A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning
3-Monochloropropane-1,2-diol (3-MCPD) and glycidol along with their esters are commonly found in chemical production, wastewater treatment, food processing, and exhibit toxicity. Accurate exposure assessment is essential for evaluating the environmental hazards and health risks posed by these contam...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Ecotoxicology and Environmental Safety |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651325008954 |
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| author | Yimei Tian Sunan Gao Fan Zhang Xuzhi Wan Wei Jia Jingjing Jiao Yilei Fan Yu Zhang |
| author_facet | Yimei Tian Sunan Gao Fan Zhang Xuzhi Wan Wei Jia Jingjing Jiao Yilei Fan Yu Zhang |
| author_sort | Yimei Tian |
| collection | DOAJ |
| description | 3-Monochloropropane-1,2-diol (3-MCPD) and glycidol along with their esters are commonly found in chemical production, wastewater treatment, food processing, and exhibit toxicity. Accurate exposure assessment is essential for evaluating the environmental hazards and health risks posed by these contaminants. We collected demographic data from 1587 participants and developed seven models using machine-learning algorithms to investigate urinary metabolite exposure and dietary exposure associations of 3-MCPD and glycidol and their esters. Urinary dihydroxypropyl mercapturic acid concentrations, edible oils, and total energy were identified as key predictors of dietary exposure to these contaminants (p < 0.001). The seven machine learning models demonstrated strong predictive capabilities for internal urinary metabolite exposure and dietary exposure associations (average R > 0.6). Among these, generalized additive model and extreme gradient boosting exhibited the strongest correlation and highest accuracy in predicting the associations. We utilized machine learning techniques to link dietary exposure to 3-MCPD, glycidol, and their esters with internal urinary metabolite exposure, providing an innovative and accurate method for risk exposure assessment. |
| format | Article |
| id | doaj-art-310e82249dba4cfc9e10f34671c6ce11 |
| institution | Matheson Library |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-310e82249dba4cfc9e10f34671c6ce112025-06-28T05:29:02ZengElsevierEcotoxicology and Environmental Safety0147-65132025-09-01302118550A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learningYimei Tian0Sunan Gao1Fan Zhang2Xuzhi Wan3Wei Jia4Jingjing Jiao5Yilei Fan6Yu Zhang7Department of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, ChinaDepartment of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, ChinaDepartment of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, ChinaDepartment of Endocrinology, The Second Affiliated Hospital, Department of Nutrition, School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, ChinaDepartment of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, ChinaDepartment of Endocrinology, The Second Affiliated Hospital, Department of Nutrition, School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, ChinaKey Laboratory of Drug Prevention and Control Technology of Zhejiang Province, Zhejiang Police College, Hangzhou 310053, ChinaDepartment of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, China; Corresponding author.3-Monochloropropane-1,2-diol (3-MCPD) and glycidol along with their esters are commonly found in chemical production, wastewater treatment, food processing, and exhibit toxicity. Accurate exposure assessment is essential for evaluating the environmental hazards and health risks posed by these contaminants. We collected demographic data from 1587 participants and developed seven models using machine-learning algorithms to investigate urinary metabolite exposure and dietary exposure associations of 3-MCPD and glycidol and their esters. Urinary dihydroxypropyl mercapturic acid concentrations, edible oils, and total energy were identified as key predictors of dietary exposure to these contaminants (p < 0.001). The seven machine learning models demonstrated strong predictive capabilities for internal urinary metabolite exposure and dietary exposure associations (average R > 0.6). Among these, generalized additive model and extreme gradient boosting exhibited the strongest correlation and highest accuracy in predicting the associations. We utilized machine learning techniques to link dietary exposure to 3-MCPD, glycidol, and their esters with internal urinary metabolite exposure, providing an innovative and accurate method for risk exposure assessment.http://www.sciencedirect.com/science/article/pii/S01476513250089543-monochloropropane-1,2-diolGlycidolDietary exposureUrinary biomarkersExposure assessmentMachine learning |
| spellingShingle | Yimei Tian Sunan Gao Fan Zhang Xuzhi Wan Wei Jia Jingjing Jiao Yilei Fan Yu Zhang A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning Ecotoxicology and Environmental Safety 3-monochloropropane-1,2-diol Glycidol Dietary exposure Urinary biomarkers Exposure assessment Machine learning |
| title | A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning |
| title_full | A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning |
| title_fullStr | A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning |
| title_full_unstemmed | A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning |
| title_short | A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning |
| title_sort | new method for internal urinary metabolite exposure and dietary exposure association assessment of 3 mcpd and glycidol and their esters based on machine learning |
| topic | 3-monochloropropane-1,2-diol Glycidol Dietary exposure Urinary biomarkers Exposure assessment Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S0147651325008954 |
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