Untargeted metabolomics and machine learning unveil the exposome and metabolism linked with the risk of early pregnancy loss by Yixuan Shi instant download

Untargeted metabolomics and machine learning unveil the exposome and metabolism linked with the risk of early pregnancy loss by Yixuan Shi instant download

ABSTRACTKeywords:Early pregnancy loss (EPL) may result from exposure to emerging contaminants (ECs), although the underlying Early pregnancy lossmechanisms remain poorly understood. This case-control study measured over 2000 serum features, including 37 ExposomeECs, 6 biochemicals, and 2057 endogenous metabolites, in serum samples collected from 48 EPL patients and Metabolomicshealthy pregnant women. The median total concentration of targeted EC in the EPL group (65.9 ng/mL) was Machine learningsignificantly higher than in controls (43.0 ng/mL; p < 0.05). Four machine learning algorithms were employed to Bis(2-ethylhexyl)phthalateidentify key molecular features and develop EPL risk prediction models. A random forest model based on chemical data achieved a predictive accuracy of 95 %, suggesting a potential association between EPL and chemical exposure, with phthalic acid esters identified as significant contributors. Ninety-five potential metabolite biomarkers were selected, which were predominantly enriched in pathways related to spermidine and spermine biosynthesis, ubiquinone biosynthesis, and pantothenate and coenzyme A biosynthesis. C17- sphinganine was identified as a leading biomarker with an area under the curve of 0.93. Furthermore,