DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies.

RESEARCH Open Access DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies Florianne O. L. Vehmeijer 1,2,3† , Leanne K. Küpers 4,5,6† , Gemma C. Sharp 4 , Lucas A. Salas 7,8,9,10 , Samantha Lent 11 , Dereje D. Jima 12,13 , Gwen Tindula 14 , Sarah Reese 15 , Cancan Qi 16,17 , Olena Gruzieva 18,19 , Christian Page 20,21 , Faisal I. Rezwan 22,23 , Philip E. Melton 24,25 , Ellen Nohr 26,27 , Geòrgia Escaramís 10,28,29 , Peter Rzehak 30 , Anni Heiskala 31 , Tong Gong 32 , Samuli T. Tuominen 33 , Lu Gao 34 , Jason P. Ross 35 , Anne P. Starling 36,37 , John W. Holloway 23,38 , Paul Yousefi 4 , Gunn Marit Aasvang 39 , Lawrence J. Beilin 40 , Anna Bergström 18,19 , Elisabeth Binder 41,42 , Leda Chatzi 43 , Eva Corpeleijn 6 , Darina Czamara 41 , Brenda Eskenazi 44 , Susan Ewart 45 , Natalia Ferre 46 , Veit Grote 30 , Dariusz Gruszfeld 47 , Siri E. Håberg 20 , Cathrine Hoyo 13,48 , Karen Huen 14 , Robert Karlsson 32 , Inger Kull 49,50 , Jean-Paul Langhendries 51 , Johanna Lepeule 52 , Maria C. Magnus 4,5,20 , Rachel L. Maguire 48,53 , Peter L. Molloy 35 , Claire Monnereau 1,3 , Trevor A. Mori 40 , Emily Oken 54 , Katri Räikkönen 33 , Sheryl Rifas-Shiman 54 , Carlos Ruiz-Arenas 8,9,10 , Sylvain Sebert 31 , Vilhelmina Ullemar 32 , Elvira Verduci 55 , Judith M. Vonk 6,17 , Cheng-jian Xu 16,17,56,57 , Ivana V. Yang 36,58,59 , Hongmei Zhang 60 , Weiming Zhang 61 , Wilfried Karmaus 60 , Dana Dabelea 36,37,62 , Beverly S. Muhlhausler 63 , Carrie V. Breton 34 , Jari Lahti 33,64 , Catarina Almqvist 32,65 , Marjo-Riitta Jarvelin 31,66,67,68 , Berthold Koletzko 30 , Martine Vrijheid 8,9,10 , Thorkild I. A. Sørensen 4,69 , Rae-Chi Huang 70 , Syed Hasan Arshad 38,71 , Wenche Nystad 72 , Erik Melén 49,50 , Gerard H. Koppelman 16,17 , Stephanie J. London 15 , Nina Holland 14 , Mariona Bustamante 8,9,10 , Susan K. Murphy 53 , Marie-France Hivert 54,73,74 , Andrea Baccarelli 75† , Caroline L. Relton 4† , Harold Snieder 6† , Vincent W. V. Jaddoe 1,2,3† and Janine F. Felix 1,2*† Abstract Background: DNA methylation has been shown to be associated with adiposity in adulthood. However, whether similar DNA methylation patterns are associated with childhood and adolescent body mass index (BMI) is largely unknown. More insight into this relationship at younger ages may have implications for future prevention of obesity and its related traits. (Continued on next page) © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. * Correspondence: [email protected] † Florianne O.L. Vehmeijer and Leanne K. Küpers are authors who contributed equally to the work. † Andrea Baccarelli, Caroline L. Relton, Harold Snieder, Vincent W.V. Jaddoe, and Janine F. Felix are authors jointly supervised the work. 1 The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Room Na-2918, Erasmus MC, PO Box 2040, 3000 CA Rotterdam, the Netherlands 2 Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands Full list of author information is available at the end of the article Vehmeijer et al. Genome Medicine (2020) 12:105 https://doi.org/10.1186/s13073-020-00810-w (Continued from previous page) Methods: We examined whether DNA methylation in cord blood and whole blood in childhood and adolescence was associated with BMI in the age range from 2 to 18 years using both cross-sectional and longitudinal models. We performed meta-analyses of epigenome-wide association studies including up to 4133 children from 23 studies. We examined the overlap of findings reported in previous studies in children and adults with those in our analyses and calculated enrichment. Results: DNA methylation at three CpGs (cg05937453, cg25212453, and cg10040131), each in a different age range, was associated with BMI at Bonferroni significance, P<1.06×10 −7 , with a 0.96 standard deviation score (SDS) (standard error (SE) 0.17), 0.32 SDS (SE 0.06), and 0.32 BMI SDS (SE 0.06) higher BMI per 10% increase in methylation, respectively. DNA methylation at nine additional CpGs in the cross-sectional childhood model was associated with BMI at false discovery rate significance. The strength of the associations of DNA methylation at the 187 CpGs previously identified to be associated with adult BMI, increased with advancing age across childhood and adolescence in our analyses. In addition, correlation coefficients between effect estimates for those CpGs in adults and in children and adolescents also increased. Among the top findings for each age range, we observed increasing enrichment for the CpGs that were previously identified in adults (birth P enrichment = 1; childhood P enrichment =2.00×10 −4 ;adolescenceP enrichment =2.10×10 −7 ). Conclusions: There were only minimal associations of DNA methylation with childhood and adolescent BMI. With the advancing age of the participants across childhood and adolescence, we observed increasing overlap with altered DNA methylation loci reported in association with adult BMI. These findings may be compatible with the hypothesis that DNA methylation differences are mostly a consequence rather than a cause of obesity. Keywords: Body mass index, Childhood obesity, DNA methylation, Epigenetics Background An accumulating body of evidence suggests that exposures in early life are associated with childhood BMI [1]. It is hypothesized that changes in DNA methylation may underlie the associations of early-life exposures with childhood adiposity [2–4]. Thus far, most of the evidence regarding DNA methylation and adiposity stems from adult studies [5–9]. The largest epigenome-wide association study (EWAS) in adults identified cross-sectional associations between DNA methylation at 187 loci and BMI in over 10, 000 participants [5]. Previous studies of the associations between epigenome-wide DNA methylation and childhood and adolescent adiposity were small and inconclusive [10– 16]. Candidate gene studies in childhood identified associations of DNA methylation in cord and childhood blood with measures of adiposity [17–24]. Epigenome-wide association studies in children and adolescents, with sample sizes ranging from 40 to 700 individuals, identified a limited number of cytosine-phosphate-guanine sites (CpGs) associated with BMI [11–13,15,25]. Although findings of some studies suggest that differences in DNA methylation may precede the development of adiposity, recent studies in adults, using methods such as Mendelian randomization, posit that alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause [4,5,9,26,27]. The direction of any causal pathway has not been robustly appraised in children. Obtaining more knowledge on the association between DNA methylation and adiposity already in childhood may have implications for future prevention of obesity and its related traits. We performed a meta-analysis of epigenome-wide association studies of BMI in up to 4133 participants from 23 studies. We assessed associations of DNA methylation in cord blood, in childhood and adolescence with BMI in children aged 2–18 years. We also compared the effect estimates and examined whether there was enrichment in our data for CpGs previously identified for their association with adolescent and adult adiposity. Methods Participants We meta-analyzed epigenome-wide association studies of cord or whole blood methylation with childhood or adolescent body mass index (BMI). We used data from up to 4133 participants from 23 studies collaborating in the Pregnancy And Childhood Epigenetics (PACE) Consortium, LifeCycle Project, and NutriProgram Project (Additional file 1: Table S1A-D and Additional file 2: Supplementary Methods) [28,29]: ALSPAC, BAMSE, CHAMACOS, CHOP Study, CHS, DOMInO Trial, GECKO Drenthe cohort, Generation R Study, GOYA study, Healthy Start Study, HELIX, INMA, IOW F1, IOW F2, MoBa1, MoBa2, NEST, NFBC 1986, PIAMA study, PREDO study, Project Viva, Raine, and STOPPA (full names in Supplementary Methods). Cohort participants were mainly of European ancestry, but there were also cohorts with (partly) non-European ethnicities (African, Hispanic, and Aboriginals). Most cohorts are prospective birth cohorts. We excluded multiple births, siblings (maximum one child per family), physiciandiagnosed syndromic obesity cases, and any type of maternal diabetes (including gestational diabetes). Informed consent was obtained for all participants, and all studies Vehmeijer et al. Genome Medicine (2020) 12:105 Page 2 of 15 received approval from their local ethics committees (see Additional file 2: Supplementary Methods). DNA methylation DNA methylation was measured in cord blood and whole blood samples, in children and adolescents using the Illumina Infinium® HumanMethylation450 BeadChip assay (Illumina, San Diego, CA, USA) [30]. Each cohort independently conducted their preferred quality control and normalization method, see Additional file 2: Supplementary Methods for details. Untransformed normalized beta values of individual CpG sites were used as exposure variables. If multiple measurements of DNA methylation and BMI were available within an age range, we used the oldest age within that range for which BMI and DNA methylation were available at the same time point. Outlying methylation beta values were excluded using the following method: values < (25th percentile −3*interquartile range (3IQR)) and values > (75th percentile + 3IQR) were removed [31]. DNA methylation is expressed as the proportion of alleles at which the DNA was methylated at a specific site and hence takes values from zero to one. Childhood BMI Height and weight were measured in each study using established protocols as described in detail in the Additional file 2: Supplementary Methods. The primary outcome was BMI, calculated as weight/height 2 in kg/m 2 ,ona continuous scale measured in three age ranges: 2–5 years (early childhood), 5–10 years (late childhood), and 14–18 years (adolescence). If multiple BMI and DNA methylation measurements were available, we used the measurements attheoldestagewithintheagerangeforwhichBMIand DNA methylation were available at the same time point. BMI values were then transformed into sexand ageadjusted standard deviation scores (SDS) using LMSGrowth [32–34]. The International Obesity Task Force (IOTF) standard was used to define cutoffs for BMI for underweight, normal weight, overweight, and obesity in children, created with the British 1990 growth reference and information of participants on BMI, sex, and age [35,36]. In secondary analyses, we used a binary outcome variable with normal-weight children as controls and overweight or obese children as cases. Underweight children were excluded from these secondary analyses. If a study had ≤10 participants in one of the (case or control) groups, this study was excluded from the secondary analyses. Covariates Covariates included in all models were maternal covariates: maternal age, maternal educational level (cohort definition), maternal smoking status during pregnancy (any smoking versus no smoking), maternal pre-pregnancy or early pregnancy BMI and parity (multiparous versus nulliparous), and gestational age at birth. For details on cohort-specific collection methods, see Additional file 2: Supplementary Methods. We estimated white blood cell proportions (B cells, CD8+ T cells, CD4+ T cells, granulocytes, NK cells, and monocytes) using the reference-based Houseman method with the Reinius reference in the minfi package in R [37–40]. A sensitivity analysis using the cord blood-specific Bakulski reference was performed in the Generation R and ALSPAC studies [41]. Batch effects were adjusted for using cohort-specific methods, see Additional file 2: Supplementary Methods. Additional covariates added in the cross-sectional childhood analyses were birth weight and breastfeeding. The adolescent analyses were additionally adjusted for adolescent age, sex, own smoking status, and puberty status. Puberty status was categorized into early puberty (if both breast and pubic hair Tanner stages (or comparable classification) were 1, 2, or 3 and if girls were pre-menarcheal or boys did not have voice change yet) and late puberty (if either breast or pubic hair Tanner stages (or comparable classification) were 4 or 5 or if girls were post-menarcheal or boys had had their voice change) [42–44]. Further details are provided in the study-specific Additional file 2: Supplementary Methods. Study-specific analyses Associations of DNA methylation with childhood or adolescent BMI were performed in individual studies on participants with complete data on all covariates. In studies with more than one ethnic group, each group was analyzed separately. We used robust linear regression models for the continuous outcome of BMI-SDS and generalized linear regression models for the case/ control analyses of overweight and obesity versus normal weight, according to a pre-specified analysis plan. EWAS analyses were conducted using DNA methylation at three time points: birth, childhood and adolescence, and BMI data collected at three time points: early childhood (2–5 years), late childhood (5–10 years), and adolescence (12-18y) (Table 1). We categorized the childhood period into early and late childhood to overcome any agespecific effects and the potential influence of the adiposity rebound on the results [45]. Depending on data availability, cohorts participated in one or more of four analyses: (analysis A) longitudinal associations of cord blood DNA methylation with early childhood BMI (2– 5 years; 3295 children from 13 studies), (analysis B) longitudinal associations of cord blood DNA methylation with late childhood BMI (5–10 years; 4133 children from 12 studies), (analysis C) cross-sectional associations of childhood blood DNA methylation with childhood BMI (2–10 years; 3371 children from 11 studies), and (analysis D) cross-sectional associations of adolescent blood Vehmeijer et al. Genome Medicine (2020) 12:105 Page 3 of 15 DNA methylation with adolescent BMI (14–18 years; 2842 adolescents from 7 studies) (Table 1). Participating studies per analysis are shown in Additional file 1: Table S1A-D. Cord blood analyses were adjusted for maternal age, educational level, smoking status, pre-pregnancy or early pregnancy BMI, parity, gestational age, batch, and estimated cell type proportions. The cross-sectional analysis in childhood was additionally adjusted for child covariates birth weight and breastfeeding; in contrast, the cross-sectional analysis in adolescence was adjusted for the same covariates as analysis C plus adolescent sex, age, and smoking and puberty status. Meta-analyses After performing quality control on all studies, we combined results in a fixed-effects inverse variance-weighted meta-analysis using METAL [46,47]. All follow-up analyses were conducted in R [39]. The meta-analyses were done independently by two study groups, and the results were compared. After exclusion of probes that were measured in only one study, that mapped to X and Y chromosomes and probes that co-hybridized to alternate sequences (cross-reactive probes), we included 429,959 probes for analysis A, 429,959 probes for analysis B, 429, 957 probes for analysis C, and 428,967 probes for analysis D [48,49]. In the result files of the main metaanalyses, we flagged probes that map to DNA containing a single nucleotide polymorphism (SNP), to repetitive sequence elements, or to DNA harboring an INDEL (Additional file 3: Table S2A-D) [48,49]. We corrected for multiple testing using both the Bonferroni correction, which gives a significance threshold of P< 1.16 × 10 −7 (0.05/429,959), and the less stringent false discovery rate (FDR) threshold using the method by Benjamini and Hochberg [50]. EWAS results were summarized as mean (and standard error) differences in BMI-SDS per 10% increase in methylation for each CpG. We created volcano plots to visualize magnitude and direction of effect (reduced or increased methylation) along with the level of statistical significance. We calculated the I 2 statistic to explore heterogeneity across studies. The I 2 estimates the proportion of variation in the meta-analysis results for each CpG site that is due to between-study differences rather than random/sampling variation. Heterogeneity was defined as an I 2 value of > 50 and shown graphically in forest plots. We performed leave-one-out analyses, in which we reran the main meta-analysis repeatedly with one of the 23 studies removed each time, to explore if any study influenced individual findings. We enhanced the annotation provided by Illumina using the UCSC Genome Browser. All of the annotations use the human February 2009 (GRCh37/hg19) assembly. We updated the gene names manually in all result files using HUGO gene nomenclature, and in case they were not found there, we used the NCBI gene website on November 5, 2019 [51–53]. To explore the associations for the extreme upper values of the BMI distribution, we performed case/control analyses (overweight and obesity versus normal weight). Underweight children were excluded from these analyses, leading to sample sizes of N= 491 cases and 2540 controls (analysis A), N= 707 cases and 3217 controls (analysis B), N= 644 cases and 2567 controls (analysis C), and N= 507 cases and 2188 controls (analysis D) (Additional file 4: Table S3A-D). To examine whether any of the Bonferroni-significant or FDR-significant CpGs in our analyses were close to Table 1 Overview of main analyses, secondary analyses, and sensitivity analyses Analysis Main analyses Secondary analyses: binary model (N), cases = overweight and obesity, controls = normal weight Sensitivity analyses DNA methylation in the blood BMI SD scores NEuropeans only (N) Without studies > 30% overweight and obesity (N) Cord blood analyses A Birth (cord blood) Early childhood (2–5 years) 3295 Cases = 491 Controls = 2540 2902 2989 B Birth (cord blood) Late childhood (5–10 years) 4133 Cases = 707 Controls = 3217 3657 3489 Cross-sectional analyses C Childhood (whole blood) Childhood (2–10 years) 3371 Cases = 644 Controls = 2567 3026 3171 D Adolescence (whole blood) Adolescence (12–18 years) 2842 Cases = 507 Controls = 2188 NA NA Analyses A and B were adjusted for maternal age, educational level, smoking status, pre-pregnancy or early pregnancy BMI, parity, gestational age at birth, batch, and estimated cell type proportions Analyses C was adjusted for maternal age, educational level, smoking status, pre-pregnancy or early pregnancy BMI, parity, gestational age at birth, batch, estimated cell type proportions, birth weight, and breastfeeding Analyses D was adjusted for maternal age, educational level, smoking status, pre-pregnancy or early pregnancy BMI, parity, gestational age at birth, batch, estimated cell type proportions, birth weight, breastfeeding, adolescent sex, age smoking and puberty status Vehmeijer et al. Genome Medicine (2020) 12:105 Page 4 of 15 BMI SNPs, we assessed if these CpGs were located within a 4-Mb window (± 2 Mb) surrounding the 15 genetic loci associated with childhood body mass index [2, 54]. For the FDR-significant CpGs that were flagged because they were potentially influenced by a SNP, we visually inspected density plots in the Generation R Study to see whether these deviated from unimodality (Additional file 5: Supplementary Information, Fig. S6). To explore DNA methylation patterns in the regions around the significant CpGs, we assessed the associations of all CpGs located within a 10-kb window (± 5 kb) surrounding these CpGs with BMI in the relevant models (Additional file 6: Table S4). Sensitivity analyses To explore whether ethnic heterogeneity may have affected our results, we repeated the meta-analyses including studies with participants of European ancestry only (N= 2902 (excluding three studies for analysis A), N= 3657 (excluding three studies for analysis B), N=3026 (excluding two studies for analysis C)), the largest ethnic subgroup (Additional file 7: Table S5A-C). Ethnicity was defined using self-reported questionnaires unless specified otherwise in the study-specific Supplementary Methods (Additional file 2). We performed additional analyses excluding studies with a high percentage (> 30% (percentage calculated after exclusion of underweight children)) of children with overweight and obesity to explore whether any associations found may be driven by more extreme values of BMI (included N= 2989 (excluding two studies for analysis A), N= 3489 (excluding four studies for analysis B), N= 3171 (excluding one study for analysis C) (Additional file 8: Table S6A-C). We also performed a third, conservative, sensitivity analysis in all age groups, excluding cohorts of non-Europeans, studies with a high percentage (> 30%) of children with overweight or obesity, and studies in which the sample was selected on or enriched for any particular exposure or outcome (Additional file 9: Table S7A-D). Comparison with previous findings We explored whether CpG sites associated with childhood, adolescent, or adult BMI in previous studies were associated with BMI in our data. For previous candidate gene studies and smaller EWASs (N< 1000), we performed a look-up of the hits, using a Bonferroni-adjusted Pvalue cutoff per study, so for each study, the cutoff was 0.05/(NCpGs from that study) (Additional file 10:Table S8) [7,15,17,18,20,21,23,24,55]. If the specific CpGs from a study were not available in our dataset, we looked up all CpGs annotated to the relevant genes [17,24]. To establish whether the CpG sites associated with BMI in previously reported larger EWASs (N≥1000) were overrepresented among our CpGs with the smallest Pvalues, we examined the absolute overlap of the top CpGs from literature with the top CpGs in our analyses [5,6,9,25, 26,56–59]. The latter were defined using two cutoffs: a stringent cutoff of Pvalue < 1 × 10 −5 and a more lenient one of Pvalue < 0.05. (Additional file 11: Table S9). We used a hypergeometric test to calculate enrichment with the phyper function in the R Stats package in R. We examined the 187 CpGs identified in the largest adult study (N= 10,261) to date in more detail in our results [5]. We tested whether the enriched CpGs significantly overlapped between our analyses using chi-square tests. We used Pearson’s correlation coefficients to examine the correlations between the effect estimates of these 187 CpGs in adults and those in our analyses [5]. Using Fisher’s exact test, we calculated whether the correlation coefficients at the various ages were significantly different from each other. Functional analyses We explored the potential functional interpretation of the most significantly associated CpGs (Pvalue < 1 × 10 −4 ) in all models using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We used the missMethyl package, which enabled us to correct for the number of probes per gene on the 450K array, based on the May 5, 2020, version of the GO and the October 23, 2019, version of the KEGG source databases [60]. To filter out the large, general pathways, we set the number of genes for each gene set between 5 and 2000, respectively. We report nominal Pvalues < 0.05 and FDR for enrichment (Additional file 12, Table S10). Results Participants We included 2842 to 4133 participants from 23 independent cohorts from the Pregnancy And Childhood Epigenetics (PACE) Consortium [28]. We assessed associations of DNA methylation in cord blood with BMI in early childhood (2–5years)(N= 3295, analysis A), DNA methylation in cord blood with BMI in late childhood (5–10 years) (N= 4133, analysis B), DNA methylation in childhood with BMI in childhood (2–10 years) (N= 3371, analysis C), and DNA methylation in adolescence with BMI in adolescence (12–18 years) (N= 2842, analysis D). Details of participants and studies used in the different analyses are presented in Table 1, Additional file 1: Table S1A-D and Additional file 2: Supplementary Methods. Meta-analyses The main, secondary, and sensitivity analyses are outlined in Table 1. Genomic inflation factors (lambdas) for the main meta-analyses ranged between 0.97 and 1.27 (Additional file 5: Supplementary information, Fig. 1a-d). Vehmeijer et al. Genome Medicine (2020) 12:105 Page 5 of 15 Genomic inflation factors (lambdas) of all cohort-specific analyses are shown in Additional file 1: Table S1A-D. The main results are shown in Table 2and Fig. 1. We did not identify associations at genome-wide significance of DNA methylation in cord blood with BMI in early childhood (analysis A, Fig. 1a, and Additional file 3: Table S2A). DNA methylation at one CpG, cg05937453 (SFRP5), in cord blood was significantly associated with latechildhood BMI (analysis B, Fig. 1b, and Additional file 3: Table S2B). For each 10% increase in DNA methylation at cg05937453 in cord blood, late-childhood BMI increased 0.96 SD (standard error (SE) 0.17). Cord blood DNA methylation at this CpG was nominally significantly associated with BMI in early-childhood (Pvalue = 0.004), but DNA methylation in childhood and adolescence was not associated with BMI in the cross-sectional analyses (Additional file 13: Table S11). In the cross-sectional analysis (analysis C), childhood DNA methylation at cg25212453 (SLC43A2) was associated with childhood BMI after Bonferroni correction. A 10% increase in DNA methylation at cg25212453 was associated with a 0.32 SD (SE 0.06) increase in childhood BMI (Fig. 1c and Additional file 3:TableS2C).DNAmethylationatthis CpG at birth and in adolescence was not associated with BMI (Additional file 13: Table S11). DNA methylation in childhood at nine additional CpGs in or near other genes was associated with childhood BMI using FDR Pvalue < 0.05 (Fig. 1c and Additional file 3:TableS2C).DNAmethylation in adolescence at cg10040131 (SFXN5) was associated with adolescent BMI after Bonferroni correction (analysis D, Fig. 1d and Additional file 3: Table 2d). A 10% increase in DNA methylation at cg10040131 was associated with a 0.32 SD (SE 0.06) higher BMI in adolescence. DNA methylation at this CpG in childhood was nominally significantly associated with childhood BMI (Pvalue = 0.0002). The association of DNA methylation at this CpG in cord blood and BMI in childhood was not significant (Additional file 13: Table S11). Associations of DNA methylation with BMI did not show a preferential direction of effect in any of the analyses (volcano plots, Additional file 5: Supplementary Information, Fig. S2A-D). We observed very little evidence of heterogeneity between studies among the Bonferroni-significantly associated CpG sites, with all I 2 ≤50 (Additional file 3: Table 2a-d and forest plots, Additional file 5: Supplementary Information, Fig. S3A, B and L). We found evidence of Fig. 1 Manhattan plots for the meta-analyses of DNA methylation and childhood or adolescent BMI. Manhattan plots showing the meta-analysis results for associations of DNA methylation in cord blood with early childhood BMI (a) and late childhood BMI (b), of DNA methylation in whole blood in childhood with childhood BMI (c), and of DNA methylation in whole blood in adolescence with adolescent BMI (d). The gray line shows the Bonferroni-corrected significance threshold for multiple testing (P< 1.06 × 10 −7 ). The orange line shows the FDR-corrected significance threshold for multiple testing Vehmeijer et al. Genome Medicine (2020) 12:105 Page 6 of 15 between-study heterogeneity (I 2 > 50) for 3 of the 9 FDRsignificantly associated CpG sites (Additional file 3:Table2c and forest plots, Additional file 5: Supplementary Information, Fig. S3C-K). The results for the twelve Bonferroni or FDR-significantly associated CpGs were stable after omitting one study at a time (leave-one-out analyses, Additional file 5: Supplementary Information, Fig. S4A-L). When BMI was dichotomized into normal and overweight/obesity, only one CpG in the cross-sectional model in childhood, cg06991974 (PRDM16-DT), showed evidence of association. In the cross-sectional model during childhood, which included 644 children with overweight/obesity and 2567 normal-weight children, DNA methylation at cg06991974 was associated with an increased risk of overweight/obesity in childhood (odds ratio (OR) 3.10, 95% confidence interval (CI) 2.08, 4.63) (Additional file 4: Table S3A-D). None of the three individual Bonferroni-significant CpGs in the three different age ranges nor the 9 FDR-significant CpGs was within a 4-Mb window surrounding the 15 known genetic loci associated with childhood body mass index [54]. Four of the 12 FDR significant CpGs contained a single-nucleotide polymorphism (SNP) [48,49]. We found no indication of non-unimodal distribution for any of these CpGs suggesting that methylation measurements at these sites were not markedly affected by SNPs (Additional file 5: Supplementary Information, Fig. S6). Two of the three Bonferroni-significant CpGs (cg05937453 and cg25212453) had other nearby CpGs within a 10-kb window (± 5 kb) measured on the 450K array (Additional file 6: Table S4). Cg05937453 (model B) was surrounded by 24 other CpGs, of which one was nominally significantly associated with BMI (Pvalue < 0.05). Both were located in the TSS200 region of SFRP5 with effect estimates in the same direction. Cg25212453 (model C) was surrounded by 13 other CpGs, of which three were nominally significant (Pvalues < 0.05). All were located in the gene body of SLC43A2 with effect estimates in the same direction. Results for Bonferroniand FDR-significant CpGs are shown in Additional file 6:TableS4. Sensitivity analyses Findings were consistent with the main results when restricted to up to 3657 participants of European ethnicity (Pearson correlation coefficients of the effect estimates across all CpG sites were 0.86–0.97 and were 0.99 across top CpG sites (Pvalue < 1 × 10 −4 ) for all models (Additional file 7: Table S5A-C)). Similarly, when the studies with a high percentage (> 30%) of children with overweight or obesity were excluded, the results were also consistent with the main analyses (Pearson correlation coefficients of the effect estimates across all CpG sites were 0.89–0.98 and were 0.99 across top CpG sites (P value < 1 × 10 −4 ) for all models (Additional file 8: Table S6A-C)). Lastly, when the studies of non-Europeans participants, a high percentage of children with overweight or obesity and studies in which the sample was selected on or enriched for any particular exposure or outcome Table 2 CpG sites at which DNA methylation was associated with child or adolescent BMI CpG CHR Location Coef SE Pvalue FDR Pvalue Nearest gene Analysis B = association of cord blood DNA methylation with late childhood BMI (5–10 years) cg05937453 10 99531765 0.96288 0.16871 1.15 × 10 −8 0.0049 SFRP5 Analysis C = cross-sectional association of whole blood DNA methylation with childhood BMI (2–10 years) cg25212453 17 1509953 0.31925 0.05978 9.27 × 10 −8 0.02075 SLC43A2 cg03500056 16 8814507 0.30577 0.05767 1.15 × 10 −7 0.02075 ABAT cg05281708 3 44690673 0.65856 0.12614 1.78 × 10 −7 0.02075 ZNF35 cg15125798 5 122621645 0.49705 0.09548 1.93 × 10 −7 0.02075 – cg04456029 12 113496126 0.27587 0.05358 2.63 × 10 −7 0.0226 DTX1 cg03431111 11 62621406 0.19261 0.03791 3.77 × 10 −7 0.0270 SNORD30;SNORD22;SNORD29; SNORD31;SNHG1 cg26889953 15 22915992 0.31743 0.06391 6.81 × 10 −7 0.0304 CYFIP1 cg19743522 12 113495566 0.33854 0.0682 6.92 × 10 −7 0.0304 DTX1 cg25877069 8 95003236 −0.45126 0.09092 6.94 × 10 −7 0.0304 – cg13931559 20 33146515 −0.84718 0.17082 7.07 × 10 −7 0.0304 MAP1LC3A Analysis D = cross-sectional association of whole blood DNA methylation with adolescent BMI (12–18 years) cg10040131 2 73178866 0.32434 0.0566 1.00 × 10 −8 0.0043 SFXN5 Coefficients (Coef) and standard errors (SE) are presented per 10% increase in the methylation level Analyses B was adjusted for maternal age, educational level, smoking status, pre-pregnancy or early pregnancy BMI, parity, gestational age, batch, and estimated cell type proportions. Analysis C was additionally adjusted for child covariates birth weight and breastfeeding, whereas analysis D was adjusted for the same covariates as analysis C plus adolescent sex, age, smoking, and puberty status Vehmeijer et al. Genome Medicine (2020) 12:105 Page 7 of 15 were all excluded, results remained strongly correlated to those from the main models. Pearson correlation coefficients of the effect estimates across all CpG sites were 0.64–0.97 and 0.95–0.99 across top CpG sites (P value < 1 × 10–4) for all models (Additional file 9: Table S7A-D). Comparison with previous findings Most CpGs identified to be associated with BMI in previous candidate gene studies or smaller EWASs (N< 1000) did not replicate in our results (Additional file 10: Table S8) [7,15,17,18,20,21,23,24,55]. When comparing the genome-wide significant findings from the largest BMI EWASs (N> 1000) in adults to our most significant findings across the four age ranges, we found an increasing overlap with age (Table 3and Additional file 11: Table S9) [5,6,9,25,26,56–59]. We used two cutoffs to select the most significant findings in our results: a Pvalue < 1 × 10 −5 , to identify “suggestive”findings, and a less stringent, nominal Pvalue < 0.05. The number of CpGs that met these criteria are provided in Table 3. First, we examined the absolute number of overlapping CpGs between the studies in adults and our findings with a Pvalue < 1 × 10 −5 and calculated enrichment. With advancing age across childhood and adolescence, we observed increasing enrichment for the 187 CpGs previously reported to be associated with adult BMI in the largest study to date (N= 10,261) [5]. For the two cord blood models, there was no overlap with the adult findings (P enrichment = 1), for the cross-sectional model in childhood 2/187 adult hits overlapped, (P enrichment = 0.0002), and for the cross-sectional model in adolescence 3/187 overlapped (P enrichment = 2.10 × 10 −7 )(Table3and Additional file 11:Table S9). Using the less stringent cutoff (Pvalue < 0.05), this trend was even clearer. The overlap between the 187 CpGs from the adult EWAS and the CpGs in our data with a Pvalue < 0.05 was 8/187 CpGs (P enrichment = 0.77, analysis A) for the association of cord blood DNA methylation and early childhood BMI and 11/187 CpGs (P enrichment = 0.30, analysis B) for the association of cord blood DNA methylation and late childhood BMI. For the cross-sectional model in childhood, the overlap was 61/187 CpGs (P enrichment = 1.97 × 10 −20 ,analysis C), and in adolescence, the overlap was 77/187 CpGs (P enrichment = 1.68 × 10 −44 , analysis D) (Table 3and Additional file 11: Table S9). Twenty-seven CpGs were among the enriched CpGs in both the childhood and the adolescent model. This overlap was not significant (P= 0.88). Correlation coefficients between the effect estimates of the 187 hits and the effect estimates for those CpGs in the four models increased with age (analysis A = −0.186 (P= 0.01), analysis B = −0.013 (P= 0.86), analysis C = 0.604 (P= 5.31 × 10 −20 ), and analysis D = 0.816 (P= 7.89 × 10 −46 ). The difference in correlation coefficients was significant for all comparisons (P’s for comparison between correlation coefficients < 0.01) except for the comparison between models A and B (P= 0.09). Effect sizes of the associations for these 187 adult BMI CpGs in our analyses increased with advancing age of children in our analyses (Additional file 5: Supplementary Information, Fig. S5). We found similar trends for enrichment of CpGs from other EWASs in adults and adolescents (Table 3)[6,9,25,26,56–59]. Of those findings from adult studies that had a nominal Pvalue (< 0.05) in our models, 17–35%werereportedbymorethanone adult study. Most of these were found in two or three studies, but four, cg06500161, cg19750657, cg12593793, and cg18181703, were reported in six or seven previous analyses. Functional analyses A functional enrichment analysis using genes linked to the CpGs with Pvalues < 1 × 10 −4 in each of the models showed no functional enrichment of Gene Ontology (GO) terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) terms (FDR < 0.05) (Additional file 12: Table S10). Discussion In this large meta-analysis of EWASs of childhood and adolescent BMI, we found little evidence of an association between DNA methylation and childhood or adolescent BMI. DNA methylation at three different CpGs, each one in a different age range, was associated with BMI in early life. With the advancing age of children in our analyses, we observed increasing enrichment of CpGs previously identified for their relation with adolescent or adult adiposity. In addition, for the 187 CpGs identified in the largest previous study of adult BMI, we found increasing effect sizes and increasing correlations between the adult effect sizes and those in our analyses, with age. Interpretation of main findings Childhood obesity is a major public health problem and associated with shortand long-term morbidity and mortality [61]. Although there is some evidence from previous studies that DNA methylation may mediate associations of pregnancy-related exposures with offspring adiposity, only few specific CpG sites have been identified [4,27]. Thus far, most of the evidence for associations of DNA methylation with adiposity stems from adult studies. In this study, we found little evidence of an association between DNA methylation and childhood or adolescent BMI. DNA methylation at three CpGs (cg05937453, cg25212453, and cg10040131), each in a different age range, was associated with BMI at Bonferroni significance, P< 1.06 × 10 −7 . However, we did observe increasing enrichment and increasing point estimates of CpGs Vehmeijer et al. Genome Medicine (2020) 12:105 Page 8 of 15 Table 3 Absolute number of overlapping CpGs and Pvalues for the enrichment of significant CpGs from previous EWASs (N> 1000) in our data Previous study (Nsites associated with BMI) Significance level Analysis A: association of cord blood DNA methylation with early childhood BMI (2–5 years) Analysis B: association of cord blood DNA methylation with late childhood BMI (5–10 years) Analysis C: crosssectional analysis of whole blood DNA methylation with childhood BMI (2–10 years) Analysis D: cross-sectional analysis of whole blood DNA methylation with adolescent BMI (12–18 years) 1×10 −5 N=7 N=8 N=51 N=26 0.05 N= 22,687 N= 20,645 N= 37,074 N= 25,292 Ali et al. [56] (3 CpGs) 1 × 10 −5 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 0.05 0 P enrichment =1 0 P enrichment =1 1/3 P enrichment = 0.24 0 P enrichment =1 Aslibekyan et al. [6](8 CpGs) 1×10 −5 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 0.05 0 P enrichment =1 0 P enrichment =1 1/8 P enrichment = 0.51 2/8 P enrichment = 0.08 Campanella et al. [57] (26 CpGs) 1×10 −5 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 1/26 P enrichment = 0.002 0.05 3/26 P enrichment = 0.16 1/26 P enrichment = 0.72 6/26 P enrichment = 0.02 11/26 P enrichment = 1.006 × 10 −7 Geurts et al. [58] (310 CpGs) 1×10 −5 0 P enrichment =1 0 P enrichment =1 2/310 P enrichment = 0.0006 2/310 P enrichment = 0.0002 0.05 12/310 P enrichment = 0.90 13/310 P enrichment = 0.73 103/310 P enrichment = 3.92 × 10 −34 125/310 P enrichment = 6.63 × 10 −70 Mendelson et al. [9] (83 CpGs) 1×10 −5 0 P enrichment =1 0 P enrichment =1 2/83 P enrichment = 4.67 × 10 −5 3/83 P enrichment = 1.81 × 10 −8 0.05 4/83 P enrichment = 0.64 8/83 P enrichment = 0.045 28/83 P enrichment = 1.36 × 10 −10 45/83 P enrichment = 3.02 × 10 −33 Sayols-Baixeras et al. [59] (96 CpGs) 1×10 −5 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 0.05 8/96 P enrichment = 0.13 9/96 P enrichment = 0.04 24/96 P enrichment = 1.53 × 10 −6 30/96 P enrichment = 1.85 × 10 −14 Sun et al. [26]black participants (36 CpGs) 1×10 −5 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 1/36 P enrichment = 0.002 0.05 3/36 P enrichment = 0.30 6/36 P enrichment = 0.007 13/36 P enrichment = 4.98 × 10 −6 22/36 P enrichment = 1.50 × 10 −18 Sun et al. [26]white participants (349 CpGs) 1×10 −5 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 0.05 12/349 P enrichment = 0.959 22/349 P enrichment = 0.12 86/349 P enrichment = 4.13 × 10 −19 116/349 P enrichment = 1.75 × 10 −54 Wahl et al. [5] (187 CpGs) 1×10 −5 0 P enrichment =1 0 P enrichment =1 2/187 P enrichment = 0.0002 3/187 P enrichment = 2.10 × 10 −7 0.05 8/187 P enrichment = 0.77 11/187 P enrichment = 0.29 61/187 P enrichment = 1.97 × 10 −20 77/187 P enrichment = 1.68 × 10 −44 Wang et al. [25] (54 CpGs) 1×10 −5 0 P enrichment =1 0 P enrichment =1 0 P enrichment =1 1/54 P enrichment = 0.003 0.05 2/54 P enrichment = 0.79 4/54 P enrichment = 0.26 23/54 P enrichment = 2.49 × 10 −11 33/54 P enrichment = 3.98 × 10 −27 NCpGs in ≥2 adult studies 0.05 9/52 (17.3%) 23/75 (30.7%) 98/347 (28.2%) 163/465 (35.1%) Two cutoffs were used to select the significant findings in our results: a Pvalue < 1 × 10 −5 , to identify “suggestive”findings, and a less stringent, nominal Pvalue < 0.05, to identify any trends. We used a hypergeometric test to calculate enrichment with the phyper function in the R Stats package in R. Results in bold are nominally significant. Of those findings from adult studies that had a nominal Pvalue (< 0.05) in our models, 17–35% were reported by more than one adult study Vehmeijer et al. Genome Medicine (2020) 12:105 Page 9 of 15