8-OHdG mediates the association of co-exposure to fifty-five typical endocrine-disrupting chemicals with renal function: a cross-section investigation in Southern Chinese adults

Chen, Qingfei; Deng, Qifei; Liao, Qilong; Liu, Yan; Zhang, Zhaorui; Wu, Dehua; Lv, Yanrong; Qin, Jingyao; Liu, Qing; Li, Shuangqi; Long, Zihao; Xing, Xiumei; Wang, Qing; Zeng, Xiaowen; Dong, Guanghui; Hou, Mengjun; Xiao, Yongmei

doi:10.1007/s11356-024-33266-1

Environ Sci Pollut Res

2024

DOI: 10.1007/s11356-024-33266-1

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8-OHdG mediates the association of co-exposure to fifty-five typical endocrine-disrupting chemicals with renal function: a cross-section investigation in Southern Chinese adults

Qingfei Chen,

Qifei Deng,

Qilong Liao

et al.

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

Liu,

Zhou,

Wang

et al. 2024

Front. Public Health

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PurposeLimited 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.MethodsOur datasets for constructing the associations between environmental phenols’ and eGFR were collected from the National Health and Nutrition Examination Survey (NHANES, 2013–2016). 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.ResultsThe 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 3,371 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.ConclusionThe RF model was efficient in identifying a correlation between phenols’ exposure and eGFR among United States NHANES 2013–2016 participants. The findings indicate that BPA, BPF, and BPS are inversely associated with eGFR.

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

Liu,

Zhou,

Wang

et al. 2024

Front. Public Health

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8-OHdG mediates the association of co-exposure to fifty-five typical endocrine-disrupting chemicals with renal function: a cross-section investigation in Southern Chinese adults

Cited by 1 publication

References 73 publications

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

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

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