Metabolomic aging and mortality clocks based on ensemble learning identify systemic aging risk across the human life span in 18,476 individuals.

Abstract

Aging is characterized by progressive functional decline, yet metabolomic signatures across the human life span remain incompletely defined. Here, we profiled plasma metabolomes from 18,476 participants aged 1 to 96 years across six centers in China using nano-assisted laser desorption/ionization mass spectrometry (LDI-MS) and identified 56 representative age-associated metabolites. In a healthy cohort, we developed a stacking ensemble model to estimate metabolomic age and quantified metabolomic age acceleration (mAA) as each individual's deviation from chronological age. Higher mAA was associated with health deterioration and unfavorable lifestyles. During follow-up from 2019 to 2025, individuals with accelerated mAA showed increased risk of incident chronic diseases, such as fatty liver disease, hypertension, and diabetes, as well as increased all-cause mortality. In a disease cohort, we further developed a mortality-linked metabolomic metric (age deviation) that quantified individualized departures from age-expected mortality risk inferred from plasma metabolites. Age deviation was associated with established composite health measures and increased with multisystem disease involvement and higher multimorbidity burden. Together, these findings support the potential of plasma metabolomic profiling as a scalable, life span-wide approach for deriving clinically interpretable metabolomic aging metrics associated with health status, chronic disease risk, and mortality.