An Enhanced Protocol to Expand Human Exposome and Machine Learning-Based Prediction for Methodology Application.

Journal: Environmental science & technology
PMID: 39928530

Abstract

The human exposome remains limited due to the challenging analytical strategies used to reveal low-level endocrine-disrupting chemicals (EDCs) and their metabolites in serum and urine. This limits the integrity of the EDC exposure assessment and hinders understanding of their cumulative health effects. In this study, we propose an enhanced protocol based on multi-solid-phase extraction (multi-SPE) to expand human exposome with polar EDCs and metabolites and train a machine learning (ML) model for methodology prediction based on molecular descriptors. The protocol enhanced the measurement of 70 (25%) and 34 (12%) out of 295 well-acknowledged EDCs in serum and urine compared to the hydrophilic-lipophilic balance sorbent alone. In a nontarget analysis of serum and urine from 20 women of childbearing age in a cohort of 498, controlling occupational factors and daily behaviors for high chemical exposure potential, the multi-SPE protocol increased the measurement of 10 (40%) and 16 (53%) target EDCs and identification of 17 (77%) and 70 (36%) nontarget chemicals (confidence ≥ level 3) in serum and urine, respectively. Interestingly, the ML model predicted that the multi-SPE protocol could identify an additional 38% of the most bioactive chemicals. In conclusion, the multi-SPE protocol advances human exposome by expanding the measurement and identification of exposure profiles.

Authors

  • Ana He
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
  • Yiming Yao
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
  • Shijie Chen
    Department of Spine Surgery, The Third Xiangya Hospital of Central South University, 138 Tongzipo Rd, Changsha, 410013, Hunan, China. shijiechencsu@csu.edu.cn.
  • Yongcheng Li
    State Key Laboratory of Robotics, Shenyang Institute of Automation, University of Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China.
  • Nan Xiao
    Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, 100081, China. xiaonan@bit.edu.cn.
  • Hao Chen
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Hongzhi Zhao
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
  • Yu Wang
    Clinical and Technical Support, Philips Healthcare, Shanghai, China.
  • Zhipeng Cheng
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, 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.
  • Jiaping Xu
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
  • Haining Luo
    Department of Center for Reproductive Medicine, Tianjin Central Hospital of Gynecology Obstetrics/Tianjin Key Laboratory of human development and reproductive regulation, Tianjin 300052, China.
  • Hongwen Sun
    MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.

Keywords

External Resources

Popular Topics
Recent Journals