Effect of traffic-related air pollution on cough in adults with polymorphisms in several cough-related genes
Bibliographic record
Abstract
Abstract With prevalent global air pollution, individuals with certain genetic predispositions and sensitivities are at of higher risk of developing respiratory symptoms including chronic cough. Studies to date have relied on patient-filled questionnaires in epidemiological studies to evaluate the gene-by-environment interactions. In a controlled human exposure study, we evaluated whether genetic risk score (GRS) based on cough-related single-nucleotide polymorphisms (SNPs) are associated with a cough count over 24 h post-exposure to diesel exhaust (DE), a model for traffic-related air pollution. DE is a mixture of several known air pollutants including PM 2.5 , CO, NO, NO 2 , and volatile organic compounds. Under closely observed circumstances, we determined that GRS constructed from 7 SNPs related to TRPA1, TRPV1, and NK-2R were correlated with cough count. Selection of channels were based on prior knowledge that SNPs in these channels lead to acute airway inflammation as a result of their increased sensitivity to particulate matter. We performed a linear regression analysis and found a significant, positive correlation between GRS and cough count following DE exposure (p = 0.002, R 2 = 0.61) and filtered air (FA) exposure (p = 0.028, R 2 = 0.37). Although that correlation was stronger for DE than for FA, we found no significant exposure-by-GRS interaction. In summary, cough-relevant GRS was associated with a higher 24 h cough count in a controlled setting, suggesting that individuals with a high GRS may be more susceptible to developing cough regardless of their exposure. The trend towards this susceptibility being more prominent in the context of traffic-related air pollution remains to be confirmed. Trial registration: ClinicalTrial.gov NCT02236039; NCT0223603. Registered on August 11, 2014, https://clinicaltrials.gov/ct2/show/NCT02236039 .
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".