Fig. 1: Study overview.

a Identification of 54 representative aging-related metabolomic biomarkers based on a least absolute shrinkage and selection operator (LASSO) Cox regression model developed amongst participants from England and Wales with all-cause mortality as the endpoint. b Links between nuclear magnetic resonance (NMR) biomarkers and aging-related adverse health outcomes. Metabolome-wide association analysis was conducted to explore the association between 54 representative aging-related biomarkers and 50 frailty-related phenotypes. Multivariable Mendelian randomization (MVMR) analysis was performed to identify potential causal relationships between 325 NMR biomarkers and 20 aging-related diseases. c Construction and application of the metabolomic aging score. The metabolomic aging score was integrated as a linear combination of 54 aging-related biomarkers weighted by the estimated coefficients from the model. The metabolomic aging score was compared with other aging metrics among participants recruited from Scotland and was further applied to discriminate future early-onset patients of aging-related diseases and to improve multiple disease-risk prediction. d Longitudinal analysis among 13,263 individuals with revisit metabolomic data. A metabolomic aging rate was calculated and used to identify personalized changing patterns in aging-related metabolomic profiles. 15 pro-aging and 25 anti-aging biomarkers were identified based on distinct changing patterns across different rate groups. COPD chronic obstructive pulmonary disease.