Researchers at King's College London have evaluated 17 machine learning algorithms trained on blood metabolite data from over 225,000 UK Biobank participants. The study, the first to comprehensively compare algorithms for developing biological aging clocks, found that 'MileAge', a measure of biological age based on metabolites, is associated with health and lifespan. Individuals with accelerated aging showed increased frailty, chronic illness, poorer self-rated health, and higher mortality risk.
King's College LondonDec 18 2024 Researchers at the Institute of Psychiatry, Psychology & Neuroscience at King's College London have conducted a comprehensive study to evaluate artificial intelligence based aging clocks, which predict health and lifespan using data from blood.
A person's metabolomic age, their "MileAge", is a measure of how old their body seems to be on the inside based on markers in the blood called metabolites. Metabolites are small molecules that are produced during the process of metabolism, for example when food is broken down into energy. The difference between a person's metabolite-predicted age and their chronological age, termed MileAge delta, indicates whether their biological aging is accelerated or decelerated.
Individuals with accelerated aging were, on average, frailer, more likely to have a chronic illness, rated their health worse, and had a higher mortality risk. They also had shorter telomeres , which are a marker of cellular aging and linked with age-related diseases such as atherosclerosis. However, decelerated biological aging was only weakly linked with good health.
Artificial Intelligence Aging Blood Metabolites Health Lifespan
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