Chronic Exposure to Fine Particulate Matter Increases Mortality Through Pathways of Metabolic and Cardiovascular Disease: Insights From a Large Mediation Analysis
Bibliographic record
Abstract
Background Long‐term exposure to outdoor fine particulate matter (PM 2.5 ) is the leading environmental risk factor for premature mortality worldwide. Characterizing important pathways through which PM 2.5 increases individuals' mortality risk can clarify the PM 2.5 –mortality relationship and identify possible points of interventions. Recent evidence has linked PM 2.5 to the onset of diabetes and cardiovascular disease, but to what extent these associations contribute to the effect of PM 2.5 on mortality remains poorly understood. Methods and Results We conducted a population‐based cohort study to investigate how the effect of PM 2.5 on nonaccidental mortality is mediated by its impacts on incident diabetes, acute myocardial infarction, and stroke. Our study population comprised ≈200 000 individuals aged 20 to 90 years who participated in population‐based health surveys in Ontario, Canada, from 1996 to 2014. Follow‐up extended until December 2017. Using causal mediation analyses with Aalen additive hazards models, we decomposed the total effect of PM 2.5 on mortality into a direct effect and several path‐specific indirect effects mediated by diabetes, each cardiovascular event, or both combined. A series of sensitivity analyses were also conducted. After adjusting for various individual‐ and neighborhood‐level covariates, we estimated that for every 1000 adults, each 10 μg/m 3 increase in PM 2.5 was associated with ≈2 incident cases of diabetes, ≈1 major cardiovascular event (acute myocardial infarction and stroke combined), and ≈2 deaths annually. Among PM 2.5 ‐related deaths, 31.7% (95% CI, 17.2%–53.2%) were attributable to diabetes and major cardiovascular events in relation to PM 2.5 . Specifically, 4.5% were explained by PM 2.5 ‐induced diabetes, 22.8% by PM 2.5 ‐induced major cardiovascular events, and 4.5% through their interaction. Conclusions This study suggests that a significant portion of the estimated effect of long‐term exposure to PM 2.5 on deaths can be attributed to its effect on diabetes and cardiovascular diseases, highlighting the significance of PM 2.5 on deteriorating cardiovascular health. Our findings should raise awareness among professionals that improving metabolic and cardiovascular health may reduce mortality burden in areas with higher exposure to air pollution.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".