Association of air pollution with risk and severity of obstructive sleep apnea: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Obstructive Sleep Apnea (OSA) is a significant health concern characterized by recurrent upper airway blockages during sleep, causing various health issues. There's growing evidence of a link between air pollution and OSA, though research results have been inconsistent. This systematic review and meta-analysis aims to consolidate and examine data on the relationship between air pollution and OSA's risk and severity. METHODS: A literature search across PubMed, EMBASE, and Web of Science was conducted until January 10, 2024. The selection criteria targeted studies involving OSA participants or those at risk, with quantitative air pollution assessments. The Nested Knowledge software facilitated screening and data extraction, while the Newcastle-Ottawa Scale was used for quality assessment. Meta-analyses, utilizing random-effects models, computed pooled odds ratios (ORs) for the OSA risk associated with PM2.5 and NO2 exposure, analyzed using R software version 4.3. RESULTS: The systematic review included twelve studies, four of which were analyzed in the meta-analysis. The meta-analysis revealed diverse results on the association of PM2.5 and NO2 with OSA risk. PM2.5 exposure showed a pooled OR of 0.987 (95 % CI: 0.836-1.138), indicating no substantial overall impact on OSA risk. Conversely, NO2 exposure was linked to a pooled OR of 1.095 (95 % CI: 0.920-1.270), a non-significant increase in risk. Many studies found a relationship between air pollution exposure and elevated Apnea-Hypopnea Index (AHI) levels, indicating a relationship between air pollution and OSA severity. CONCLUSION: The findings suggest air pollutants, especially NO2, might play a role in worsening OSA risk and severity, but the evidence isn't definitive. This highlights the variability of different pollutants' effects and the necessity for more research. Understanding these links is vital for shaping public health policies and clinical approaches to address OSA amidst high air pollution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".