Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Genetics and epigenetics

Replicating associations between DNA methylation and body mass index in a longitudinal sample of older twins

International Journal of Obesity volume 44, pages 1397–1405 (2020)Cite this article

Subjects

Abstract

Background

There is an important interplay between epigenetic factors and body weight, and previous work has identified ten sites where DNA methylation is robustly associated with body mass index (BMI) cross-sectionally. However, interpretation of the associations is complicated by the substantial changes in BMI often occurring in late-life, and the fact that methylation is often driven by genetic variation. This study therefore investigated the longitudinal association between these ten sites and BMI from midlife to late-life, and whether associations persist after controlling for genetic factors.

Methods

We used data from 535 individuals (mean age 68) in the Swedish Adoption/Twin Study of Aging (SATSA) with longitudinal measures of both DNA methylation from blood samples and BMI, spanning up to 20 years. Methylation levels were measured with the Infinium Human Methylation 450K or Infinium MethylationEpic array, with seven of the ten sites passing quality control. Latent growth curve models were applied to investigate longitudinal associations between methylation and BMI, and between–within models to study associations within twin pairs, thus adjusting for genetic factors.

Results

Baseline DNA methylation levels at five of the seven sites were associated with BMI level at age 65 (cg00574958 [CPT1A]; cg11024682 [SREBF1]), and/or change (cg06192883 [MYO5C]; cg06946797 [RMI2]; cg08857797 [VPS25]). For four of the five sites, the associations remained comparable within twin pairs. However, the effects of cg06192883 were substantially attenuated within pairs. No change in DNA methylation was detected for any of the seven evaluated sites.

Conclusion

Five of the seven sites investigated were associated with late-life level and/or change in BMI. The effects for four of the sites remained similar when examined within twin pairs, indicating that the associations are mainly environmentally driven. However, the substantial attenuation in the association between cg06192883 and late-life BMI within pairs points to the importance of genetic factors in this association.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1

Similar content being viewed by others

References

  1. Dahl AK, Reynolds CA, Fall T, Magnusson PK, Pedersen NL. Multifactorial analysis of changes in body mass index across the adult life course: a study with 65 years of follow-up. Int J Obes. 2014;38:1133–41.

    Article  CAS  Google Scholar 

  2. Dahl AK, Fauth EB, Ernsth-Bravell M, Hassing LB, Ram N, Gerstof D. Body mass index, change in body mass index, and survival in old and very old persons. J Am Geriatr Soc. 2013;61:512–8.

    Article  Google Scholar 

  3. Stokes A, Preston SH. Revealing the burden of obesity using weight histories. Proc Natl Acad Sci USA. 2016;113:572–7.

    Article  CAS  Google Scholar 

  4. Min J, Chiu DT, Wang Y. Variation in the heritability of body mass index based on diverse twin studies: a systematic review. Obes Rev. 2013;14:871–82.

    Article  CAS  Google Scholar 

  5. Winkler TW, Justice AE, Graff M, Barata L, Feitosa MF, Chu S, et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 2015;11:e1005378.

    Article  Google Scholar 

  6. Song M, Zheng Y, Qi L, Hu FB, Chan AT, Giovannucci EL. Longitudinal analysis of genetic susceptibility and BMI throughout adult life. Diabetes. 2018;67:248–55.

    Article  CAS  Google Scholar 

  7. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: linking environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593–610.

    Article  CAS  Google Scholar 

  8. Luo C, Hajkova P, Ecker JR. Dynamic DNA methylation: In the right place at the right time. Science. 2018;361:1336–40.

    Article  CAS  Google Scholar 

  9. Demerath EW, Guan W, Grove ML, Aslibekyan S, Mendelson M, Zhou YH, 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. 2015;24:4464–79.

    Article  CAS  Google Scholar 

  10. Mendelson MM, Marioni RE, Joehanes R, Liu C, Hedman AK, Aslibekyan S, et al. Association of body mass index with DNA methylation and gene expression in blood cells and relations to cardiometabolic disease: a Mendelian randomization approach. PLoS Med. 2017;14:e1002215.

    Article  Google Scholar 

  11. Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017;541:81–6.

    Article  CAS  Google Scholar 

  12. Sayols-Baixeras S, Subirana I, Fernandez-Sanles A, Senti M, Lluis-Ganella C, Marrugat J, et al. DNA methylation and obesity traits: an epigenome-wide association study. The REGICOR study. Epigenetics. 2017;12:909–16.

    Article  Google Scholar 

  13. Campanella G, Gunter MJ, Polidoro S, Krogh V, Palli D, Panico S, et al. Epigenome-wide association study of adiposity and future risk of obesity-related diseases. Int J Obes. 2018;42:2022–35.

    Article  CAS  Google Scholar 

  14. Dhana K, Braun KVE, Nano J, Voortman T, Demerath EW, Guan W, et al. An epigenome-wide association study of obesity-related traits. Am J Epidemiol. 2018;187:1662–9.

    Article  Google Scholar 

  15. Bell CG. The epigenomic analysis of human obesity. Obesity. 2017;25:1471–81.

    Article  CAS  Google Scholar 

  16. McRae AF, Marioni RE, Shah S, Yang J, Powell JE, Harris SE, et al. Identification of 55,000 replicated DNA methylation QTL. Sci. Rep. 2018;8:17605.

    Article  Google Scholar 

  17. Finkel D, Pedersen N. Processing speed and longitudinal trajectories of change for cognitive abilities: the Swedish Adoption/Twin Study of Aging. Neuropsychol Dev Cognit. 2004;11:325–45.

    Article  Google Scholar 

  18. Magnusson PK, Almqvist C, Rahman I, Ganna A, Viktorin A, Walum H, et al. The Swedish Twin Registry: establishment of a biobank and other recent developments. Twin Res Hum Genet. 2012;16:317–29.

    Article  Google Scholar 

  19. Davis SDP, Bilke S, Triche, Jr. T and Bootwalla M. methylumi: Handle Illumina methylation data. R package version 2.18.2. 2015.

  20. Pidsley R, CC YW, Volta M, et al. LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genom. 2013;14:293.

    Article  CAS  Google Scholar 

  21. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform. 2012;13:86.

    Article  Google Scholar 

  22. Leek JT, Johnson WE, Parker HS, Fertig EJ, Jaffe AE and Storey JD. SVA: Surrogate Variable Analysis. R package version 3.20.0. 2016. https://bioconductor.org/packages/release/bioc/html/sva.html.

  23. Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, et al. Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 2010;11:587.

    Article  CAS  Google Scholar 

  24. Sjölander A, Frisell T, Öberg S. Causal interpretation of between-within models for twin research. In: Epidemiol Methods. 2012;1:216–33.

  25. McArdle JJ. Latent variable modeling of differences and changes with longitudinal data. Annu Rev Psychol. 2009;60:577–605.

    Article  Google Scholar 

  26. Falconer DS, Mackay TFC. Introduction to quantitative genetics, 4th ed. Longman: Harlow; 1996.

  27. Wang Y, Karlsson R, Lampa E, Zhang Q, Hedman AK, Almgren M, et al. Epigenetic influences on aging: a longitudinal genome-wide methylation study in old Swedish twins. Epigenetics. 2018;13:975–87.

    Article  Google Scholar 

  28. Gaunt TR, Shihab HA, Hemani G, Min JL, Woodward G, Lyttleton O, et al. Systematic identification of genetic influences on methylation across the human life course. Genome Biol. 2016;17:61.

    Article  Google Scholar 

  29. Krementsova EB, Furuta K, Oiwa K, Trybus KM, Ali MY. Small teams of myosin Vc motors coordinate their stepping for efficient cargo transport on actin bundles. J Biol Chem. 2017;292:10998–1008.

    Article  CAS  Google Scholar 

  30. La Rosa S, Lloyd RV, Capella C. Expression and role of myosins in pancreatic endocrine cells and related tumors. In: Loft MA editor. Trends in pancreatic cancer research. New York: Nova Publishers; 2005. pp. 23–39.

  31. Willmer T, Johnson R, Louw J, Pheiffer C. Blood-based DNA methylation biomarkers for type 2 diabetes: potential for clinical applications. Front Endocrinol. 2018;9:744.

    Article  Google Scholar 

  32. Braun KV, Voortman T, Dhana K, Troup J, Bramer WM, Chowdhury R, et al. The role of DNA methylation in dyslipidaemia: a systematic review. Prog Lipid Res. 2016;64:178–91.

    Article  CAS  Google Scholar 

  33. Li S, Wong EM, Bui M, Nguyen TL, Joo JE, Stone J, et al. Inference about causation between body mass index and DNA methylation in blood from a twin family study. Int J Obes. 2019;43:243–52.

    Article  CAS  Google Scholar 

  34. Sherling DH, Perumareddi P, Hennekens CH. Metabolic syndrome: clinical and policy implications of the new silent killer. J Cardiovasc Pharmacol Ther. 2017;22:365–7.

    Article  CAS  Google Scholar 

  35. Kelfve S, Fors S, Lennartsson C. Getting better all the time? Selective attrition and compositional changes in longitudinal and life-course studies. Longitud Life Course Stud. 2017;8:104–20.

  36. Li W, Christiansen L, Hjelmborg J, Baumbach J, Tan Q. On the power of epigenome-wide association studies using a disease-discordant twin design. Bioinformatics. 2018;34:4073–8.

    Article  CAS  Google Scholar 

  37. Frisell T, Oberg S, Kuja-Halkola R, Sjolander A. Sibling comparison designs: bias from non-shared confounders and measurement error. Epidemiology. 2012;23:713–20.

    Article  Google Scholar 

  38. Teschendorff AE, Zheng SC. Cell-type deconvolution in epigenome-wide association studies: a review and recommendations. Epigenomics. 2017;9:757–68.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

SATSA was supported by National Institutes of Health (NIH; grants AG04563 and AG10175), the MacArthur Foundation Research Network on Successful Aging, the Swedish Research Council for Working Life and Social Research (FAS; Grants 97:0147:1B, 2009-0795), and the Swedish Research Council (825-2007-7460 and 825-2009-6141). This work was supported by the Swedish Research Council Grant (2016-03081) and the Swedish Research Council for Health, Working Life and Welfare (2018-01201). We acknowledge The Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant no 2017-00641. Methylation profiling on the Infinium MethylationEPIC BeadChip was performed by the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se). The facility is part of the National Genomics Infrastructure (NGI) Sweden and Science for Life Laboratory. The SNP&SEQ Platform is also supported by the Swedish Research Council and the Knut and Alice Wallenberg Foundation.

Author information

Authors and Affiliations

  1. Institute of Gerontology and Aging Research Network—Jönköping (ARN-J), School of Health and Welfare, Jönköping University, Jönköping, Sweden

    Ida K. Karlsson & Anna K. Dahl Aslan

  2. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Ida K. Karlsson, Malin Ericsson, Yunzhang Wang, Juulia Jylhävä, Sara Hägg, Nancy L. Pedersen & Anna K. Dahl Aslan

  3. Department of Psychology, University of Southern California, Los Angeles, CA, USA

    Nancy L. Pedersen

  4. Department of Psychology, University of California, Riverside, CA, USA

    Chandra A. Reynolds

Authors
  1. Ida K. Karlsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Malin Ericsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunzhang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juulia Jylhävä
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sara Hägg
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nancy L. Pedersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chandra A. Reynolds
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Anna K. Dahl Aslan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ida K. Karlsson.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karlsson, I.K., Ericsson, M., Wang, Y. et al. Replicating associations between DNA methylation and body mass index in a longitudinal sample of older twins. Int J Obes 44, 1397–1405 (2020). https://doi.org/10.1038/s41366-019-0498-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-019-0498-6

This article is cited by

Search

Advanced search

Quick links