Insights Into the Mechanisms and Mediators of the Effects of Air Pollution Exposure on Blood Pressure and Vascular Function in Healthy Humans
Bibliographic record
Abstract
Fine particulate matter air pollution plus ozone impairs vascular function and raises diastolic blood pressure. We aimed to determine the mechanism and air pollutant responsible. The effects of pollution on heart rate variability, blood pressure, biomarkers, and brachial flow-mediated dilatation were determined in 2 randomized, double-blind, crossover studies. In Ann Arbor, 50 subjects were exposed to fine particles (150 microg/m(3)) plus ozone (120 parts per billion) for 2 hours on 3 occasions with pretreatments of an endothelin antagonist (Bosentan, 250 mg), antioxidant (Vitamin C, 2 g), or placebo. In Toronto, 31 subjects were exposed to 4 different conditions (particles plus ozone, particles, ozone, and filtered air). In Toronto, diastolic blood pressure significantly increased (2.9 and 3.6 mm Hg) only during particle-containing exposures in association with particulate matter concentration and reductions in heart rate variability. Flow-mediated dilatation significantly decreased (2.0% and 2.9%) only 24 hours after particle-containing exposures in association with particulate matter concentration and increases in blood tumor necrosis factor alpha. In Ann Arbor, diastolic blood pressure significantly similarly increased during all of the exposures (2.5 to 4.0 mm Hg), a response not mitigated by pretreatments. Flow-mediated dilatation remained unaltered. Particulate matter, not ozone, was responsible for increasing diastolic blood pressure during air pollution inhalation, most plausibly by instigating acute autonomic imbalance. Only particles from urban Toronto additionally impaired endothelial function, likely via slower proinflammatory pathways. Our findings demonstrate credible mechanisms whereby fine particulate matter could trigger acute cardiovascular events and that aspects of exposure location may be an important determinant of the health consequences.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".