<p>Metal exposure is an emerging factor affecting the risk of gestational diabetes mellitus (GDM). This study aimed to explore the association between multiple metals (calcium [Ca], copper [Cu], iron [Fe], zinc [Zn], magnesium [Mg], and lead [Pb]) during early pregnancy and the risk of GDM using four statistical methods and further identify the critical metals within the mixture associated with GDM. A total of 763 pregnant women were included in this prospective cohort study. Blood samples were collected before 14 gestational weeks, and metal concentrations were measured by atomic absorption spectrometry. An oral glucose tolerance test was conducted at 24–28 gestational weeks to diagnose GDM. Binary logistic regression analysis and restricted cubic spline (RCS) models were applied to assess the association between individual metal concentration and GDM. Quantile g-computation (QGC) analysis and Bayesian kernel machine regression (BKMR) were used to evaluate the associations between metal mixture exposure and GDM. The mean concentrations of Zn and Pb were significantly higher in the GDM group than in the non-GDM group. In the logistic regression analyses, maternal blood Zn, Fe, and Pb were associated with an increased risk of GDM. RCS analysis showed that Zn and Pb were linearly and positively associated with the risk of GDM. According to QGC analysis and the BKMR models, the mixture of six metals was significantly and positively associated with the risk of GDM. Pb, Fe, and Zn made the major contributions. These findings underscore the importance of considering multiple metal exposures in understanding the risk factors for GDM.</p>

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Association between multiple metals and gestational diabetes mellitus: a cohort study from Eastern China

  • Peiwen Zheng,
  • Binbin Yin,
  • Ming Lei,
  • Wen Hu,
  • Lingfei He,
  • Fang Tang,
  • Hui Chen,
  • Zhuo Yuan,
  • Bo Zhu

摘要

Metal exposure is an emerging factor affecting the risk of gestational diabetes mellitus (GDM). This study aimed to explore the association between multiple metals (calcium [Ca], copper [Cu], iron [Fe], zinc [Zn], magnesium [Mg], and lead [Pb]) during early pregnancy and the risk of GDM using four statistical methods and further identify the critical metals within the mixture associated with GDM. A total of 763 pregnant women were included in this prospective cohort study. Blood samples were collected before 14 gestational weeks, and metal concentrations were measured by atomic absorption spectrometry. An oral glucose tolerance test was conducted at 24–28 gestational weeks to diagnose GDM. Binary logistic regression analysis and restricted cubic spline (RCS) models were applied to assess the association between individual metal concentration and GDM. Quantile g-computation (QGC) analysis and Bayesian kernel machine regression (BKMR) were used to evaluate the associations between metal mixture exposure and GDM. The mean concentrations of Zn and Pb were significantly higher in the GDM group than in the non-GDM group. In the logistic regression analyses, maternal blood Zn, Fe, and Pb were associated with an increased risk of GDM. RCS analysis showed that Zn and Pb were linearly and positively associated with the risk of GDM. According to QGC analysis and the BKMR models, the mixture of six metals was significantly and positively associated with the risk of GDM. Pb, Fe, and Zn made the major contributions. These findings underscore the importance of considering multiple metal exposures in understanding the risk factors for GDM.