摘要：Metals are involved in glucose metabolism, and some may alter glycemic regulation. However, joint effects of essential and non-essential metals on glucose concentrations during pregnancy are unclear. This study explored the joint associations of pregnancy exposures to essential (copper, magnesium, manganese, selenium, zinc) and non-essential (arsenic, barium, cadmium, cesium, lead, mercury) metals with gestational glucose concentrations using 1,311 women enrolled 1999–2002 in Project Viva, a Boston, MA-area pregnancy cohort. The study measured erythrocyte metal concentrations from 1 st trimester blood samples and used glucose concentrations measured 1 h after non-fasting 50-gram glucose challenge tests (GCT) from clinical gestational diabetes screening at 26–28 weeks gestation. Bayesian Kernel Machine Regression (BKMR) and quantile-based g-computation were applied to model the associations of metal mixtures—including their interactions—with glucose concentrations post-GCT. We tested for reproducibility of BKMR results using generalized additive models. The BKMR model showed an inverse U-shaped association for barium and a linear inverse association for mercury. Specifically, estimated mean glucose concentrations were highest around 75th percentile of barium concentrations [2.1 (95% confidence interval: −0.2, 4.4) mg/dL higher comparing to the 25th percentile], and each interquartile range increase of erythrocyte mercury was associated with 1.9 mg/dL lower mean glucose concentrations (95% credible interval: −4.2, 0.4). Quantile g-computation showed joint associations of all metals, essential-metals, and non-essential metals on gestational glucose concentrations were all null, however, we observed evidences of interaction for barium and lead. Overall, we found early pregnancy barium and mercury erythrocytic concentrations were associated with altered post-load glucose concentrations in later pregnancy, with potential interactions between barium and lead.
关键词：Metal mixtures ; Blood glucose concentrations ; Gestational diabetes ; Bayesian Kernel Machine Regression ; As arsenic ; Ba barium ; BKMR Bayesian Kernel Machine Regression ; BMI body mass index ; Cd cadmium ; CHEAR Children’s Health Exposure Analysis Resource ; Cs cesium ; Cu cupper ; CV coefficient of variation ; DAG directed acyclic graphs ; FFQ Food Frequency Questionnaire ; GAM generalized additive models ; GCT glucose challenge test ; GCT glucose challenge tests ; GDM gestational diabetes mellitus ; Hg mercury ; ICP-MS Inductively Coupled Plasma Mass Spectrometry ; ICP-QQQ triple quadrupole Inductively Coupled Plasma Mass Spectrometry ; IGT impaired glucose tolerance ; IQR interquartile range ; LOD limit of detection ; MCMC Markov Chain Monte Carlo ; Mg magnesium ; MIREC Maternal–Infant Research on Environmental Chemicals ; ML maximum likelihood ; Mn manganese ; OGTT oral glucose tolerance test ; OR odd ratio ; Pb lead ; PIP posterior inclusion probability ; QC quality control ; SD standard deviation ; Se selenium ; Zn zinc
