A large-scale human plasma metabolite atlas from over 380,000 participants.

Authors

Zhirong Li, Yating Miao, Lina Jin, Yanan Ma, Xinyao Zhang

Published in

Communications biology. Jun 23, 2026. Epub Jun 23, 2026.

Abstract

Large-scale metabolomics enables systematic profiling of circulating small-molecule metabolites and provides an integrative view of genetic, environmental, lifestyle, and microbial influences on human health, yet comprehensive evidence linking metabolites to diverse traits, diseases, comorbidity, prediction, and causality remains limited. Here, we analyze 251 Nuclear magnetic resonance (NMR)-derived plasma metabolites in 212,751 UK Biobank participants, with independent validation in 177,013 individuals from a newly released data wave. Testing across 884 health-related traits, 722 prevalent diseases, and 1137 incident diseases using multivariable regression and stringent Bonferroni correction, we identified 67,505 metabolite-trait, 21,982 metabolite-prevalent disease, and 41,214 metabolite-incident disease associations, with replication rates of 76.1%, 83.3%, and 74.0%, respectively. Associations were most concentrated in endocrine, metabolic, and circulatory diseases. Unsupervised clustering of disease-metabolite profiles revealed 10 and 12 disease clusters (for prevalent and incident diseases, respectively) that span International Classification of Diseases (ICD)-10 chapters yet share convergent metabolomic signatures, providing a metabolic basis for clinical comorbidity. Metabolite-based predictive models showed stronger performance for short-term than long-term outcomes, with creatinine as the most frequently selected predictor across prevalent disease models. Bidirectional Mendelian randomisation (MR) identified 61 putative metabolite-to-disease causal effects and 558 disease-driven metabolic alterations. This study provides a systematically validated plasma metabolomic atlas (https://net-matebolic.vercel.app/) linking metabolites with human traits and diseases, offering insights into comorbidity, risk prediction, and potential causal pathways.

PMID:
42337021
Bibliographic data and abstract were imported from PubMed on 24 Jun 2026.

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