Risk of Incident Diabetes in Relation to Long-term Exposure to Fine Particulate Matter in Ontario, Canada
Bibliographic record
Abstract
Background: Laboratory studies suggest that fine particulate matter (≤ 2.5 µm in diameter; PM 2.5 ) can activate pathophysiological responses that may induce insulin resistance and type 2 diabetes.However, epidemiological evidence relating PM 2.5 and diabetes is sparse, particularly for incident diabetes.oBjectives: We conducted a population-based cohort study to determine whether long-term exposure to ambient PM 2.5 is associated with incident diabetes.Methods: We assembled a cohort of 62,012 nondiabetic adults who lived in Ontario, Canada, and completed one of five population-based health surveys between 1996 and 2005.Follow-up extended until 31 December 2010.Incident diabetes diagnosed between 1996 and 2010 was ascertained using the Ontario Diabetes Database, a validated registry of persons diagnosed with diabetes (sensitivity = 86%, specificity = 97%).Six-year average concentrations of PM 2.5 at the postal codes of baseline residences were derived from satellite observations.We used Cox proportional hazards models to estimate the associations, adjusting for various individual-level risk factors and contextual covariates such as smoking, body mass index, physical activity, and neighborhood-level household income.We also conducted multiple sensitivity analyses.In addition, we examined effect modification for selected comorbidities and sociodemographic characteristics.results: There were 6,310 incident cases of diabetes over 484,644 total person-years of followup.The adjusted hazard ratio for a 10-µg/m 3 increase in PM 2.5 was 1.11 (95% CI: 1.02, 1.21).Estimated associations were comparable among all sensitivity analyses.We did not find strong evidence of effect modification by comorbidities or sociodemographic covariates.conclusions: This study suggests that long-term exposure to PM 2.5 may contribute to the development of diabetes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".