Untargeted metabolomics and machine learning unveil the exposome and metabolism linked with the risk of early pregnancy loss.

Journal: Journal of hazardous materials
PMID: 39892135

Abstract

Early pregnancy loss (EPL) may result from exposure to emerging contaminants (ECs), although the underlying mechanisms remain poorly understood. This case-control study measured over 2000 serum features, including 37 ECs, 6 biochemicals, and 2057 endogenous metabolites, in serum samples collected from 48 EPL patients and healthy pregnant women. The median total concentration of targeted EC in the EPL group (65.9 ng/mL) was significantly higher than in controls (43.0 ng/mL; p < 0.05). Four machine learning algorithms were employed to identify 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, exposure to bis(2-ethylhexyl)phthalate was linked to an increased risk of EPL by disrupting lipid metabolism. These findings indicate that combining untargeted metabolomics with machine learning approaches offers novel insights into the mechanisms of EPL related to EC exposure.

Authors

  • Yixuan Shi
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
  • Keyi Li
    Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA. Electronic address: kl734@scarletmail.rutgers.edu.
  • Ran Ding
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
  • Xiaoying Li
    Ministry of Education Key Laboratory of Metabolism and Molecular Medicine, Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Zhipeng Cheng
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
  • Jialan Liu
    Department of Obstetrics and Gynecology, Tianjin Jinnan Hospital, Tianjin 300350, China.
  • Shaoxia Liu
    Department of Obstetrics and Gynecology, Tianjin Jinnan Hospital, Tianjin 300350, China.
  • Hongkai Zhu
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
  • Hongwen Sun
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.

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