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Differences In The Resting Metabolic Profile Between Endurance-Trained And Strength-Trained Athletes Compared With Sedentary Subjects

2022; Lippincott Williams & Wilkins; Volume: 54; Issue: 9S Linguagem: Inglês

10.1249/01.mss.0000880124.72651.c3

1530-0315

Mario Parstorfer, Gernot Poschet, Dorothea Kronberger, Kirsten Brüning, Birgit Friedmann‐Bette,

Adipose Tissue and Metabolism

Regular strength or endurance training leads to distinct differences in the phenotype and energy metabolism of elite athletes. PURPOSE: To determine whether there are distinct differences in the resting metabolic profile between endurance- and strength-trained athletes compared with sedentary subjects. METHODS: Three groups of male adults, 12 strength-trained (ST, elite competitive weightlifters, 20 ± 3 years, training experience 8 ± 3 years, VO2max 42 ± 5 ml/kg/min) and 10 endurance-trained athletes (ET, elite middle distance and marathon runners, 24 ± 3 years, training experience 9 ± 4 years, VO2max 65 ± 5 ml/kg/min) and 12 sedentary subjects (CG, 25 ± 4 years, VO2max 41 ± 7 ml/kg/min). Venous blood samples were taken at the end of each of three characteristic training phases of athletes within one training year. The last two meals (dinner and breakfast) before blood sampling were standardized. Plasma samples were analyzed using the Biocrates MxP® Quant 500 kit on UPLC I-class PLUS system coupled to a SCIEX QTRAP 6500+ mass spectrometry system. ANCOVA as well as partial least-squares analysis (PLS-DA) were performed using R and MetaboAnalyst 5.0. Volcano plots and score plots with their VIP-scores were created. RESULTS: After multi step procedure to ensure data quality, a total of 367 plasma metabolites were finally included in the analysis. Univariate analysis revealed 136 significant metabolites (p < 0.05) between the three groups with most of the metabolites from the compound classes triacylglycerols (73), glycerol-phospholipids (12), amino acids and amino acids related metabolites (12). The greatest differences were detected between ET and ST (120 metabolites, p < 0.05). In addition, multivariate analysis with all groups (PC1 and PC2: 4,9%, p < 0.001) showed a clear distinction between ET and ST and between CG and ST with a less clear distinction between CG and ET. A combined analysis of univariate and multivariate analysis revealed four main metabolites: glutamate, TG(16:0_38:6), TG(16:0_34:1), trigonelline. CONCLUSIONS: Apparently, adaptation to several years of regular strength or endurance training leads to distinct differences in the resting metabolic profile of elite athletes. Compared with sedentary subjects, most clear-cut differences were found after strength training.