Concentration-dependent health effects of air pollution in controlled human exposures
Bibliographic record
Abstract
BACKGROUND: Air pollution is a leading contributor to premature mortality worldwide and is often represented by particulate matter (PM), a key contributor to its harmful health effects. Concentration-response relationships are useful for quantifying the effects of air pollution in relevant populations and in considering potential effect thresholds. Controlled human exposures can provide data on acute effects and concentration-response relationships that complement epidemiological studies. OBJECTIVES: We examined PM concentration-responses after controlled human air pollution exposures to examine exposure-response markers, assess effect modifiers, and identify potential effect thresholds. METHODS: We reviewed primary research from published controlled human exposure studies where responses were reported at multiple target PM concentrations or summarized per unit change in PM to identify concentration-dependent effects. RESULTS: Of the 191 publications identified through PubMed and supplementary searches, 31 were eligible. Eligible studies collectively represented four pollutant models: concentrated ambient particles, engineered carbon nanoparticles, diesel exhaust, and woodsmoke. We identified concentration-dependent effects on oxidative stress markers, inflammation, and cardiovascular function that overlapped across different pollutants. Metabolic syndrome and glutathione s-transferase mu 1 genotype were identified as potential effect modifiers. DISCUSSION: Improved understanding of concentration-response relationships is integral to biomonitoring and mitigation of health effects through impact assessment and policy. Although we identified potential concentration-response markers, thresholds, and modifiers, our conclusions on these relationships were limited by a dearth of eligible publications, considerable variability in methodology, and inconsistent reporting standards between studies. More research is required to validate these observations. We recommend that future studies harmonize estimate reporting to facilitate the identification of robust response markers across research and applied settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".