cb\u003eSuppl. Figure\u0026nbsp;2\u003c/b\u003e). The meta-analysis of all CpGs with heterogeneity \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.01 identified an additional 4,664 CpGs (7,492 CpGs total) that were significantly associated with GA. Only 108 CpGs that were significant in the meta-analysis showed marginal evidence for heterogeneity of effects (heterogeneity \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), however, the estimated effects of these associations were always in the same direction (\u003cb\u003eSuppl. Figure\u0026nbsp;3\u003c/b\u003e). Approximately 78% of the CpGs identified as significant in Bohlin's EWAS (11,337 of the 14,501 reported) were also significant in both CHILD and START. To benchmark our results, we highlighted these CpGs in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in purple. In general, there was good agreement in signals identified between CHILD and START, as well as those between CHILD or START and Bolin\u0026rsquo;s (\u003cb\u003eSuppl. Figure\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe retained 1509 unique gene regions from the GWAS catalog, of which 1508 were linked to T2D and 7 were associated with GDM. Of these, 130 were represented in genes mapped onto by significant CpGs identified in either CHILD, START, or both (\u003cb\u003eSuppl. Table\u0026nbsp;4\u003c/b\u003e). In both CHILD and START, we found the number of genes containing at least one significant CpGs to be overrepresented among known T2D genes (\u003cb\u003eSuppl. Figure\u0026nbsp;5\u003c/b\u003e). Specifically, 71 out of 921 genes identified in CHILD (binomial test for proportion \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) and 97 out of 1158 genes identified in START (binomial test for proportion \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) were represented in genes reported in T2D or GDM GWASs (\u003cb\u003eSuppl. Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEpigenetic GA and GAA\u003c/h2\u003e \u003cp\u003eOut of the six epigenetic GA clocks tested, the Knight and Bohlin clocks designed for the HM450 array had better overlap with CpGs in our data as compared to the EPIC clocks with high missingness (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In terms of performance, the cord blood-based Knight and Bohlin clocks had better correlation with the chronological GA, with the Bohlin clock being consistently the best in START (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;64\u003c/sup\u003e) and CHILD (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.0\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;54\u003c/sup\u003e), producing the highest correlation coefficient and the smallest median absolute difference (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Thus, all subsequent results were based on epigenetic GA estimated using Bohlin\u0026rsquo;s cord blood clock. The estimated epigenetic age using Bohlin\u0026rsquo;s clock and the chronological GA roughly followed a linear relationship, while the residual approach ensured that GAA was uncorrelated with the chronological age (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Finally, there was no statistically significant difference between the mean estimated epigenetic GA in white European (40.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81) and South Asian (40.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74) newborns, respectively. Similarly, there was no difference in the GAA derived using the residual approach (\u003cb\u003eSuppl. Table\u0026nbsp;6\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance of cord blood DNA methylation gestational age clocks in CHILD and START.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCpGs used (total)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePearson's r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMAD (days)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCord blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.74E-35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHILD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBohlin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCord blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.98E-54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMayne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlacenta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLee.RPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlacenta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e950 (1125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.50E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLee.CPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlacenta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e950 (1125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.81E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaftorn-EPIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCord blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.73E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTART\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCord blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.09E-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBohlin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCord blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.90E-64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMayne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlacenta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLee.RPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlacenta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e958 (1125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLee.CPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlacenta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e958 (1125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.40E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaftorn-EPIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCord blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMaternal characteristics that are predictive of GA, epigenetic GA, and GAA\u003c/h2\u003e \u003cp\u003eThe final models for each outcome were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Between CHILD and START, the common set of predictors for GA included pre-eclampsia and GDM, but maternal age and dietary patterns were specific to START, while pre-pregnancy weight, BMI, and gestational wight gain were unique predictors for GA in CHILD alone. These models explained 8.03% and 7.90% of the GA variance in CHILD and START, respectively. Meanwhile, parity, GDM, and newborn sex were important predictors in both CHILD and START for epigenetic GA, with gestational weight gain, pre-pregnancy weight and BMI being additionally implicated in CHILD. In particular, GDM was negatively associated with epigenetic GA, but more severely in CHILD with a reduction of 0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 weeks as compared to the 0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 weeks in START. Together, these predictors explained 8.52% and 4.50% of the epigenetic GA variance, which was higher than that for GA in CHILD but lower than that in START. Both parity and newborn female sex were also negatively associated with GAA in CHILD and START, but GDM was only associated with GAA in CHILD while dietary patterns, gestational weight gain, and maternal smoking were uniquely associated with GAA in START (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate models using maternal characteristics to predict gestational age, epigenetic gestational age, acceleration of gestational age in CHILD and START.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate association of maternal and offspring characteristics with epigenetic GA and GAA\u003c/h2\u003e \u003cp\u003eNewborns from mothers with GDM on average had a lower epigenetic GA in both CHILD (0.6 weeks, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.98\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02; \u003cb\u003eSuppl. Table\u0026nbsp;7\u003c/b\u003e) and START (0.18 weeks, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.58\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039; \u003cb\u003eSuppl. Table\u0026nbsp;8\u003c/b\u003e), but more severely in CHILD as compared to START. Parity was significantly associated with epigenetic GA in both CHILD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0016, \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and START (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0014), indicating that the number of previous births reduces epigenetic GA. Additionally, in START, the age of the mother was negatively associated with both GA and epigenetic GA (\u003cb\u003eSuppl. Table\u0026nbsp;8\u003c/b\u003e). However, a subsequent subset analysis revealed that maternal age was not associated with epigenetic age when adjusted for parity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49) and that it was not associated with epigenetic GA when stratified based on the number of previous pregnancies (\u003cb\u003eSuppl. Figure\u0026nbsp;6\u003c/b\u003e). Further, male sex was only associated with GAA in CHILD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0044; FDR adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036; \u003cb\u003eSuppl. Table\u0026nbsp;7\u003c/b\u003e) but not in START (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14; \u003cb\u003eSuppl. Table\u0026nbsp;8\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAs expected, the associations between epigenetic GA and newborn weight and length were significant in both South Asian and white European cohorts (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0025; \u003cb\u003eSuppl. Tables\u0026nbsp;7 and 8\u003c/b\u003e). The most interesting contrast was the association with newborn PI, whereby a positive association with GA (0.063 weeks; \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.89 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and epigenetic GA (0.034 weeks; \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) was observed in START, but it was negatively associated with GAA in CHILD (0.032 weeks; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0009; \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), despite newborn weight and height being similarly positively associated with GA and epigenetic GA in both cohorts. Both epigenetic GA and GAA were positively associated with 1-year length in CHILD (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.02), and additionally GAA with 1-year PI and 2-year length (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003cb\u003eSuppl. Table\u0026nbsp;7\u003c/b\u003e). We did not observe any association between anthropometrics at subsequent follow-up visits and GA, epigenetic GA, or GAA in START (\u003cb\u003eSuppl. Table\u0026nbsp;8\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to comparatively examine DNAm signatures of GA in two ethnic populations (living in the same country) using cord blood. We found consistent DNAm signatures of gestational age at individual CpG levels and confirmed that the Bohlin's epigenetic GA clock correlated well with chronological GA in white Europeans and can be generalized to South Asians. In both populations, the GA clock was positively linked to newborn weight and length, and negatively to gestational diabetes, newborn female sex, and parity. Unique to South Asians, the GA clock was also associated with a higher newborn PI. We confirmed the associations of parity and newborn sex with GAA in both populations, and discovered that white European newborn males exhibited twice as much accelerated GA as compared to South Asians.\u003c/p\u003e \u003cp\u003ePrevious EWASs of GA have found overlapping signals with other outcomes, such as birth weight, while EWASs of birthweight were enriched for CpGs previously associated with prenatal smoking, folic acid intake, and maternal hypertension or pre-eclampsia (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). Since the strongest association we found with epigenetic GA clock was GDM, we were interested in whether GDM or T2D genetics could be implicated in the epigenetics of GA. We found two CpGs significantly associated with GA in START that were present in the \u003cem\u003eTCF7L2\u003c/em\u003e gene, whose risk alleles increase the risk of T2D by reducing insulin secretion and are also associated with a lower BMI (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). This lends support to the hypothesis that GDM in South Asians is predominantly due to insulin deficiency, which is consistent with their lower birth weight (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e), whereby comparatively white Europeans may have a stronger insulin resistance component. Meanwhile, \u003cem\u003eGLIS3\u003c/em\u003e is another recognized gene for diabetes that is associated with the development of beta cells (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e) and neonatal diabetes (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). Further, \u003cem\u003eADA\u003c/em\u003e gene, which has been linked to T2D (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e) and several serum metabolites (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e), was also mapped to CpGs significantly associated in START but was just below statistical significance in CHILD. Overall, there was an overrepresentation of genes that were known to associate with T2D in both START and CHILD, but with a more pronounced enrichment in START. The shared genes could be part of the mechanisms through which metabolic risks are transmitted from mothers to offspring, contributing to a transgenerational cycle of metabolic disorders (\u003cspan additionalcitationids=\"CR89\" citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite widespread differences in DNAm patterns between South Asians and white Europeans in an adult population (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e), at an aggregate level, the Bohlin clock consisting of 132 CpGs produced a robust measure of biological GA with comparable performance in both cohorts. While the epigenetic GA clocks can adequately capture biological GA in both populations, they displayed subtle yet differential associations with maternal exposures and effects on offspring. The multitude of maternal exposures, ranging from nutrition, substance use, and metabolic factors to chronic health conditions, presented a complex tapestry of influences on GA, epigenetic GA and GAA. In general, South Asian women had low to no smoking and a diet that was mostly plant-based, and their newborns often exhibited a lower birthweight and a lower GA as compared to white Europeans. In this particular population, we found evidence of Western dietary pattern to influence GA and GAA. But this should be interpreted in the context of differences in the correlation between two populations: a plant-based diet score was positively correlated with Western and Health Conscious diets in South Asian women (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.35 and 0.32), but a choice of plant-based diet implied a reduced Western diet (\u003cem\u003er\u003c/em\u003e = -0.21) and a strong preference for Health Conscious diet (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.64) in white European women (\u003cb\u003eSuppl. Figure\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn both cohorts, parity and newborn female sex were found to be negatively associated with epigenetic GA and GAA. While both maternal age and parity have been shown to associate with GAA (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), our univariate and multivariate analyses pointed to parity being independently associated with epigenetic GA and GAA, but not maternal age. Consistent with this claim, a previous study showed a negative association between parity and maternal telomere length, which is an indicator of cellular aging. Advanced maternal age (e.g. \u0026gt;35) is typically considered a risk factor for pregnancy-related outcomes (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). Here we showed that maternal age was negatively associated with GA and epigenetic GA in the univariate analysis, but only in the South Asian cohort, suggesting it could be capturing the effect of parity. This was supported by the observation that parity was more strongly correlated with maternal age in START (Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.45) than CHILD (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27), reflecting a higher proportion of older mothers being multiparous among South Asians. This could also explain why maternal age has the most significant impact on negative outcomes in South Asian women (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e). On the other hand, our finding that newborn males have accelerated GA in both CHILD and START is consistent with established literature that male sex was associated with accelerated aging in newborns (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and in adult populations (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn maternal health outcomes, the observed prevalence of GDM was consistent with those reported for the two populations in general (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). We observed a negative relationship between GDM and epigenetic GA in both cohorts, as well as GDM and GAA in CHILD. The direction of associations was in line with a previous report (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). However, there was no evidence to support the role of GDM on GAA in the South Asian cohort despite much higher prevalence and sample size. We were able to replicate previous findings of pre-pregnancy weight and BMI having an impact on epigenetic GA in CHILD (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), but there was a coherent indication that gestational weight gain might be the more relevant exposure to GA and epigenetic GA in both cohorts.\u003c/p\u003e \u003cp\u003eThe findings of this study should be interpreted in the light of several limitations. Cord blood DNAm in newborns reflects the regulation of gene activity during embryonic development, cellular differentiation, and response to environmental factors, and is thus a valuable source of information for assessing maternal risk factors and predicting future health outcomes. However, depending on the tissue types, resolution of the epigenetic data (e.g. single cell vs. tissue), technology (HM450K vs. EPIC), and method of GA clock construction, the performance of existing GA clocks varied substantially. The main observation is that the barriers to trans-ethnic EWAS analysis lie in various choices of technical and study design. For example, different tissue types might reveal tissue-specific responses, a placental clock could produce an elevated association with maternal exposures vs. offspring outcomes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), which is complementary to existing studies of GA using cord blood. We have shown that DNAm clock, while universal in its foundational concept, can exhibit specific patterns and variations when analyzed across different ethnic groups. Factors contributing to these disparities might range from genetic backgrounds to varied environmental and socio-cultural practices that might not be readily available, such as alcohol consumption (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e) and air pollution (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e), both are known to alter epigenetics. These discrepancies, while enriching our understanding of epigenetic variability between ethnic groups, also serve as a clarion call for tailored healthcare and research approaches across populations.\u003c/p\u003e \u003cp\u003eIn summary, this paper comparatively examined the transferability and health implications of epigenetic GA and GAA in white European and South Asian birth cohorts living in the same country. Two key findings are the consistent associations of parity, GDM, and newborn sex with epigenetic GA in both South Asian and white European cohorts, but with weaker effects observed in the South Asian cohort; and the positive association between epigenetic GA and newborn ponderal index that is exclusive to South Asian newborns. The epigenetic GA clock presents a promising avenue to bridge our understanding of maternal exposures and offspring health outcomes, and these differentiating characteristics of associations across the cohorts, enrich this narrative. Further research in the South Asian population, both adult and pediatric, is needed to strengthen these findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCpGs: 5\u0026apos;\u0026mdash;C\u0026mdash;phosphate\u0026mdash;G\u0026mdash;3\u0026apos;\u003c/p\u003e\n\u003cp\u003eDNAm: DNA methylation\u003c/p\u003e\n\u003cp\u003eEWAS: epigenome-wide association study\u003c/p\u003e\n\u003cp\u003eGA: gestational age\u003c/p\u003e\n\u003cp\u003eGAA: gestational age acceleration\u003c/p\u003e\n\u003cp\u003eGDM: gestational diabetes mellitus\u003c/p\u003e\n\u003cp\u003eGWAS: genome-wide association study\u003c/p\u003e\n\u003cp\u003eOGTT: oral glucose tolerance test \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePI: ponderal index\u003c/p\u003e\n\u003cp\u003eT2D: type 2 diabetes\u003c/p\u003e\n\u003cp\u003eWHR: waist-to-hip ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement \u003c/strong\u003eEthical approval was obtained independently from the Hamilton Integrated Research Ethics Board: CHILD (REB 07\u0026ndash;2929) and START (REB 10\u0026ndash;640). CHILD was additionally approved by the respective Human Research Ethics Boards at McMaster University, the Universities of Manitoba, Alberta, and British Columbia, and the Hospital for Sick Children. Legal guardians of each participant provided written informed consent. Written informed consent was obtained from the parent/guardian (participating mother) for each study separately. We also have now obtained additional ethics board approval from HiREB (REB 16592) for using the data from the two cohorts together without additional consent from the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003eConsent for publication \u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e The EWAS summary statistics are available from the EWAS catalog at https://www.ewascatalog.org/?study=27717397_gestational_age_ultrasoundestimated_gestational_age. Summary statistics generated in the current study, including a total of 2 primary association studies and the meta-analyzed results are available upon request. All scripts to reproduce the results and figures can be found at https://github.com/WeiAkaneDeng/EpigeneticResearch/tree/main/EpigenticGA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003eFunding \u003c/strong\u003eThis study was funded by the Canadian Institutes of Health Research Metabolomics Team Grant: MWG-146332. The Canadian Institutes of Health Research (CIHR), Debbie and Don Morrison and the Allergy, Genes, and Environment Network of Centres of Excellence (AllerGen NCE), Women\u0026apos;s and Children\u0026apos;s Health Research Institute provided core support for establishing the CHILD Study. Dr. Anand is supported by a Tier 1 Canada Research Chair in Ethnicity and CVD and Heart, Stroke Foundation Chair in Population Health, a grant from the Canadian Partnership Against Cancer, Heart and Stroke Foundation of Canada and Canadian Institutes of Health Research. Dr. Azad is supported by a Tier 2 Canada Research Chair in the Developmental Origins of Chronic Disease. Dr. Subbarao is supported by a Tier 1 Canada Research Chair in Pediatric Asthma and Lung Health. Dr. Turvey is supported by a Tier 1 Canada Research Chair in Pediatric Precision Health.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions \u003c/strong\u003eWQD conceptualized the study, designed the methodology, performed data analyses and visualization, and prepared the first draft and subsequent revisions. MP, SMA, SLW, KMM, GP, SAA contributed to the interpretation of data and results and substantively revised the manuscript. NCawte contributed to methylation data curation, pre-processing, and quality control methodology. NC was responsible for phenotypic data curation and pre-processing. PS, SET, TJM, PM, MBA, and ES contributed to data curation, the interpretation of data, and revision. SMA, NC, SSA contributed to project administration. GP and SSA contributed to study conceptualization and data curation. SSA was responsible for funding acquisition and resources. All authors edited and critically reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003eWe express our sincere gratitude to all the participating families and the START and CHILD study teams, including interviewers, nurses, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, and receptionists. We thank the CHILD Cohort Study (CHILD) participant families for their dedication and commitment to advancing health research. CHILD was initially funded by CIHR and AllerGen NCE. Visit CHILD at childstudy.ca. We would like to acknowledge the Genetic and Molecular Epidemiology Laboratory (GMEL), an associate of Hamilton Health Sciences and McMaster University, for their indispensable contributions to this work. The technical staff of GMEL conducted all epigenetic profiling, including sample processing and other technical operations.\u003c/p\u003e\n\u003cp\u003eWe thank the members of the Nutrigen Alliance for providing the data: Sonia S. Anand; Stephanie A. Atkinson; Meghan Azad; Allan B. Becker; Jeffrey Brook; Judah A Denburg; Dipika Desai; Russell J. de Souza; Milan K. Gupta; Michael Kobor; Diana L. Lefebvre; Wendy Lou; Piushkumar J. Mandhane; Sarah McDonald; Andrew Mente; David Meyre; Theo J. Moraes; Katherine M. Morrison; Guillaume Par\u0026eacute;; Malcolm R. Sears; Padmaja Subbarao; Koon K. Teo; Stuart E. Turvey; Julie Wilson; Salim Yusuf; Gita Wahi; Michael A. Zulyniak.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHorvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013 Oct 21;14(10). \u003c/li\u003e\n\u003cli\u003eHannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda SV, et al. Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates. Mol Cell. 2013;49(2). \u003c/li\u003e\n\u003cli\u003eBocklandt S, Lin W, Sehl ME, S\u0026aacute;nchez FJ, Sinsheimer JS, Horvath S, et al. Epigenetic predictor of age. PLoS One. 2011;6(6). \u003c/li\u003e\n\u003cli\u003eMcEwen LM, O\u0026rsquo;Donnell KJ, McGill MG, Edgar RD, Jones MJ, MacIsaac JL, et al. The PedBE clock accurately estimates DNA methylation age in pediatric buccal cells. 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Epigenome-wide meta-analysis of methylation in children related to prenatal NO2 air pollution exposure. Environ Health Perspect. 2017;125(1). \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 3","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cord blood DNA methylation, gestational age at birth, epigenetic gestational age, accelerated gestational age","lastPublishedDoi":"10.21203/rs.3.rs-3997065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3997065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEpigenetic modifications, particularly DNA methylation (DNAm) in cord blood, are an important biological marker of how external exposures during gestation can influence the \u003cem\u003ein-utero\u003c/em\u003e environment and subsequent offspring development. Despite the recognized importance of DNAm during gestation, comparative studies to determine the consistency of these epigenetic signals across different ethnic groups are largely absent. To address this gap, we first performed epigenome-wide association studies (EWAS) of gestational age (GA) using newborn cord blood DNAm comparatively in a white European (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;342) and a South Asian (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;490) birth cohort living in Canada. Then, we capitalized on established cord blood epigenetic GA clocks to examine the associations between maternal exposures, offspring characteristics and epigenetic GA, as well as GA acceleration, defined as the residual difference between epigenetic and chronological GA at birth.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIndividual EWASs identified 1,211 and 1,543 differentially methylated CpGs associated with GA in white European and South Asian cohorts, respectively, with a similar distribution of effects. We confirmed that Bohlin\u0026rsquo;s cord blood GA clock was robustly correlated with GA in white Europeans (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.0\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;54\u003c/sup\u003e) and South Asians (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;64\u003c/sup\u003e). In both cohorts, Bohlin\u0026rsquo;s clock was positively associated with newborn weight and length, and negatively associated with parity, newborn female sex, and gestational diabetes. Exclusive to South Asians, the GA clock was positively associated with the newborn ponderal index, while pre-pregnancy weight and gestational weight gain were strongly predictive of increased epigenetic GA in white Europeans. Important predictors of GA acceleration included gestational diabetes mellitus, newborn sex, and parity in both cohorts.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results demonstrate the consistent DNAm signatures of GA and the utility of Bohlin\u0026rsquo;s GA clock across the two populations. Although the overall pattern of DNAm is similar, its connections with the mother's environment and the baby's anthropometrics can differ between the two groups. Further research is needed to understand these unique relationships.\u003c/p\u003e","manuscriptTitle":"Consistent Cord Blood DNA Methylation Signatures of Gestational Age between South Asian and white European Cohorts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 19:50:16","doi":"10.21203/rs.3.rs-3997065/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-22T04:52:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-17T16:45:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6b926fde-4761-4c22-bbd3-ea1a63aebe8d","date":"2024-03-08T09:35:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-08T06:28:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-04T23:30:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-04T23:29:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Epigenetics","date":"2024-02-28T15:11:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"668886b5-45c1-44d1-8013-5fa23ba4c93e","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-23T09:01:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 19:50:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3997065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3997065","identity":"rs-3997065","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}