ArticleMetabolomics : Official journal of the Metabolomic Society2026
Serum metabolomic profiling reveals load-specific adaptations to resistance training.
Article in Metabolomics : Official journal of the Metabolomic Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionResistance training (RT) imposes repeated mechanical stimuli triggering systemic metabolic reprogramming whose molecular underpinnings remain incompletely characterized. Univariate approaches fail to capture pathway-level shifts defining the metabolomic response to RT in protocols differing in load.
objectivesTo evaluate whether high-load (HL) and low-load (LL) RT protocols performed to volitional failure induce serum biochemical patterns specific to training status and loading condition, undetectable by univariate approaches.
methodsSeventeen healthy young men completed an 8-week RT intervention (HL: 80% 1-RM, n = 9; LL: 30% 1-RM, n = 8). Fasting serum profiles were acquired by untargeted
resultsOf 10 metabolites significantly altered, five were consistently modulated across both protocols (3-hydroxyisovalerate, 3-hydroxybutyrate, acetone, isobutyrate, and lactate), reflecting shared adaptations in amino acid turnover and ketone body metabolism. Training-status separation achieved AUC = 1.00 (p < 0.001), with 3-hydroxyisovalerate as the dominant feature. Load-specific divergence was captured only by an exploratory multivariate signature of choline, glucose, and alanine (AUC = 0.94; p = 0.008), none of which was individually significant in univariate testing. Pathway integration demonstrated that discriminant metabolites are consistently related to well-established metabolic pathways: leucine catabolism, ketone body turnover, and glycolytic-oxidative rebalancing.
conclusionsRT induces biologically coherent, load-modulated serum metabolomic shifts detectable only through multivariate analysis. These findings are hypothesis-generating and require external validation in independent cohorts before applied implementation.
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