ArticleFrontiers in physiology2026
Serum metabolic signatures associated with running performance in healthy men living in a cold environment: insights into amino acid and energy metabolism.
Article in Frontiers in physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Chronic exposure to cold environments may impose distinct metabolic demands that are relevant to physical performance; however, the metabolic characteristics associated with physical performance in individuals living in cold environments remain incompletely understood. We aimed to characterize serum metabolomic profiles associated with differences in physical performance among healthy adults residing in a cold region. Methods: Among 178 healthy males residing in a cold-region environment, 141 participants with available serum metabolomics data were included and stratified according to 3,000-meter running performance into Good (≤14 minutes 13 seconds, n = 76) and Poor (>14 minutes 13 seconds, n = 65) groups. Fasting serum samples underwent untargeted ultra-performance liquid chromatography-tandem mass spectrometry profiling. Differentially abundant metabolites were identified using covariate-adjusted linear models implemented in the limma framework, with appendicular skeletal muscle mass index included as a continuous covariate. Differentially abundant metabolites were defined using a Benjamini-Hochberg FDR-adjusted Results: A total of 141 males aged 28-40 years were included. After adjustment for ASMI, 10 differentially abundant metabolites were identified between the Good and Poor groups. Six metabolites showed higher adjusted abundances in the Good group, including cortisol (FDR = 0.031, log Conclusions: Differences in running performance were associated with distinct serum metabolic patterns involving amino-acid and nitrogen metabolism, phospholipid metabolism, and stress- and redox-associated metabolic processes. These findings may provide metabolic hypotheses on inter-individual differences in running performance and identify candidate metabolic pathways for further investigation.
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