Individual and combined association between nutritional trace metals and the risk of preterm birth in a recurrent pregnancy loss cohort

Yilin Liu; Tingting Wang; Yunpeng Ge; Hongfei Shen; Jiapo Li; Chong Qiao

doi:10.3389/fnut.2023.1205748

Individual and combined association between nutritional trace metals and the risk of preterm birth in a recurrent pregnancy loss cohort

Front Nutr. 2023 Nov 30:10:1205748. doi: 10.3389/fnut.2023.1205748. eCollection 2023.

Authors

Yilin Liu^{1

2

3

4}, Tingting Wang^{1

2

3

4}, Yunpeng Ge^{1

2

3

4}, Hongfei Shen^{1

2

3

4}, Jiapo Li^{1

2

3

4}, Chong Qiao^{1

2

3

4}

Affiliations

¹ Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
² Key Laboratory of Reproductive and Genetic Medicine, National Health Commission, Shenyang, China.
³ Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Shenyang, China.
⁴ Research Center of China Medical University Birth Cohort, China Medical University, Shenyang, China.

Abstract

Background: Recurrent pregnancy loss (RPL) was associated with an elevated risk of pregnancy complications, particularly preterm birth (PTB). However, the risk factors associated with PTB in RPL remained unclear. Emerging evidence indicated that maternal exposure to metals played a crucial role in the development of PTB. The objective of our study was to investigate the individual and combined associations of nutritional trace metals (NTMs) during pregnancy with PTB in RPL.

Methods: Using data from a recurrent pregnancy loss cohort (n = 459), propensity score matching (1:3) was performed to control for covariates. Multiple logistic regression and multiple linear regression were employed to identify the individual effects, while elastic-net regularization (ENET) and Bayesian kernel machine regression (BKMR) were used to examine the combined effects on PTB in RPL.

Results: The logistic regression model found that maternal exposure to copper (Cu) (quantile 4 [Q4] vs. quantile 1 [Q1], odds ratio [OR]: 0.21, 95% confidence interval [CI]: 0.05, 0.74) and zinc (Zn) (Q4 vs. Q1, OR: 0.19, 95%CI: 0.04, 0.77) was inversely associated with total PTB risk. We further constructed environmental risk scores (ERSs) using principal components and interaction terms derived from the ENET model to predict PTB accurately (p < 0.001). In the BKMR model, we confirmed that Cu was the most significant component (PIP = 0.85). When other metals were fixed at the 25^th and 50^th percentiles, Cu was inversely associated with PTB. In addition, we demonstrated the non-linear relationships of Zn with PTB and the potential interaction between Cu and other metals, including Zn, Ca, and Fe.

Conclusion: In conclusion, our study highlighted the significance of maternal exposure to NTMs in RPL and its association with PTB risk. Cu and Zn were inversely associated with PTB risk, with Cu identified as a crucial factor. Potential interactions between Cu and other metals (Zn, Ca, and Fe) further contributed to the understanding of PTB etiology in RPL. These findings suggest opportunities for personalized care and preventive interventions to optimize maternal and infant health outcomes.

Keywords: Bayesian kernel machine regression; metal mixture; nutritional trace metals; preterm birth; recurrent pregnancy loss.

Grants and funding

This study was supported by the National Key R&D Program of China (grant number: 2016YFC1000404), National Natural Science Foundation of China (grant number: 81370735), the National Natural Science Foundation of China (grant number: 81771610), Outstanding Scientific Fund of Shengjing Hospital (grant number: 201706), the Distinguished Professor of Liaoning Province (grant number: 2017), the Science and Technology Project of Shenyang (grant number: 20-205-4-004), the Leading Talents of Talent Project (grant number: XLYC2005008), and the Livelihood Science and Technology Joint Project of Liaoning Province (grant number: 2021JH2/10300123).