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The relationship between air pollutants and gestational diabetes: an updated systematic review and meta-analysis
S Nazarpour1,2, F Ramezani Tehrani3, R Valizadeh4,5
1Reproductive Endocrinology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, 24 Parvaneh, Yaman Street, Velenjak, P.O. Box: 19395-4763, Tehran, 1985717413, Islamic Republic of Iran.
Purpose:
Air pollution is an environmental stimulus that may predispose pregnant women to gestational diabetes mellitus (GDM). This systematic review and meta-analysis were conducted to investigate the relationship between air pollutants and GDM.
Methods:
PubMed, Web of Science, and Scopus were systematically searched for retrieving English articles published from January 2020 to September 2021, investigating the relationship of exposure to ambient air pollution or levels of air pollutants with GDM and related parameters, including fasting plasma glucose (FPG), insulin resistance, and impaired glucose tolerance. Heterogeneity and publication bias were evaluated using I-squared (I2), and Begg's statistics, respectively. We also performed the subgroup analysis for particulate matters (PM2.5, PM10), Ozone (O3), and sulfur dioxide (SO2) in the different exposure periods.
Results:
A total of 13 studies examining 2,826,544 patients were included in this meta-analysis. Compared to non-exposed women, exposure to PM2.5 increases the odds (likelihood of occurrence outcome) of GDM by 1.09 times (95% CI 1.06, 1.12), whereas exposure to PM10 has more effect by OR of 1.17 (95% CI 1.04, 1.32). Exposure to O3 and SO2 increases the odds of GDM by 1.10 times (95% CI 1.03, 1.18) and 1.10 times (95% CI 1.01, 1.19), respectively.
Conclusions:
The results of the study show a relationship between air pollutants PM2.5, PM10, O3, and SO2 and the risk of GDM. Although evidence from various studies can provide insights into the linkage between maternal exposure to air pollution and GDM, more well-designed longitudinal studies are recommended for precise interpretation of the association between GDM and air pollution by adjusting all potential confounders.
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