Asthma and Obstructive Sleep Apnea (OSA): Examining the Mechanism of Worsened Asthma Control in Obese Patients with OSA
Bibliographic record
Abstract
Obesity is known to worsen asthma control. One potential mechanism by which it worsens asthma is through obstructive sleep apnea (OSA). Its prevalence is up to 2 to 3 times higher in patients with asthma and as many as 95% of patients with severe steroid dependent asthma have OSA. The inflammatory pathway generated by derangements in the metabolic pathways of L-arginine and nitric oxide (NO) may explain the negative underlying effects of OSA on asthma control. We modeled the effect of biomarkers of L-arginine metabolism and OSA on each outcome (Emergency room (ER)/steroid use, asthma control questionnaire (ACQ), and asthma quality of life questionnaire (AQLQ)) using generalized linear models, where biomarkers and their ratios were log-transformed and where confounders included metabolic syndrome criteria (MSX), asthma age, smoking status, sex, body mass index (BMI), and race. For OSA, unadjusted and adjusted regression models were fit to assess whether the relationship in question was mediated by differences in biomarker ratios or demographics. Our sample consists of 295 asthmatics who were primarily female (81.3%), with a median age of 49 (IQR: 22, range: 21-76) years. OSA was reported in 87 participants (29.5%). In patients with OSA, we observed a significantly higher mean age (mean difference = 9.56 years; p<0.001) as well as a significantly higher mean BMI (mean difference = 7.08 units; p<0.001). Further, we found that OSA prevalence was slightly higher among males (40% vs 27% in females, p = 0.071), and not discernably different among different race categories (p = 0.335). Patients in the OSA group were more likely to have utilized steroids or the ER in the last 12 months (OR=1.874, p=0.017), however this effect is somewhat mediated by differences in demographics between OSA/non-OSA participants. However, even after adjusting for differences due to demographics, patients with OSA had a significantly higher ACQ score (p=0.01) and had significantly lower AQLQ scores (p=0.001). OSA worsens asthma control. The prevalence of OSA is directly proportional to age. The increase in healthcare utilization in the OSA may be attributable differences in demographics. The observed discrepancy in asthma control for those with OSA was not mediated by markers of arginine metabolism nor by differences in other key demographic characteristics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".