Ying Wang1, Ping Zhang1, Xi Chen2, Weiwei Wu1, Yongliang Feng1, Hailan Yang3, Mei Li1, Bingjie Xie1, Pengge Guo1, Joshua L Warren4, Xiaoming Shi2, Suping Wang1, Yawei Zhang5. 1. Department of Epidemiology, Shanxi Medical University School of Public Health, Taiyuan, China. 2. National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China. 3. Department of Obstetrics, The First Affiliated Hospital, Shanxi Medical University, Taiyuan, China. 4. Department of Biostatistics, Yale School of Medicine, New Haven, CT, USA. 5. Department of Epidemiology, Shanxi Medical University School of Public Health, Taiyuan, China; Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Department of Surgery, Yale School of Medicine, New Haven, CT, USA. Electronic address: yawei.zhang@yale.edu.
Abstract
BACKGROUND: The association between multiple metal concentrations and gestational diabetes mellitus (GDM) is poorly understood. METHODS: A total of 776 women with GDM and an equal number of controls were included in the study. Concentrations of metals in participants' blood (nickel (Ni), arsenic (As), cadmium (Cd), antimony (Sb), thallium (Tl), mercury (Hg), lead (Pb)) were measured using inductively coupled plasma-mass. We used unconditional logistical regression models to estimate the associations between metals and GDM. We also employed weighted quantile sum (WQS) regression and principal components analysis (PCA) to examine metal mixtures in relation to GDM. RESULTS: An increased risk of GDM was associated with As (OR = 1.49, 95% CI: 1.11, 2.01 for the 2nd tertile vs. the 1st tertile) and Hg (OR = 1.43, 95% CI: 1.09, 1.88 for the 3rd tertile vs. the 1st tertile). In WQS analysis, the WQS index was significantly associated with GDM (OR = 1.20, 95% CI: 1.02, 1.41). The major contributor to the metal mixture index was Hg (69.2%), followed by Pb (12.8%), and As (11.3%). Based on PCA, the second principal component, which was characterized by Hg, Ni, and Pb, was associated with an increased risk of GDM (OR = 1.46, 95% CI: 1.02, 2.08 for the highest quartile vs. the lowest quartile). CONCLUSIONS: Our study results suggest that high metal levels are associated with an increased risk of GDM, and this increased risk is mainly driven by Hg and, to a lesser extent, by Ni, Pb, and As.
BACKGROUND: The association between multiple metal concentrations and gestational diabetes mellitus (GDM) is poorly understood. METHODS: A total of 776 women with GDM and an equal number of controls were included in the study. Concentrations of metals in participants' blood (nickel (Ni), arsenic (As), cadmium (Cd), antimony (Sb), thallium (Tl), mercury (Hg), lead (Pb)) were measured using inductively coupled plasma-mass. We used unconditional logistical regression models to estimate the associations between metals and GDM. We also employed weighted quantile sum (WQS) regression and principal components analysis (PCA) to examine metal mixtures in relation to GDM. RESULTS: An increased risk of GDM was associated with As (OR = 1.49, 95% CI: 1.11, 2.01 for the 2nd tertile vs. the 1st tertile) and Hg (OR = 1.43, 95% CI: 1.09, 1.88 for the 3rd tertile vs. the 1st tertile). In WQS analysis, the WQS index was significantly associated with GDM (OR = 1.20, 95% CI: 1.02, 1.41). The major contributor to the metal mixture index was Hg (69.2%), followed by Pb (12.8%), and As (11.3%). Based on PCA, the second principal component, which was characterized by Hg, Ni, and Pb, was associated with an increased risk of GDM (OR = 1.46, 95% CI: 1.02, 2.08 for the highest quartile vs. the lowest quartile). CONCLUSIONS: Our study results suggest that high metal levels are associated with an increased risk of GDM, and this increased risk is mainly driven by Hg and, to a lesser extent, by Ni, Pb, and As.
Authors: Abby F Fleisch; Sudipta Kumer Mukherjee; Subrata K Biswas; John F Obrycki; Sheikh Muhammad Ekramullah; D M Arman; Joynul Islam; David C Christiani; Maitreyi Mazumdar Journal: Environ Health Date: 2022-01-14 Impact factor: 5.984