Associations between Mixture of Perfluoroalkyl Substances and Lipid Profile in a Highly Exposed Adult Community in the Veneto Region

Erich Batzella, Maryam Zare Jeddi, Gisella Pitter, Francesca Russo, Tony Fletcher and Cristina Canova ()
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Erich Batzella: Unit of Biostatistics, Epidemiology and Public Health, Department of Cardio-Thoraco-Vascular Sciences and Public Health, 35131 Padova, Italy
Maryam Zare Jeddi: RIVM-National Institute for Public Health and the Environment, 3720 Bilthoven, The Netherlands
Gisella Pitter: Screening and Health Impact Assessment Unit, Azienda Zero-Veneto Region, 35131 Padua, Italy
Francesca Russo: Directorate of Prevention, Food Safety, Veterinary Public Health-Veneto Region, 30123 Venice, Italy
Tony Fletcher: Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
Cristina Canova: Unit of Biostatistics, Epidemiology and Public Health, Department of Cardio-Thoraco-Vascular Sciences and Public Health, 35131 Padova, Italy

IJERPH, 2022, vol. 19, issue 19, 1-14

Abstract: Background: Residents of a large area in the Veneto Region (Northeastern Italy) were exposed to drinking water contaminated by perfluoroalkyl substances (PFAS) for decades. While exposure to PFAS has been consistently associated with elevated serum lipids, combined exposures to multiple PFASs have been poorly investigated. Utilising different statistical approaches, we examine the association between chemical mixtures and lipid parameters. Methods: Cross-sectional data from the regional health surveillance program (34,633 individuals aged 20–64 years) were used to examine the combined effects of PFAS mixture (Perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoic acid (PFNA) and perfluorohexane sulfonate (PFHxS)) on total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C). Weighted Quantile Sum (WQS) regression, Quantile-based G-computation (Q-Gcomp) and Bayesian Kernel Machine Regression (BKMR) were used based on their ability to handle highly correlated chemicals. Results: We observed that each quartile increase in the WQS index was associated with an increase in the levels of TC (β: 4.09, 95% CI: 3.47–4.71), HDL-C (β: 1.13, 95% CI: 0.92–1.33) and LDL-C (β: 3.14, 95% CI: 2.65–3.63). Q-Gcomp estimated that a quartile increase in the PFAS mixture was associated with increased TC (ψ: 4.04, 95% CI 3.5–4.58), HDL-C (ψ: 1.07, 95% CI 20.87–1.27) and LDL-C (ψ: 2.71, 95% CI 2.23–3.19). In the BKMR analysis, the effect of PFAS mixture on serum lipids increased significantly when their concentrations were at their 75th percentiles or above, compared to those at their 50th percentile. All methods revealed a major contribution of PFOS and PFNA, although the main exposure was due to PFOA. We found suggestive evidence that associations varied when stratified by gender. Conclusions: The PFAS mixture was positively associated with lipid parameters, regardless of the applied method. Very similar results obtained from the three methods may be attributed to the linear positive association with the outcomes and no interaction between each PFAS.

Keywords: perfluoroalkyl substances; lipid; cholesterol; contaminated water; combined exposure; mixture; WQS; G-computation; BKMR (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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