Metabolic Profiling of Epigenetic Aging and Its Associations With Aging‐Related Phenotypes and Modifiable Lifestyle Factors
TL;DR
These five metabolic signatures of epigenetic age acceleration (EAA) exhibited significant associations with aging‐related phenotypes including higher disease risk, poorer health status, and adverse clinical indicators including Gallstones, chronic kidney disease, and hepatitis.
Abstract
Epigenetic aging biomarkers are well‐established hallmarks of biological aging, yet their metabolic underpinnings remain largely unexplored. Here, we characterized metabolic signatures associated with five epigenetic aging biomarkers (HorvathAge, HannumAge, DNAmPhenoAge, DunedinPACE, and DNAmTL) and examined their clinical relevance and potential determinants in 7162 Chinese older adults from two cohorts (primary and validation). We observed both shared and distinct metabolic associations across epigenetic aging biomarkers. Metabolic signatures of epigenetic aging biomarkers were derived using elastic net regression, showing moderate correlations with the corresponding epigenetic aging biomarkers (r = 0.21–0.36 in internal testing set, p < 0.05), with external replication further validating metabolic signatures of DNAmPhenoAge, DunedinPACE, and DNAmTL (r = 0.18–0.29, p < 0.05). These five metabolic signatures of epigenetic age acceleration (EAA) exhibited 279 significant associations with aging‐related phenotypes including higher disease risk, poorer health status, and adverse clinical indicators. Gallstones, chronic kidney disease, and hepatitis, along with renal‐, hepatic‐ and metabolic‐related clinical indicators, were consistently associated with multiple metabolic signatures of EAA. Smoking status, alcohol consumption, body mass index (BMI), and physical activity were identified as modifiable lifestyle factors associated with metabolic signatures of EAA, with BMI showing the most consistent associations. Metabolic signatures of DunedinPACE and DNAmPhenoAA exhibited the most extensive associations with aging‐related phenotypes and modifiable lifestyle factors in both primary and validation cohorts. These findings provide novel insights into the metabolic correlates of epigenetic aging biomarkers and underscore the potential of metabolomics‐informed metrics of epigenetic aging as informative indicators of physiological decline and lifestyle effects.