Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk

Abstract

Multi-organ biological aging clocks across different organ systems have been shown to predict human disease and mortality. Here, we extend this multi-organ framework to plasma metabolomics, developing five organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derivatized metabolites from 274,247 UK Biobank participants. Our age prediction models achieve a mean absolute error of approximately 6 years (0.25<r < 0.42). Crucially, including composite metabolites (e.g. sums or ratios of raw metabolites) results in poor generalizability to independent test data due to multicollinearity. Genome-wide associations identify 405 MetBAG-locus pairs (P < 5 × 10−8/5). Using SBayesS, we estimate the SNP-based heritability (0.09< $${h}_{{SNP}}^{2}$$ < 0.18), negative selection signatures (−0.93 < S < −0.76), and polygenicity (0.001<Pi < 0.003) for the 5 MetBAGs. Genetic correlation and Mendelian randomization analyses reveal potential causal links between the 5 MetBAGs and cardiometabolic conditions (e.g., metabolic disorders and hypertension). Integrating multi-organ and multi-omics features improves disease category and mortality predictions. The 5 MetBAGs extend existing biological aging clocks to study human aging and disease across multiple biological scales. All results are publicly available at https://labs-laboratory.com/medicine/ . Aging affects multiple organs and tracking these changes could improve our understanding of disease risk. Here, the authors show that metabolomics-based organ-specific aging clocks can predict future risk of cardiometabolic disease and mortality.