The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects
IntroductionThis study examines the synergistic effects of multi-pollutant exposure on hepatic lipid accumulation in non-alcoholic fatty liver disease (NAFLD) through the application of an explainable machine learning framework. This approach addresses the limitations of traditional models in managi...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1598639/full |
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| author | Chunying Yan Zhanfang Zhu Xueyan Guo Wei Zong Guisheng Liu Yan Jin Shiyuan Cui Fuqiang Liu Shujuan Gao |
| author_facet | Chunying Yan Zhanfang Zhu Xueyan Guo Wei Zong Guisheng Liu Yan Jin Shiyuan Cui Fuqiang Liu Shujuan Gao |
| author_sort | Chunying Yan |
| collection | DOAJ |
| description | IntroductionThis study examines the synergistic effects of multi-pollutant exposure on hepatic lipid accumulation in non-alcoholic fatty liver disease (NAFLD) through the application of an explainable machine learning framework. This approach addresses the limitations of traditional models in managing complex environmental interactions.MethodsUsing data from the National Health and Nutrition Examination Survey (NHANES) 2015–2016 (n = 494), we developed a stacked ensemble model that integrates LASSO, support vector machines (SVM), neural networks, and XGBoost to analyze urinary biomarkers of heavy metals, polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs). The Environmental Pollution Exposure Index (EPEI) was constructed to quantify cumulative effects, with SHAP values employed to identify critical pollutants and thresholds. Subgroup analyses were conducted to assess heterogeneity across different Body Mass Index (BMI), diabetes, and hyperlipidemia statuses.Results2-Hydroxynaphthalene was identified as the predominant pollutant (SHAP = 0.89), with cobalt and VOC metabolites (e.g., N-Acetyl-S-(2-carbamoylethyl)-L-cysteine) also contributing significantly. The EPEI demonstrated strong associations with obesity-related parameters (PLF: 7.02 vs. 3.41 in high/low-exposure groups, p < 0.0001) and hyperlipidemia (OR = 2.28 vs. 1.08, p = 2.7e-06). The model demonstrated an amplification of effects in subgroups with severe obesity (OR = 2.66, 95% CI: 2.08–3.24) and impaired fasting glucose.DiscussionThis study establishes a machine learning framework for assessing multi-pollutant risks in NAFLD, identifying 2-Hydroxynaphthalene as a significant hepatotoxicant and EPEI as a quantifiable metric of exposure. The findings highlight the metabolic vulnerabilities associated with obesity and early dysglycemia, thereby informing precision prevention strategies. Methodological advancements integrate exposomics with interpretable artificial intelligence, facilitating targeted interventions in environmental health. |
| format | Article |
| id | doaj-art-2e1c606256bf4203a01123378ae89cd6 |
| institution | OA Journals |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Public Health |
| spelling | doaj-art-2e1c606256bf4203a01123378ae89cd62025-08-20T01:55:37ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-05-011310.3389/fpubh.2025.15986391598639The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effectsChunying Yan0Zhanfang Zhu1Xueyan Guo2Wei Zong3Guisheng Liu4Yan Jin5Shiyuan Cui6Fuqiang Liu7Shujuan Gao8Department of Gastroenterology, Shaanxi Provincial People's Hospital, Xi’an, ChinaXi'an Jiaotong University Hospital, Xi’an, ChinaDepartment of Gastroenterology, Shaanxi Provincial People's Hospital, Xi’an, ChinaDepartment of Gastroenterology, Shaanxi Provincial People's Hospital, Xi’an, ChinaDepartment of Gastroenterology, Shaanxi Provincial People's Hospital, Xi’an, ChinaDepartment of Gastroenterology, Shaanxi Provincial People's Hospital, Xi’an, ChinaDepartment of Gastroenterology, Shaanxi Provincial People's Hospital, Xi’an, ChinaDepartment of Cardiology, Shaanxi Provincial People's Hospital, Xi’an, ChinaDepartment of Gastroenterology, Shaanxi Provincial People's Hospital, Xi’an, ChinaIntroductionThis study examines the synergistic effects of multi-pollutant exposure on hepatic lipid accumulation in non-alcoholic fatty liver disease (NAFLD) through the application of an explainable machine learning framework. This approach addresses the limitations of traditional models in managing complex environmental interactions.MethodsUsing data from the National Health and Nutrition Examination Survey (NHANES) 2015–2016 (n = 494), we developed a stacked ensemble model that integrates LASSO, support vector machines (SVM), neural networks, and XGBoost to analyze urinary biomarkers of heavy metals, polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs). The Environmental Pollution Exposure Index (EPEI) was constructed to quantify cumulative effects, with SHAP values employed to identify critical pollutants and thresholds. Subgroup analyses were conducted to assess heterogeneity across different Body Mass Index (BMI), diabetes, and hyperlipidemia statuses.Results2-Hydroxynaphthalene was identified as the predominant pollutant (SHAP = 0.89), with cobalt and VOC metabolites (e.g., N-Acetyl-S-(2-carbamoylethyl)-L-cysteine) also contributing significantly. The EPEI demonstrated strong associations with obesity-related parameters (PLF: 7.02 vs. 3.41 in high/low-exposure groups, p < 0.0001) and hyperlipidemia (OR = 2.28 vs. 1.08, p = 2.7e-06). The model demonstrated an amplification of effects in subgroups with severe obesity (OR = 2.66, 95% CI: 2.08–3.24) and impaired fasting glucose.DiscussionThis study establishes a machine learning framework for assessing multi-pollutant risks in NAFLD, identifying 2-Hydroxynaphthalene as a significant hepatotoxicant and EPEI as a quantifiable metric of exposure. The findings highlight the metabolic vulnerabilities associated with obesity and early dysglycemia, thereby informing precision prevention strategies. Methodological advancements integrate exposomics with interpretable artificial intelligence, facilitating targeted interventions in environmental health.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1598639/fullmachine learningpercentage of liver fat (PLF)heavy metalspolycyclic aromatic hydrocarbons (PAHs)volatile organic compounds (VOCs) |
| spellingShingle | Chunying Yan Zhanfang Zhu Xueyan Guo Wei Zong Guisheng Liu Yan Jin Shiyuan Cui Fuqiang Liu Shujuan Gao The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects Frontiers in Public Health machine learning percentage of liver fat (PLF) heavy metals polycyclic aromatic hydrocarbons (PAHs) volatile organic compounds (VOCs) |
| title | The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects |
| title_full | The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects |
| title_fullStr | The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects |
| title_full_unstemmed | The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects |
| title_short | The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects |
| title_sort | impact of multipollutant exposure on hepatic steatosis a machine learning based investigation into multipollutant synergistic effects |
| topic | machine learning percentage of liver fat (PLF) heavy metals polycyclic aromatic hydrocarbons (PAHs) volatile organic compounds (VOCs) |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1598639/full |
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