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Metabolomic and proteomic signatures of cardiorespiratory fitness for predicting all-cause mortality and non-communicable disease risk: a prospective study in the UK Biobank

Miras Moreno, S.; Torres, A.; Ruiz, J.; Carter, J.; Abreu de Carvalho, C.; AGUILERA, c. M.; Piernas, C.; Martinez Tellez, B.

2025-08-11 epidemiology
10.1101/2025.08.07.25332939 medRxiv
Show abstract

Cardiorespiratory fitness (CRF) is a strong predictor of mortality and non-communicable disease risk, but its underlying molecular mechanisms are poorly understood. In this study, we identified CRF-associated metabolomics (n=30,010) and proteomics (n=4,235) signatures in UK Biobank participants. These signatures were validated in an independent sample of UK participants with data on metabolomics (n=198,871) and proteomics (n=29,961) to investigate prospective associations with all-cause mortality and non-communicable diseases. Our findings reveal that higher CRF is characterized by downregulation of pathways related to inflammation, triglyceride metabolism, glycolysis, and vascular dysfunction, and upregulation of pathways related to cholesterol transport, apolipoprotein particle size, and cytoskeletal remodeling. Leveraging these insights, we developed two novel metabolic CRF signatures--one metabolomic and one proteomic--that robustly reflect CRF levels (R2: 0.49-0.60). Over an average of 9_years of follow-up, we observed 27,659 cases of all-cause mortality. Across the discovery and validation cohorts, we found that the metabolomic CRF signature was strongly associated with a 34-39% lower risk of all-cause mortality and markedly reduced risk of type 2 diabetes (89-91%), cardiovascular disease (35-39%), and colorectal cancer (32-54%). Additionally, the proteomic CRF signature was associated with a 17% lower risk of all-cause mortality, and with a 22-39% lower risk of type 2 diabetes and cardiovascular disease. Together, these findings suggest that circulating metabolites and proteins can capture the physiological imprint of CRF and may serve as indirect biomarkers for predicting mortality and non-communicable disease risk.

Published in Circulation: Genomic and Precision Medicine (predicted rank #21) · training set

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