Association of exposure to multiple heavy metals during pregnancy with the risk of gestational diabetes mellitus and insulin secretion phase after glucose stimulation
Epidemiological evidence for the association between heavy metals exposure during pregnancy and gestational diabetes mellitus (GDM) is still inconsistent. Additionally, that is poorly understood about the potential cause behind the association, for instance, whether heavy metal exposure is related t...
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Veröffentlicht in: | Environmental research 2024-05, Vol.248, p.118237-118237, Article 118237 |
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Zusammenfassung: | Epidemiological evidence for the association between heavy metals exposure during pregnancy and gestational diabetes mellitus (GDM) is still inconsistent. Additionally, that is poorly understood about the potential cause behind the association, for instance, whether heavy metal exposure is related to the change of insulin secretion phase is unknown.
We aimed to explore the relationships of blood levels of arsenic (As), lead (Pb), thallium (Tl), nickel (Ni), cadmium (Cd), cobalt (Co), barium (Ba), chromium (Cr), mercury (Hg) and copper (Cu) during early pregnancy with the odds of GDM, either as an individual or a mixture, as well as the association of the metals with insulin secretion phase after glucose stimulation.
We performed a nested case−control study consisting of 302 pregnant women with GDM and 302 controls at the First Affiliated Hospital of Anhui Medical University in Hefei, China. Around the 12th week of pregnancy, blood samples of pregnant women were collected and levels of As, Pb, Tl, Ni, Cd, Co, Ba, Cr, Hg and Cu in blood were measured. An oral glucose tolerance test (OGTT) was done in each pregnant woman during the 24−28th week of pregnancy to diagnose GDM and C−peptide (CP) levels during OGTT were measured simultaneously. The four metals (As, Pb, Tl and Ni) with the highest effect on odds of GDM were selected for the subsequent analyses via the random forest model. Conditional logistic regression models were performed to analyze the relationships of blood As, Pb, Tl and Ni levels with the odds of GDM. The weighted quantile sum (WQS) regression and bayesian kernel machine regression (BKMR) were used to assess the joint effects of levels of As, Pb, Tl and Ni on the odds of GDM as well as to evaluate which metal level contributed most to the association. Latent profile analysis (LPA) was conducted to identify profiles of glycemic and C−peptide levels at different time points. Multiple linear regression models were employed to explore the relationships of metals with glycaemia−related indices (fasting blood glucose (FBG), 1−hour blood glucose (1h BG), 2−hour blood glucose (2h BG), fasting C−peptide (FCP), 1−hour C−peptide (1h CP), 2−hour C-peptide (2h CP), FCP/FBG, 1h CP/1h BG, 2h CP/2h BG, area under the curve of C−peptide (AUCP), area under the curve of glucose (AUCG), AUCP/AUCG and profiles of BGs and CPs, respectively. Mixed−effects models with repeated measures data were used to explore the relationship between As (the ultimately selected m |
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ISSN: | 0013-9351 1096-0953 |
DOI: | 10.1016/j.envres.2024.118237 |