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

Vehmeijer, Florianne O L; Küpers, Leanne K.; Sharp, Gemma C; Salas, Lucas A.; Lent, Samantha; Jima, Dereje D.; Tindula, Gwen; Reese, Sarah E.; Qi, Cancan; Gruzieva, Olena; Page, Christian M.; Rezwan, Faisal I.; Melton, Phillip E.; Nøhr, Ellen Aagaard; Escaramís, Geòrgia; Rzehak, Peter; Heiskala, Anni; Gong, Tong; Tuominen, Samuli; Gao, Lu; Ross, Jason P.; Starling, Anne P.; Holloway, John W.; Yousefi, Paul; Aasvang, Gunn Marit; Beilin, Lawrence J.; Bergström, Anna; Binder, Elisabeth B.; Chatzi, Leda; Corpeleijn, Eva; Czamara, Darina; Eskenazi, Brenda; Ewart, Susan; Ferré, Natàlia; Grote, Veit; Gruszfeld, Dariusz; Håberg, Siri E.; Hoyo, Cathrine; Huen, Karen; Karlsson, Robert; Kull, Inger; Langhendries, Jean‐Paul; Lepeule, Johanna; Magnus, Maria C; Maguire, Rachel; Molloy, Peter L.; Monnereau, Claire; Mori, Trevor A.; Oken, Emily; Räikkönen, Katri; Rifas‐Shiman, Sheryl L.; Ruiz‐Arenas, Carlos; Sebért, Sylvain; Ullemar, Vilhelmina; Verduci, Elvira; Vonk, Judith M.; Xu, Cheng‐Jian; Yang, Ivana V.; Zhang, Hongmei; Zhang, Weimin; Karmaus, Wilfried; Dabelea, Dana; Mühlhäusler, Beverly S.; Breton, Carrie V.; Lahti, Jari; Almqvist, Catarina; Järvelin, Marjo‐Riitta; Koletzko, Berthold; Vrijheid, Martine; Sørensen, Thorkild I A; Huang, Rae‐Chi; Arshad, Syed Hasan; Nystad, Wenche; Melén, Erik; Koppelman, Gerard H.; London, Stephanie J.; Holland, Nina; Bustamante, Mariona; Murphy, Susan K.; Hivert, Marie‐France; Baccarelli, Andrea A.; Relton, Caroline L; Snieder, Harold; Jaddoe, Vincent W. V.; Felix, Janine F.

doi:10.1186/s13073-020-00810-w

Cited by 44 publications

(51 citation statements)

References 81 publications

(201 reference statements)

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“…In the case of BMI, Mendelian randomization approaches have been used in epigenome-wide association studies (EWAS) to assess causality in large cohorts with complex phenotypic stratification. Conflicting results were obtained, as both BMI-to-DNAm and the opposite causality direction were demonstrated (Huan et al, 2019;Sun et al, 2019;Vehmeijer et al, 2020). As for lipid-DNAm causality, circulating lipids cause DNAm and not vice versa (Dekkers et al, 2016).…”

Section: Introductionmentioning

confidence: 97%

Distinct Associations of BMI and Fatty Acids With DNA Methylation in Fasting and Postprandial States in Men

et al. 2021

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We have previously shown that blood global DNA methylation (DNAm) differs between postprandial state (PS) and fasting state (FS) and is associated with BMI and polyunsaturated fatty acid (PUFA) (negatively and positively, respectively) in 12 metabolically healthy adult Mexican men (AMM cohort) equally distributed among conventional BMI classes. Here, we detailed those associations at CpG dinucleotide level by exploiting the Infinium methylation EPIC array (Illumina). We sought differentially methylated CpG (dmCpG) that were (1) associated with BMI (BMI-dmCpG) and/or fatty acids (FA) (FA-dmCpG) in FS or PS and (2) different across FS and PS within a BMI class. BMI-dmCpG and FA-dmCpG were more numerous in FS compared to PS and largely prandial state-specific. For saturated and monounsaturated FA, dmCpG overlap was higher across than within the respective saturation group. Several BMI- and FA-dmCpG mapped to genes involved in metabolic disease and in some cases matched published experimental data sets. Notably, SETDB1 and MTHFS promoter dmCpG could explain the previously observed associations between global DNAm, PUFA content, and BMI in FS. Surprisingly, overlap between BMI-dmCpG and FA-dmCpG was limited and the respective dmCpG were differentially distributed across functional genomic elements. BMI-dmCpG showed the highest overlap with dmCpG of the saturated FA palmitate, monounsaturated C20:1 and PUFA C20:2. Of these, selected promoter BMI-dmCpG showed opposite associations with palmitate compared to C20:1 and C20:2. As for the comparison between FS and PS within BMI classes, dmCpG were strikingly more abundant and variably methylated in overweight relative to normoweight or obese subjects (∼70–139-fold, respectively). Overweight-associated dmCpG-hosting genes were significantly enriched in targets for E47, SREBP1, and RREB1 transcription factors, which are known players in obesity and lipid homeostasis, but none overlapped with BMI-dmCpG. We show for the first time that the association of BMI and FA with methylation of disease-related genes is distinct in FS and PS and that limited overlap exists between BMI- and FA-dmCpG within and across prandial states. Our study also identifies a transcriptional regulation circuitry in overweight that might contribute to adaptation to that condition or to transition to obesity. Further work is necessary to define the pathophysiological implications of these findings.

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Section: Introductionmentioning

confidence: 97%

Distinct Associations of BMI and Fatty Acids With DNA Methylation in Fasting and Postprandial States in Men

et al. 2021

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“…Future studies can examine the dose, duration, and composition of PM's impact on DNA methylation. In other studies, DNA methylation has been associated with children's health, such as asthma [32], body mass index [33], and attention deficit hyperactivity disorder [34]. Future research in the Fragile Families and Childhood Wellbeing Study may test similar associations between DNA methylation and health outcomes.…”

Section: Discussionmentioning

confidence: 86%

Prenatal Particulate Matter Exposure Is Associated with Saliva DNA Methylation at Age 15: Applying Cumulative DNA Methylation Scores as an Exposure Biomarker

Bakulski

Fisher

Dou

et al. 2021

Toxics

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Background: Exposure in utero to particulate matter (PM2.5 and PM10) is associated with maladaptive health outcomes. Although exposure to prenatal PM2.5 and PM10 has cord blood DNA methylation signatures at birth, signature persistence into childhood and saliva cross-tissue applicability has not been tested. Methods: In the Fragile Families and Child Wellbeing Study, a United States 20-city birth cohort, average residential PM2.5 and PM10 during the three months prior to birth was estimated using air quality monitors with inverse distance weighting. Saliva DNA methylation at ages 9 (n = 749) and 15 (n = 793) was measured using the Illumina HumanMethylation 450 k BeadArray. Cumulative DNA methylation scores for particulate matter were estimated by weighting participant DNA methylation at each site by independent meta-analysis effect estimates and standardizing the sums. Using a mixed-effects regression analysis, we tested the associations between cumulative DNA methylation scores at ages 9 and 15 and PM exposure during pregnancy, adjusted for child sex, age, race/ethnicity, maternal income-to-needs ratio, nonmartial birth status, and saliva cell-type proportions. Results: Our study sample was 50.5% male, 56.3% non-Hispanic Black, and 19.8% Hispanic, with a median income-to-needs ratio of 1.4. Mean exposure levels for PM2.5 were 27.9 μg/m3/day (standard deviation: 7.0; 23.7% of observations exceeded safety standards) and for PM10 were 15.0 μg/m3/day (standard deviation: 3.1). An interquartile range increase in PM2.5 exposure (10.73 μg/m3/day) was associated with a −0.0287 standard deviation lower cumulative DNA methylation score for PM2.5 (95% CI: −0.0732, 0.0158, p = 0.20) across all participants. An interquartile range increase in PM10 exposure (3.20 μg/m3/day) was associated with a −0.1472 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: −0.3038, 0.0095, p = 0.06) across all participants. The PM10 findings were driven by the age 15 subset where an interquartile range increase in PM10 exposure was associated with a −0.024 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: −0.043, −0.005, p = 0.012). Findings were robust to adjustment for PM exposure at ages 1 and 3. Conclusion: In utero PM10-associated DNA methylation differences were identified at age 15 in saliva. Benchmarking the timing and cell-type generalizability is critical for epigenetic exposure biomarker assessment.

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“…School-age outcomes showed a ΔR 2 Methylation near zero for BMI, IQ and ADHD symptoms at age 6 in both cohorts. In contrast to gestational-age and birth weight, these analyses present prospective associations over at least 6 years and have resulted in fewer genome-wide significant findings in previous EWAS (13)(14)(15). This temporal component together with perhaps lower contribution of DNAm may weaken associations and result in lower variance explained estimates.…”

Section: Discussionmentioning

confidence: 94%

“…Cord blood has also the potential to predict later development, e.g. nine CpG sites were associated with ADHD symptoms in school-age according to a recent EWAS in 2,477 children (13) and one CpG site predicted BMI in late childhood (n=4133) (14). In contrast, no genome-wide significant sites in cord blood were identified for BMI in early childhood (14) nor IQ in school-age (n=3798) (15).…”

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confidence: 99%

Epigenome-wide contributions to individual differences in childhood phenotypes: A GREML approach

Neumann

Pingault

et al. 2021

Preprint

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Background: DNA methylation is an epigenetic mechanism involved in human development. Numerous epigenome-wide association studies (EWAS) have investigated the associations of DNA methylation at single CpG sites with childhood outcomes. However, the overall contribution of DNA methylation across the genome (R2Methylation) towards childhood phenotypes is unknown. An estimate of R2Methylation would provide context regarding the importance of DNA methylation explaining variance in health outcomes. Methods: We estimated the variance explained by epigenome-wide cord blood methylation (R2Methylation) for five childhood phenotypes: gestational age, birth weight, and body mass index (BMI), IQ and ADHD symptoms at school age. We adapted a genome-based restricted maximum likelihood (GREML) approach with cross-validation (CV) to DNA methylation data and applied it in two population-based birth cohorts: ALSPAC (n=775) and Generation R (n=1382). Results: Using information from >470,000 autosomal probes we estimated that DNA methylation at birth explains 45% (SDCV = 0.07) of gestational age variance and 16% (SDCV = 0.05) of birth weight variance. The R2Methylation estimates for BMI, IQ and ADHD symptoms at school age estimates were near 0% across almost all cross-validation iterations. Conclusions: The results suggest that cord blood methylation explains a moderate to large degree of variance in gestational age and birth weight, in line with the success of previous EWAS in identifying numerous CpG sites associated with these phenotypes. In contrast, we could not obtain a reliable estimate for school-age BMI, IQ and ADHD symptoms. This may reflect a null bias due to insufficient sample size to detect variance explained in more weakly associated phenotypes, although the true R2Methylation for these phenotypes is likely below that of gestational age and birth weight when using DNA methylation at birth.

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

Cited by 44 publications

References 81 publications

Distinct Associations of BMI and Fatty Acids With DNA Methylation in Fasting and Postprandial States in Men

Distinct Associations of BMI and Fatty Acids With DNA Methylation in Fasting and Postprandial States in Men

Prenatal Particulate Matter Exposure Is Associated with Saliva DNA Methylation at Age 15: Applying Cumulative DNA Methylation Scores as an Exposure Biomarker

Epigenome-wide contributions to individual differences in childhood phenotypes: A GREML approach

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