DunedinPACE predicts incident metabolic syndrome: cross-sectional and longitudinal data from the Berlin Aging Study II

Background Aim of the study was a comparative analysis of different epigenetic clocks with regard to their ability to predict a future onset of the Metabolic Syndrome (MetS). In addition, cross-sectional relationships between epigenetic age measures and MetS were investigated. Methods MetS was diagnosed in participants of the Berlin Aging Study II at baseline (n = 1671, mean age 68.8 ± 3.7 years, 51.6% women) and at follow-up (n = 1083; 7.4 ± 1.5 years later). DNA methylation age acceleration (DNAmAA) was calculated for a total of ten epigenetic clocks at baseline. In addition, DunedinPACE, a DNAm-based measure of the pace of aging, was calculated. The relationship between MetS, DNAmAA, and DunedinPACE was investigated by fitting regression models adjusted for potential confounders and calculating receiver operating characteristic statistics. Results Among all biomarkers investigated, DunedinPACE was the only DNAm-based predictor that was significantly associated with incident MetS at follow-up on average 7.4 years later (OR: 9.84, P =. 028). Logistic regression models predicting MetS that either included solely clinical parameters or solely epigenetic clock estimates (DNAmAA) or DunedinPACE revealed that GrimAge DNAmAA had an area under the curve most comparable to the model considering clinical variables only. Cross-sectional differences between participants with and without MetS remained statistically significant for DunedinPACE only after covariate adjustment (baseline: β = 0.042, follow-up: β = 0.031, P <. 0001 in both cases). Conclusion Comparison of epigenetic clocks in relation to MetS showed strong and consistent associations with DunedinPACE. Our results highlight the potential of using certain DNAm-based measures of biological ageing in predicting the onset of clinical outcomes, such as MetS.