| Title: | A large-scale human plasma metabolite atlas from over 380,000 participants |
| Journal: | Communications Biology |
| Published: | 23 Jun 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42337021/ |
| DOI: | https://doi.org/10.1038/s42003-026-10505-4 |
Publication 20565
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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.</p>
5 Authors
- Zhirong Li
- Yating Miao
- Lina Jin
- Yanan Ma
- Xinyao Zhang
2 Applications
| Application ID | Title |
|---|---|
| 769193 | Multifactorial Analysis of Brain Function, Structure, and Disease Risk: Investigating Genetic, Lifestyle, and Social Determinants of Neurological and Mental Health Using UK Biobank Data |
| 1127192 | Associations of diet and lifestyle with chronic non-communicable diseases: a multi-modal analysis using genetic, biomarker, and imaging data from UK Biobank |
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