Associations between Particulate Matter Air Pollution, Presence and Progression of Subclinical Coronary and Carotid Atherosclerosis: A Systematic Review
Bibliographic record
Abstract
Abstract Background and aims Long-term exposure to particulate matter (PM) air pollution has been linked with increased cardiovascular events and mortality, however, studies had shown inconsistent associations between PM exposure and subclinical atherosclerosis. Methods We performed an updated systematic literature review to identify studies evaluating the associations between PM and subclinical atherosclerosis, measured using presence/progression of coronary artery calcium (CAC) or carotid intima-media thickness (CIMT) in adult populations. Quality was assessed using the Newcastle-Ottawa scale. Results Eighteen studies were included: 5 cohorts and 13 cross-sectional. Amongst 7 studies that evaluated the associations between PM and prevalence of CAC, 4 reported significantly higher odds of detectable CAC>0 or CAC>400 with increased PM exposure. Nine studies evaluated the association between exposure to at least one of the particulates and CIMT; of these, 6 reported significant independent associations. Two studies evaluated PM2.5 and CAC progression, with 1 reporting a greater progression of CAC with increased exposure to PM, while 3 out of 4 studies evaluating CIMT progression showed no significant difference in CIMT progression with a higher PM2.5 exposure. Additionally, 3 studies found significant associations between proximity to major roadways and measures of subclinical atherosclerosis. Among null studies, most displayed non-significant trends towards higher atherosclerosis burden with higher PM exposure. Conclusions Overall, available observational studies support a positive association between PM exposure and subclinical atherosclerosis. Further longitudinal studies are needed to better establish this relationship and assess the efficacy of previously identified interventions on mitigation of clinical cardiovascular disease.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".