0774 Glucagon-Like Peptide 1 Receptor Agonists for Obstructive Sleep Apnea: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Introduction Glucagon-like peptide-1 (GLP-1) receptor agonists have gained recent popularity in its usage for weight loss in people with diabetes and obesity. There is also emerging evidence of its usage for treating obstructive sleep apnea (OSA) which can be caused by or worsened by obesity. However, there is limited synthesized evidence for the usage of GLP-a receptor agonists for people with OSA. Methods We performed a PRISMA compliant systematic review and meta-analysis of the effects of GLP-1 receptor agonists in people with OSA. MEDLINE, EMBASE, Cochrane Library, and PsycINFO were searched up to June 30th, 2024. Randomized controlled trials (RCT) investigating the usage of any GLP-1 receptor agonists in people with OSA that reported OSA symptom outcomes as measured by the apnea-hypopnea index (AHI) were included. Standardized mean difference (SMD) measured effect size. Q and I2 tests measured heterogeneity. Cochrane Risk of Bias Tool 2 assessed risk of bias. Results Six studies were included which consisted of 1023 participants with moderate to severe OSA. 516 participants received GLP-1 receptor agonist treatment for their OSA with 507 in the control group (receiving either placebo or continuous positive airway pressure (CPAP)). GLP-1 receptor agonist therapy for OSA when compared to placebo groups had an medium effect size of SMD = - 0.601 (95% CI: - 0.969 to - 0.233, p = 0.001). When compared to CPAP treatment, there was no significant difference in AHI differences post-treatment (p = 0.297). Conclusion This systematic review and meta-analysis synthesizes the current evidence which supports the potential usage of GLP-1 receptor agonists for the treatment of OSA. This pharmaceutical treatment option would increase the treatment options available for people with OSA. However, the literature is limited by the few RCTs available, small sample sizes, and lack of long-term follow-up data. Further studies are needed in this area. Support (if any)
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".