预测冲刺潜力:基于年轻男性运动员血液代谢物概况的机器学习模型
Jingfeng Chen1, Yuhang Qian1, Yuansheng Xu2
1School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK.
European journal of sport science
|February 24, 2025
概括
男人血液代谢物签名,特别是斯芬戈米林 (d18:0/14:0) 和酸乙烯胺 (d20:0/18:1),可以区分运动员和健康个体,并预测冲刺表现. 这些生物标志物为优化运动员查和训练策略提供了潜力.
科学领域:
- 代谢学 代谢学 代谢学
- 运动科学 运动科学 运动科学
- 生物标志物发现发现
背景情况:
- 运动员查和绩效预测对于优化训练和比赛至关重要.
- 识别可靠的生物标志物可以增强这些过程.
- 目前的方法可能缺乏特异性或效率.
研究的目的:
- 识别血液代谢物签名,以区分运动员和健康个体.
- 为了预测男性短跑者 (100米,200米,400米) 的运动表现.
- 为了优化运动员的查和比赛前的准备.
主要方法:
- 血液样本上的非目标和目标代谢量.
- 差异分析,WGCNA,UMAP和LASSO-Cox回归用于代谢物识别.
- 机器学习分类 (13种方法) 用于区分.
- 与冲刺时间的相关性分析.
主要成果:
- 确定了两个关键的差异表达代谢物 (DEM):HMDB0012085 (斯芬哥米林) 和HMDB0009224 (酸丁乙醇胺).
- 与健康个体相比,运动员中发现了这些DEM的较高水平.
- 机器学习模型有效地根据DEM水平区分运动员,而HMDB0012085显示出更高的重要性.
- 在DEM水平和冲刺表现之间存在显著的负相关性.
结论:
- 在男性血液中发现的斯芬戈米林 (HMDB0012085) 和酸乙醇胺 (HMDB0009224) 是有前途的生物标志物.
- 这些代谢物可以区分运动员,并预测冲刺表现.
- 在运动员查和性能提升方面的潜在应用.
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