Associations between multiple metals during early pregnancy and gestational diabetes mellitus under four statistical models

Abstract

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy. Metal exposure is an emerging factor affecting the risk of GDM. However, the effects of metal mixture on GDM and key metals within the mixture remain unclear. This study was aimed at investigating the association between metal mixture during early pregnancy and the risk of GDM using four statistical methods and further at identifying the key metals within the mixture associated with GDM. A nested case-control study including 128 GDM cases and 318 controls was conducted in Beijing, China. Urine samples were collected before 13 gestational weeks and the concentrations of 13 metals were measured. Single-metal analysis (unconditional logistic regression) and mixture analyses (Bayesian kernel machine regression (BKMR), quantile g-computation, and elastic-net regression (ENET) models) were applied to estimate the associations between exposure to multiple metals and GDM. Single-metal analysis showed that Ni was associated with lower risk of GDM, while positive associations of Sr and Sb with GDM were observed. Compared with the lowest quartile of Ni, the ORs of GDM in the highest quartiles were 0.49 (95% CI 0.24, 0.98). In mixture analyses, Ni and Mg showed negative associations with GDM, while Co and Sb were positively associated with GDM in BKMR and quantile g-computation models. No significant joint effect of metal mixture on GDM was observed. However, interestingly, Ni was identified as a key metal within the mixture associated with decreased risk of GDM by all three mixture methods. Our study emphasized that metal exposure during early pregnancy was associated with GDM, and Ni might have important association with decreased GDM risk.

Keywords: Bayesian kernel machine regression; Elastic-net regression; Gestational diabetes mellitus; Metal; Nested case–control study; Quantile g-computation.