Analytical workflows in salivary metabolomics for athlete stress monitoring: sampling, platform selection, and translation challenges

Abstract

Salivary metabolomics has emerged as a promising non-invasive strategy for athlete stress monitoring, but its translational value depends as much on analytical rigor as on biomarker discovery. This review examines salivary metabolomics from a methods-centered perspective, focusing on how collection protocols, pre-analytical control, platform selection, and statistical design shape the evidence base for monitoring exercise load, fatigue, recovery, and training adaptation. Current athlete and exercise studies show that salivary metabolic profiles are responsive to acute exertion and repeated competition, with recurrent signals involving amino acids, hydrophilic stress-related metabolites, and broader multimetabolite signatures. However, the literature remains limited by small cohorts, cross-sectional designs, inconsistent sampling procedures, and weak external validation. Comparative analysis suggests that no single analytical platform is universally optimal. NMR offers reproducibility and suitability for longitudinal phenotyping; LC-MS provides high sensitivity and broad chemical coverage but demands stronger quantitative discipline; GC-MS remains useful for derivatizable central metabolites; and CE-MS is particularly effective for polar stress-related metabolites. Across platforms, the dominant translational bottleneck is not a lack of measurable salivary change but insufficient standardization of how saliva is collected, processed, normalized, and modeled. The most credible route toward routine sports application is a platform-aware workflow that combines standardized passive-drool sampling, repeated within-athlete baselines, targeted or pseudotargeted validation panels, and multimodal integration with performance and training data. Under these conditions, salivary metabolomics is best viewed not as a blood substitute, but as a low-burden biochemical layer that can enhance longitudinal athlete monitoring.