Close association of PFASs exposure with hepatic fibrosis than steatosis: evidences from NHANES 2017-2018

Abstract

Multiple animals and in vitro studies have demonstrated that perfluoroalkyl and polyfluoroalkyl substances (PFASs) exposure causes liver damage associated with fat metabolism. However, it is lack of population evidence for the correlation between PFAS exposure and nonalcoholic fatty liver disease (NAFLD). A cross-sectional analysis was performed of 1150 participants aged over 20 from the US. Liver ultrasound transient elastography was to identify the participants with NAFLD and multiple biomarkers were the indicators for hepatic steatosis and hepatic fibrosis. Logistics regression and restricted cubic splines models were used to estimate the association between PFASs and NAFLD. PFASs had not a significant association with NAFLD after adjustment. The hepatic steatosis indicators including fatty liver index, NAFLD liver fat score, and Framingham steatosis index were almost not significantly correlated with PFASs exposure respectively. But fibrosis indicators including fibrosis-4 index (FIB-4), NAFLD fibrosis score, and Hepamet fibrosis score were positively correlated with each type of PFASs exposure. After adjustment by gender, age, race, education, and poverty income rate, there was also a significant correlation between PFOS and FIB-4 with 0.07 (0.01, 0.13). The mixed PFASs were associated with FIB-4, with PFOS contributing the most (PIP = 1.000) by the Bayesian kernel machine regression model. The results suggested PFASs exposure appeared to be more closely associated with hepatic fibrosis than steatosis, and PFOS might be the main cause of PFASs associated with hepatic fibrosis.Key messagesCurrent exposure doses of PFAS did not significantly change the risk of developing NAFLD.PFASs exposure appeared to be more closely associated with hepatic fibrosis than steatosis.PFOS might be the main cause of PFASs associated with hepatic fibrosis.

Keywords: Bayesian kernel machine regression (BKMR); Generalized linear model (GLM); Nonalcoholic fatty liver disease (NAFLD); Perfluoroalkyl and polyfluoroalkyl substances (PFASs); Restricted cubic spline (RCS).

Figure 1.

Flowchart of participants selected from the NHANES 2017–2018.

Figure 2.

DAG was made based on the results of baseline characteristics comparison and prior knowledge.

Figure 3.

Effect of PFASs exposure on the risk of developing NAFLD. (A) Logistic regression result of NAFLD and single PFASs. Odds ratios (OR)1 and P1 for model 1 without adjusted; OR2 and P2 for model 2 with adjusted by gender, age, race and education and PIR. ‘#’ was the result of P for trend. (B) RCS results of NAFLD and ln (PFDeA). The median value of ln (PFDeA) was used as a reference point. Blue was the density of the ln (PFDeA) distribution.

Figure 4.

Correlation analysis (A) Spearman correlation coefficients between PFASs and NAFLD biomarkers. (B) Pearman correlation coefficients among PFASs logarithm. The correlation coefficient was shown in color. Blue was a positive correlation and red was a negative correlation. Color darker, circle bigger implied a stronger correlation. Demerit marks showed p > 0.05.

Figure 5.

The association between PFASs and hepatic fibrosis-related biomarkers was analyzed by GLM. Model 1 was unadjusted; model 2 was adjusted by gender, age, race and education and PIR.

Figure 6.

The overall and individual exposure effect of PFASs on FIB-4. (A) The overall effect of the PFASs mixture being fixed to different percentiles as compared to their 50 percentiles. (B) The estimated values of the individual effect were calculated by comparing the FIB-4 increase risk when a single PFAS was at its 75th percentile as compared to when that was at its 25th percentile, where all of the remaining PFAS were fixed at their 25th (red line), 50th (green line), or 75th (blue line) percentile.