ArticleCommunications biology2026
A large-scale human plasma metabolite atlas from over 380,000 participants.
Article in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.