期刊
FRONTIERS IN PHYSIOLOGY
卷 13, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fphys.2022.858869
关键词
athletic status; metabolomics; metabolites; swimmers; nuclear magnetic resonance
类别
资金
- Scientific Research Foundation of SUMHS [SSF-21-03-008, SSF-21-03-004]
- Science and Technology Commission of Shanghai Municipality [16JC1400500]
- Shanghai Sports Science and Technology Plan: Eaglet Project [20C008]
This study aimed to explore novel metabolite biomarkers in professional swimmers with different competition levels using nuclear magnetic resonance (NMR) metabolomics. The researchers identified ten metabolite biomarkers with differentially expressed levels between elite and sub-elite swimmers, and established a model using four metabolites (HDL, glutamine, methanol, and alpha-glucose) to predict athletic status. The model achieved a high accuracy in differentiating professional swimmers with different competitive levels.
Objective: Metabolites in body fluids, such as lactate, glucose, and creatinine, have been measured by conventional methods to evaluate physical function and performance or athletic status. The objectives of the current study were to explore the novel metabolite biomarkers in professional swimmers with different competition levels using nuclear magnetic resonance (NMR) metabolomics, and try to establish a model to identify the athletic status or predict the competitive potential.Methods: Serum samples were collected from 103 elite and 84 sub-elite level Chinese professional swimmers, and were profiled by NMR analysis.Results: Out of the thirty-six serum metabolites profiled, ten were associated with the athletic status of swimmers (with p < 0.05). When compared with sub-elite swimmers, elite swimmers had higher levels of high-density lipoprotein (HDL), unsaturated fatty acid, lactic acid, and methanol. Elite swimmers had lower levels of isoleucine, 3-hydroxybutyric acid, acetoacetate, glutamine, glycine, and alpha-glucose. A model with four metabolites, including HDL, glutamine, methanol, and alpha-glucose, was established to predict athletic status by adjusting with different covariates. The area under the curve (AUC) of the best model was 0.904 (95% CI: 0.862-0.947), with a sensitivity and specificity of 75.5 and 90.2%, respectively.Conclusion: We have identified ten metabolite biomarkers with differentially expressed levels between elite and sub-elite swimmers, the differences could result from genetic or sports level between the two cohorts. A model with four metabolites has successfully differentiated professional swimmers with different competitive levels.
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