Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning
Background: Exposure to per- and polyfluoroalkyl substances (PFAS) may linked to thyroid cancer (TC) risk, but inconsistent findings and a lack of studies on mixed exposures exist, especially regarding novel PFAS compounds. Additionally, little is known about the potential mechanisms underlying the...
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Elsevier
2025-01-01
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| Series: | Environment International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412024007906 |
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| author | Fei Wang Yuanxin Lin Lian Qin Xiangtai Zeng Hancheng Jiang Yanlan Liang Shifeng Wen Xiangzhi Li Shiping Huang Chunxiang Li Xiaoyu Luo Xiaobo Yang |
| author_facet | Fei Wang Yuanxin Lin Lian Qin Xiangtai Zeng Hancheng Jiang Yanlan Liang Shifeng Wen Xiangzhi Li Shiping Huang Chunxiang Li Xiaoyu Luo Xiaobo Yang |
| author_sort | Fei Wang |
| collection | DOAJ |
| description | Background: Exposure to per- and polyfluoroalkyl substances (PFAS) may linked to thyroid cancer (TC) risk, but inconsistent findings and a lack of studies on mixed exposures exist, especially regarding novel PFAS compounds. Additionally, little is known about the potential mechanisms underlying the association. Objectives: Explore the effects of PFAS exposure on the serum metabolome and its correlation with TC. Methods: A 1:1 age- and sex-matched case-control study was administered with 746 TC cases and healthy controls. Liquid chromatography-high resolution mass spectrometry determined serum 11 PFAS and untargeted metabolome profile. ENET and LightGBM models were used to explore the exposure patterns and perform variable selection. The mixed exposure effects were assessed using Weighted quantile sum regression and Bayesian kernel machine regression. Metabolome-wide association analyses were performed to assess metabolic dysregulation associated with PFAS, and a structural synthesis analysis was used to detect latent groups of individuals with TC based on PFAS levels and metabolite patterns. Results: Ten of the 11 PFAS were detected in > 80 % of the population. PFHxA and PFDoA exposure associated with increased TC risk, while PFHxS and PFOA associated with decreased TC risk in single compound models (all P < 0.05). Machine learning algorithms identified PFHxA, PFDoA, PFHxS, PFOA, and PFHpA as the key PFAS influencing the development of TC, and mixed exposures have an overall positive effect on TC risk, with PFHxA making the primary contribution. A novel integrative analysis identified a cluster of TC patients characterized by increased PFHxA, PFDoA, PFHpA and decreased PFOA, PFHxS levels, and altered metabolite patterns highlighted by the upregulation of free fatty acids. Conclusions: PFAS exposure is linked to a higher risk of TC, possibly through changes in fatty acid metabolism. Larger, prospective studies are needed to confirm these findings, and the role of short-chain PFAS requires more attention. |
| format | Article |
| id | doaj-art-d9bbfd7fa75f464c92198d02e970fc13 |
| institution | Kabale University |
| issn | 0160-4120 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj-art-d9bbfd7fa75f464c92198d02e970fc132025-01-24T04:44:02ZengElsevierEnvironment International0160-41202025-01-01195109203Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learningFei Wang0Yuanxin Lin1Lian Qin2Xiangtai Zeng3Hancheng Jiang4Yanlan Liang5Shifeng Wen6Xiangzhi Li7Shiping Huang8Chunxiang Li9Xiaoyu Luo10Xiaobo Yang11Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China; Guangxi Key Laboratory of Environment and Health Research, Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, ChinaThe Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, Guangxi, ChinaThe First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, ChinaLiuzhou Workers’ Hospital, Liuzhou, Guangxi, ChinaDepartment of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, ChinaDepartment of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, ChinaGuangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China; Department of Public Health, School of Medicine, Guangxi University of Science and Technology, Liuzhou, Guangxi, ChinaDepartment of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, ChinaDepartment of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, ChinaDepartment of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, Guangxi, China; Guangxi Key Laboratory of Environment and Health Research, Guangxi Medical University, Nanning, Guangxi, China; Corresponding authors at: Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Key Laboratory of Environment and Health Research, Guangxi Medical University, Nanning 530021, China.Background: Exposure to per- and polyfluoroalkyl substances (PFAS) may linked to thyroid cancer (TC) risk, but inconsistent findings and a lack of studies on mixed exposures exist, especially regarding novel PFAS compounds. Additionally, little is known about the potential mechanisms underlying the association. Objectives: Explore the effects of PFAS exposure on the serum metabolome and its correlation with TC. Methods: A 1:1 age- and sex-matched case-control study was administered with 746 TC cases and healthy controls. Liquid chromatography-high resolution mass spectrometry determined serum 11 PFAS and untargeted metabolome profile. ENET and LightGBM models were used to explore the exposure patterns and perform variable selection. The mixed exposure effects were assessed using Weighted quantile sum regression and Bayesian kernel machine regression. Metabolome-wide association analyses were performed to assess metabolic dysregulation associated with PFAS, and a structural synthesis analysis was used to detect latent groups of individuals with TC based on PFAS levels and metabolite patterns. Results: Ten of the 11 PFAS were detected in > 80 % of the population. PFHxA and PFDoA exposure associated with increased TC risk, while PFHxS and PFOA associated with decreased TC risk in single compound models (all P < 0.05). Machine learning algorithms identified PFHxA, PFDoA, PFHxS, PFOA, and PFHpA as the key PFAS influencing the development of TC, and mixed exposures have an overall positive effect on TC risk, with PFHxA making the primary contribution. A novel integrative analysis identified a cluster of TC patients characterized by increased PFHxA, PFDoA, PFHpA and decreased PFOA, PFHxS levels, and altered metabolite patterns highlighted by the upregulation of free fatty acids. Conclusions: PFAS exposure is linked to a higher risk of TC, possibly through changes in fatty acid metabolism. Larger, prospective studies are needed to confirm these findings, and the role of short-chain PFAS requires more attention.http://www.sciencedirect.com/science/article/pii/S0160412024007906Per- and polyfluoroalkyl substances (PFAS)Thyroid cancerUntargeted metabolomeMixed exposureSHapley Additive exPlanation (SHAP)Case−control study |
| spellingShingle | Fei Wang Yuanxin Lin Lian Qin Xiangtai Zeng Hancheng Jiang Yanlan Liang Shifeng Wen Xiangzhi Li Shiping Huang Chunxiang Li Xiaoyu Luo Xiaobo Yang Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning Environment International Per- and polyfluoroalkyl substances (PFAS) Thyroid cancer Untargeted metabolome Mixed exposure SHapley Additive exPlanation (SHAP) Case−control study |
| title | Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning |
| title_full | Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning |
| title_fullStr | Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning |
| title_full_unstemmed | Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning |
| title_short | Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning |
| title_sort | serum metabolome associated with novel and legacy per and polyfluoroalkyl substances exposure and thyroid cancer risk a multi module integrated analysis based on machine learning |
| topic | Per- and polyfluoroalkyl substances (PFAS) Thyroid cancer Untargeted metabolome Mixed exposure SHapley Additive exPlanation (SHAP) Case−control study |
| url | http://www.sciencedirect.com/science/article/pii/S0160412024007906 |
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