The Influence of Airborne Particulate Matter on the Risk of Gestational Diabetes Mellitus: A Large Retrospective Study in Chongqing, China

Xiaoling Zeng; Yu Zhan; Wei Zhou; Zhimei Qiu; Tong Wang; Qing Chen; Dandan Qu; Qiao Huang; Jia Cao; Niya Zhou

doi:10.3390/toxics12010019

The Influence of Airborne Particulate Matter on the Risk of Gestational Diabetes Mellitus: A Large Retrospective Study in Chongqing, China

Toxics. 2023 Dec 24;12(1):19. doi: 10.3390/toxics12010019.

Authors

Xiaoling Zeng^{1

2}, Yu Zhan³, Wei Zhou⁴, Zhimei Qiu³, Tong Wang¹, Qing Chen¹, Dandan Qu^{5

6}, Qiao Huang⁴, Jia Cao¹, Niya Zhou^{5

6}

Affiliations

¹ Institute of Toxicology, Facutly of Military Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China.
² School of Public Health, China Medical University, Shenyang 110122, China.
³ Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China.
⁴ Department of Obstetrics and Gynecology, Chongqing Health Center for Women and Children (Women and Children's Hospital of Chongqing Medical University), Chongqing 401147, China.
⁵ Clinical Research Centre, Women and Children's Hospital of Chongqing Medical University, Chongqing 401147, China.
⁶ Chongqing Research Centre for Prevention & Control of Maternal and Child Diseases and Public Health, Women and Children's Hospital of Chongqing Medical University, Chongqing 401147, China.

Abstract

Emerging research findings suggest that airborne particulate matter might be a risk factor for gestational diabetes mellitus (GDM). However, the concentration-response relationships and the susceptible time windows for different types of particulate matter may vary. In this retrospective analysis, we employ a novel robust approach to assess the crucial time windows regarding the prevalence of GDM and to distinguish the susceptibility of three GDM subtypes to air pollution exposure. This study included 16,303 pregnant women who received routine antenatal care in 2018-2021 at the Maternal and Child Health Hospital in Chongqing, China. In total, 2482 women (15.2%) were diagnosed with GDM. We assessed the individual daily average exposure to air pollution, including PM_2.5, PM₁₀, O₃, NO₂, SO₂, and CO based on the volunteers' addresses. We used high-accuracy gridded air pollution data generated by machine learning models to assess particulate matter per maternal exposure levels. We further analyzed the association of pre-pregnancy, early, and mid-pregnancy exposure to environmental pollutants using a generalized additive model (GAM) and distributed lag nonlinear models (DLNMs) to analyze the association between exposure at specific gestational weeks and the risk of GDM. We observed that, during the first trimester, per IQR increases for PM₁₀ and PM_2.5 exposure were associated with increased GDM risk (PM₁₀: OR = 1.19, 95%CI: 1.07~1.33; PM_2.5: OR = 1.32, 95%CI: 1.15~1.50) and isolated post-load hyperglycemia (GDM-IPH) risk (PM₁₀: OR = 1.23, 95%CI: 1.09~1.39; PM_2.5: OR = 1.38, 95%CI: 1.18~1.61). Second-trimester O₃ exposure was positively correlated with the associated risk of GDM, while pre-pregnancy and first-trimester exposure was negatively associated with the risk of GDM-IPH. Exposure to SO₂ in the second trimester was negatively associated with the risk of GDM-IPH. However, there were no observed associations between NO₂ and CO exposure and the risk of GDM and its subgroups. Our results suggest that maternal exposure to particulate matter during early pregnancy and exposure to O₃ in the second trimester might increase the risk of GDM, and GDM-IPH is the susceptible GDM subtype to airborne particulate matter exposure.

Keywords: airborne particulate matter; gestational diabetes mellitus; isolated post-load hyperglycemia; sensitive subtypes; sensitive time windows.

Abstract

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