Machine learning-driven proteomic profiling reveals seminal plasma biomarkers linking trihalomethane exposure to impaired sperm quality.

作者：Song Xu, Haixia Zhou, Yingjun Chen, Heng-Gui Chen, Bin Sun, Zhong-Hua Lu, Tianzheng Meng, Chen-Liang Xiong, Yi-Xin Wang, Jie Zhang · 发表于：Ecotoxicology and Environmental Safety · 年份：2026 · DOI：10.1016/j.ecoenv.2026.120319 · 研究领域：Medicine

The decline in sperm quality has emerged as a major public health concern with significant implications for male fertility and population sustainability. However, high-accuracy biomarkers for sperm quality are lacking, and the environmental drivers remain obscure. Trihalomethanes (THMs), ubiquitous disinfection by-products in drinking water, have been linked to declining semen quality worldwide, yet the molecular mechanisms underlying THM-induced reproductive toxicity remain underexplored. In this propensity score-matched case-control study designed to maximize internal validity, we identified SP biomarkers of oligospermia and teratospermia among Chinese sperm donors and investigated their associations with THM exposure. Using deep data-independent acquisition (DIA) proteomics, we quantified 1666 SP proteins, including 73 proteins enriched in male reproductive tissues. A robust XGBoost machine learning algorithm, validated to mitigate overfitting, determined the optimal biomarker combination, achieving an AUC of 0.95. Ten oligospermia biomarkers-including five testis- or epididymis-specific proteins-were inversely associated with blood THM concentrations. These proteins regulate interconnected pathways related to oxidative stress and energy metabolism. We propose that THM-induced disruption of these fundamental metabolic machineries not only impairs sperm motility but also arrests spermatogenesis, leading to reduced sperm count. This study provides the first evidence linking ...