Associations of Exposure to Air Pollution with Insulin Resistance: A Systematic Review and Meta-Analysis

Jiajia Dang, Mengtong Yang, Xinge Zhang, Haotian Ruan, Guiyu Qin, Jialin Fu, Ziqiong Shen, Anran Tan, Rui Li and Justin Moore
Additional contact information
Jiajia Dang: School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan 430071, China
Mengtong Yang: School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan 430071, China
Xinge Zhang: School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan 430071, China
Haotian Ruan: School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan 430071, China
Guiyu Qin: School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan 430071, China
Jialin Fu: School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan 430071, China
Ziqiong Shen: School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan 430071, China
Anran Tan: School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan 430071, China
Rui Li: School of Health Sciences, Wuhan University, 115 Donghu Road, Wuhan 430071, China
Justin Moore: Department of Family & Community Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA

IJERPH, 2018, vol. 15, issue 11, 1-16

Abstract: In this article, we review the available evidence and explore the association between air pollution and insulin resistance (IR) using meta-analytic techniques. Cohort studies published before January 2018 were selected through English-language literature searches in nine databases. Six cohort studies were included in our sample, which assessed air pollutants including PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm), NO 2 (nitrogen dioxide), and PM 10 (particulate matter with an aerodynamic diameter less than 10 μm). Percentage change in insulin or insulin resistance associated with air pollutants with corresponding 95% confidence interval (CI) was used to evaluate the risk. A pooled effect (percentage change) was observed, with a 1 μg/m 3 increase in NO 2 associated with a significant 1.25% change (95% CI: 0.67, 1.84; I 2 = 0.00%, p = 0.07) in the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and a 0.60% change (95% CI: 0.17, 1.03; I 2 = 30.94%, p = 0.27) in insulin. Similar to the analysis of NO 2 , a 1 μg/m 3 increase in PM 10 was associated with a significant 2.77% change (95% CI: 0.67, 4.87; I 2 = 94.98%, p < 0.0001) in HOMA-IR and a 2.75% change in insulin (95% CI: 0.45, 5.04; I 2 = 58.66%, p = 0.057). No significant associations were found between PM 2.5 and insulin resistance biomarkers. We conclude that increased exposure to air pollution can lead to insulin resistance, further leading to diabetes and cardiometabolic diseases. Clinicians should consider the environmental exposure of patients when making screening and treatment decisions for them.

Keywords: air pollution; insulin resistance; meta-analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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