Chen Jingfeng, Qian Yuhang, Xu Yuansheng
School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK.
School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough, UK.
Eur J Sport Sci. 2025 Mar;25(3):e12272. doi: 10.1002/ejsc.12272.
This study aims to utilize male blood metabolite signatures for (i) distinguishing between healthy individuals and athletes, thereby optimizing the athlete screening process; and (ii) predicting athletic performance in 100, 200, and 400 m sprints, enhancing precompetition preparation and intervention strategies. Initially, we employed nontargeted metabolomics to analyze the blood metabolome of healthy individuals (n = 10) and athletes (n = 10), identifying differential expressed metabolites (DEMs) potentially related to athletic performance through differential analysis, consensus clustering, WGCNA, and UMAP analysis. Subsequently, using LASSO-Cox analysis, we refined our selection to two core DEMs: HMDB0012085 (Sphingomyelin (d18:0/14:0)) and HMDB0009224 (Phosphatidylethanolamine(20:0/18:1(9Z))) associated with athletic performance. We then applied targeted metabolomics to measure the levels of these DEMs in a larger cohort, including healthy individuals (n = 50) and athletes (n = 100), revealing a significant increase in the levels of HMDB0012085 and HMDB0009224 in athletes compared to healthy individuals. Utilizing 13 machine learning classification methods, we demonstrated that the levels of HMDB0012085 and HMDB0009224 in blood effectively differentiate between healthy individuals and athletes. Notably, HMDB0012085 exhibits greater feature importance across multiple algorithms compared to HMDB0009224. Specifically, in decision trees (94.1 vs. 5.9), random forests (60.7 vs. 39.3), gradient boosting trees (91.5 vs. 8.5), CatBoost (61.7 vs. 38.3), ExtraTrees (64.7 vs. 35.3), and XGBoost (74.5 vs. 25.5). Finally, we found a significant negative correlation between the levels of HMDB0012085 and HMDB0009224 in whole blood and sprint times for 100, 200, and 400 m races. In conclusion, HMDB0012085 and HMDB0009224 in whole blood hold promise as biomarkers for predicting athletic potential in males.
（i）区分健康个体和运动员，从而优化运动员筛选过程；（ii）预测100米、200米和400米短跑项目的运动表现，加强赛前准备和干预策略。首先，我们采用非靶向代谢组学方法分析了健康个体（n = 10）和运动员（n = 10）的血液代谢组，通过差异分析、共识聚类、加权基因共表达网络分析（WGCNA）和均匀流形近似与投影（UMAP）分析，确定了可能与运动表现相关的差异表达代谢物（DEM）。随后，通过套索-考克斯分析，我们将选择范围缩小到两种核心DEM：与运动表现相关的HMDB0012085（鞘磷脂（d18:0/14:0））和HMDB0009224（磷脂酰乙醇胺（20:0/18:1(9Z)））。然后，我们应用靶向代谢组学方法在更大的队列中测量这些DEM的水平，该队列包括健康个体（n = 50）和运动员（n = 100），结果显示与健康个体相比，运动员体内HMDB0012085和HMDB0009224的水平显著升高。利用13种机器学习分类方法，我们证明血液中HMDB0012085和HMDB0009224的水平能够有效区分健康个体和运动员。值得注意的是，与HMDB0009224相比，HMDB0012085在多种算法中表现出更高的特征重要性。具体而言，在决策树（94.1对5.9）、随机森林（60.7对39.3）、梯度提升树（91.5对8.5）、CatBoost（61.7对38.3）、极端随机树（64.7对35.3）和XGBoost（74.5对25.5）中。最后，我们发现全血中HMDB0012085和HMDB0009224的水平与100米、200米和400米比赛的短跑时间呈显著负相关。总之，全血中的HMDB0012085和HMDB0009224有望作为预测男性运动潜力的生物标志物。
