Cross-sectional and longitudinal association of seven DNAm-based predictors with metabolic syndrome and type 2 diabetes.

Suet Mei Chew, Alexander Teumer, Pamela R Matías-García, Christian Gieger, Juliane Winckelmann, Karsten Suhre, Christian Herder, Wolfgang Rathmann, Annette Peters, Melanie Waldenberger

Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. [email protected] Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. [email protected]. · pubmed

Paper

Abstract

To date, various epigenetic clocks have been constructed to estimate biological age, most commonly using DNA methylation (DNAm). These include "first-generation" clocks such as DNAmAgeHorvath and "second-generation" clocks such as DNAmPhenoAge and DNAmGrimAge. The divergence of one's predicted DNAm age from chronological age, termed DNAmAge acceleration (AA), has been linked to mortality and various aging-related conditions, albeit with varying findings. In metabolic syndrome (MetS) and type 2 diabetes (T2D), it remains inconclusive which DNAm-based predictor(s) is/are closely related to these two metabolic conditions. Therefore, we examined the cross-sectional associations between seven DNAm-based predictors and prevalent metabolic conditions in participants with methylation data from the KORA study. We also analyzed the longitudinal association with time-to-incident T2D and the relative prognostic value compared to clinical predictors from the Framingham 8-year T2D risk function in predicting incident disease over eight years.

The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.

Research summary

The paper investigates the associations between DNA methylation-based predictors and metabolic syndrome and type 2 diabetes. This research is relevant as it explores biological age indicators that may relate to aging processes and age-related diseases, contributing to the understanding of metabolic conditions in the context of longevity.

Author of this paper? Send a correction.