Abstract
BACKGROUND: The association between recurrent pregnancy loss (RPL) and environmental exposure has attracted increasing attention. However, associations between RPL and metal exposure in northwestern China remained unclear. METHODS: This case-control study (318 RPL women, 326 controls) investigated associations between serum metal concentrations and RPL. Five machine learning algorithms identified significant variables. Bayesian kernel machine regression (BKMR) and quartile g-computation (Qgcomp) models assessed the combined effects of metal mixtures on RPL risk. Untargeted metabolomics integrated with metal exposure data explored potential mechanisms underlying metal-induced disruption. RESULTS: Compared to controls, RPL women exhibited higher BMI (P<0.001) and elevated serum Ti, Cu, and Se levels (P<0.05), while controls had higher Li, V, Cr, Sr, Pb, Ni, Zn, and Fe (P<0.05). Machine learning algorithms (AUC = 0.99-1.0) identified V, Li, Cr, Ti, and Ni as top five discriminative metals. Mixture analyses (BKMR/Qgcomp) revealed a significantly increased RPL risk with mixed metals (β=0.37, 95% CI: 0.31-0.42). Ti contributed positively to this risk, whereas V contributed negatively after adjusted for con-founders. Metabolomic analysis in a subset (n=100) linked these metals primarily to perturbations in purine metabolism, pantothenate and CoA biosynthesis, retinol metabolism, and ubiquinone/terpenoid-quinone biosynthesis. CONCLUSION: Our study provides valuable insights into the metabolic and environmental factors associated with RPL.