Urinary metabolomic profile distinguishes muscle injury in elite football players
The study reports targeted UPLC-MS analysis of urinary metabolites from 121 elite male football players to identify metabolic patterns linked to muscle injury. Investigators found coordinated alterations in amino acid turnover, purine metabolism, and tryptophan catabolism, with a multivariate model achieving an AUROC of 0.88 (sensitivity 73%, specificity 88%). Discriminant compounds included β-aminoisobutyric acid, hypoxanthine, xanthurenic acid, β-alanine, and alanine, while longitudinal tracking suggested metabolite normalization during rehabilitation. The findings position urinary metabolomics as a potential non-invasive adjunct to external load monitoring, though larger validation cohorts are required before routine laboratory implementation.
The original study
Urinary metabolomic signatures of muscle injury and recovery in elite football players.
- Authors
- Quintás G, Pruna R, Wong M, Mechó S, Madrero P, Sanjuán-Herráez JD, et al.
- Journal
- Metabolomics : Official journal of the Metabolomic Society
- Type
- Journal Article, Observational Study
- PMID
- 42776338
Original abstract
INTRODUCTION: Muscle injuries are a leading cause of time-loss in elite football, highlighting the need for objective biomarkers capable of reflecting the athlete's physiological state. Monitoring strategies rely predominantly on external load metrics and may not fully capture internal physiological responses to training and competition. Urinary metabolomics offers a non-invasive approach to characterize injury related physiological processes. OBJECTIVES: To identify urinary metabolomic signatures associated with muscle injury in elite football players and to explore their potential utility for monitoring injury-related physiological stress and recovery. METHODS: This observational longitudinal study included 287 urine samples collected from 121 elite male football players across two consecutive seasons. Samples were clinically classified as muscle injury (n = 30) or control (n = 257). Targeted UPLC-MS quantified amino acids and tryptophan-related metabolites. Univariate analysis, pathway over-representation analysis, and PLS-DA were applied to identify injury-associated metabolic patterns RESULTS: Muscle injury was associated with alterations in amino acid turnover, purine metabolism, energetic stress, and tryptophan catabolism. Exploratory pathway analysis suggested enrichment of amino acid-related pathways, although interpretation was limited by the targeted nature of the analytical panel. PLS-DA identified an injury-associated metabolic signature with a cross-validated AUROC of 0.88 (sensitivity 73%, specificity 88%; p < 0.003). Key discriminant metabolites included β-aminoisobutyric acid, hypoxanthine, xanthurenic acid, β-alanine, and alanine. Exploratory visual assessment of individual longitudinal trajectories suggested a return toward baseline during rehabilitation. CONCLUSIONS: Urinary metabolomics identifies coordinated metabolic alterations associated with muscle injury in elite football players, supporting its potential as a future complementary tool to external load monitoring. Further validation in larger cohorts is required before these systemic signatures can be applied to clinical monitoring or recovery assessment.