Association of maternal multi-metal exposure and dyslipidemia: a study of air pollution on pregnancy outcomes

Abstract

Background: Exposure to air pollutants, including heavy metals, is a major environmental concern of public health and these environmental toxicants have been associated with pregnancy complications.

Objectives: An air pollution on pregnancy outcome (APPO) study was performed to investigate the adverse effects of fine particulate matter (PM_2.5) exposure on pregnancy outcomes. This study examined the association between maternal urinary metal mixtures and pregnancy complications, including dyslipidemia and preterm birth (PTB).

Methods: The concentrations of 16 metals were measured in 60 urine samples collected during the second trimester pregnancy. Logistic regression and Bayesian kernel machine regression (BKMR) models were used to analyze the single and overall effects of metal exposure on pregnancy complications, respectively.

Results: Logistic regression analysis showed a significant difference in urinary Ni and Zn concentrations between those exposed to high and low concentrations of fine particulate matter with an aerodynamic diameter of less than 2.5 µm (PM_2.5) and those not exposed. Four metals (Ni, Sc, Mo, and Cs) were positively associated with total cholesterol (TC) levels, but not with triglyceride (TG) levels and PTB. The BKMR model showed that the overall mixture of 16 metals was positively correlated with high TC and TG levels during the third trimester of pregnancy, and the individual effects of Mo and Pb were the most significant. However, we were only able to identify a trend between maternal exposure to metal mixtures and PTB.

Conclusions: BKMR analyses showed a positive association between exposure to multi-metal mixtures and higher maternal TC and TG levels, a factor that contributes to PTB. Therefore, this also suggests that multi-metal exposure during pregnancy may be a potential risk factor for PTB.

Keywords: Heavy metals; Overall effect; Pregnancy outcomes; Total cholesterol; Triglyceride.

Fig. 1

Correlation of multi-metal concentrations in urine with total cholesterol levels using BKMR model (A) The plot represents the estimated joint effects of a latent of triglyceride increase from the median between the 25th and 75th percentiles with 95% confidence intervals (CI). B Plots (blue line) show the univariate exposure–response functions with 95% CI (gray region) for exposure to each metal on total cholesterol when other metals were fixed at the median concentration. The model was adjusted for normalized age, body mass index (BMI), income, education level, and particulate matter < 2.5 μm diameter (PM_2.5) concentration

Fig. 2

Correlation of multi-metal concentrations in urine with triglyceride levels using BKMR model (A) The plot represents the estimated joint effects of a latent of triglyceride increase from the median between the 25th and 75th percentiles with 95% confidence intervals (CI). B Plots (blue line) show the univariate exposure–response functions with 95% CI (gray region) for exposure to each metal on triglycerides when other metals were fixed at the median concentration. The model was adjusted for normalized age, BMI, income, education level, and PM_2.5 concentration

Fig. 3

Overall effects of multi-metal exposures in urine and PTB using BKMR model (A) PIP > 0.5 indicate the importance of each exposure metal. B The plot represents the estimated joint effects in a latent probability of preterm birth from the median between the 25th and 75th percentiles with 95% CI. The model was adjusted for normalized age, body mass index (BMI), income, education level, and particulate matter < 2.5 μm diameter (PM_2.5) concentration