Comprehensive Metabolomic Analysis of Saliva Using SWATH–DIA Reveals Systemic Metabolic Adaptations to Exercise

Saliva is an accessible, noninvasive biofluid for real-time monitoring of physiological and metabolic changes. However, its potential to capture physical-exciton-induced biochemical responses has been limited by the metabolite identification depth and reproducibility of conventional metabolomics tools. In this study, we established a Sequential Window Acquisition of All Theoretical Fragment-Ion Spectra (SWATH–DIA)–based untargeted LC–MS metabolomics workflow for comprehensive profiling and relative quantitation of the salivary metabolome before and after physical exercise. Saliva samples were collected from 27 recreational runners before and immediately after a standardized 5 km run to investigate acute metabolic fluctuations in participants. The Zeno SWATH–DIA method enabled the simultaneous acquisition of precursor and fragment ion spectra across the full m/z range (50–800 Da) in positive and negative mode of electrospray ionization (ESI), resulting in detection and validation metabolites spanning lipids, amino acids, organic acids, carbohydrates, and short-chain carnitines. Compared with traditional data-dependent acquisition (DDA) approaches, Zeno SWATH–DIA provided enhanced metabolite coverage, improved reproducibility, and reduced precursor selection bias (a statement of quantitation of how much more). Multivariate analyses (PCA, OPLS-DA) revealed clear separation between pre- and postexercise samples, highlighting metabolic shifts involving carbohydrate metabolism (lactate, pyruvate), fatty acid oxidation (acylcarnitines, glycerol), amino acid turnover (BCAAs, arginine, ornithine), and nitrogen metabolism (urea, spermidine). Collectively, these findings establish Zeno SWATH–DIA saliva metabolomics as a robust, high-coverage analytical approach for noninvasive assessment of acute metabolic responses to the exercise-induced physiological changes. The workflow provides a methodological foundation for future integrative studies linking saliva-based metabolomics with performance, fatigue, and metabolic health monitoring.