Urinary metabolomic signatures of muscle injury and recovery in elite football players.

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.