Co-Exposure of Polycyclic Aromatic Hydrocarbons and Phthalates with Blood Cell-Based Inflammation in Early Pregnant Women

Abstract

Cumulative evidence has demonstrated that exposure to polycyclic aromatic hydrocarbons (PAHs) or phthalates (PAEs) contributes to a variety of adverse health effects. However, the association of PAHs and PAEs co-exposure with blood cell-based inflammatory indicators during early pregnancy is still unclear. We aimed to investigate the single and mixed associations of exposure to PAHs and PAEs with blood cell-based inflammatory indicators among early pregnant women. A total of 318 early pregnant women were included in this study. General linear regressions were used to estimate the relationships of individual OH-PAHs and mPAEs with blood cell-based inflammatory indicators. The key pollutants were selected by an adapted least absolute shrinkage and selection operator (LASSO) penalized regression model and wasemployed to build the Bayesian kernel machine regression (BKMR) and quantile g-computation (Q-g) models, which can assess the joint association of OH-PAHs and mPAEs with blood cell-based inflammatory indicators. General linear regression indicated that each 1% increase in MOP was associated with a 4.92% (95% CI: 2.12%, 7.68%), 3.25% (95% CI: 0.50%, 6.18%), 5.87% (95% CI: 2.22%, 9.64%), and 6.50% (95% CI: 3.46%, 9.64%) increase in WBC, lymphocytes, neutrophils, and monocytes, respectively. BKMR and Q-g analysis showed that the mixture of OH-PAHs and mPAEs was linked with increased levels of white blood cells (WBC), neutrophils, monocytes, and lymphocytes, and MOP was identified as the dominant contributor. OH-PAHs and mPAEs co-exposure in early pregnancy was associated with elevated blood cell-based inflammatory indicators reactions. More attention should be paid to the inflammation induced by environmental pollution for perinatal women, especially early pregnant women.

Keywords: BKMR; Inflammation; PAEs; PAHs; Q-g; pregnancy.

Figure 1

Spearman correlation coefficient between OH-PAHs and mPAEs in urine. * p < 0.05, ** p < 0.01.

Figure 2

Association of OH-PAHs and PAEs with blood cell-based biomarkers of inflammation in the urine of pregnant women. Note: Linear regression models are adjusted for maternal age, pre-pregnancy BMI, marital status, categorical education, race, smoking, passive smoking, exercise, and alcohol consumption. * p < 0.05.

Figure 3

Adaptive lasso penalty regression was used to analyze the relationship between phthalate and PAH metabolite exposure and WBC. Note: WBC was the dependent variable and OH-PAHs and PAEs metabolites were the independent variables in the lasso penalty regression model. The model adjusted for maternal age, pre-pregnancy BMI, marital status, categorical education, race, smoking, passive smoking, exercise, and alcohol consumption. The red line in (A) indicates λmin. Coefficient curves for OH-PAHs and m-PAEs are shown in (B). (for the interpretation of the curves in this figure, the reader is referred to the labeling in the figure).

Figure 4

The overall association of screened OH-PAHs and PAEs mixtures with WBCs (A), lymphocytes (B), neutrophils (C), and monocytes (D) was estimated by the Bayesian kernel machine regression (BKMR) model. Note: The model adjusted for maternal age, pre-pregnancy BMI, marital status, categorical education, race, smoking, passive smoking, exercise, and alcohol consumption.

Figure 5

The single associations of screened OH-PAHs and PAEs with WBCs (A), lymphocytes (B), neutrophils (C), and lymphocytes (D) were estimated by the Bayesian kernel machine regression (BKMR) model. Note: The model adjusted for maternal age, pre-pregnancy BMI, marital status, categorical education, race, smoking, passive smoking, exercise, and alcohol consumption.

Figure 6

Univariate exposure-response relationships between the concentration of each substance and WBCs (A), lymphocytes (B), neutrophils (C), and monocytes (D) when the other substances were fixed at median concentrations. Note: The model adjusted for maternal age, pre-pregnancy BMI, marital status, categorical education, race, smoking, passive smoking, exercise, and alcohol consumption.

Figure 7

Estimation of the effect of screening the four mixtures of OH-PAHs and PAEs in WBCs (A), lymphocytes (B), neutrophils (C), and monocytes (D) and scaled weights corresponding to the proportion of the effect for each chemical in quantile g-computation. Note: The model adjusted for maternal age, pre-pregnancy BMI, marital status, categorical education, race, smoking, passive smoking, exercise, and alcohol consumption.