Exposure to Diesel Exhaust and Plasma Cortisol Response: A Randomized Double-Blind Crossover Study
Bibliographic record
Abstract
Health Canada conducts research to better understand the health impacts of air pollutants. Exposure to traffic-related air pollutants is linked to a variety of health impacts (e.g. cardiovascular disease, asthma, cognitive decline, dementia, depression, diabetes), but research efforts continue to explore how exposure to these pollutants results in such diverse health impacts. In the present study, Health Canada and University of British Columbia scientists evaluated whether short-term exposure to diesel exhaust increased blood levels of the stress hormone cortisol, and examined whether sex, asthma status, genetic variability, or antioxidants modified the effect. For the week prior to exposure, participants took an antioxidant pill or a placebo. On three separate days (minimum of two weeks between exposures), study participants entered an exposure chamber and breathed clean air or diluted diesel exhaust for 2 hours. Participants were not aware of which exposure they were experiencing. Breathing diesel exhaust increased blood cortisol levels. While effects were similar in males and females, they were driven by responses in people diagnosed with asthma, and those with variations in genes important for antioxidant response. Taking antioxidants prior to exposure reduced but did not eliminate the effect. Our results show that breathing diesel exhaust increases stress hormone levels, particularly in those with asthma. The study sheds light on a potential mechanism underlying health impacts of traffic-related air pollutants, and highlights how individual differences in our health and genes may contribute to vulnerability. Such information provides support to risk assessment and management exercises aimed at mitigating the adverse health impacts of air pollutants.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".