An exploratory study on the association of multiple metals in serum with preeclampsia

Abstract

Background: Individual metal levels are potential risk factors for the development of preeclampsia (PE). However, understanding of relationship between multiple metals and PE remains elusive.

Purpose: The purpose of this study was to explore whether eight metals [zinc (Zn), manganese (Mn), copper (Cu), nickel (Ni), lead (Pb), arsenic (As), cadmium (Cd), and mercury (Hg)] in serum had a certain relationship with PE.

Methods: A study was conducted in Dongguan, China. The concentrations of metals in maternal serum were assessed using inductively coupled plasma mass spectrometry (ICP-MS). Data on various factors were collected through a face-to-face interview and hospital electronic medical records. The unconditional logistic regression model, principal component analysis (PCA) and Bayesian Kernel Machine Regression (BKMR) were applied in our study.

Results: The logistic regression model revealed that the elevated levels of Cu, Pb, and Hg were associated with an increased risk of PE. According to PCA, principal component 1 (PC1) was predominated by Hg, Pb, Mn, Ni, Cu, and As, and PC1 was associated with an increased risk of PE, while PC2 was predominated by Cd and Zn. The results of BKMR indicated a significant positive cumulative effect of serum metals on PE risk, with Ni and Cu exhibiting a significant positive effect. Moreover, BKMR results also revealed the nonlinear effects of Ni and Cd.

Conclusion: The investigation suggests a potential positive cumulative impact of serum metals on the occurrence of PE, with a particular emphasis on Cu as a potential risk factor for the onset and exacerbation of PE. These findings offer valuable insights for guiding future studies on this concern.

Keywords: Bayesian kernel machine regression; copper; logistic regression model; metal; preeclampsia; principal component analysis.