DNA methylation supports accelerated biological age in Type 2 Diabetes which can be reversed with pharmacological treatments: Retrospective Cohort Study

BackgroundBiological age (BA) closely depicts age-related changes at a cellular level. Type 2 Diabetes mellitus (T2D) accelerates BA when calculated using clinical biomarkers. However, there is a large spread of individual BA within these groups and it is unclear what clinical biomarkers correlate with different speeds of aging and whether pharmacological treatment of diabetes alter BA. We hypothesized that accelerated BA would be seen at the DNA methylation (DNAm) level, the gold standard to determine BA, and biomarkers and treatments would correlate the rate of BA in T2D. MethodsPublicly available DNAm samples were obtained from the GEO NCBI database and the NHANES 2017-2018 and ACCORD Cohorts were used for our analysis. We used the DNA Methylation Phenotypic Age algorithm and the Klemera and Doubal (KDM) methods to calculate BA with DNA methylation and clinical biomarkers, respectively. ResultsDNAm showed increased BA in whole blood and pancreatic islets in T2D in aging-related pathways, such as DNA damage and inflammation. Using the NHANES and ACCORD Trial cohorts, we found that avoidance of fried and fatty foods, and vigorous activity correlated with decreased BA in T2D. Cardiovascular, glycemic, and inflammatory biomarkers associated with the rate of aging in DM. Intensive blood pressure and T2D treatment associated with a greater deceleration in the speed of aging as compared to the standard. ConclusionsOverall, we show that certain tissues age faster in people with T2D and this strongly associates with blood glucose control, inflammation and cardiovascular health. Effective treatment of the disease can decelerate aging and decrease BA suggesting the latter as a novel and integrated index to evaluate and follow people with T2D. FundingThis study was supported by Institutional Startup Funds to C.A.M. (Joslin Diabetes Center) and NIH grants P30 DK036836 Joslin Diabetes Research Center (Bioinformatic Core).