Controlled Diesel Exhaust Exposure Induces a Concentration-dependent Increase in Airway Inflammation: A Clinical Trial
Bibliographic record
Abstract
Abstract Rationale Air pollution exposure is harmful to human airways, and its impacts are best studied using concentration–response relationships. However, most concentration–response research on airway health has investigated chronic exposures, with less being known about acute effects, which can be robustly studied using controlled human exposures. Objectives To investigate the concentration relationship between airway health measures and diesel exhaust (DE). Methods We conducted a double-blind crossover study with 17 healthy nonsmokers exposed to filtered air and DE standardized to 20, 50, and 150 μg/m3 of particulate matter ⩽2.5 μm in aerodynamic diameter for 4 hours. Before, during, and up to 24 hours from the exposure start, we measured lung function, airway responsiveness, and airway inflammation using spirometry, methacholine challenge, and fractional exhaled nitric oxide (Fe NO), respectively. In addition, we measured nasal airway inflammation using differential cell counts and cytokines in nasal lavage and epithelial lining fluid at 24 hours. We assessed DE concentration responses and associations between outcomes using linear mixed effects models and repeated measures correlations, respectively, thereafter adjusting for multiple comparisons. Results DE exposure increased percentage ΔFe NO at 4 hours (β = 0.16 ± 0.06). Compared with filtered air, percentage ΔFe NO trended toward an increase at concentrations of 20 μg/m3 (β = 18.66 ± 8.76) and 50 μg/m3 (β = 19.33 ± 8.92) and increased significantly at 150 μg/m3 (β = 34.43 ± 8.92). In addition, DE exposure induced a trend toward increased nasal IL-6 at 24 hours (percentage difference, 0.88; 95% confidence interval, 0.08, 1.70). There were no effects of DE exposure on Fe NO at 24 hours, lung function, airway responsiveness, or nasal cell counts. Conclusions DE induces a concentration-dependent increase in Fe NO, indicating that it may be a sensitive marker of an acute inflammatory response in the airways. We report responses at concentrations below those in previous controlled DE exposure studies, and we document particulate matter ⩽2.5 μm in aerodynamic diameter concentration–response estimates at exposure levels routinely experienced in the community and occupational settings. Clinical trial registered with www.clinicaltrials.gov (NCT 03234790).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".