TY - JOUR
T1 - Early pregnancy essential and non-essential metal mixtures and gestational glucose concentrations in the 2nd trimester
T2 - Results from project viva
AU - Zheng, Yinnan
AU - Lin, Pi I.Debby
AU - Williams, Paige L.
AU - Weisskopf, Marc G.
AU - Cardenas, Andres
AU - Rifas-Shiman, Sheryl L.
AU - Wright, Robert O.
AU - Amarasiriwardena, Chitra
AU - Henn, Birgit Claus
AU - Hivert, Marie France
AU - Oken, Emily
AU - James-Todd, Tamarra
N1 - Funding Information:
Support for this research was provided by grants from the US National Institutes of Health (R01 HD034568, UH3 OD023286, U2CES026561, and U2CES026555).
Funding Information:
The authors would like to thank the participants of Project Viva for their time and willingness to participate in the study; all members of the Project Viva team at the Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute for collecting and managing data, The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The Children's Health Exposure Analysis Resource (CHEAR) funded the measurement of the first trimester metals (CHEAR award #2017-1740; PI: Cardenas) carried out at the Mount Sinai CHEAR Network Laboratory (PI: Wright). Support for this research was provided by grants from the US National Institutes of Health (R01 HD034568, UH3 OD023286, U2CES026561, and U2CES026555).
Publisher Copyright:
© 2021
PY - 2021/10
Y1 - 2021/10
N2 - 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 1st 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.
AB - 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 1st 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.
KW - Bayesian Kernel Machine Regression
KW - Blood glucose concentrations
KW - Gestational diabetes
KW - Metal mixtures
UR - http://www.scopus.com/inward/record.url?scp=85107696092&partnerID=8YFLogxK
U2 - 10.1016/j.envint.2021.106690
DO - 10.1016/j.envint.2021.106690
M3 - Article
C2 - 34120006
AN - SCOPUS:85107696092
SN - 0160-4120
VL - 155
JO - Environment international
JF - Environment international
M1 - 106690
ER -