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

An epigenome-wide association study of waist circumference in Chinese monozygotic twins

Abstract

Objectives

Central obesity poses significant health risks because it increases susceptibility to multiple chronic diseases. Epigenetic features such as DNA methylation may be associated with specific obesity traits, which could help us understand how genetic and environmental factors interact to influence the development of obesity. This study aims to identify DNA methylation sites associated with the waist circumference (WC) in Northern Han Chinese population, and to elucidate potential causal relationships.

Methods

A total of 59 pairs of WC discordant monozygotic twins (ΔWC >0) were selected from the Qingdao Twin Registry in China. Generalized estimated equation model was employed to estimate the methylation levels of CpG sites on WC. Causal relationships between methylation and WC were assessed through the examination of family confounding factors using FAmiliaL CONfounding (ICE FALCON). Additionally, the findings of the epigenome-wide analysis were corroborated in the validation stage.

Results

We identified 26 CpG sites with differential methylation reached false discovery rate (FDR) < 0.05 and 22 differentially methylated regions (slk-corrected p < 0.05) strongly linked to WC. These findings provided annotations for 26 genes, with notable emphasis on MMP17, ITGA11, COL23A1, TFPI, A2ML1-AS1, MRGPRE, C2orf82, and NINJ2. ICE FALCON analysis indicated the DNA methylation of ITGA11 and TFPI had a causal effect on WC and vice versa (p < 0.05). Subsequent validation analysis successfully replicated 10 (p < 0.05) out of the 26 identified sites.

Conclusions

Our research has ascertained an association between specific epigenetic variations and WC in the Northern Han Chinese population. These DNA methylation features can offer fresh insights into the epigenetic regulation of obesity and WC as well as hints to plausible biological mechanisms.

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

Access options

Buy this article

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

Fig. 1: Circular Manhattan plot for epigenome-wide association study of WC.
Fig. 2: Differential methylation patterns for the identified DMRs.

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Piche ME, Tchernof A, Despres JP. Obesity phenotypes, diabetes, and cardiovascular diseases. Circ Res. 2020;126:1477–500.

    Article  CAS  PubMed  Google Scholar 

  2. Bastien M, Poirier P, Lemieux I, Despres JP. Overview of epidemiology and contribution of obesity to cardiovascular disease. Prog Cardiovasc Dis. 2014;56:369–81.

    Article  PubMed  Google Scholar 

  3. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309:71–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9:373–92.

    Article  PubMed  Google Scholar 

  5. Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet. 2019;28:166–74.

    Article  CAS  PubMed  Google Scholar 

  6. Vazquez G, Duval S, Jacobs DR Jr, Silventoinen K. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: a meta-analysis. Epidemiol Rev. 2007;29:115–28.

    Article  PubMed  Google Scholar 

  7. Huxley R, James WP, Barzi F, Patel JV, Lear SA, Suriyawongpaisal P, et al. Ethnic comparisons of the cross-sectional relationships between measures of body size with diabetes and hypertension. Obes Rev. 2008;9:53–61.

    Article  PubMed  Google Scholar 

  8. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120:1640–5.

    Article  CAS  PubMed  Google Scholar 

  9. Heymsfield SB, Wadden TA. Mechanisms, pathophysiology, and management of obesity. N Engl J Med. 2017;376:1492.

    Article  PubMed  Google Scholar 

  10. Bray MS, Loos RJ, McCaffery JM, Ling C, Franks PW, Weinstock GM, et al. NIH working group report-using genomic information to guide weight management: from universal to precision treatment. Obesity. 2016;24:14–22.

    Article  PubMed  Google Scholar 

  11. Pigeyre M, Yazdi FT, Kaur Y, Meyre D. Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin Sci. 2016;130:943–86.

    Article  CAS  Google Scholar 

  12. Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012;13:484–92.

    Article  CAS  PubMed  Google Scholar 

  13. 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  PubMed  PubMed Central  Google Scholar 

  14. Xie T, Gorenjak V, Stathopoulou MG, Dade S, Marouli E, Masson C, et al. Epigenome-wide association study detects a novel loci associated with central obesity in healthy subjects. BMC Med Genomics. 2021;14:233.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Taylor JY, Huang Y, Zhao W, Wright ML, Wang Z, Hui Q, et al. Epigenome-wide association study of BMI in Black populations from InterGEN and GENOA. Obesity. 2023;31:243–55.

    Article  CAS  PubMed  Google Scholar 

  16. Aslibekyan S, Demerath EW, Mendelson M, Zhi D, Guan W, Liang L, et al. Epigenome-wide study identifies novel methylation loci associated with body mass index and waist circumference. Obesity. 2015;23:1493–501.

    Article  CAS  PubMed  Google Scholar 

  17. Sun D, Zhang T, Su S, Hao G, Chen T, Li QZ, et al. Body mass index drives changes in DNA methylation: a longitudinal study. Circ Res. 2019;125:824–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen Y, Kassam I, Lau SH, Kooner JS, Wilson R, Peters A, et al. Impact of BMI and waist circumference on epigenome-wide DNA methylation and identification of epigenetic biomarkers in blood: an EWAS in multi-ethnic Asian individuals. Clin Epigenetics. 2021;13:195.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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  PubMed  Google Scholar 

  20. Geurts YM, Dugue PA, Joo JE, Makalic E, Jung CH, Guan W, et al. Novel associations between blood DNA methylation and body mass index in middle-aged and older adults. Int J Obes. 2018;42:887–96.

    Article  CAS  Google Scholar 

  21. Ling C, Ronn T. Epigenetics in human obesity and type 2 diabetes. Cell Metab. 2019;29:1028–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Tan Q, Christiansen L, von Bornemann Hjelmborg J, Christensen K. Twin methodology in epigenetic studies. J Exp Biol. 2015;218:134–9.

    Article  PubMed  Google Scholar 

  23. Bell JT, Saffery R. The value of twins in epigenetic epidemiology. Int J Epidemiol. 2012;41:140–50.

    Article  PubMed  Google Scholar 

  24. 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  PubMed  Google Scholar 

  25. Li S, Bui M, Hopper JL. Inference about causation from examination of familial confounding (ICE FALCON): a model for assessing causation analogous to Mendelian randomization. Int J Epidemiol. 2020;49:1259–69.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Duan H, Ning F, Zhang D, Wang S, Zhang D, Tan Q, et al. The Qingdao Twin Registry: a status update. Twin Res Hum Genet. 2013;16:79–85.

    Article  PubMed  Google Scholar 

  27. Becker A, Busjahn A, Faulhaber HD, Bahring S, Robertson J, Schuster H, et al. Twin zygosity. Automated determination with microsatellites. J Reprod Med. 1997;42:260–6.

    CAS  PubMed  Google Scholar 

  28. Jackson RW, Snieder H, Davis H, Treiber FA. Determination of twin zygosity: a comparison of DNA with various questionnaire indices. Twin Res. 2001;4:12–8.

    Article  CAS  PubMed  Google Scholar 

  29. Tomsey CS, Kurtz M, Kist F, Hockensmith M, Call P. Comparison of PowerPlex 16, PowerPlex1.1/2.1, and ABI AmpfISTR Profiler Plus/COfiler for forensic use. Croat Med J. 2001;42:239–43.

    CAS  PubMed  Google Scholar 

  30. Xu C, Zhang D, Tian X, Wu Y, Pang Z, Li S, et al. Genetic and environmental basis in phenotype correlation between physical function and cognition in aging Chinese twins. Twin Res Hum Genet. 2017;20:60–65.

    Article  PubMed  Google Scholar 

  31. Joint Committee for Developing Chinese guidelines on P, Treatment of Dyslipidemia in A. [Chinese guidelines on prevention and treatment of dyslipidemia in adults]. Zhonghua Xin Xue Guan Bing Za Zhi. 2007;35:390–419.

    Google Scholar 

  32. Suchiman HE, Slieker RC, Kremer D, Slagboom PE, Heijmans BT, Tobi EW. Design, measurement and processing of region-specific DNA methylation assays: the mass spectrometry-based method EpiTYPER. Front Genet. 2015;6:287.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011;27:1571–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hebestreit K, Dugas M, Klein HU. Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics. 2013;29:1647–53.

    Article  CAS  PubMed  Google Scholar 

  35. Michels KB, Binder AM, Dedeurwaerder S, Epstein CB, Greally JM, Gut I, et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat Methods. 2013;10:949–55.

    Article  CAS  PubMed  Google Scholar 

  36. Rahmani E, Zaitlen N, Baran Y, Eng C, Hu D, Galanter J, et al. Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nat Methods. 2016;13:443–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Korthauer K, Kimes PK, Duvallet C, Reyes A, Subramanian A, Teng M, et al. A practical guide to methods controlling false discoveries in computational biology. Genome Biol. 2019;20:118.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Pedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics. 2012;28:2986–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Crujeiras AB, Pissios P, Moreno-Navarrete JM, Diaz-Lagares A, Sandoval J, Gomez A, et al. An epigenetic signature in adipose tissue is linked to nicotinamide N-methyltransferase gene expression. Mol Nutr Food Res. 2018;62:e1700933.

    Article  PubMed  Google Scholar 

  40. Bader M, Alenina N, Andrade-Navarro MA, Santos RA. MAS and its related G protein-coupled receptors, Mrgprs. Pharm Rev. 2014;66:1080–105.

    Article  CAS  PubMed  Google Scholar 

  41. Barella LF, Jain S, Kimura T, Pydi SP. Metabolic roles of G protein-coupled receptor signaling in obesity and type 2 diabetes. FEBS J. 2021;288:2622–44.

    Article  CAS  PubMed  Google Scholar 

  42. Bar-Or A. Analyses of all matrix metalloproteinase members in leukocytes emphasize monocytes as major inflammatory mediators in multiple sclerosis. Brain. 2003;126:2738–49.

    Article  PubMed  Google Scholar 

  43. Uversky V, Srichai MB, Colleta H, Gewin L, Matrisian L, Abel TW, et al. Membrane-type 4 matrix metalloproteinase (MT4-MMP) modulates water homeostasis in mice. PLoS ONE. 2011;6:e17099.

    Article  Google Scholar 

  44. Sohail A, Marco M, Zhao H, Shi Q, Merriman S, Mobashery S, et al. Characterization of the dimerization interface of membrane type 4 (MT4)-matrix metalloproteinase. J Biol Chem. 2011;286:33178–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sternson SM. Hypothalamic survival circuits: blueprints for purposive behaviors. Neuron. 2013;77:810–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Augustine V, Lee S, Oka Y. Neural control and modulation of thirst, sodium appetite, and hunger. Cell. 2020;180:25–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jourjine N. Hunger and thirst interact to regulate ingestive behavior in flies and mammals. Bioessays. 2017;39.

  48. Eiselt AK, Chen S, Chen J, Arnold J, Kim T, Pachitariu M, et al. Hunger or thirst state uncertainty is resolved by outcome evaluation in medial prefrontal cortex to guide decision-making. Nat Neurosci. 2021;24:907–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Swanson LW. Cerebral hemisphere regulation of motivated behavior. Brain Res. 2000;886:113–64.

    Article  CAS  PubMed  Google Scholar 

  50. Cakir I, Nillni EA. Endoplasmic reticulum stress, the hypothalamus, and energy balance. Trends Endocrinol Metab. 2019;30:163–76.

    Article  CAS  PubMed  Google Scholar 

  51. Cioanca AV, Wu CS, Natoli R, Conway RM, McCluskey PJ, Jager MJ, et al. The role of melanocytes in the human choroidal microenvironment and inflammation: insights from the transcriptome. Pigment Cell Melanoma Res. 2021;34:928–45.

    Article  CAS  PubMed  Google Scholar 

  52. Iwai M, Tulafu M, Togo S, Kawaji H, Kadoya K, Namba Y, et al. Cancer-associated fibroblast migration in non-small cell lung cancers is modulated by increased integrin alpha11 expression. Mol Oncol. 2021;15:1507–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Grassot V, Da Silva A, Saliba J, Maftah A, Dupuy F, Petit JM. Highlights of glycosylation and adhesion related genes involved in myogenesis. BMC Genomics. 2014;15:621.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Muscella A, Stefano E, Lunetti P, Capobianco L, Marsigliante S. The regulation of fat metabolism during aerobic exercise. Biomolecules. 2020;10:1699.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Luo C, Pook E, Wang F, Archacki SR, Tang B, Zhang W, et al. ADTRP regulates TFPI expression via transcription factor POU1F1 involved in coronary artery disease. Gene. 2020;753:144805.

    Article  CAS  PubMed  Google Scholar 

  56. Barbitoff YA, Serebryakova EA, Nasykhova YA, Predeus AV, Polev DE, Shuvalova AR, et al. Identification of novel candidate markers of type 2 diabetes and obesity in Russia by exome sequencing with a limited sample size. Genes. 2018;9:415.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Christakoudi S, Evangelou E, Riboli E, Tsilidis KK. GWAS of allometric body-shape indices in UK Biobank identifies loci suggesting associations with morphogenesis, organogenesis, adrenal cell renewal and cancer. Sci Rep. 2021;11:10688.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Venkataraghavan S, Pankow JS, Boerwinkle E, Fornage M, Selvin E, Ray D. Epigenome-wide association study of incident type 2 diabetes in Black and White participants from the Atherosclerosis Risk in Communities Study. Preprint. medRxiv. 2023;2023.08.09.23293896. 2023.

  59. Goyal R, Singhai M, Faizy AF. Glutathione peroxidase activity in obese and nonobese diabetic patients and role of hyperglycemia in oxidative stress. J Midlife Health. 2011;2:72–6.

    PubMed  PubMed Central  Google Scholar 

  60. Pei J, Pan X, Wei G, Hua Y. Research progress of glutathione peroxidase family (GPX) in redoxidation. Front Pharm. 2023;14:1147414.

    Article  CAS  Google Scholar 

  61. Pedram P, Wadden D, Amini P, Gulliver W, Randell E, Cahill F, et al. Food addiction: its prevalence and significant association with obesity in the general population. PLoS ONE. 2013;8:e74832.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Bauer M. Cell-type-specific disturbance of DNA methylation pattern: a chance to get more benefit from and to minimize cohorts for epigenome-wide association studies. Int J Epidemiol. 2018;47:917–27.

    Article  PubMed  Google Scholar 

  63. Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aissi D, Wahl S, et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014;383:1990–8.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors appreciate the local Health Bureau, local Center for Disease Control and Prevention, other relevant governments and persons, investigators, and respondents for their support to this research.

Funding

This work was supported by the National Natural Science Foundation of China (grant number 81773506) and the Natural Science Foundation of Shandong Province (grant number ZR2023MH374).

Author information

Authors and Affiliations

Authors

Contributions

FX participated to data analysis and wrote the manuscript. FH participated to data analysis. YW participated to data curation. BL participated to data curation. HT assisted in reviewing and revising the manuscript. WW participated in the visualization. XT participated to the investigation process. CX coordinated the planning of research activities. HD participated to the investigation process. DZ participated to data collection. YW designed the study and managed the execution of research activities. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Yili Wu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This research was approved by Regional Ethics Committee of the Qingdao CDC Institutional Review Boards (QDCDC- IRB) and carried out in compliance with the guidelines of the Helsinki Declaration. Informed written consent was obtained from all individual participants included in the study.

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

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xing, F., Han, F., Wu, Y. et al. An epigenome-wide association study of waist circumference in Chinese monozygotic twins. Int J Obes (2024). https://doi.org/10.1038/s41366-024-01538-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41366-024-01538-y

Search

Quick links