Linking the metals to metabolism in recurrent pregnancy loss through untargeted metabolomics and machine learning.

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

SUBMITTER: Liu C 

PROVIDER: S-EPMC12719250 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature