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ORIGINAL RESEARCH article

Front. Public Health
Sec. Environmental Health and Exposome
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1405533
This article is part of the Research Topic Effect-Based Tools: Safeguarding Public Health by Assessing Environmental Contamination View all 4 articles

Effects of environmental phenols on eGFR: Machine learning modeling methods applied to cross-sectional studies

Provisionally accepted
Rongrong Huang Rongrong Huang 1*Lei Liu Lei Liu 1Hao Zhou Hao Zhou 1Xueli Wang Xueli Wang 2Fukang Wen Fukang Wen 3Guibin Zhang Guibin Zhang 4Jinao Yu Jinao Yu 5Hui Shen Hui Shen 6
  • 1 Affiliated Hospital of Nantong University, Nantong, China
  • 2 Qingdao Eighth People's Hospital, Qingdao, Shandong Province, China
  • 3 Sun Yat-sen University, Guangzhou, Guangdong Province, China
  • 4 Tongji University, Shanghai, Shanghai Municipality, China
  • 5 University of Wisconsin-Madison, Madison, Wisconsin, United States
  • 6 The Ohio State University, Columbus, Ohio, United States

The final, formatted version of the article will be published soon.

    Purpose: Limited investigation is available on the correlation between environmental phenols' exposure and estimated glomerular filtration rate (eGFR). Our target is established a robust and explainable machine learning (ML) model that associates environmental phenols' exposure with eGFR.Our datasets for constructing the associations between environmental phenols' and eGFR were collected from the National Health and Nutrition Examination Survey (NHANES, 2013(NHANES, -2016)). Five ML models were contained and fine-tuned to eGFR regression by phenols' exposure. Regression evaluation metrics were used to extract the limitation of the models. The most effective model was then utilized for regression, with interpretation of its features carried out using shapley additive explanations (SHAP) and the game theory python package to represent the model's regression capacity. The study identified the top-performing random forest (RF) regressor with a mean absolute error of 0.621 and a coefficient of determination of 0.998 among 3371 participants. Six environmental phenols with eGFR in linear regression models revealed that the concentrations of triclosan (TCS) and bisphenol S (BPS) in urine were positively correlated with eGFR, and the correlation coefficients were β= 0.010 (P=0.026) and β=0.007 (P=0.004) respectively. SHAP values indicate that BPS (1.38), bisphenol F (BPF) (0.97), 2,5-dichlorophenol (0.87), TCS (0.78), BP3 (0.60), bisphenol A (BPA) (0.59) and 2,4-dichlorophenol (0.47) in urinary contributed to the model.The RF model was efficient in identifying a correlation between phenols' exposure and eGFR among US NHANES 2013-2016 participants. The findings indicate that BPA, BPF, and BPS are inversely associated with eGFR.

    Keywords: Environmental Exposure, Phenols, machine learning, Glomerular Filtration Rate, NHANES

    Received: 23 Mar 2024; Accepted: 26 Jul 2024.

    Copyright: © 2024 Huang, Liu, Zhou, Wang, Wen, Zhang, Yu and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Rongrong Huang, Affiliated Hospital of Nantong University, Nantong, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.