Outdoor air pollution exposure and the risk of type 2 diabetes mellitus: A systematic umbrella review and meta-analysis
Bibliographic record
Abstract
The association between different air pollutants and Type 2 Diabetes Mellitus (T2DM) is a growing topic of interest in public health research. This umbrella review and meta-analysis aimed to consolidate current literature on the association between various outdoor air pollutants and T2DM. Subgroups and dose-response relationships were also analyzed to further quantify the association, especially by the factors such as the type of pollutants, duration of exposure, and geographical variation, etc. A thorough literature search of three databases revealed a total of 71 records for umbrella review and 1524 records for meta-analysis where 8 studies were included in the final review of umbrella review and 46 studies for meta-analysis. The evaluation of the study's quality in umbrella review and meta-analysis were conducted using the AMSTAR 2 criteria and the Newcastle-Ottawa Scale (NOS), respectively. Exposure to Particulate Matter (PM) 2.5, PM10, Nitrogen dioxides (NO2) and Ozone (O3) were significantly associated with the risk of T2DM [OR = 1.12 (95% Confidence Interval (CI): 1.09, 1.15), 1.12 (95% CI: 1.06, 1.18), 1.09 (95%CI: 1.07, 1.12), 1.05 (95%CI: 1.03, 1.08), respectively] and subgroup analysis further revealed that PM2.5, PM10, and NO2 associations were confounded by factors such as ages, study design, regions of exposure and air pollution concentration levels. Lastly, only exposure to PM10 had a significant dose-response relationship with the risk of T2DM (p-value = 0.000). These findings further emphasized the need for standardized methods in conducting air pollution research and additional research on other air pollutants to further explore the relationships between these air pollutants and T2DM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".