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Correlation between fasting plasma glucose, HbA1c and DNA methylation in adult twins
Received date: 2020-02-15
Online published: 2020-06-30
Supported by
Special Found for Health Scienti-fic Research in the Public Welfare(201502006);Special Found for Health Scienti-fic Research in the Public Welfare(201002007);National Natural Science Foundation of China(81573223)
Objective: To explore the cytidine-phosphate-guanosine (CPG) sites associated with fas-ting plasma glucose (FPG) and glycated haemoglobin (HbA1c) in twins.Methods: In the study, 169 pairs of monozygotic twins were recruited in Qingdao, Zhejiang, Jiangsu, Sichuan and Heilongjiang in June to December of 2013 and June 2017 to October 2018. The methylation was detected by Illumina Infinium HumanMethylation450 BeadChip and Illumina Infinium MethylationEPIC BeadChip. According to the Linear Mixed Effect model (LME model), fasting plasma glucose and HbA1c were taken as the main effects, the methylation level (β value) was taken as the dependent variable, continuous variables, such as age, body mass index (BMI), blood pressure, components of blood cells, surrogate variables generated by SVA, and categorical variables, such as gender, smoking and drinking status, hypoglycemic drugs taking, were included in the fixed effect model as covariates, and the identity numbers (ID) of the twins was included in the random effect model. The intercept was set as a random. Regression analysis was carried out to find out the CpG sites related to fasting blood glucose or HbA1c, respectively.Results: In this study, 338 monozygotic twins (169 pairs) were included, with 412 459 CpG loci. Among them, 114 pairs were male, and 55 pairs were female, with an average age of (48.2±11.9) years. After adjustment of age, gender, BMI, blood pressure, smoking, drinking, blood cell composition, and other covariates, and multiple comparison test, 7 CpG sites (cg19693031, cg01538969, cg08501915, cg04816311, ch.8.1820050F, cg06721411, cg26608667) were found related to fasting blood glucose, 3 of which (cg08501915, ch.8.1820050f, cg26608667) were the newly found sites in this study; whereas 10 CpG sites (cg19693031, cg04816311, cg01538969, cg01339781, cg01676795, cg24667115, cg09029192, cg20697417, ch.4.1528651F, cg16097041) were found related to HbA1c, and 4 of which(cg01339781, cg24667115, cg20697417, and ch.4.1528651f) were new. We found that cg19693031 in TXNIP gene was the lowest P-value site in the association analysis between DNA methylation and fas-ting plasma glucose and HbA1c (PFPG=2.42×10-19, FDRFPG<0.001; PHbA1c=1.72×10-19, FDRHbA1c<0.001).Conclusion: In this twin study, we found new CpG sites related to fasting blood glucose and HbA1c, and provided some clues that partly revealed the potential mechanism of blood glucose metabolism in terms of DNA methylation, but it needed further verification in external larger samples.
Key words: Fasting plasma glucose; Glycated haemoglobin; DNA methylation; Twin
Zhao-nian WANG , Wen-jing GAO , Bi-qi WANG , Wei-hua CAO , Jun LV , Can-qing YU , Zeng-chang PANG , Li-ming CONG , Hua WANG , Xian-ping WU , Yu LIU , Li-ming LI . Correlation between fasting plasma glucose, HbA1c and DNA methylation in adult twins[J]. Journal of Peking University(Health Sciences), 2020 , 52(3) : 425 -431 . DOI: 10.19723/j.issn.1671-167X.2020.03.005
| [1] | Cho NH, Shaw JE, Karuranga S, et al. IDF diabetes atlas: Glo-bal estimates of diabetes prevalence for 2017 and projections for 2045[J]. Diabetes Res Clin Pract, 2018,138:271-281. |
| [2] | Wang L, Gao P, Zhang M, et al. Prevalence and ethnic pattern of diabetes and prediabetes in China in 2013[J]. JAMA, 2017,317(24):2515-2523. |
| [3] | Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications[J]. Nat Rev Endocrinol, 2018,14(2):88-98. |
| [4] | American Diabetes Association. Classification and diagnosis of diabetes: Standards of medical care in diabetes-2018[J]. Diabetes Care, 2018,41(Suppl 1):13-27. |
| [5] | World Health Organization. Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus: Abbreviated report of a who consultation[R/OL]. (2011-01-12)[2020-01-05]. https://www.who.int/diabetes/publications/report-hba1c_2011.pdf. |
| [6] | Singh GM, Danaei G, Farzadfar F, et al. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: A pooled analysis[J]. PLoS One, 2013,8(7):e65174. |
| [7] | 高文静, 李立明. 以分开抚养双生子为基础的研究进展[J]. 中华医学遗传学杂志, 2014,31(3):327-329. |
| [8] | Tan Q, Christiansen L, von Bornemann Hjelmborg J, et al. Twin methodology in epigenetic studies[J]. J Exp Biol, 2015,218(Pt 1):134-139. |
| [9] | Hwang JY, Lee HJ, Go MJ, et al. Genome-wide methylation ana-lysis identifies ELOVL5 as an epigenetic biomarker for the risk of type 2 diabetes mellitus[J]. Sci Rep, 2018,8(1):14862. |
| [10] | Liu F, Sun Q, Wang L, et al. Bioinformatics analysis of abnormal DNA methylation in muscle samples from monozygotic twins discordant for type 2 diabetes[J]. Mol Med Rep, 2015,12(1):351-356. |
| [11] | Yuan W, Xia Y, Bell CG, et al. An integrated epigenomic analysis for type 2 diabetes susceptibility loci in monozygotic twins[J]. Nat Commun, 2014,5:5719. |
| [12] | Fortin JP, Triche TJ Jr., Hansen KD. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi[J]. Bioinformatics, 2017,33(4):558-560. |
| [13] | Pidsley R, Cc YW, Volta M, et al. A data-driven approach to preprocessing Illumina 450K methylation array data[J]. BMC Genomics, 2013,14:293. |
| [14] | Wang B, Gao W, Yu C, et al. Determination of zygosity in adult Chinese twins using the 450K methylation array versus questionnaire data[J]. PLoS One, 2015,10(4):e0123992. |
| [15] | Houseman EA, Accomando WP, Koestler DC, et al. DNA methylation arrays as surrogate measures of cell mixture distribution[J]. BMC Bioinformatics, 2012,13:86. |
| [16] | Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis[J]. PLoS Genet, 2007,3(9):1724-1735. |
| [17] | Michels KB, Binder AM. Considerations for design and analysis of DNA methylation studies[J]. Methods Mol Biol, 2018,1708:31-46. |
| [18] | Carlin JB, Gurrin LC, Sterne JA, et al. Regression models for twin studies: A critical review[J]. Int J Epidemiol, 2005,34(5):1089-1099. |
| [19] | Soriano-Tarraga C, Jimenez-Conde J, Giralt-Steinhauer E, et al. Epigenome-wide association study identifies TXNIP gene associated with type 2 diabetes mellitus and sustained hyperglycemia[J]. Hum Mol Genet, 2016,25(3):609-619. |
| [20] | Meeks KAC, Henneman P, Venema A, et al. Epigenome-wide association study in whole blood on type 2 diabetes among sub-Saharan African individuals: Findings from the RODAM study[J]. Int J Epidemiol, 2019,48(1):58-70. |
| [21] | Pena GG, Dutra MS, Gazzinelli A, et al. Heritability of phenotypes associated with glucose homeostasis and adiposity in a rural area of Brazil[J]. Ann Hum Genet, 2014,78(1):40-49. |
| [22] | Cardona A, Day FR, Perry JRB, et al. Epigenome-wide association study of incident type 2 diabetes in a british population: EPIC-Norfolk study[J]. Diabetes, 2019,68(12):2315-2326. |
| [23] | Al Muftah WA, Al-Shafai M, Zaghlool SB, et al. Epigenetic associations of type 2 diabetes and BMI in an Arab population[J]. Clin Epigenetics, 2016,8:13. |
| [24] | Kulkarni H, Kos MZ, Neary J, et al. Novel epigenetic determinants of type 2 diabetes in Mexican-American families[J]. Hum Mol Genet, 2015,24(18):5330-5344. |
| [25] | Galmozzi A, Kok BP, Kim AS, et al. PGRMC2 is an intracellular haem chaperone critical for adipocyte function[J]. Nature, 2019,576(7785):138-142. |
| [26] | Alhawiti NM, Al Mahri S, Aziz MA, et al. TXNIP in metabolic regulation: Physiological role and therapeutic outlook[J]. Curr Drug Targets, 2017,18(9):1095-1103. |
| [27] | Waldhart AN, Dykstra H, Peck AS, et al. Phosphorylation of TXNIP by AKT mediates acute influx of glucose in response to insulin[J]. Cell Rep, 2017,19(10):2005-2013. |
| [28] | Gencheva M, Kato M, Newo AN, et al. Contribution of deah-box protein DHX16 in human pre-mRNA splicing[J]. Biochem J, 2010,429(1):25-32. |
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