News-Medical.Net January 1, 2025
Metabolite data and AI combine to redefine how we measure aging and predict health spans.
In a recent study published in the journal Science Advances, researchers at King’s College London explored metabolomic aging clocks using machine learning models trained on plasma metabolite data from the United Kingdom (U.K.) Biobank. The study aimed to assess the potential of metabolomic aging clocks in predicting health outcomes and life span by benchmarking their accuracy, robustness, and relevance to biological aging indicators beyond chronological age.
Background
Biological aging, distinct from chronological age, reflects molecular and cellular damage influencing health and disease susceptibility. Chronological age alone cannot capture the variability in aging-related physiological states among individuals. However, recent advances in omics technologies, particularly metabolomics, have...
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