Asthma Exacerbations and Glucagon-Like Peptide-1 Receptor Agonists: a Review of the Current Evidence
Bibliographic record
Abstract
Asthma is a chronic inflammatory disease involving multiple mediators and cytokines. While our current treatments have shown significant therapeutic benefits, there still appear to be some patients who, despite aggressive therapy, good adherence, and inhaler technique, continue to have exacerbations. Exacerbations lead to loss of lung function, exposure to systemic corticosteroids, effects on quality of life, and even mortality. There is a large number of glucagon-like peptide-1 (GLP-1) receptors in the lung even compared with other organs, and studies have shown evidence of reduced exacerbations in asthmatics treated with GLP-1 receptor agonists (GLP-1 RA). While weight loss may affect lung mechanics, evidence of inflammatory changes has been revealed that could explain this relationship. This article will review the data behind these conjectures and outline potential clinical utility and the need for future studies to truly understand the role of GLP-1 receptors in the lung. Obesity is a common issue and a comorbidity that negatively impacts asthma outcomes. Weight loss can improve asthma outcomes, and evidence shows that a particular type of therapy currently indicated for diabetes that assists in weight loss and targets receptors that are abundant in the lungs will outperform other therapies. GLP-1-receptor agonists may particularly help overweight patients who have asthma to control the disease as best as possible and prevent exacerbations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.005 | 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".