Combined Effects of Multiple Per- and Polyfluoroalkyl Substances Exposure on Allostatic Load Using Bayesian Kernel Machine Regression
This study aims to investigate the combined effects of per- and polyfluoroalkyl substances (PFAS) on allostatic load, an index of chronic stress that is linked to several chronic diseases, including cardiovascular disease and cancer. Using data from the National Health and Nutrition Examination Surv...
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| Veröffentlicht in: | International journal of environmental research and public health 2023-05, Vol.20 (10), p.5808 |
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| description | This study aims to investigate the combined effects of per- and polyfluoroalkyl substances (PFAS) on allostatic load, an index of chronic stress that is linked to several chronic diseases, including cardiovascular disease and cancer. Using data from the National Health and Nutrition Examination Survey (NHANES) 2007-2014, this study examines the relationship between six PFAS variables (PFDE, PFNA, PFOS, PFUA, PFOA, and PFHS) and allostatic load using Bayesian Kernel Machine Regression (BKMR) analysis. The study also investigates the impact of individual and combined PFAS exposure on allostatic load using various exposure-response relationships, such as univariate, bivariate, or multivariate models. The analysis reveals that the combined exposure to PFDE, PFNA, and PFUA had the most significant positive trend with allostatic load when it was modeled as a binary variable, while PFDE, PFOS, and PFNA had the most significant positive trend with allostatic load when modeled as a continuous variable. These findings provide valuable insight into the consequences of cumulative exposure to multiple PFAS on allostatic load, which can help public health practitioners identify the dangers associated with potential combined exposure to select PFAS of interest. In summary, this study highlights the critical role of PFAS exposure in chronic stress-related diseases and emphasizes the need for effective strategies to minimize exposure to these chemicals to reduce the risk of chronic diseases. It underscores the importance of considering the combined effects of PFAS when assessing their impact on human health and offers valuable information for policymakers and regulators to develop strategies to protect public health. |
| doi_str_mv | 10.3390/ijerph20105808 |
| format | Article |
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Using data from the National Health and Nutrition Examination Survey (NHANES) 2007-2014, this study examines the relationship between six PFAS variables (PFDE, PFNA, PFOS, PFUA, PFOA, and PFHS) and allostatic load using Bayesian Kernel Machine Regression (BKMR) analysis. The study also investigates the impact of individual and combined PFAS exposure on allostatic load using various exposure-response relationships, such as univariate, bivariate, or multivariate models. The analysis reveals that the combined exposure to PFDE, PFNA, and PFUA had the most significant positive trend with allostatic load when it was modeled as a binary variable, while PFDE, PFOS, and PFNA had the most significant positive trend with allostatic load when modeled as a continuous variable. These findings provide valuable insight into the consequences of cumulative exposure to multiple PFAS on allostatic load, which can help public health practitioners identify the dangers associated with potential combined exposure to select PFAS of interest. In summary, this study highlights the critical role of PFAS exposure in chronic stress-related diseases and emphasizes the need for effective strategies to minimize exposure to these chemicals to reduce the risk of chronic diseases. It underscores the importance of considering the combined effects of PFAS when assessing their impact on human health and offers valuable information for policymakers and regulators to develop strategies to protect public health.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph20105808</identifier><identifier>PMID: 37239535</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Acids ; Alkanesulfonic Acids ; Allostasis ; Ammonium perfluorooctanoate ; Analysis ; Bayes Theorem ; Bayesian analysis ; Bivariate analysis ; Blood pressure ; Cardiovascular diseases ; Chronic diseases ; Chronic illnesses ; Dose-response effects ; Environmental Pollutants - analysis ; Epidemiology ; Exposure ; Fluorocarbons ; Health risks ; Humans ; Investigations ; Laboratories ; Mathematical models ; Nutrition ; Nutrition Surveys ; Perfluoroalkyl & polyfluoroalkyl substances ; Perfluorooctane sulfonic acid ; Perfluorooctanoic acid ; Physiology ; Pollutants ; Public health ; Sample size ; Stress (Psychology) ; Variables</subject><ispartof>International journal of environmental research and public health, 2023-05, Vol.20 (10), p.5808</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Using data from the National Health and Nutrition Examination Survey (NHANES) 2007-2014, this study examines the relationship between six PFAS variables (PFDE, PFNA, PFOS, PFUA, PFOA, and PFHS) and allostatic load using Bayesian Kernel Machine Regression (BKMR) analysis. The study also investigates the impact of individual and combined PFAS exposure on allostatic load using various exposure-response relationships, such as univariate, bivariate, or multivariate models. The analysis reveals that the combined exposure to PFDE, PFNA, and PFUA had the most significant positive trend with allostatic load when it was modeled as a binary variable, while PFDE, PFOS, and PFNA had the most significant positive trend with allostatic load when modeled as a continuous variable. These findings provide valuable insight into the consequences of cumulative exposure to multiple PFAS on allostatic load, which can help public health practitioners identify the dangers associated with potential combined exposure to select PFAS of interest. In summary, this study highlights the critical role of PFAS exposure in chronic stress-related diseases and emphasizes the need for effective strategies to minimize exposure to these chemicals to reduce the risk of chronic diseases. It underscores the importance of considering the combined effects of PFAS when assessing their impact on human health and offers valuable information for policymakers and regulators to develop strategies to protect public health.</description><subject>Acids</subject><subject>Alkanesulfonic Acids</subject><subject>Allostasis</subject><subject>Ammonium perfluorooctanoate</subject><subject>Analysis</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bivariate analysis</subject><subject>Blood pressure</subject><subject>Cardiovascular diseases</subject><subject>Chronic diseases</subject><subject>Chronic illnesses</subject><subject>Dose-response effects</subject><subject>Environmental Pollutants - analysis</subject><subject>Epidemiology</subject><subject>Exposure</subject><subject>Fluorocarbons</subject><subject>Health risks</subject><subject>Humans</subject><subject>Investigations</subject><subject>Laboratories</subject><subject>Mathematical models</subject><subject>Nutrition</subject><subject>Nutrition Surveys</subject><subject>Perfluoroalkyl & polyfluoroalkyl substances</subject><subject>Perfluorooctane sulfonic acid</subject><subject>Perfluorooctanoic acid</subject><subject>Physiology</subject><subject>Pollutants</subject><subject>Public health</subject><subject>Sample size</subject><subject>Stress (Psychology)</subject><subject>Variables</subject><issn>1660-4601</issn><issn>1661-7827</issn><issn>1660-4601</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptkk1v1DAQhiMEoqVw5YgsceGS4o_YTk5oWS0fYisqoGfLcca7Xrx2aieI_QH8b1xRSosqH8YaP_OOZ_RW1XOCTxnr8Gu3gzRuKSaYt7h9UB0TIXDdCEwe3rofVU9y3mHM2kZ0j6sjJinrOOPH1a9l3PcuwIBW1oKZMooWnc1-cqMHdA6pRjoM6Dz6g_VzTFH77wePvs59nnQwkNHq5xjznADFgBbex5KfnEHrqAd0kV3YoLf6ANnpgD5BCuDRmTbb0hJ9gU2CnF0MT6tHVvsMz67jSXXxbvVt-aFef37_cblY16bBpK01a3pitbActxQLye0AFHeSiJ5JQrTkumNEE0sobwj0jWBEMq6pGXQBOTup3vzRHed-D4OBMCXt1ZjcXqeDitqpuy_BbdUm_lAEU9JihovCq2uFFC9nyJPau2zAex0gzlnR8rHC8o4V9OV_6C7OKZT5CkW6ppFUyn_URntQLthYGpsrUbWQnDKBBWkLdXoPVc4Ae2diAOtK_r4Ck2LOCezNkASrK-eou84pBS9ur-YG_2sV9htu179H</recordid><startdate>20230512</startdate><enddate>20230512</enddate><creator>Bashir, Tahir</creator><creator>Obeng-Gyasi, Emmanuel</creator><general>MDPI AG</general><general>MDPI</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-3195-706X</orcidid></search><sort><creationdate>20230512</creationdate><title>Combined Effects of Multiple Per- and Polyfluoroalkyl Substances Exposure on Allostatic Load Using Bayesian Kernel Machine Regression</title><author>Bashir, Tahir ; Obeng-Gyasi, Emmanuel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4018-a34b1fa6f50820675fde209716b3711a75a931a1f12541eb4631735a2cdae2053</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Acids</topic><topic>Alkanesulfonic Acids</topic><topic>Allostasis</topic><topic>Ammonium perfluorooctanoate</topic><topic>Analysis</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Bivariate analysis</topic><topic>Blood pressure</topic><topic>Cardiovascular diseases</topic><topic>Chronic diseases</topic><topic>Chronic illnesses</topic><topic>Dose-response effects</topic><topic>Environmental Pollutants - analysis</topic><topic>Epidemiology</topic><topic>Exposure</topic><topic>Fluorocarbons</topic><topic>Health risks</topic><topic>Humans</topic><topic>Investigations</topic><topic>Laboratories</topic><topic>Mathematical models</topic><topic>Nutrition</topic><topic>Nutrition Surveys</topic><topic>Perfluoroalkyl & polyfluoroalkyl substances</topic><topic>Perfluorooctane sulfonic acid</topic><topic>Perfluorooctanoic acid</topic><topic>Physiology</topic><topic>Pollutants</topic><topic>Public health</topic><topic>Sample size</topic><topic>Stress (Psychology)</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bashir, Tahir</creatorcontrib><creatorcontrib>Obeng-Gyasi, Emmanuel</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database (ProQuest)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of environmental research and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bashir, Tahir</au><au>Obeng-Gyasi, Emmanuel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combined Effects of Multiple Per- and Polyfluoroalkyl Substances Exposure on Allostatic Load Using Bayesian Kernel Machine Regression</atitle><jtitle>International journal of environmental research and public health</jtitle><addtitle>Int J Environ Res Public Health</addtitle><date>2023-05-12</date><risdate>2023</risdate><volume>20</volume><issue>10</issue><spage>5808</spage><pages>5808-</pages><issn>1660-4601</issn><issn>1661-7827</issn><eissn>1660-4601</eissn><abstract>This study aims to investigate the combined effects of per- and polyfluoroalkyl substances (PFAS) on allostatic load, an index of chronic stress that is linked to several chronic diseases, including cardiovascular disease and cancer. Using data from the National Health and Nutrition Examination Survey (NHANES) 2007-2014, this study examines the relationship between six PFAS variables (PFDE, PFNA, PFOS, PFUA, PFOA, and PFHS) and allostatic load using Bayesian Kernel Machine Regression (BKMR) analysis. The study also investigates the impact of individual and combined PFAS exposure on allostatic load using various exposure-response relationships, such as univariate, bivariate, or multivariate models. The analysis reveals that the combined exposure to PFDE, PFNA, and PFUA had the most significant positive trend with allostatic load when it was modeled as a binary variable, while PFDE, PFOS, and PFNA had the most significant positive trend with allostatic load when modeled as a continuous variable. These findings provide valuable insight into the consequences of cumulative exposure to multiple PFAS on allostatic load, which can help public health practitioners identify the dangers associated with potential combined exposure to select PFAS of interest. In summary, this study highlights the critical role of PFAS exposure in chronic stress-related diseases and emphasizes the need for effective strategies to minimize exposure to these chemicals to reduce the risk of chronic diseases. It underscores the importance of considering the combined effects of PFAS when assessing their impact on human health and offers valuable information for policymakers and regulators to develop strategies to protect public health.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>37239535</pmid><doi>10.3390/ijerph20105808</doi><orcidid>https://orcid.org/0000-0003-3195-706X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Acids Alkanesulfonic Acids Allostasis Ammonium perfluorooctanoate Analysis Bayes Theorem Bayesian analysis Bivariate analysis Blood pressure Cardiovascular diseases Chronic diseases Chronic illnesses Dose-response effects Environmental Pollutants - analysis Epidemiology Exposure Fluorocarbons Health risks Humans Investigations Laboratories Mathematical models Nutrition Nutrition Surveys Perfluoroalkyl & polyfluoroalkyl substances Perfluorooctane sulfonic acid Perfluorooctanoic acid Physiology Pollutants Public health Sample size Stress (Psychology) Variables |
| title | Combined Effects of Multiple Per- and Polyfluoroalkyl Substances Exposure on Allostatic Load Using Bayesian Kernel Machine Regression |
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