0434 THE ASSOCIATION OF TRAFFIC-RELATED AIR POLLUTION WITH SLEEP APNEA AND INFLAMMATORY BIOMARKERS
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is associated with inflammatory biomarkers which may predispose to premature cardiovascular disease. Air pollution is also associated with systemic inflammation, and may therefore also be associated with worsening OSA. Our objective was to assess whether traffic-related pollution (TRAP) is associated with OSA severity or systemic inflammation. 1858 consenting patients who had a polysomnography (PSG) for suspected OSA were recruited between 2007 and 2013 into a research database. Information from a detailed questionnaire, BMI, and PSG were included. In a subset (n=494), serum was collected the morning after PSG, and levels of inflammatory biomarkers (e-selectin, intracellular adhesion molecule, vascular cell adhesion molecule, interleukin 6, interleukin 8) were measured using Luminex. For each patient, residential 6-digit postal code (corresponding to ~ 1 block face) was used to estimate each subject’s TRAP exposure (nitrogen oxides, black carbon and fine particulate matter) using land-use regression, with mean nitrogen dioxide concentration of 16.2 ± 5.6 ppb (Vancouver, BC). SAS 9.4 used for analysis. 1339 participants (69.6% male, mean and SD age: 57.6 ± 12.2 years, AHI: 22.5 ± 22.1/hr) had a postal code within the air pollution model domain. 255 patients had no OSA (AHI <5/hr); 390 had mild OSA (AHI 5–15/hr); 336 had moderate OSA (AHI 15–30/hr); and 358 had severe OSA (AHI >30/hr). Pollution measures were not significantly correlated with AHI (Pearson correlation coefficients -0.005 to -0.061, p>0.1 for all variables) or with OSA severity using categorical variables by ANOVA; the lack of association persisted after controlling for age and gender. None of the inflammatory biomarkers were associated with pollution levels. In our cohort, we did not find an association between air pollution exposure and either OSA severity or inflammatory biomarkers. This work is funded through grants from the Canadian Institutes of Health Research and the Canadian Sleep and Circadian Network.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".