Long-term exposure to iron and copper in fine particulate air pollution and their combined impact on reactive oxygen species concentration in lung fluid: a population-based cohort study of cardiovascular disease incidence and mortality in Toronto, Canada
Bibliographic record
Abstract
BACKGROUND: Exposure to fine particulate (PM2.5) air pollution is associated with increased cardiovascular disease (CVD), but less is known about its specific components, such as metals originating from non-tailpipe emissions. We investigated the associations of long-term exposure to metal components [iron (Fe) and copper (Cu)] in PM2.5 with CVD incidence. METHODS: We conducted a population-based cohort study in Toronto, Canada. Exposures to Fe and Cu in PM2.5 and their combined impact on the concentration of reactive oxygen species (ROS) in lung fluid were estimated using land use regression models. Incidence of acute myocardial infarction (AMI), congestive heart failure (CHF) and CVD death was ascertained using health administrative datasets. We used mixed-effects Cox regression models to examine the associations between the exposures and health outcomes. A series of sensitivity analyses were conducted, including indirect adjustment for individual-level cardiovascular risk factors (e.g. smoking), and adjustment for PM2.5 and nitrogen dioxide (NO2). RESULTS: In single-pollutant models, we found positive associations between the three exposures and all three outcomes, with the strongest associations detected for the estimated ROS. The associations of AMI and CHF were sensitive to indirect adjustment, but remained robust for CVD death in all sensitivity analyses. In multi-pollutant models, the associations of the three exposures generally remained unaltered. Interestingly, adjustment for ROS did not substantially change the associations between PM2.5 and CVD, but attenuated the associations of NO2. CONCLUSIONS: Long-term exposure to Fe and Cu in PM2.5 and their combined impact on ROS were consistently associated with increased CVD death.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".