Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity


Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances1,2. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation3,4,5,6, a key regulator of gene expression and molecular phenotype7. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10−7, range P = 9.2 × 10−8 to 6.0 × 10−46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10−6, range P = 5.5 × 10−6 to 6.1 × 10−35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07–2.56); P = 1.1 × 10−54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: Circos plot of the epigenome-wide association of DNA methylation in blood with BMI.
Figure 2: Genetic association studies to investigate the potential relationships between BMI and DNA methylation in blood.
Figure 3: Relationship between DNA methylation in blood and BMI amongst 1,435 participants of the KORA S4/F4 population cohort.
Figure 4: Relative risk of incident type 2 diabetes by quartile of methylation risk score amongst Indian Asians.

Accession codes

Primary accessions

Gene Expression Omnibus


  1. Wang, Y. C., McPherson, K., Marsh, T., Gortmaker, S. L. & Brown, M. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet 378, 815–825 (2011)

    Article  Google Scholar 

  2. Ng, M. et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384, 766–781 (2014)

    Article  Google Scholar 

  3. Dick, K. J. et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet 383, 1990–1998 (2014)

    Article  CAS  Google Scholar 

  4. Feinberg, A. P. et al. Personalized epigenomic signatures that are stable over time and covary with body mass index. Sci. Transl. Med. 2, 49ra67 (2010)

    Article  Google Scholar 

  5. Xu, X. et al. A genome-wide methylation study on obesity: differential variability and differential methylation. Epigenetics 8, 522–533 (2013)

    Article  CAS  Google Scholar 

  6. Demerath, E. W. et al. Epigenome-wide association study (EWAS) of BMI, BMI change and waist circumference in African American adults identifies multiple replicated loci. Hum. Mol. Genet. 24, 4464–4479 (2015)

    Article  CAS  Google Scholar 

  7. Portela, A. & Esteller, M. Epigenetic modifications and human disease. Nat. Biotechnol. 28, 1057–1068 (2010)

    Article  CAS  Google Scholar 

  8. Danaei, G. et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country–years and 2·7 million participants. Lancet 378, 31–40 (2011)

    Article  CAS  Google Scholar 

  9. Slieker, R. C. et al. Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array. Epigenetics Chromatin 6, 26 (2013)

    Article  CAS  Google Scholar 

  10. Rosen, E. D. & Spiegelman, B. M. What we talk about when we talk about fat. Cell 156, 20–44 (2014)

    Article  CAS  Google Scholar 

  11. Relton, C. L. & Davey Smith, G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int. J. Epidemiol. 41, 161–176 (2012)

    Article  Google Scholar 

  12. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010)

    Article  CAS  Google Scholar 

  13. Bochukova, E. G. et al. Large, rare chromosomal deletions associated with severe early-onset obesity. Nature 463, 666–670 (2010)

    Article  ADS  CAS  Google Scholar 

  14. Johansson, L. E. et al. Differential gene expression in adipose tissue from obese human subjects during weight loss and weight maintenance. Am. J. Clin. Nutr. 96, 196–207 (2012)

    Article  CAS  Google Scholar 

  15. Aron-Wisnewsky, J. et al. Effect of bariatric surgery-induced weight loss on SR-BI-, ABCG1-, and ABCA1-mediated cellular cholesterol efflux in obese women. J. Clin. Endocrinol. Metab. 96, 1151–1159 (2011)

    Article  CAS  Google Scholar 

  16. Pfeifferm, L. et al. DNA methylation of lipid-related genes affects blood lipid levels. Circ Cardiovasc Genet 8, 334–342 (2015)

    Article  Google Scholar 

  17. Hidalgo, B. et al. Epigenome-wide association study of fasting measures of glucose, insulin, and HOMA-IR in the Genetics of Lipid Lowering Drugs and Diet Network study. Diabetes 63, 801–807 (2014)

    Article  CAS  Google Scholar 

  18. Donkin, I. et al. Obesity and Bariatric surgery drive epigenetic variation of spermatozoa in humans. Cell Metab. 23, 369–378 (2016)

    Article  CAS  Google Scholar 

  19. Karin, M. & Ben-Neriah, Y. Phosphorylation meets ubiquitination: the control of NF-κB activity. Annu. Rev. Immunol. 18, 621–663 (2000)

    Article  CAS  Google Scholar 

  20. Brightling, C. E. et al. Benralizumab for chronic obstructive pulmonary disease and sputum eosinophilia: a randomised, double-blind, placebo-controlled, phase 2a study. Lancet Respir. Med. 2, 891–901 (2014)

    Article  CAS  Google Scholar 

  21. Chambers, J. C. et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case–control study. Lancet Diabetes Endocrinol. 3, 526–534 (2015)

    Article  CAS  Google Scholar 

  22. Houseman, E. A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012)

    Article  Google Scholar 

  23. Lehne, B. et al. A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies. Genome Biol. 16, 37 (2015)

    Article  Google Scholar 

  24. Lyons, A. B. & Parish, C. R. Determination of lymphocyte division by flow cytometry. J. Immunol. Methods 171, 131–137 (1994)

    Article  CAS  Google Scholar 

  25. Park, D. et al. Noninvasive imaging of cell death using an Hsp90 ligand. J. Am. Chem. Soc. 133, 2832–2835 (2011)

    Article  CAS  Google Scholar 

  26. Burgess, S. Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int. J. Epidemiol. 43, 922–929 (2014)

    Article  Google Scholar 

  27. Spalding, K. L. et al. Dynamics of fat cell turnover in humans. Nature 453, 783–787 (2008)

    Article  ADS  CAS  Google Scholar 

  28. Ahrens, M. et al. DNA methylation analysis in nonalcoholic fatty liver disease suggests distinct disease-specific and remodeling signatures after bariatric surgery. Cell Metab. 18, 296–302 (2013)

    Article  CAS  Google Scholar 

  29. Schurmann, C. et al. Analyzing illumina gene expression microarray data from different tissues: methodological aspects of data analysis in the metaxpress consortium. PLoS One 7, e50938 (2012)

    Article  ADS  CAS  Google Scholar 

  30. Döring, A. et al. SLC2A9 influences uric acid concentrations with pronounced sex-specific effects. Nat. Genet. 40, 430–436 (2008)

    Article  Google Scholar 

Download references


Detailed acknowledgments are provided in the Supplementary Information.

Author information

Authors and Affiliations



Data collection and analysis in the contributing population studies, ALSPAC study: T.R.G., C.L.R., R.C.R., G.D.S.; EGCUT study: K.F., S.Ka., L.M., N.P.; EPICOR study: G.F., S.G., V.K., G.M., S.P., R.T., P.V.; KORA study: M.C.K., C.G., H.G., C.H., T.I., J.K., S.Ku., C.M., T.M., A.P., H.P., J.S.R., M.R., W.R., K.Sc., K.St., B.T., M.W., S.W.; Leiden Longevity Study: M.B., A.J.M.d.C., B.T.H., P.E.S.; LIFELINES study: M.J.B., L.F., P.v.d.H., E.F.T., C.W., A.Z.; LOLIPOP study: B.A., U.A., C.B, P.A.B., V.B., J.C.C., A.Dr., P.E., M.R.J., S.J., J.S.K., M.A.K., N.K., B.L., C.M.L., M.Lo., S.d.L., M.I.M., V.M., Z.Y.M., H.K.N., F.R., M.A.R., J.S., P.S., R.So., W.R.S., E.S.T., L.T., S.T., A.R.W., W.Z.; Rotterdam Study: A.De., C.v.D., O.H.F., A.H., A.I., J.B.J.v.M., L.S., A.G.U.; TwinsUK study: J.T.B., P.D., J.K.S., T.D.S., P.C.T., T.P.Y., WY. Data collection and molecular analyses in isolated cell subsets; adipocytes: M.A., R.L.B., J.C.C., M.E., M.H., A.J., J.S.K., Z.Y.M., H.K.N., M.A.R., J.S., R.So., W.R.S., S.T.; hepatocytes: O.A., M.Br., J.H., C.S., R.Si.; leucocytes: J.F.A., S.L.B., J.C.C., J.S.K., M.La., Z.Y.M., H.K.N., G.N., Z.N., M.A.R., R.So., W.R.S., S.T., Y.Y. Data analysis and writing group; J.C.C., A.Dr., P.E., J.S.K., C.G., H.G., B.L., M.Lo., G.M., M.I.M., J.S., W.R.S., S.W.

Corresponding authors

Correspondence to Jaspal S. Kooner, Harald Grallert or John C. Chambers.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks M. Boehnke, J. M. Greally, B. Voight and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Study design.

Epigenome-wide association and replication testing was performed in order to identify methylation sites associated with adiposity. In the discovery step, four large cohorts were included with Illumina 450k DNA methylation data available, which were preprocessed and quality controlled according to a harmonized protocol. Epigenome-wide association was performed in every single study with BMI as response variable and methylation β-value as independent variable, adjusting for covariates as described in the Methods. At a genome-wide significance level of P < 1 × 10−7, 278 methylation sites from 207 regions were identified. In the replication step, 187 of these were replicated in independent samples. Genetic association and causality analyses were used in order to investigate whether the identified methylation signals underlie the development of adiposity or are the consequence of adiposity. The findings were supported by longitudinal analyses. The cross-tissue analyses represent a first step towards extending our observations in blood to metabolically relevant tissues. The functional genomics and gene expression analyses help to link the observed methylation associations to transcriptional outcomes, while the gene-set enrichment analysis provides a way to summarize the potentially affected metabolic pathways. Finally, we studied the relationships between methylation and adiposity-related metabolic traits and type 2 diabetes to address the clinical relevance of our findings.

Extended Data Figure 2 Distribution of methylation values at the 187 sentinel CpG sites compared to the approximately 473,000 CpG sites assayed by the Illumina Infinium 450K Human Methylation array.

The 187 identified methylation–BMI associations are strongly enriched for CpG sites with intermediate levels of methylation, consistent with the presence of epigenetic heterogeneity at these loci in blood (157 out of 187 sites with 20–80% methylation, a 3.0-fold enrichment compared to microarray background, P = 1.4 × 10−22 Fisher’s test).

Extended Data Figure 3 DNA methylation at the sentinel CpG sites in whole blood and in 4 isolated cell subsets (monocytes, neutrophils, CD4+ and CD8+ T cells) from 60 individuals (30 obese individuals and 30 normal weight controls) by Illumina MethylationEPIC array, which quantifies 179 of the 187 sentinel markers.

Results are shown as a heatmap, coded by methylation value (hypomethylation < 0.2; intermediate methylation 0.2–0.8; hypermethylation > 0.8). Results show the presence of intermediate methylation (and hence epigenetic heterogeneity) at the majority of loci, and in the majority of cell types, in both cases and controls.

Extended Data Figure 4 Association of DNA methylation with obesity in the 4 cell subsets studied, based on quantification of methylation at 179 sentinel methylation markers in 30 obese individuals and 30 normal weight controls.

Results are presented as QQ plots of the observed association test statistics in each of the isolated cell subsets. λ, the genomic control inflation factor.

Extended Data Figure 5 Comparison of effect sizes between isolated white cell subsets.

Results are presented as the difference in methylation between obese cases and normal weight controls (methylation in cases−methylation in controls, in absolute terms on percentage scale) in the respective isolated white-blood-cell subset (y axis) compared to the average case−control difference across all 4 cell subsets studied (x axis).

Extended Data Figure 6 Mean methylation levels at the 187 sentinel methylation markers associated with BMI, across 7 tissue types.

Bottom, pairwise scatterplots (trend line in red). Top, the Pearson correlation coefficient and P values. Blood, n = 6; liver, n = 5; muscle, n = 6; omentum, n = 6; pancreas, n = 4; subcutaneous (SC) fat, n = 6; spleen, n = 3.

Extended Data Figure 7 Causality analysis in adipose tissue to investigate the potential relationships between BMI and DNA methylation.

Left, causality analysis in adipose tissue investigating whether DNA methylation at sentinel CpG sites influences BMI. Units are change in BMI per copy of effect allele. For each sentinel CpG site, we determined the effect of a previously identified cis SNP on BMI predicted through methylation (x axis) and the directly observed effect of a SNP on BMI (y axis). No CpG passed multiple testing corrections for all three comparisons. Overall there was little relationship between the effects of SNPs on BMI predicted through methylation and the directly observed effect (R = −0.04, P = 0.58). Right, causality analysis in adipose tissue investigating whether DNA methylation at sentinel CpG sites is the consequence of BMI. Units are change in methylation per unit change in weighted genetic risk score. We identified SNPs reported to influence BMI in GWAS meta-analysis, and calculated a weighted genetic risk score. For each sentinel CpG site we then determined the effect of genetic risk score on methylation predicted via BMI (x axis) and the directly observed effect of genetic risk score on methylation (y axis). No CpG passed multiple testing corrections for all three comparisons. The overall correlation between observed and predicted effects (R = 0.73, P = 1.6 × 10−32) replicates our findings in blood that methylation at the majority of CpG sites is consequential to BMI.

Extended Data Figure 8 The 187 sentinel CpGs are enriched for association with gene-expression in cis in blood.

ad, To derive an expectation under the null-hypothesis we generated 10,000 sets of matched CpGs (matched for mean methylation and for s.d. of methylation, see Methods), and tested their association with expression of the nearest gene (a), the gene allocated to the CpG by the Illumina annotation (b), all genes within a 500 kb distance (c) and all genes within a 500 kb distance excluding the nearest gene (d). We observed significantly more expression-probes associated with the sentinel markers (red arrow) in blood compared to the 10,000 permuted sets (green bars).

Extended Data Figure 9 Summary statistics for the causality analyses investigating the relationship between DNA methylation in blood and metabolic disturbances.

a, DNA methylation in blood as a potential determinant of the metabolic disturbances associated with adiposity (causal analysis). For each of the sentinel CpG sites, we identified the cis SNP (1 Mb) most closely associated with DNA methylation levels. For each of the SNPs, we then determined the effect of SNP on phenotype predicted via methylation and the directly observed effect of SNP on phenotype. Results are presented as the R2 between phenotype-specific observed and predicted effects across the 187 CpG sites, calculated using linear regression. b, DNA methylation in blood as a potential consequence of the metabolic disturbances associated with adiposity (consequential analysis). We identified the SNPs reported to influence each phenotypic trait (using the most recent GWAS meta-analysis; Supplementary Table 24), and calculated phenotype-specific weighted genetic risk scores. For each of the CpG sites, and each of the phenotypes, we then determined the effect of genetic risk score on methylation predicted through phenotype, with the directly observed effect of genetic risk score on methylation. Results are presented as the R2 between phenotype-specific observed and predicted effects across the 187 CpG sites, calculated using linear regression. P values are shown for correlations between observed and predicted effects that reach P < 0.05.

Extended Data Figure 10 Association of established and emergent biomarkers with type 2 diabetes.

Results are presented as risk of type 2 diabetes associated with the specified biomarkers in three models: model 1, adjusted for age and sex; model 2, as for model 1, but additionally adjusted for body mass index and impaired fasting glucose; and model 3, as for model 2, but additionally adjusted for central obesity and insulin concentrations. CRP, C-reactive protein; MRS, methylation risk score. Results for quantitative traits (amino acids, C-reactive protein, insulin and methylation risk score) are presented as risk of type 2 diabetes in Q4 compared to Q1.

Supplementary information

Supplementary Information

This file contains Supplementary Text, Legends for Supplementary Tables 1-30 (see separate excel file), Supplementary Figures legends 1-2, (see separate files for figures), Supplementary Figures 3-10 and additional references. (PDF 1778 kb)

Supplementary Figure 1

This file contains Supplementary Figure 1 (see Supplementary Information file for legend). (PDF 7680 kb)

Supplementary Figure 2

This file contains Supplementary Figure 2 (see Supplementary Information file for legend). (PDF 645 kb)

Supplementary Tables

This file contains Supplementary Tables 1-30 (see Supplementary Information file for legends). (XLSX 14339 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wahl, S., Drong, A., Lehne, B. et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541, 81–86 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing