Robust metabolomic age prediction based on a wide selection of metabolites

Chronological age is a major risk factor for numerous diseases. However, chronological age does not capture the complex biological aging process. the difference between the chronological age and biologically driven aging could be more informative in reflecting health status. Here, we set out to develop a metabolomic age prediction model by applying ridge regression and bootstrapping with 826 metabolites (678 endogenous and 148 xenobiotics) measured by an untargeted platform in relatively healthy blood donors aged 18-75 years from the INTERVAL study (N=11,977;50.2% men). After bootstrapping internal validation, the metabolomic age prediction models demonstrated high performance with an adjusted R2 of 0.83 using all metabolites and 0.82 using only endogenous metabolites. The former was significantly associated with obesity and cardiovascular disease (CVD) in the NEO study (N=599;47.0% men; age range=45-65) due to the contribution of medication derived metabolites—namely salicylate and ibuprofen—and environmental exposures such as cotinine. Additional metabolomic age prediction models using all metabolites were developed for men and women separately. The models had high performance (R²=0.85 and 0.86) but shared a moderate correlation of 0.72. Furthermore, we observed 163 sex-dimorphic metabolites, including threonine, glycine, cholesterol, and androgenic and progesterone-related metabolites. Our strongest predictors across all models were novel and included hydroxyasparagine (Model Endo+Xeno β=4.74), vanillylmandelate (β=4.07), and 5,6-dihydrouridine (β=-4.2). Our study presents a robust metabolomic age model that reveals distinct sex-based age-related metabolic patterns and illustrates the value of including xenobiotic to enhance metabolomic prediction accuracy.