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

DNA methylation and adiposity phenotypes: an epigenome-wide association study among adults in the Strong Heart Study

International Journal of Obesity volume 44, pages 2313–2322 (2020)Cite this article

Subjects

Abstract

Background

Elevated adiposity is often posited by medical and public health researchers to be a risk factor associated with cardiovascular disease, diabetes, and other diseases. These health challenges are now thought to be reflected in epigenetic modifications to DNA molecules, such as DNA methylation, which can alter gene expression.

Methods

Here we report the results of three Epigenome Wide Association Studies (EWAS) in which we assessed the differential methylation of DNA (obtained from peripheral blood) associated with three adiposity phenotypes (BMI, waist circumference, and impedance-measured percent body fat) among American Indian adult participants in the Strong Heart Study.

Results

We found differential methylation at 8264 CpG sites associated with at least one of our three response variables. Of the three adiposity proxies we measured, waist circumference had the highest number of associated differentially methylated CpGs, while percent body fat was associated with the lowest. Because both waist circumference and percent body fat relate to physiology, we focused interpretations on these variables. We found a low degree of overlap between these two variables in our gene ontology enrichment and Differentially Methylated Region analyses, supporting that waist circumference and percent body fat measurements represent biologically distinct concepts.

Conclusions

We interpret these general findings to indicate that highly significant regions of the genome (DMR) and synthesis pathways (GO) in waist circumference analyses are more likely to be associated with the presence of visceral/abdominal fat than more general measures of adiposity. Our findings confirmed numerous CpG sites previously found to be differentially methylated in association with adiposity phenotypes, while we also found new differentially methylated CpG sites and regions not previously identified.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: Diagram illustrating data processing procedure used for this study.
Fig. 2: Venn Diagram of results of three EWAS studies (BMI, waist circumference, and percent body fat).
Fig. 3: Manhattan plot of three EWAS studies (BMI, waist circumference, and percent body fat).
Fig. 4: Venn diagram of differentially methylated CpG sites.
Fig. 5: Venn diagram of differentially methylated CpG regions.
Fig. 6: Graphical representation of selected results from DMR analyses.
Fig. 7: Venn diagram of Gene Ontology Enrichment Association results.

Similar content being viewed by others

Data availability

Access to Strong Heart Study raw data is subject to approval by participating tribes following the procedures established by the Strong Heart Study in agreement with the tribes. Detailed information is available in the Strong Heart Study website https://strongheartstudy.org/.

Code availability

Code used to analyze data in this paper is available from the senior author and the corresponding author.

References

  1. 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.

    CAS  PubMed  Google Scholar 

  2. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, 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. 2014;384:766–81.

    PubMed  PubMed Central  Google Scholar 

  3. Dhana K, Braun K, Nano J, Voortman R, Joyce M, Uitterlinden A, et al. An epigenome-wide association study (EWAS) of obesity-related traits. Annals Nutr Metab. 2017;71:287–287.

    Google Scholar 

  4. Shai I, Jiang R, Manson JE, Stampler MJ, Willett WC, Colditz GA, et al. Ethnicity, obesity, and risk of type 2 diabetes in women - A 20-year follow-up study. Diabetes Care. 2006;29:1585–90.

    PubMed  Google Scholar 

  5. Deurenberg-Yap M, Schmidt G, van Staveren WA, Deurenberg P. The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore. Int J Obes. 2000;24:1011–7.

    CAS  Google Scholar 

  6. Rush EC, Goedecke J, Jennings C, Micklesfield L, Dugas L, Lambert EV, et al. BMI, fat and muscle differences in urban women of five ethnicities from two countries. Int J Obes. 2007;31:1232–9.

    CAS  Google Scholar 

  7. Muller MJ, Geisler C, Blundell J, Dulloo A, Schutz Y, Krawczak M. The case of GWAS of obesity: does body weight control play by the rules? Int J Obes. 2018;42:1395–405.

    Google Scholar 

  8. Bosy-Westphal A, Braun W, Geisler C, Norman K, Muller MJ. Body composition and cardiometabolic health: the need for novel concepts. Eur J Clin Nutr. 2018;72:638–44.

    PubMed  Google Scholar 

  9. Demerath EW, Guan WH, 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. Human Mol Gen. 2015;24:4464–79.

    CAS  Google Scholar 

  10. Al Muftah WA, Al-Shafai M, Zaghlool SB, Visconti A, Tsai PC, Kumar P, et al. Epigenetic associations of type 2 diabetes and BMI in an Arab population. Clin Epigen. 2016;8:13.

    Google Scholar 

  11. Meeks KAC, Henneman P, Venema A, Burr T, Galbete C, Danquah I, et al. An epigenome-wide association study in whole blood of measures of adiposity among Ghanaians: the RODAM study. Clin Epigen. 2017;9:103.

    Google Scholar 

  12. Schlesinger S, Aleksandrova K, Pischon T, Fedirko V, Jenab M, Trepo E, et al. Abdominal obesity, weight gain during adulthood and risk of liver and biliary tract cancer in a European cohort. Int J Cancer. 2013;132:645–57.

    CAS  PubMed  Google Scholar 

  13. Pang YJ, Kartsonaki C, Guo Y, Chen YP, Yang L, Bian Z, et al. Central adiposity in relation to risk of liver cancer in Chinese adults: a prospective study of 0.5 million people. Int J Cancer. 2019;145:1245–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Campbell PT, Newton CC, Freedman ND, Koshiol J, Alavanja MC, Freeman LEB, et al. Body mass index, waist circumference, diabetes, and risk of liver cancer for US adults. Cancer Res. 2016;76:6076–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Shuster A, Patlas M, Pinthus JH, Mourtzakis M. The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis. Br J Radiol. 2012;85:1–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Lee ET, Welty TK, Fabsitz R, Cowan LD, Le NA, Oopik AJ, et al. The strong heart study - a study of cardiovascular disease in American Indians: designs and methods. Am J Epidemiol. 1990;132:1141–55.

    CAS  PubMed  Google Scholar 

  17. The Strong Heart Study Manual Chapter 5: Clinical Examination. 2012 (Modified May 2, 2003). https://strongheartstudy.org/portals/1288/Assets/documents/manuals/Phase%20I%20Operations%20Manual.pdf?ver=2017-11-15-134631-313.

  18. Rising R, Swinburn B, Larson K, Ravussin E. Body composition in Pima Indians: validation of bioelectrical resistance. Am J Clin Nutr. 1991;53:594–8.

    CAS  PubMed  Google Scholar 

  19. Stolarczyk LM, Heyward VH, Hicks VL, Baumgartner RN. Predictive accuracy of bioelectrical impedance in estimating body composition of Native American women. Am J Clin Nutr. 1994;59:964–70.

    CAS  PubMed  Google Scholar 

  20. Fortin JP, Triche TJ, Hansen KD. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics. 2017;33:558–60.

    CAS  PubMed  Google Scholar 

  21. Triche TJ, Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013;41:e90.

    PubMed  PubMed Central  Google Scholar 

  22. 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.

    Google Scholar 

  23. McCartney DL, Walker RM, Morris SW, McIntosh AM, Porteous DJ, Evans KL. Identification of polymorphic and off-target probe binding sites on the Illumina Infinium MethylationEPIC BeadChip. Genomics Data. 2016;9:22–24.

    PubMed  PubMed Central  Google Scholar 

  24. Ritchie ME, Phipson B, Wu D, Hu YF, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl Acids Res. 2015;43:e47.

    PubMed  Google Scholar 

  25. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B-Methodol. 1995;57:289–300.

    Google Scholar 

  26. Demerath NJ, The American Soldier: Volume I, adjustment during army life. By S. A. Stouffer, E. A. Suchman, L. C. DeVinney, S. A. Star, R. M. Williams, Jr. Volume II, Combat and Its Aftermath. By S. A. Stouffer, A. A. Lumsdaine, M. H. Lumsdaine, R. M. Williams, Jr., M. B. Smith, I. L. Janis, S. A. Star, L. S. Cottrell, Jr. Princeton, New Jersey: Princeton University Press, 1949. Vol. I, 599 pp., Vol. II, 675 pp. $7.50 each; $13.50 together. Social Forces. 1949;28:87–90.

  27. Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, Lee A, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377:13–27.

    PubMed  Google Scholar 

  28. Gonzalez MC, Correia M, Heymsfield SB. A requiem for BMI in the clinical setting. Current Opin Clin Nutr Metab Care. 2017;20:314–21.

    Google Scholar 

  29. Gearon E, Tanamas SK, Stevenson C, Loh VHY, Peeters A. A., Changes in waist circumference independent of weight: Implications for population level monitoring of obesity. Preventive Med. 2018;111:378–83.

    Google Scholar 

  30. Prentice AM, Jebb SA. Beyond body mass index. Obes Rev. 2001;2:141–7.

    CAS  PubMed  Google Scholar 

  31. Martí A, Marcos A, Martínez JA. Obesity and immune function relationships. Obes Rev. 2001;2:131–40.

    PubMed  Google Scholar 

  32. O’Rourke RW, Metcalf MD, White AE, Madala A, Winters BR, Maizlin II, et al. Depot-specific differences in inflammatory mediators and a role for NK cells and IFN-gamma in inflammation in human adipose tissue. Int J Obes. 2009;33:978–90.

    Google Scholar 

  33. Lappalainen T, Greally JM. Associating cellular epigenetic models with human phenotypes. Nat Rev Gen. 2017;18:441–51.

    CAS  Google Scholar 

  34. Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacology. 2013;38:23–38.

    CAS  PubMed  Google Scholar 

  35. Buchta CM, Boi KS, Miller BJ, Milhem MM, Norian LA. Obesity does not exacerbate the protumorigenic systemic environment in sarcoma subjects. ImmunoHorizons. 2017;1:20.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Joffe L, Dwyer S, Bender JLG, Frazier AL, Ladas EJ. Nutritional status and clinical outcomes in pediatric patients with solid tumors: A systematic review of the literature. Sem Oncol. 2019;46:48–56.

    Google Scholar 

  37. Choromanska B, Mysliwiec P, Hady HR, Dadan J, Mysliwiec H, Bonda T, et al. The implication of adipocyte ATP-binding cassette A1 and G1 transporters in metabolic complications of obesity. J Physiol Pharmacol. 2019;70:143–52.

    CAS  Google Scholar 

  38. Samaras K, Botelho NK, Chisholm DJ, Lord RV. Subcutaneous and visceral adipose tissue gene expression of serum adipokines that predict type 2 diabetes. Obesity. 2010;18:884–9.

    CAS  PubMed  Google Scholar 

  39. Pateyjohns IR, Brinkworth GD, Buckley JD, Noakes M, Clifton PM. Comparison of three bioelectrical impedance methods with DXA in overweight and obese men. Obesity. 2006;14:2064–70.

    PubMed  Google Scholar 

  40. Borga M, West J, Bell JD, Harvey NC, Romu T, Heymsfield SB, et al. Advanced body composition assessment: from body mass index to body composition profiling. J Invest Med. 2018;66:887–95.

    Google Scholar 

  41. Hunger JM, Major B. Weight stigma mediates the association between bmi and self-reported health. Health Psych. 2015;34:172–5.

    Google Scholar 

  42. Pause C. Borderline: the ethics of fat stigma in public health. J Law Med Ethics. 2017;45:510–7.

    Google Scholar 

  43. Hill JH, Solt C, Foster MT. Obesity associated disease risk: the role of inherent differences and location of adipose depots. Hormone Molecular Biol Clin Invest. 2018;33.

  44. Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M, et al. Maternal and child undernutrition 1—maternal and child undernutrition: global and regional exposures and health consequences. Lancet. 2008;371:243–60.

    PubMed  Google Scholar 

  45. Miller SL, Wolfe RR. The danger of weight loss in the elderly. J Nutr Health Aging. 2008;12:487–91.

    CAS  PubMed  Google Scholar 

  46. Wells JCK, Fewtrell MS. Measuring body composition. Arch Dis Child. 2006;91:612–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Klopfenstein BJ, Kim MS, Krisky CM, Szumowski J, Rooney WD, Purnell JQ. Comparison of 3 T MRI and CT for the measurement of visceral and subcutaneous adipose tissue in humans. Br J Radiol. 2012;85:e826–e830.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Barcelona V, Huang YF, Brown K, Liu JX, Zhao W, Yu M, et al. Novel DNA methylation sites associated with cigarette smoking among African Americans. Epigenetics. 2019;14:383–91.

    PubMed  PubMed Central  Google Scholar 

  49. Fasanelli F, Baglietto L, Ponzi E, Guida F, Campanella G, Johansson M, et al. Hypomethylation of smoking-related genes is associated with future lung cancer in four prospective cohorts. Nat Commun. 2015;6:10192.

    PubMed  PubMed Central  Google Scholar 

  50. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28:882–3.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by grants for the National Heart, Lung, and Blood Institute (NHLBI) (under contract numbers 75N92019D00027, 75N92019D00028, 75N92019D00029, and 75N92019D00030) and previous grants (R01HL090863, R01HL109315, R01HL109301, R01HL109284, R01HL109282, and R01HL109319 and cooperative agreements: U01HL41642, U01HL41652, U01HL41654, U01HL65520, and U01HL65521) and by the National Institutes of Health Sciences (R01ES021367, R01ES025216, P42ES010349, P30ES009089). Thanks to Dr. Pascale R Leroueil for computational expertise and Ali Thompson for illuminating discussions. All analyses and writing of this manuscript were done on the occupied territory of the Lenape and Wappinger Nations.

Author information

Authors and Affiliations

  1. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA

    Katherine C. Crocker, Arce Domingo-Relloso & Ana Navas-Acien

  2. Texas Biomedical Research Institute, San Antonio, TX, USA

    Karin Haack & Shelley A. Cole

  3. Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA

    Amanda M. Fretts

  4. Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Wan-Yee Tang & M. Daniele Fallin

  5. Institute for Biomedical Research Hospital Clinic de Valencia (INCLIVA), Valencia, Spain

    Miguel Herreros

  6. Department of Chronic Disease Epidemiology, National Center for Epidemiology, Carlos III Health Institute, Madrid, Spain

    Maria Tellez-Plaza

Authors
  1. Katherine C. Crocker
    View author publications

    Search author on:PubMed Google Scholar

  2. Arce Domingo-Relloso
    View author publications

    Search author on:PubMed Google Scholar

  3. Karin Haack
    View author publications

    Search author on:PubMed Google Scholar

  4. Amanda M. Fretts
    View author publications

    Search author on:PubMed Google Scholar

  5. Wan-Yee Tang
    View author publications

    Search author on:PubMed Google Scholar

  6. Miguel Herreros
    View author publications

    Search author on:PubMed Google Scholar

  7. Maria Tellez-Plaza
    View author publications

    Search author on:PubMed Google Scholar

  8. M. Daniele Fallin
    View author publications

    Search author on:PubMed Google Scholar

  9. Shelley A. Cole
    View author publications

    Search author on:PubMed Google Scholar

  10. Ana Navas-Acien
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Katherine C. Crocker.

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

Crocker, K.C., Domingo-Relloso, A., Haack, K. et al. DNA methylation and adiposity phenotypes: an epigenome-wide association study among adults in the Strong Heart Study. Int J Obes 44, 2313–2322 (2020). https://doi.org/10.1038/s41366-020-0646-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41366-020-0646-z

This article is cited by

Search

Advanced search

Quick links