Yi Sun1, Xia Li2, Tarik Benmarhnia3, Jiu-Chiuan Chen4, Chantal Avila2, David A Sacks5, Vicki Chiu2, Jeff Slezak2, John Molitor6, Darios Getahun7, Jun Wu8. 1. Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, CA, USA. 2. Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA. 3. Herbert Wertheim School of Public Health and Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive #0725, CA La Jolla 92093, USA. 4. Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90033, USA. 5. Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA; Department of Obstetrics and Gynecology, University of Southern California, Keck School of Medicine, Los Angeles, CA, USA. 6. College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA. 7. Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA; Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA. Electronic address: Darios.T.Getahun@kp.org. 8. Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, CA, USA. Electronic address: junwu@hs.uci.edu.
Abstract
BACKGROUND: Epidemiological findings are inconsistent regarding the associations between air pollution exposure during pregnancy and gestational diabetes mellitus (GDM). Several limitations exist in previous studies, including potential outcome and exposure misclassification, unassessed confounding, and lack of simultaneous consideration of air pollution mixtures and particulate matter (PM) constituents. OBJECTIVES: To assess the association between GDM and maternal residential exposure to air pollution, and the joint effect of the mixture of air pollutants and PM constituents. METHODS: Detailed clinical data were obtained for 395,927 pregnancies in southern California (2008-2018) from Kaiser Permanente Southern California (KPSC) electronic health records. GDM diagnosis was based on KPSC laboratory tests. Monthly average concentrations of fine particulate matter < 2.5 μm (PM2.5), <10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) were estimated using kriging interpolation of Environmental Protection Agency's routine monitoring station data, while PM2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon) were estimated using a fine-resolution geoscience-derived model. A multilevel logistic regression was used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of air pollution and PM component mixtures. Main analyses adjusted for maternal age, race/ethnicity, education, median family household income, pre-pregnancy BMI, smoking during pregnancy, insurance type, season of conception and year of delivery. RESULTS: The incidence of GDM was 10.9% in the study population. In single-pollutant models, we observed an increased odds for GDM associated with exposures to PM2.5, PM10, NO2 and PM2.5 constituents. The association was strongest for NO2 [adjusted odds ratio (OR) per interquartile range: 1.176, 95% confidence interval (CI): 1.147-1.205)]. In multi-pollutant models, increased ORs for GDM in association with one quartile increase in air pollution mixtures were found for both kriging-based regional air pollutants (NO2, PM2.5, and PM10, OR = 1.095, 95% CI: 1.082-1.108) and PM2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon, OR = 1.258, 95% CI: 1.206-1.314); NO2 (78%) and black carbon (48%) contributed the most to the overall mixture effects among all krigged air pollutants and all PM2.5 constituents, respectively. The risk of GDM associated with air pollution exposure were significantly higher among Hispanic mothers, and overweight/obese mothers. CONCLUSION: This study found that exposure to a mixture of ambient PM2.5, PM10, NO2, and PM2.5 chemical constituents was associated with an increased risk of GDM. NO2 and black carbon PM2.5 contributed most to GDM risk.
BACKGROUND: Epidemiological findings are inconsistent regarding the associations between air pollution exposure during pregnancy and gestational diabetes mellitus (GDM). Several limitations exist in previous studies, including potential outcome and exposure misclassification, unassessed confounding, and lack of simultaneous consideration of air pollution mixtures and particulate matter (PM) constituents. OBJECTIVES: To assess the association between GDM and maternal residential exposure to air pollution, and the joint effect of the mixture of air pollutants and PM constituents. METHODS: Detailed clinical data were obtained for 395,927 pregnancies in southern California (2008-2018) from Kaiser Permanente Southern California (KPSC) electronic health records. GDM diagnosis was based on KPSC laboratory tests. Monthly average concentrations of fine particulate matter < 2.5 μm (PM2.5), <10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) were estimated using kriging interpolation of Environmental Protection Agency's routine monitoring station data, while PM2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon) were estimated using a fine-resolution geoscience-derived model. A multilevel logistic regression was used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of air pollution and PM component mixtures. Main analyses adjusted for maternal age, race/ethnicity, education, median family household income, pre-pregnancy BMI, smoking during pregnancy, insurance type, season of conception and year of delivery. RESULTS: The incidence of GDM was 10.9% in the study population. In single-pollutant models, we observed an increased odds for GDM associated with exposures to PM2.5, PM10, NO2 and PM2.5 constituents. The association was strongest for NO2 [adjusted odds ratio (OR) per interquartile range: 1.176, 95% confidence interval (CI): 1.147-1.205)]. In multi-pollutant models, increased ORs for GDM in association with one quartile increase in air pollution mixtures were found for both kriging-based regional air pollutants (NO2, PM2.5, and PM10, OR = 1.095, 95% CI: 1.082-1.108) and PM2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon, OR = 1.258, 95% CI: 1.206-1.314); NO2 (78%) and black carbon (48%) contributed the most to the overall mixture effects among all krigged air pollutants and all PM2.5 constituents, respectively. The risk of GDM associated with air pollution exposure were significantly higher among Hispanic mothers, and overweight/obese mothers. CONCLUSION: This study found that exposure to a mixture of ambient PM2.5, PM10, NO2, and PM2.5 chemical constituents was associated with an increased risk of GDM. NO2 and black carbon PM2.5 contributed most to GDM risk.
Authors: Heejoo Jo; Sandrah P Eckel; Jiu-Chiuan Chen; Myles Cockburn; Mayra P Martinez; Ting Chow; Frederick W Lurmann; William E Funk; Anny H Xiang; Rob McConnell Journal: Environ Int Date: 2019-10-11 Impact factor: 9.621
Authors: Eric V Balti; Justin B Echouffo-Tcheugui; Yandiswa Y Yako; Andre P Kengne Journal: Diabetes Res Clin Pract Date: 2014-09-10 Impact factor: 5.602
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