Combined Effect of per- and Polyfluoroalkyl Substances, Toxic Metals, and Essential Elements on Chronic Kidney Disease
Chronic kidney disease (CKD) is a noteworthy global health issue affecting 10% of the world’s populace. It is increasingly linked to environmental exposures; however, the interplay of toxic metals, per- and polyfluoroalkyl substances (PFAS), and essential elements has not been fully elucidated. This...
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MDPI AG
2025-05-01
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| author | Issah Haruna Emmanuel Obeng-Gyasi |
| author_facet | Issah Haruna Emmanuel Obeng-Gyasi |
| author_sort | Issah Haruna |
| collection | DOAJ |
| description | Chronic kidney disease (CKD) is a noteworthy global health issue affecting 10% of the world’s populace. It is increasingly linked to environmental exposures; however, the interplay of toxic metals, per- and polyfluoroalkyl substances (PFAS), and essential elements has not been fully elucidated. This cross-sectional study analyzed 5800 out of the 9245 participants from the 2017–2018 NHANES dataset to evaluate the combined effect of PFAS, essential elements, and toxic metals on CKD using logistic regression and advanced environmental mixture models, namely, Bayesian Kernel Machine Regression (BKMR), quantile g-computation (qgcomp), and Weighted Quantile Sum (WQS) regression. Our results showed cadmium (Cd) emerging as a significant contributor to CKD (OR = 2.16, <i>p</i> = 0.023) from the logistic regression analysis. Mercury (Hg) demonstrated the highest contribution in mixtures (posterior inclusion probability = 0.908) from our BKMR analysis, with a non-linear U-shaped dose–response relationship. Essential elements like selenium (Se) and manganese (Mn) exhibited protective correlations but complex non-linear interactions, moderating toxic metal effects from our qgcomp and WQS regression. Notably, antagonistic interactions between essential elements and some pollutants reduced the overall mixture impact on CKD, showing an overall decreasing joint effect of the combined PFAS, toxic metals, and essential elements on CKD, from the 25th to the 75th quantile. This study highlights the role of environmental co-exposures in CKD risk and highlights the need for advanced statistical and machine learning approaches in studying complex environmental mixture interactions on human health. |
| format | Article |
| id | doaj-art-e19b7821f02f429eaaf928ddbf1cd929 |
| institution | Kabale University |
| issn | 2673-4672 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Pollutants |
| spelling | doaj-art-e19b7821f02f429eaaf928ddbf1cd9292025-08-20T03:27:25ZengMDPI AGPollutants2673-46722025-05-01521210.3390/pollutants5020012Combined Effect of per- and Polyfluoroalkyl Substances, Toxic Metals, and Essential Elements on Chronic Kidney DiseaseIssah Haruna0Emmanuel Obeng-Gyasi1Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USADepartment of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USAChronic kidney disease (CKD) is a noteworthy global health issue affecting 10% of the world’s populace. It is increasingly linked to environmental exposures; however, the interplay of toxic metals, per- and polyfluoroalkyl substances (PFAS), and essential elements has not been fully elucidated. This cross-sectional study analyzed 5800 out of the 9245 participants from the 2017–2018 NHANES dataset to evaluate the combined effect of PFAS, essential elements, and toxic metals on CKD using logistic regression and advanced environmental mixture models, namely, Bayesian Kernel Machine Regression (BKMR), quantile g-computation (qgcomp), and Weighted Quantile Sum (WQS) regression. Our results showed cadmium (Cd) emerging as a significant contributor to CKD (OR = 2.16, <i>p</i> = 0.023) from the logistic regression analysis. Mercury (Hg) demonstrated the highest contribution in mixtures (posterior inclusion probability = 0.908) from our BKMR analysis, with a non-linear U-shaped dose–response relationship. Essential elements like selenium (Se) and manganese (Mn) exhibited protective correlations but complex non-linear interactions, moderating toxic metal effects from our qgcomp and WQS regression. Notably, antagonistic interactions between essential elements and some pollutants reduced the overall mixture impact on CKD, showing an overall decreasing joint effect of the combined PFAS, toxic metals, and essential elements on CKD, from the 25th to the 75th quantile. This study highlights the role of environmental co-exposures in CKD risk and highlights the need for advanced statistical and machine learning approaches in studying complex environmental mixture interactions on human health.https://www.mdpi.com/2673-4672/5/2/12chronic kidney diseaseper- and polyfluoroalkyl substancestoxic metalsessential elementsenvironmental mixturesBayesian Kernel Machine Regression |
| spellingShingle | Issah Haruna Emmanuel Obeng-Gyasi Combined Effect of per- and Polyfluoroalkyl Substances, Toxic Metals, and Essential Elements on Chronic Kidney Disease Pollutants chronic kidney disease per- and polyfluoroalkyl substances toxic metals essential elements environmental mixtures Bayesian Kernel Machine Regression |
| title | Combined Effect of per- and Polyfluoroalkyl Substances, Toxic Metals, and Essential Elements on Chronic Kidney Disease |
| title_full | Combined Effect of per- and Polyfluoroalkyl Substances, Toxic Metals, and Essential Elements on Chronic Kidney Disease |
| title_fullStr | Combined Effect of per- and Polyfluoroalkyl Substances, Toxic Metals, and Essential Elements on Chronic Kidney Disease |
| title_full_unstemmed | Combined Effect of per- and Polyfluoroalkyl Substances, Toxic Metals, and Essential Elements on Chronic Kidney Disease |
| title_short | Combined Effect of per- and Polyfluoroalkyl Substances, Toxic Metals, and Essential Elements on Chronic Kidney Disease |
| title_sort | combined effect of per and polyfluoroalkyl substances toxic metals and essential elements on chronic kidney disease |
| topic | chronic kidney disease per- and polyfluoroalkyl substances toxic metals essential elements environmental mixtures Bayesian Kernel Machine Regression |
| url | https://www.mdpi.com/2673-4672/5/2/12 |
| work_keys_str_mv | AT issahharuna combinedeffectofperandpolyfluoroalkylsubstancestoxicmetalsandessentialelementsonchronickidneydisease AT emmanuelobenggyasi combinedeffectofperandpolyfluoroalkylsubstancestoxicmetalsandessentialelementsonchronickidneydisease |