Comprehensive Cross-Sectional Study of the Triglyceride Glucose Index, Organophosphate Pesticide Exposure, and Cardiovascular Diseases: A Machine Learning Integrated Approach
Using NHANES data from 2003 to 2008, 2011 to 2012, and 2015 to 2020, we examined the relationship between urinary organophosphate pesticide (OPP) metabolites and the triglyceride glucose (TyG) index. The TyG index evaluates insulin resistance, a crucial factor in metabolic diseases. Linear regressio...
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2025-02-01
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| author | Xuehai Wang Mengxin Tian Zengxu Shen Kai Tian Yue Fei Yulan Cheng Jialing Ruan Siyi Mo Jingjing Dai Weiyi Xia Mengna Jiang Xinyuan Zhao Jinfeng Zhu Jing Xiao |
| author_facet | Xuehai Wang Mengxin Tian Zengxu Shen Kai Tian Yue Fei Yulan Cheng Jialing Ruan Siyi Mo Jingjing Dai Weiyi Xia Mengna Jiang Xinyuan Zhao Jinfeng Zhu Jing Xiao |
| author_sort | Xuehai Wang |
| collection | DOAJ |
| description | Using NHANES data from 2003 to 2008, 2011 to 2012, and 2015 to 2020, we examined the relationship between urinary organophosphate pesticide (OPP) metabolites and the triglyceride glucose (TyG) index. The TyG index evaluates insulin resistance, a crucial factor in metabolic diseases. Linear regression analyzed urinary metabolites in relation to the TyG index and OPPs. An RCS (restricted cubic spline) model explored the nonlinear relationship of a single OPP metabolite to TyG. A weighted quantile regression and quantile-based g-computation assessed the impact of combined OPP exposure on the TyG index. XGBoost, Random Forest, Support Vector Machines, logistic regression, and SHapley Additive exPlanations models investigated the impact of OPPs on the TyG index and cardiovascular disease. Network toxicology identified CVD targets associated with OPPs. This study included 4429 participants based on specific criteria. Linear regression analysis indicated that diethyl thiophosphate was positively correlated with the TyG index. The positive correlation between OPP metabolites and the TyG index at low to moderate concentrations was confirmed by WQS and QGC analyses. The machine learning results aligned with traditional statistical findings. Network toxicology identified PTGS3, PPARG, HSP40AA1, and CXCL8 as targets influenced by OPPs. OPP exposure influences IR and cardiometabolic health, highlighting the importance of public health prevention. |
| format | Article |
| id | doaj-art-d4f0e0b51b054ed58c48c190dfd3df06 |
| institution | DOAJ |
| issn | 2305-6304 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj-art-d4f0e0b51b054ed58c48c190dfd3df062025-08-20T02:45:38ZengMDPI AGToxics2305-63042025-02-0113211810.3390/toxics13020118Comprehensive Cross-Sectional Study of the Triglyceride Glucose Index, Organophosphate Pesticide Exposure, and Cardiovascular Diseases: A Machine Learning Integrated ApproachXuehai Wang0Mengxin Tian1Zengxu Shen2Kai Tian3Yue Fei4Yulan Cheng5Jialing Ruan6Siyi Mo7Jingjing Dai8Weiyi Xia9Mengna Jiang10Xinyuan Zhao11Jinfeng Zhu12Jing Xiao13Nantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaNantong Hospital to Nanjing University of Chinese Medicine, Nanjing 210023, ChinaNantong Key Laboratory of Environmental Toxicology, Department of Occupational Medicine and Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, ChinaUsing NHANES data from 2003 to 2008, 2011 to 2012, and 2015 to 2020, we examined the relationship between urinary organophosphate pesticide (OPP) metabolites and the triglyceride glucose (TyG) index. The TyG index evaluates insulin resistance, a crucial factor in metabolic diseases. Linear regression analyzed urinary metabolites in relation to the TyG index and OPPs. An RCS (restricted cubic spline) model explored the nonlinear relationship of a single OPP metabolite to TyG. A weighted quantile regression and quantile-based g-computation assessed the impact of combined OPP exposure on the TyG index. XGBoost, Random Forest, Support Vector Machines, logistic regression, and SHapley Additive exPlanations models investigated the impact of OPPs on the TyG index and cardiovascular disease. Network toxicology identified CVD targets associated with OPPs. This study included 4429 participants based on specific criteria. Linear regression analysis indicated that diethyl thiophosphate was positively correlated with the TyG index. The positive correlation between OPP metabolites and the TyG index at low to moderate concentrations was confirmed by WQS and QGC analyses. The machine learning results aligned with traditional statistical findings. Network toxicology identified PTGS3, PPARG, HSP40AA1, and CXCL8 as targets influenced by OPPs. OPP exposure influences IR and cardiometabolic health, highlighting the importance of public health prevention.https://www.mdpi.com/2305-6304/13/2/118TyG indexmachine learningnetwork toxicology analysisorganophosphorus pesticides |
| spellingShingle | Xuehai Wang Mengxin Tian Zengxu Shen Kai Tian Yue Fei Yulan Cheng Jialing Ruan Siyi Mo Jingjing Dai Weiyi Xia Mengna Jiang Xinyuan Zhao Jinfeng Zhu Jing Xiao Comprehensive Cross-Sectional Study of the Triglyceride Glucose Index, Organophosphate Pesticide Exposure, and Cardiovascular Diseases: A Machine Learning Integrated Approach Toxics TyG index machine learning network toxicology analysis organophosphorus pesticides |
| title | Comprehensive Cross-Sectional Study of the Triglyceride Glucose Index, Organophosphate Pesticide Exposure, and Cardiovascular Diseases: A Machine Learning Integrated Approach |
| title_full | Comprehensive Cross-Sectional Study of the Triglyceride Glucose Index, Organophosphate Pesticide Exposure, and Cardiovascular Diseases: A Machine Learning Integrated Approach |
| title_fullStr | Comprehensive Cross-Sectional Study of the Triglyceride Glucose Index, Organophosphate Pesticide Exposure, and Cardiovascular Diseases: A Machine Learning Integrated Approach |
| title_full_unstemmed | Comprehensive Cross-Sectional Study of the Triglyceride Glucose Index, Organophosphate Pesticide Exposure, and Cardiovascular Diseases: A Machine Learning Integrated Approach |
| title_short | Comprehensive Cross-Sectional Study of the Triglyceride Glucose Index, Organophosphate Pesticide Exposure, and Cardiovascular Diseases: A Machine Learning Integrated Approach |
| title_sort | comprehensive cross sectional study of the triglyceride glucose index organophosphate pesticide exposure and cardiovascular diseases a machine learning integrated approach |
| topic | TyG index machine learning network toxicology analysis organophosphorus pesticides |
| url | https://www.mdpi.com/2305-6304/13/2/118 |
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