Large clinical study in China uncovers “clocks” of human aging that may predict chronic disease and mortality
Summary author: Joseph Cariz
By parsing metabolic data from more than 18,000 participants in China, scientists have constructed “clock” models of human aging that can give clues about the risk of chronic diseases and mortality. The comprehensive study provides a treasure trove of data about aging’s impact on metabolism and yields clinically relevant metrics that could guide health monitoring and disease prevention efforts. Aging is a highly complicated process, and scientists have only begun to tease apart how the body’s molecular pathways shift as we get older. In recent years, researchers have sought to identify biomarkers of aging – such as epigenetic markers and changes in metabolic signatures – and integrate them into models called aging clocks. These models might help clinicians identify people who have a higher risk of chronic diseases and could inform early interventions. Now, Linlin Cao and colleagues present results from one of the most comprehensive studies to date of metabolic aging, involving 18,476 participants ranging from one to 96 years of age from six clinical centers in China. They profiled metabolites in plasma samples from the participants and linked 56 metabolites to age-associated shifts. Based on these data, the team derived a metric called metabolomic age acceleration (mAA), which correlated with a higher risk of fatty liver disease, hypertension, diabetes, and all-cause mortality as the cohort aged. The team also examined a disease cohort and uncovered a second metabolic metric called age deviation that was higher in patients with more severe and systemic disease. “Together, these findings support plasma metabolomics as a scalable and clinically interpretable framework for studies of disease prevention, early detection, healthy aging, and aging-related heterogeneity across the life course,” Cao et al. conclude. They add that their age prediction framework is available to the public via a web interface.
Journal
Science Translational Medicine
Article Title
Metabolomic aging and mortality clocks based on ensemble learning identify systemic aging risk across the human lifespan in 18,476 individuals
Article Publication Date
14-Oct-2026
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