Long-term exposure to air pollution, coronary artery calcification, and carotid artery plaques in the population-based Swedish SCAPIS Gothenburg cohort
Bibliographic record
Abstract
Long-term exposure to air pollution is associated with cardiovascular events. A main suggested mechanism is that air pollution accelerates the progression of atherosclerosis, yet current evidence is inconsistent regarding the association between air pollution and coronary artery and carotid artery atherosclerosis, which are well-established causes of myocardial infarction and stroke. We studied associations between low levels of long-term air pollution, coronary artery calcium (CAC) score, and the prevalence and area of carotid artery plaques, in a middle-aged population-based cohort. The Swedish CArdioPulmonary bioImage Study (SCAPIS) Gothenburg cohort was recruited during 2013–2017 and thoroughly examined for cardiovascular risk factors, including computed tomography of the heart and ultrasonography of the carotid arteries. In 5070 participants (age 50–64 years), yearly residential exposures to air pollution (PM2.5, PM10, PMcoarse, NOx, and exhaust-specific PM2.5 1990–2015) were estimated using high-resolution dispersion models. We used Poisson regression to examine associations between long-term (26 years’ mean) exposure to air pollutants and CAC score, and prevalence of carotid artery plaques, adjusted for potential confounders. Among participants with carotid artery plaques, we also examined the association with plaque area using linear regression. Mean exposure to PM2.5 was low by international standards (8.5 μg/m3). There were no consistent associations between long-term total PM2.5 exposure and CAC score or presence of carotid artery plaques, but an association between total PM2.5 and larger plaque area in participants with carotid plaques. Associations with traffic-related air pollutants were consistently positive for both a high CAC score and bilateral carotid artery plaques. These associations were independent of road traffic noise. We found stronger associations among men and participants with cardiovascular risk factors. The results lend some support to atherosclerosis as a main modifiable pathway between low levels of traffic-related ambient air pollution and cardiovascular disease, especially in vulnerable individuals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".