Abstract

Exercise induces rapid systemic metabolic perturbations, but the chemical organization of post-exercise recovery remains incompletely resolved across public LC-MS datasets. Here, we performed a public-data reanalysis of human exercise metabolomics to determine whether named metabolites can be organized into chemically interpretable recovery landscapes. The primary dataset was the Metabolomics Workbench race-walking study ST003348/PR002083, which includes serum LC-MS profiles from race-walking athletes sampled at rest, immediately after exercise, and after 3 h and 22 h of recovery. Two independent public exercise datasets, ST003662/PR002271 and ST001789/PR001133, were used to externally validate the direction of change. Named features were deduplicated by RefMet identity, log2 fold-changes were estimated with paired subject-level contrasts, and false-discovery-rate correction was applied within each time point. We assigned rule-based chemical classes and summarized residual 22 h displacement as the median absolute paired log2 fold-change within each chemical class. This quantity was used only as a transparent descriptive aggregation of the observed 22 h contrasts, not as an independent, validated, or outcome-linked recovery index. In the primary dataset, 561 deduplicated features were retained from 19 subjects with a complete time point structure. Immediate post-exercise perturbation was detected in 188 features, whereas only seven features remained significantly displaced at 22 h. Lipids and lipid mediators showed the largest immediate chemical-class perturbation, while vitamins/steroids/signaling molecules and lipids had the highest class-level residual 22 h displacement—kinetic classification identified 180 acute-and-recovered features, 33 delayed/3 h-dominant features, and seven persistent features. External validation was strongest in the independent plasma running dataset ST001789, where primary acute responders showed 90.7% sign concordance at Time 0 and 75.9% at 60 min. By contrast, the DBS/VAMS dataset ST003662 showed weak and non-significant direction-of-change concordance, indicating limited transportability to that matrix/protocol. Overall, this study does not claim discovery of new exercise metabolites or pathways; it provides a reproducible applied chemistry framework for describing exercise recovery as a structured metabolochemical process rather than a purely physiological endpoint.

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