Association between exposure to perfluoroalkyl substances and uric acid in Chinese adults


Background: A growing body of evidence suggests the deleterious effects of perfluoroalkyl substances (PFASs) on kidney, but little is known on the association between PFASs joint exposure and uric acid.

Methods: Serum PFASs concentrations were measured in 661 participants recruited from Tianjin, China using liquid chromatography/mass spectrometry. The associations of single PFASs exposure with uric acid levels and hyperuricemia were assessed using multivariable linear and logistic regression models, respectively. Restricted cubic spline models were established to investigate the dose-response relationships between PFASs concentrations and uric acid levels. Bayesian Kernel Machine Regression (BKMR) model with a hierarchical variable selection was performed to assess the joint effect of PFASs on uric acid.

Results: Potassium perfluoro-1-octanesulfonate (PFOS) and perfluoro-n-octanoic acid (PFOA) were the dominated contributors with median concentrations of 16.80 ng/ml and 9.42 ng/ml, respectively. Increased PFOA concentration (per log2-unit) was associated with elevated uric acid level (β = 0.088, 95% CI: 0.033-0.143) and higher risk of hyperuricemia (OR = 1.134, 95% CI: 1.006-1.289). Conversely, the estimated change of uric acid associated with log2-unit increment in perfluoro-n-decanoic acid (PFDA) was -0.081 mg/dL (95% CI: 0.154, -0.009). A significant linear dose-response pattern was found between log2-transformed PFOA concentration and uric acid level. BKMR analyses indicated a non-significant overall effect of PFASs mixture on uric acid.

Conclusions: Significant associations between PFOA and PFDA and uric acid, and between PFOA and hyperuricemia were found in the single-pollutant models, but the joint effect of PFASs mixture on uric acid was not observed in the BKMR model, which provided new insights in regulation policies and risk assessment of PFASs.

Authors: Ze Yang, Kun Men, Jiaxin Guo, Ruifang Liu, Hongbo Liu, Jiemin Wei, Jingyun Zhang, Liangpo Liu, Xiaohui Lin, Mingyue Zhang, Yong Liu, Yu Chen, Nai-Jun Tang
; Full Source: Chemosphere 2022 Nov 6;137164. doi: 10.1016/j.chemosphere.2022.137164.