Metabolomics has emerged as a powerful analytical tool in anti-doping research, enabling the detection of performance-altering substances through comprehensive profiling of metabolic changes. In equine sports, the physiological stress of racing induces significant metabolic shifts, this study employed a non-targeted metabolomic approach to analyze plasma samples collected from racehorses under two conditions: (1) out-of-competition (resting state) and (2) immediately post-race. The primary objective was to establish a Post-race Biomarkers Database (PBD), systematically documenting differentially expressed metabolites, their fold changes, and associated metabolic pathways to distinguish racing-related physiological adaptations from potential doping effects. Statistical evaluation of the PBD revealed two key metabolite combinations, namely pentadecanoyl carnitine/galactosylglycerol (P/G) and heptadecanoyl carnitine/galactosylglycerol (H/G), as robust discriminators between resting and post-race samples. Both biomarkers exhibited pronounced involvement in lipid metabolism, consistent with the heightened energy demands of intense exercise. A single-blind study was performed to validate the applicability of such biomarkers. In addition, time-dependent changes of the biomarkers were monitored using total 20 pre and post-race plasma samples collected at four time-points: pre-race, post-race 30 min, 120 min, 180 min. This research highlights the efficacy of integrating biomarkers databases with pathway analysis to identify physiologically relevant biomarkers. The PBD not only provides a reference for normal post-race metabolic changes but also establishes a framework for detecting irregular patterns indicative of doping.
More importantly, our future perspective is to extend the concept of this integrated platform (combining biomarkers database with pathway analysis) to identify common biomarkers of the same drug classes (e.g. NSAIDs and hypoxia-inducible factor stabilizers) by comparing the corresponding reference sample datasets comprising both positive samples (from drug administration studies or positive doping cases) and negative controls. Such approach can significantly enhance the efficiency and scope of routine anti-doping testing particularly for screening prohibited substances of the studied drug classes without reference materials.