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Meta-Analysis
. 2023 May 31:68:1605718.
doi: 10.3389/ijph.2023.1605718. eCollection 2023.

Long-Term Exposure to Traffic-Related Air Pollution and Diabetes: A Systematic Review and Meta-Analysis

Meltem Kutlar Joss  1   2   3 , Hanna Boogaard  4 , Evangelia Samoli  5 , Allison P Patton  4 , Richard Atkinson  6 , Jeff Brook  7 , Howard Chang  8 , Pascale Haddad  3 , Gerard Hoek  9 , Ron Kappeler  1   2 , Sharon Sagiv  10 , Audrey Smargiassi  11 , Adam Szpiro  12 , Danielle Vienneau  1   2 , Jennifer Weuve  13 , Fred Lurmann  14 , Francesco Forastiere  15 , Barbara H Hoffmann  3
Affiliations
Meta-Analysis

Long-Term Exposure to Traffic-Related Air Pollution and Diabetes: A Systematic Review and Meta-Analysis

Meltem Kutlar Joss et al. Int J Public Health. .

Abstract

Objectives: We report results of a systematic review on the health effects of long-term traffic-related air pollution (TRAP) and diabetes in the adult population. Methods: An expert Panel appointed by the Health Effects Institute conducted this systematic review. We searched the PubMed and LUDOK databases for epidemiological studies from 1980 to July 2019. TRAP was defined based on a comprehensive protocol. Random-effects meta-analyses were performed. Confidence assessments were based on a modified Office for Health Assessment and Translation (OHAT) approach, complemented with a broader narrative synthesis. We extended our interpretation to include evidence published up to May 2022. Results: We considered 21 studies on diabetes. All meta-analytic estimates indicated higher diabetes risks with higher exposure. Exposure to NO2 was associated with higher diabetes prevalence (RR 1.09; 95% CI: 1.02; 1.17 per 10 μg/m3), but less pronounced for diabetes incidence (RR 1.04; 95% CI: 0.96; 1.13 per 10 μg/m3). The overall confidence in the evidence was rated moderate, strengthened by the addition of 5 recently published studies. Conclusion: There was moderate evidence for an association of long-term TRAP exposure with diabetes.

Keywords: NO2; confidence assessment; diabetes; particulate matter; traffic-related air pollution.

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Conflict of interest statement

Author FL was employed by the company Sonoma Technology, Inc. The remaining authors declare that they do not have any conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Meta-analysis of associations between traffic-related air pollutants and diabetes prevalence (empty squares) and incidence (filled squares) (Global 2022). The following increments were used: 10 µg/m3 for NO2, 20 μg/m3 for NOx, 1 μg/m3 for EC, 10 μg/m3 for PM10, and 5 μg/m3 for PM2.5. Effect estimates cannot be directly compared across the different traffic-related pollutants because the selected increments do not necessarily represent the same contrast in exposure.
FIGURE 2
FIGURE 2
Forest plots of adjusted RRs (95% CIs) for diabetes prevalence with NO2, PM10, and PM2.5 (Global 2022). The size of the grey squares represents the weight of the study in the meta-analysis. The following increments were used: 10 μg/m3 for NO2, 20 μg/m3 for NOx, 1 μg/m3 for EC, 10 μg/m3 for PM10, and 5 μg/m3 for PM2.5. Effect estimates cannot be directly compared across the different traffic-related pollutants because the selected increments do not necessarily represent the same contrast in exposure.
FIGURE 3
FIGURE 3
Forest plots of adjusted RRs (95% CIs) for diabetes incidence with NO2, NOx, EC and PM2.5 (Global 2022). The size of the grey squares represents the weight of the study in the meta-analysis. The following increments were used: 10 µg/m3 for NO2, 20 µg/m3 for NOx, 1 µg/m3 for EC, 10 µg/m3 for PM10, and 5 µg/m3 for PM2.5. Effect estimates cannot be directly compared across the different traffic-related pollutants because the selected increments do not necessarily represent the same contrast in exposure.
See this image and copyright information in PMC

References

    1. Goyal R, Jialal I. Diabetes Mellitus Type 2. StatPearls. Treasure Island (FL): StatPearls Publishing LLC. (2021). Available at: https://www.ncbi.nlm.nih.gov/books/NBK513253/ (Accessed November 12, 2021).
    1. International Diabetes Federation. IDF Diabetes Atlas. Brussels, Belgium: International Diabetes Federation; (2021).
    1. Beulens JWJ, Pinho MGM, Abreu TC, den Braver NR, Lam TM, Huss A, et al. Environmental Risk Factors of Type 2 Diabetes-An Exposome Approach. Diabetologia (2021) 65(2):263–74. 10.1007/s00125-021-05618-w - DOI - PubMed
    1. Wu Y, Fu R, Lei C, Deng Y, Lou W, Wang L, et al. Estimates of Type 2 Diabetes Mellitus Burden Attributable to Particulate Matter Pollution and its 30-Year Change Patterns: A Systematic Analysis of Data from the Global Burden of Disease Study 2019. Front Endocrinol (Lausanne) (2021) 12:689079. 10.3389/fendo.2021.689079 - DOI - PMC - PubMed
    1. Yang BY, Fan S, Thiering E, Seissler J, Nowak D, Dong GH, et al. Ambient Air Pollution and Diabetes: A Systematic Review and Meta-Analysis. Environ Res (2020) 180:108817. 10.1016/j.envres.2019.108817 - DOI - PubMed