Association of Combined Per- and Polyfluoroalkyl Substances and Metals with Chronic Kidney Disease

Abstract

Background: Exposure to environmental pollutants such as metals and Per- and Polyfluoroalkyl Substances (PFAS) has become common and increasingly associated with a decrease in the estimated Glomerular Filtration Rate (eGFR), which is a marker often used to measure chronic kidney disease (CKD). However, there are limited studies involving the use of both eGFR and the urine albumin creatinine ratio (uACR), which are more comprehensive markers to determine the presence of CKD and the complexity of pollutant exposures and response interactions, especially for combined metals and PFAS, which has not been comprehensively elucidated. Objective: This study aims to assess the individual and combined effects of perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), Cadmium (Cd), Mercury (Hg), and Lead (Pb) exposure on CKD using data from the National Health and Nutritional Examination Survey (NHANES) 2017-2018. Methods: We employed the use of bivariate logistic regression and Bayesian Kernel Machine Regression (BKMR) in our analysis of the data. Results: Logistic regression results revealed a positive association between PFOA and CKD. Our BKMR analysis revealed a non-linear and bi-phasic relationship between the metal exposures and CKD. In our univariate exposure-response function plot, Cd and Hg exhibited a U and N-shaped interaction, which indicated a non-linear and non-additive relationship with both low and high exposures associated with CKD. In addition, the bivariate exposure-response function between two exposures in a mixture revealed that Cd had a U-shaped relationship with CKD at different quantiles of Pb, Hg, PFOA, and PFOS, indicating that both low and high levels of Cd is associated with CKD, implying a non-linear and complex biological interaction. Hg's interaction plot demonstrated a N-shaped association across all quantiles of Cd, with the 75th quantile of Pb and the 50th and 75th quantiles of PFOA and PFOS. Furthermore, the PIP results underscored Cd's consistent association with CKD (PIP = 1.000) followed by Hg's (PIP = 0.9984), then PFOA and PFOS with a closely related PIP of 0.7880 and 0.7604, respectively, and finally Pb (PIP = 0.6940), contributing the least among the five environmental pollutants on CKD, though significant. Conclusions: Our findings revealed that exposure to environmental pollutants, particularly Hg and Cd, are associated with CKD. These findings highlight the need for public health interventions and strategies to mitigate the cumulative effect of PFAS and metal exposure and elucidate the significance of utilizing advanced statistical methods and tools to understand the impact of environmental pollutants on human health. Further research is needed to understand the mechanistic pathways of PFAS and metal-induced kidney injury and CKD, and longitudinal studies are required to ascertain the long-term impact of these environmental exposures.

Keywords: Bayesian kernel machine regression; NHANES; PFAS; chronic kidney disease; environmental pollutants; estimated glomerular filtration rate; metals; posterior inclusion probability; univariate and bivariate exposure–response.

Figure 1

Spearman correlation among variables of interest.

Figure 2

Pearson correlation matrix heatmap among outcome and exposure variables.

Figure 3

Univariate exposure–response functions and 95% confidence interval for the association between single pollutant exposure when other pollutant exposures are fixed at the median. Adjusted for age, gender, ethnicity, BMI, diabetes, annual income, alcohol intake, smoking, and hypertension.

Figure 4

Bivariate exposure–response function of metals and PFAS with CKD.

Figure 5

Bivariate exposure–response function of every two exposures with CKD—investigating the predictor-response function with varying quantiles of the second predictor, while other predictors are fixed. Adjusted for age, gender, ethnicity, BMI, diabetes, annual income, alcohol intake, smoking, and hypertension.

Figure 6

The summary of the overall health effects of the exposures (multiple pollutants) on the outcome depends on various percentiles (from 25th to 75th percentiles). Adjusted for age, gender, ethnicity, BMI, diabetes, annual income, alcohol intake, smoking and hypertension.

Figure 7

Single-variable effect of PFAS and metals at increasing quartiles for CDK. Adjusted for age, gender, ethnicity, BMI, diabetes, annual income, alcohol intake, smoking, and hypertension.