Exposure to multiple metals in early pregnancy and gestational diabetes mellitus: A prospective cohort study

妊娠期糖尿病 医学 怀孕 产科 人口 妊娠期 糖尿病 队列研究 相对风险 泌尿系统 妇科 置信区间 内科学 内分泌学 环境卫生 生物 遗传学
作者
Xiaoyi Wang,Duan Gao,Guofu Zhang,Xu Zhang,Qian Li,Qin Feng Gao,Renjuan Chen,Shangzhi Xu,Li Huang,Yu Zhang,Lixia Lin,Chunrong Zhong,Xi Chen,Guoqiang Sun,Yang Song,Xuefeng Yang,Liping Hao,Hongying Yang,Lei Yang,Nianhong Yang
出处
期刊:Environment International [Elsevier BV]
卷期号:135: 105370-105370 被引量:153
标识
DOI:10.1016/j.envint.2019.105370
摘要

BACKGROUND: A growing number of epidemiologic studies have estimated associations between type 2 diabetes mellitus and exposure to metals. However, studies on the associations of internal assessments of metal exposure and gestational diabetes mellitus (GDM) are limited in scope and have inconsistent outcomes. OBJECTIVES: This investigation aimed to explore the associations between urinary nickel (Ni), arsenic (As), cadmium (Cd), antimony (Sb), cobalt (Co), or vanadium (V) in early pregnancy and the subsequent risk of GDM in Chinese pregnant women. METHODS: The study population included 2090 women with singleton pregnancy from the Tongji Maternal and Child Health Cohort (TMCHC). Urine samples were collected before 20 gestational weeks, and an oral glucose tolerance test (OGTT) was conducted at 24-28 gestational weeks to diagnose GDM. The concentrations of urinary metals were measured using inductively coupled plasma mass spectrometry (ICP-MS) and were corrected for urinary creatinine. The associations between the risk of GDM and urinary metals were assessed using Poisson regression with a robust error variance with generalized estimating equations (GEE) models and Bayesian kernel machine regression (BKMR). RESULTS: A total of 241 participants (11.53%) were diagnosed with GDM. Five metals (Ni, As, Sb, Co, and V) were found significantly and positively associated with GDM based on single-metal models. In multiple-metal models, for each unit increase of ln-transformed urinary Ni or Sb, the risk of GDM increased 18% [relative risk (RR):1.18, 95%confidence interval (CI): 1.00, 1.38 or RR: 1.18, 95%CI: 1.00, 1.39, respectively]. The BKMR analysis revealed a statistically significant and positive joint effect of the six metals on the risk of GDM, when the urinary levels of the six metals were all above the 55th percentile, compared to the median levels. The effect of metal Ni was significant when the concentrations of the other metals were all fixed at their 25th percentile, and metal Sb displayed a significant and positive effect when all the other metals were fixed at 25th, 50th, and 75th percentiles. CONCLUSIONS: To the best of our knowledge, this study is the first to demonstrate that increased concentrations of urinary Ni in early pregnancy are associated with an elevated risk of GDM, either evaluated individually or as a metal mixture. All six metals mixed exposure was positively associated with the risk of GDM, while Sb and Ni were demonstrated more important effects than the other four metals in the mixture.
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