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:

Integrated analysis of probability of type 2 diabetes mellitus with polymorphisms and methylation of SLC30A8 gene: a nested case-control study

Journal of Human Genetics volume 67, pages 651–660 (2022)Cite this article

Subjects

Abstract

To estimate the associations between single-nucleotide polymorphisms (SNPs) and methylation of SLC30A8 gene and T2DM risk, and the interactions among SNPs, methylation, and environmental factors on T2DM risk. We genotyped 9 SNPs and tested methylation at 46 CpG loci of SLC30A8 in the baseline DNA of 290 T2DM cases and 290 matched controls nested in the Rural Chinese Cohort Study. A conditional logistic regression model was used to estimate the associations between SNPs and SLC30A8 methylation and T2DM risk. Multifactor Dimensionality Reduction analysis was used to estimate the effect of interactions among SNPs, methylation, and environment on T2DM risk. Probability of T2DM was decreased with rs11558471 (GG vs. AA, OR = 0.55, 95% CI 0.32, 0.96), with rs13266634 (TT vs. CC, OR = 0.55, 95% CI 0.32, 0.94), with rs3802177 (AA vs. GG, OR = 0.54, 95%CI 0.31, 0.94), and its probability was increased with rs2466293 of SLC30A8 (GA vs. AA, OR = 1.63, 95% CI 1.08–2.47). Its probability was also significantly associated with methylation of CG9 and CG45 (OR = 0.56 [95% CI 0.33–0.97] and 1.61 [95%CI 1.03–-2.51]). T2DM probability was significantly associated with the interaction effect between rs2466293 and hypertension (p = 0.045). T2DM probability was also significantly associated with the combination effects of rs2466293 with BMI, hypertension, and hypertriglyceridemia, with the combination effects of hypertriglyceridemia with rs11558471, rs13266634, and methylation of CG45.

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

Similar content being viewed by others

References

  1. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pr. 2018;138:271–81.

    Article  CAS  Google Scholar 

  2. Thomas CC, Philipson LH. Update on diabetes classification. Med Clin North Am. 2015;99:1–16.

    Article  PubMed  Google Scholar 

  3. Selph S, Dana T, Blazina I, Bougatsos C, Patel H, Chou R. Screening for type 2 diabetes mellitus: a systematic review for the U.S. Preventive Services Task Force. Ann Intern Med. 2015;162:765–76.

    Article  PubMed  Google Scholar 

  4. Kwak SH, Park KS. Recent progress in genetic and epigenetic research on type 2 diabetes. Exp Mol Med. 2016;48:e220.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bacos K, Gillberg L, Volkov P, Olsson AH, Hansen T, Pedersen O, et al. Blood-based biomarkers of age-associated epigenetic changes in human islets associate with insulin secretion and diabetes. Nat Commun. 2016;7:11089.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40:638–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. van Otterdijk SD, Binder AM, Szarc Vel Szic K, Schwald J, Michels KB. DNA methylation of candidate genes in peripheral blood from patients with type 2 diabetes or the metabolic syndrome. PLoS One. 2017;12:e0180955.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Drong AW, Lindgren CM, McCarthy MI. The genetic and epigenetic basis of type 2 diabetes and obesity. Clin Pharm Ther. 2012;92:707–15.

    Article  CAS  Google Scholar 

  9. Chang YC, Liu PH, Yu YH, Kuo SS, Chang TJ, Jiang YD, et al. Validation of type 2 diabetes risk variants identified by genome-wide association studies in Han Chinese population: a replication study and meta-analysis. PLoS One. 2014;9:e95045.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Chimienti F, Devergnas S, Favier A, Seve M. Identification and cloning of a beta-cell-specific zinc transporter, ZnT-8, localized into insulin secretory granules. Diabetes 2004;53:2330–7.

    Article  CAS  PubMed  Google Scholar 

  11. Chimienti F, Favier A, Seve M. ZnT-8, a pancreatic beta-cell-specific zinc transporter. Biometals 2005;18:313–7.

    Article  CAS  PubMed  Google Scholar 

  12. Palmer ND, Goodarzi MO, Langefeld CD, Ziegler J, Norris JM, Haffner SM, et al. Quantitative trait analysis of type 2 diabetes susceptibility loci identified from whole genome association studies in the Insulin Resistance Atherosclerosis Family Study. Diabetes 2008;57:1093–100.

    Article  CAS  PubMed  Google Scholar 

  13. Gu HF. Genetic, epigenetic and biological effects of zinc transporter (SLC30A8) in Type 1 and Type 2 Diabetes. Curr Diabetes Rev. 2017;13:132–40.

    Article  CAS  PubMed  Google Scholar 

  14. Tamaki M, Fujitani Y, Hara A, Uchida T, Tamura Y, Takeno K, et al. The diabetes-susceptible gene SLC30A8/ZnT8 regulates hepatic insulin clearance. J Clin Invest. 2013;123:4513–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Fan M, Li W, Wang L, Gu S, Dong S, Chen M, et al. Association of SLC30A8 gene polymorphism with type 2 diabetes, evidence from 46 studies: a meta-analysis. Endocrine 2016;53:381–94.

    Article  CAS  PubMed  Google Scholar 

  16. Dong F, Zhang BH, Zheng SL, Huang XX, Du XB, Zhu KH, et al. Association between SLC30A8 rs13266634 polymorphism and risk of T2DM and IGR in Chinese population: a systematic review and meta-analysis. Front Endocrinol. 2018;9:564.

    Article  Google Scholar 

  17. Volkmar M, Dedeurwaerder S, Cunha DA, Ndlovu MN, Defrance M, Deplus R, et al. DNA methylation profiling identifies epigenetic dysregulation in pancreatic islets from type 2 diabetic patients. EMBO J. 2012;31:1405–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Seman NA, Mohamud WN, Ostenson CG, Brismar K, Gu HF. Increased DNA methylation of the SLC30A8 gene promoter is associated with type 2 diabetes in a Malay population. Clin Epigenetics. 2015;7:30.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Toperoff G, Aran D, Kark JD, Rosenberg M, Dubnikov T, Nissan B, et al. Genome-wide survey reveals predisposing diabetes type 2-related DNA methylation variations in human peripheral blood. Hum Mol Genet. 2012;21:371–83.

    Article  CAS  PubMed  Google Scholar 

  20. Ren Y, Liu Y, Sun X, Wang B, Zhao Y, Luo X, et al. Cohort study to determine the waist circumference cutoffs for predicting type 2 diabetes mellitus in rural China. Diabetes Metab Res Rev. 2018;34:e3007.

    Article  PubMed  Google Scholar 

  21. Zhao Y, Zhang M, Luo X, Wang C, Li L, Zhang L, et al. Association of 6-year waist circumference gain and incident hypertension. Heart 2017;103:1347–52.

    Article  PubMed  Google Scholar 

  22. Wang C, Li L, Wang L, Ping Z, Flory MT, Wang G, et al. Evaluating the risk of type 2 diabetes mellitus using artificial neural network: an effective classification approach. Diabetes Res Clin Pr. 2013;100:111–8.

    Article  Google Scholar 

  23. Bassett DR Jr. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1396.

    Article  PubMed  Google Scholar 

  24. Chen C, Lu FC. Department of Disease Control Ministry of Health PRC. The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci. 2004;17:1–36.

    PubMed  Google Scholar 

  25. Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Bohm M, et al. 2013 ESH/ESC guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J. 2013:2159–219.

  26. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18:499–502.

    Article  CAS  PubMed  Google Scholar 

  27. Wallace TM, Matthews DR. The assessment of insulin resistance in man. Diabet Med. 2002;19:527–34.

    Article  CAS  PubMed  Google Scholar 

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

  29. McGrath SB, Bounpheng M, Torres L, Calavetta M, Scott CB, Suh Y, et al. High-speed, multicolor fluorescent two-dimensional gene scanning. Genomics 2001;78:83–90.

    Article  CAS  PubMed  Google Scholar 

  30. Knott C, Bell S, Britton A. Alcohol consumption and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of more than 1.9 million individuals from 38 observational studies. Diabetes Care. 2015;38:1804–12.

    Article  CAS  PubMed  Google Scholar 

  31. Pan A, Wang Y, Talaei M, Hu FB, Wu T. Relation of active, passive, and quitting smoking with incident type 2 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2015;3:958–67.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yu HJ, Ho M, Liu X, Yang J, Chau PH, Fong DYT. Association of weight status and the risks of diabetes in adults: a systematic review and meta-analysis of prospective cohort studies. Int J Obes (Lond). 2022;46:1101–13.

    Article  Google Scholar 

  33. Zaccardi F, O’Donovan G, Webb DR, Yates T, Kurl S, Khunti K, et al. Cardiorespiratory fitness and risk of type 2 diabetes mellitus: A 23-year cohort study and a meta-analysis of prospective studies. Atherosclerosis 2015;243:131–7.

    Article  CAS  PubMed  Google Scholar 

  34. Yang X, Chen J, Pan A, Wu JHY, Zhao F, Xie Y, et al. Association between higher blood pressure and risk of diabetes mellitus in middle-aged and elderly Chinese adults. Diabetes Metab J. 2020;44:436–45.

    Article  PubMed  Google Scholar 

  35. Wright AK, Kontopantelis E, Emsley R, Buchan I, Mamas MA, Sattar N, et al. Cardiovascular risk and risk factor management in Type 2 Diabetes Mellitus. Circulation 2019;139:2742–53.

    Article  PubMed  Google Scholar 

  36. Wang Y, Meng RW, Kunutsor SK, Chowdhury R, Yuan JM, Koh WP, et al. Plasma adiponectin levels and type 2 diabetes risk: a nested case-control study in a Chinese population and an updated meta-analysis. Sci Rep. 2018;8:406.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Coleman CI, Reinhart K, Kluger J, White CM. The effect of statins on the development of new-onset type 2 diabetes: a meta-analysis of randomized controlled trials. Curr Med Res Opin. 2008;24:1359–62.

    Article  PubMed  Google Scholar 

  38. Wagner R, Thorand B, Osterhoff MA, Muller G, Bohm A, Meisinger C, et al. Family history of diabetes is associated with higher risk for prediabetes: a multicentre analysis from the German Center for Diabetes Research. Diabetologia 2013;56:2176–80.

    Article  CAS  PubMed  Google Scholar 

  39. Motsinger AA, Ritchie MD. Multifactor dimensionality reduction: an analysis strategy for modelling and detecting gene-gene interactions in human genetics and pharmacogenomics studies. Hum Genomics. 2006;2:318–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Xu M, Bi Y, Xu Y, Yu B, Huang Y, Gu L, et al. Combined effects of 19 common variations on type 2 diabetes in Chinese: results from two community-based studies. PLoS One. 2010;5:e14022.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lin Y, Li P, Cai L, Zhang B, Tang X, Zhang X, et al. Association study of genetic variants in eight genes/loci with type 2 diabetes in a Han Chinese population. BMC Med Genet. 2010;11:97.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wang Y, Duan L, Yu S, Liu X, Han H, Wang J, et al. Association between “solute carrier family 30 member 8” (SLC30A8) gene polymorphism and susceptibility to type 2 diabetes mellitus in Chinese Han and minority populations: an updated meta-analysis. Asia Pac J Clin Nutr. 2018;27:1374–90.

    CAS  PubMed  Google Scholar 

  43. Jing YL, Sun QM, Bi Y, Shen SM, Zhu DL. SLC30A8 polymorphism and type 2 diabetes risk: evidence from 27 study groups. Nutr Metab Cardiovasc Dis. 2011;21:398–405.

    Article  CAS  PubMed  Google Scholar 

  44. Pineda-Tenor D, Micheloud D, Berenguer J, Jimenez-Sousa MA, Fernandez-Rodriguez A, Garcia-Broncano P, et al. SLC30A8 rs13266634 polymorphism is related to a favorable cardiometabolic lipid profile in HIV/hepatitis C virus-coinfected patients. AIDS 2014;28:1325–32.

    Article  CAS  PubMed  Google Scholar 

  45. Dayeh TA, Olsson AH, Volkov P, Almgren P, Ronn T, Ling C. Identification of CpG-SNPs associated with type 2 diabetes and differential DNA methylation in human pancreatic islets. Diabetologia 2013;56:1036–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Liu C, Marioni RE, Hedman AK, Pfeiffer L, Tsai PC, Reynolds LM, et al. A DNA methylation Biomark of alcohol Consumption. Mol Psychiatry. 2018;23:422–33.

    Article  CAS  PubMed  Google Scholar 

  47. Li S, Hursting SD, Davis BJ, McLachlan JA, Barrett JC. Environmental exposure, DNA methylation, and gene regulation: lessons from diethylstilbesterol-induced cancers. Ann N. Y Acad Sci. 2003;983:161–9.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was funded by the National Natural Science Foundation of China (Grant nos. 81373074, 81402752, and 81673260); the Natural Science Foundation of Guangdong Province (Grant no. 2017A030313452); the Medical Research Foundation of Guangdong Province (Grant no. A2017181); and the Science and Technology Development Foundation of Shenzhen (Grant nos. JCYJ20140418091413562, JCYJ20160307155707264, JCYJ20170302143855721, and JCYJ20170412110537191).

Author information

Authors and Affiliations

  1. School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China

    Fulan Hu, Yanyan Zhang, Pei Qin, Xiaoyan Wu, Yang Li, Dongsheng Hu & Ming Zhang

  2. Department of Epidemiology and Health Statistics, School of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China

    Yang Zhao, Dechen Liu, Qionggui Zhou, Gang Tian, Quanman Li, Chunmei Guo, Ranran Qie, Shengbing Huang & Minghui Han

Authors
  1. Fulan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanyan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pei Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dechen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qionggui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gang Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Quanman Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Chunmei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xiaoyan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ranran Qie
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Shengbing Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Minghui Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Yang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Dongsheng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

FH, MZ, and DH contributed to study design, manuscript drafting, data analysis and interpretation. YZ, PQ, YZ, DL, GT, QL, CG, XW, RQ, SH, MH, and YL contributed to data collection. All authors approved submission of the manuscript in its present form.

Corresponding author

Correspondence to Ming Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

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 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

Hu, F., Zhang, Y., Qin, P. et al. Integrated analysis of probability of type 2 diabetes mellitus with polymorphisms and methylation of SLC30A8 gene: a nested case-control study. J Hum Genet 67, 651–660 (2022). https://doi.org/10.1038/s10038-022-01067-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s10038-022-01067-4

Search

Advanced search

Quick links