The Relationship Between Diabetes Mellitus and Traffic-Related Air Pollution
Bibliographic record
Abstract
OBJECTIVE: Air pollution is associated with an increased risk for cardiovascular events. Many of the biological pathways involved could also promote diabetes mellitus (DM). We therefore investigated the association between DM prevalence and exposure to traffic-related air pollution (nitrogen dioxide [NO 2]). METHODS: Study participants were patients who attended two respiratory clinics in Hamilton (n = 5228) and Toronto (n = 2406). The diagnosis of DM was ascertained by linkage to administrative databases of the Ontario universal Health Insurance Plan for patients aged 40 years and above. Geographic Information systems methodology was used to assign individual estimates of NO2 based on a network of samplers in each city. Logistic regression was used to estimate the relations between NO2 exposures and the odds of DM diagnosis. RESULTS: After adjusting for age, body mass index, and neighborhood income there were positive effects in women on the odds ratio for DM for each 1 ppb NO2 exposure in Toronto (OR 1.055, 95% CI: 0.99 to 1.11) and Hamilton (OR 1.029, 95% CI: 0.98 to 1.08). In a meta-analytic model including both cities, there was a significant effect in women (OR = 1.04; 95% CI: 1.00 to 1.08). Across the inter-quartile range (approximately 4 ppb NO2) there was nearly a 17% increase in the odds of DM for women. There were no positive associations among men. CONCLUSIONS: Exposure to NO2, a marker of traffic-related air pollutants, was associated with DM prevalence among women. Exposure estimate errors in men may explain the apparent gender difference. These results suggest that common air pollutants are associated with DM and warrant more investigation to determine if this is a cause-and-effect relationship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".