Dataset Information

DNA methylation mediates development of HbA1c-associated complications in type 1 diabetes.

ABSTRACT: Metabolic memory, the persistent benefits of early glycaemic control on preventing and/or delaying the development of diabetic complications, has been observed in the Diabetes Control and Complications Trial (DCCT) and in the Epidemiology of Diabetes Interventions and Complications (EDIC) follow-up study, but the underlying mechanisms remain unclear. Here, we show the involvement of epigenetic DNA methylation (DNAme) in metabolic memory by examining its associations with preceding glycaemic history, and with subsequent development of complications over an 18-yr period in the blood DNA of 499 randomly selected DCCT participants with type 1 diabetes who are also followed up in EDIC. We demonstrate the associations between DNAme near the closeout of DCCT and mean HbA1c during DCCT (mean-DCCT HbA1c) at 186 cytosine-guanine dinucleotides (CpGs) (FDR < 15%, including 43 at FDR < 5%), many of which were located in genes related to complications. Exploration studies into biological function reveal that these CpGs are enriched in binding sites for the C/EBP transcription factor, as well as enhancer/transcription regions in blood cells and haematopoietic stem cells, and open chromatin states in myeloid cells. Mediation analyses show that, remarkably, several CpGs in combination explain 68-97% of the association of mean-DCCT HbA1c with the risk of complications during EDIC. In summary, DNAme at key CpGs appears to mediate the association between hyperglycaemia and complications in metabolic memory, through modifying enhancer activity at myeloid and other cells.

SUBMITTER: Chen Z

PROVIDER: S-EPMC7590966 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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DNA methylation mediates development of HbA1c-associated complications in type 1 diabetes.

Chen Zhuo Z Miao Feng F Braffett Barbara H BH Lachin John M JM Zhang Lingxiao L Wu Xiwei X Roshandel Delnaz D Carless Melanie M Li Xuejun Arthur XA Tompkins Joshua D JD Kaddis John S JS Riggs Arthur D AD Paterson Andrew D AD Natarajan Rama R

Nature metabolism 20200720 8

Metabolic memory, the persistent benefits of early glycaemic control on preventing and/or delaying the development of diabetic complications, has been observed in the Diabetes Control and Complications Trial (DCCT) and in the Epidemiology of Diabetes Interventions and Complications (EDIC) follow-up study, but the underlying mechanisms remain unclear. Here, we show the involvement of epigenetic DNA methylation (DNAme) in metabolic memory by examining its associations with preceding glycaemic hist ...[more]

PMID: 32694834

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Project description:Type 2 diabetes mellitus (T2D) has a complex genetic and environmental architecture that underlies its development and clinical presentation. Despite the identification of well over a hundred genetic variants and CpG sites that associate with T2D, a robust biosignature that could be used to prevent or forestall clinical disease has not been developed. Based on the premise that underlying genetic variation influences DNA methylation (DNAm) independently of or in combination with environmental exposures, we assessed the ability of local and distal gene x methylation (GxMeth) interactive effects to improve cg19693031 models for predicting T2D status in an African American cohort. Using genome-wide genetic data from 506 subjects, we identified a total of 1476 GxMeth terms associated with HbA1c values. The GxMeth SNPs map to biological pathways associated with the development and complications of T2D, with genetically contextual differences in methylation observed only in diabetic subjects for two GxMeth SNPs (rs2390998 AG vs. GG, p = 4.63 × 10-11, Δβ = 13%, effect size = 0.16 [95% CI = 0.05, 0.32]; rs1074390 AA vs. GG, p = 3.93 × 10-4, Δβ = 9%, effect size = 0.38 [95% CI = 0.12, 0.56]. Using a repeated stratified k-fold cross-validation approach, a series of balanced random forest classifiers with random under-sampling were built to evaluate the addition of GxMeth terms to cg19693031 models to discriminate between normoglycemic controls versus T2D subjects. The results were compared to those obtained from models incorporating only the covariates (age, sex and BMI) and the addition of cg19693031. We found a post-pruned classifier incorporating 10 GxMeth SNPs and cg19693031 adjusted for covariates predicted the T2D status, with the AUC, sensitivity, specificity and precision of the positive target class being 0.76, 0.81, 0.70 and 0.63, respectively. Comparatively, the AUC, sensitivity, specificity and precision using the covariates and cg19693031 were only 0.71, 0.74, 0.67 and 0.59, respectively. Collectively, we demonstrate correcting for genetic confounding of cg19693031 improves its ability to detect type 2 diabetes. We conclude that an integrated genetic-epigenetic approach could inform personalized medicine programming for more effective prevention and treatment of T2D.

Project description:AimsThe aim of the present study was to evaluate, by means of a meta-analysis approach, whether new available data, appeared on qualified literature, can support the effectiveness of an association of HbA1c variability with the risk of macro- and/or micro-vascular complications in type 2 diabetes mellitus (T2DM).MethodsThe meta-analysis was conducted according to PRISMA Statement guidelines and considered published studies on T2DM, presenting HbA1c variability as standard deviation (SD) or its derived coefficient of variation (CV). Literature search was performed on PubMed in the time range 2015-July 2022, with no restrictions of language.ResultsTwenty-three selected studies fulfilled the aims of the present investigation. Overall, the analysis of the risk as hazard ratios (HR) indicated a significant association between the HbA1c variability, expressed either as SD or CV, and the complications, except for neuropathy. Macro-vascular complications were all significantly associated with HbA1c variability, with HR 1.40 (95%CI 1.31-1.50, p < 0.0001) for stroke, 1.30 (95%CI 1.25-1.36, p < 0.0001) for transient ischaemic attack/coronary heart disease/myocardial infarction, and 1.32 (95%CI 1.13-1.56, p = 0.0007) for peripheral arterial disease. Micro-vascular complications yielded HR 1.29 (95%CI 1.22-1.36, p < 0.0001) for nephropathy, 1.03 (95%CI 0.99-1.08, p = 0.14) for neuropathy, and 1.15 (95%CI 1.08-1.24, p < 0.0001) for retinopathy. For all-cause mortality, HR was 1.33 (95%CI 1.27-1.39, p < 0.0001), and for cardiovascular mortality 1.25 (95%CI 1.17-1.34, p < 0.0001).ConclusionsOur meta-analysis on HbA1c variability performed on the most recent published data since 2015 indicates positive association between HbA1c variability and macro-/micro-vascular complications, as well as mortality events, in T2DM, suggesting that this long-term glycaemic parameter merits further attention as a predictive, independent risk factor for T2DM population.

Dataset Information

DNA methylation mediates development of HbA1c-associated complications in type 1 diabetes.

Publications

DNA methylation mediates development of HbA1c-associated complications in type 1 diabetes.

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