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Current metabolomic aging clocks inadequately capture individual heterogeneity in biological aging trajectories, constraining their clinical utility. Here, we developed a metabolomic age clock in the UK Biobank (n = 196,790) using a comprehensive panel of 249 plasma metabolites. This framework was trained to predict phenotypic age (PhenoAge), a validated composite biomarker that integrates clinical chemistry across multiple systems, and was evaluated for its utility to predict incident cardiovascular diseases (CVDs) and dementia. We found that this new measure accurately predicted actual PhenoAge (Pearson's r = 0.90) and was significantly associated with the incidence of seven CVDs, including major adverse cardiovascular events, atherosclerotic cardiovascular disease, myocardial infarction, stroke, aortic stenosis, heart failure, and abdominal aortic aneurysm, but not dementia. Furthermore, metabolomic aging was associated with biological, physical, and cognitive age-related phenotypes, comprising telomere length, frailty index, and reaction time. Incorporating the metabolomic age clock with PREVENT (Predicting Risk of CVD Events) risk score modestly improved the performance, as measured by C-statistic and net reclassification index. Genetic analyses revealed 91 genomic loci and 168 genes (e.g., SERPINA1, FADS cluster), with tissue-enrichment analysis highlighting the liver's significant role in metabolic aging. By bridging metabolomic profiles with multisystem aging information, this framework provides a measure of biological aging that is associated with age-related functional status and cardiovascular risk.