Ambient air pollution and gestational diabetes mellitus: An updated systematic review and meta-analysis

Weiqi Liang; Hui Zhu; Jin Xu; Zhijia Zhao; Liming Zhou; Qiong Zhu; Jie Cai; Lindan Ji

doi:10.1016/j.ecoenv.2023.114802

Ambient air pollution and gestational diabetes mellitus: An updated systematic review and meta-analysis

Ecotoxicol Environ Saf. 2023 Apr 15:255:114802. doi: 10.1016/j.ecoenv.2023.114802. Epub 2023 Mar 17.

Authors

Weiqi Liang¹, Hui Zhu², Jin Xu³, Zhijia Zhao¹, Liming Zhou⁴, Qiong Zhu⁵, Jie Cai⁶, Lindan Ji⁷

Affiliations

¹ Department of Preventive Medicine, School of Medicine, Ningbo University, Ningbo, China.
² Department of Internal Medicine, School of Medicine, Ningbo University, Ningbo, China.
³ Department of Preventive Medicine, School of Medicine, Ningbo University, Ningbo, China; Zhejiang Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, China.
⁴ Center for Reproductive Medicine, Ningbo Women and Children's Hospital, Ningbo, China.
⁵ Department of Pediatrics, Affiliated People's Hospital of Ningbo University, Ningbo, China.
⁶ Center for Reproductive Medicine, Ningbo Women and Children's Hospital, Ningbo, China. Electronic address: doctor_caijie@hotmail.com.
⁷ Zhejiang Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, China; Department of Biochemistry, School of Medicine, Ningbo University, Ningbo, China. Electronic address: jilindan@nbu.edu.cn.

PMID: 36934545
DOI: 10.1016/j.ecoenv.2023.114802

Abstract

Objective: We aimed to evaluate the relationship between the composition of particulate matter (PM) and gestational diabetes mellitus (GDM) by a comprehensively review of epidemiological studies.

Methods: We systematically identified cohort studies related to air pollution and GDM risk before February 8, 2023 from six databases (PubMed, Embase, Web of Science Core Collection, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform and Chongqing VIP Chinese Science and Technology Periodical databases). We calculated the relative risk (RR) and its 95% confidence intervals (CIs) to assess the overall effect by using a random effects model.

Results: This meta-analysis of 31 eligible cohort studies showed that exposure to PM_2.5, PM₁₀, SO₂, and NO₂ was associated with a significantly increased risk of GDM, especially in preconception and first trimester. Analysis of the components of PM_2.5 found that the risk of GDM was strongly linked to black carbon (BC) and nitrates (NO₃^-). Specifically, BC exposure in the second trimester and NO₃^- exposure in the first trimester elevated the risk of GDM, with the RR of 1.128 (1.032-1.231) and 1.128 (1.032-1.231), respectively. The stratified analysis showed stronger correlations of GDM risk with higher levels of pollutants in Asia, except for PM_2.5 and BC, which suggested that the specific composition of particulate pollutants had a greater effect on the exposure-outcome association than the concentration.

Conclusions: Our study found that ambient air pollutant is a critical factor for GDM and further studies on specific particulate matter components should be considered in the future.

Keywords: Gaseous pollutant; Gestational diabetes mellitus; Meta-analysis; Particulate matter.

Publication types

Meta-Analysis
Systematic Review
Review

MeSH terms

Air Pollutants* / analysis
Air Pollutants* / toxicity
Air Pollution* / adverse effects
Air Pollution* / analysis
Cohort Studies
Diabetes, Gestational* / chemically induced
Diabetes, Gestational* / epidemiology
Environmental Exposure / analysis
Female
Humans
Particulate Matter / analysis
Particulate Matter / toxicity
Pregnancy

Substances

Air Pollutants
Particulate Matter