Late Breaking Abstract - Do asthma patients prescribed a GLP1 have improved asthma and weight loss outcomes?
Bibliographic record
Abstract
Glucagon-Like Peptide-1’s (GLP1) are used for diabetes and weight management in the UK. There is evidence that high blood sugar levels in people with type 2 diabetes impair lung function with increases in average blood sugar levels from 4 mmol/L to 12 mmol/L, resulting in a 20% drop in lung function1. This analysis aimed to determine if there were improved asthma outcomes in patients prescribed GLP1. Data from the Optimum Patient Care Research Database for the cases and controls were extracted. Cases were those with treated asthma, a GLP1 and a BMI ≥30 (n=10,111). Control group (5:1) patients were matched on age, gender, prior exacerbations, and baseline SABA prescribing (50,555). Asthma outcomes were measured using two composite indexes, Risk Domain Asthma Control (RDAC) and Overall Asthma Control (OAC). RDAC “uncontrolled” had either an asthma-related hospital attendance, an acute OCS and/or an antibiotic with a respiratory review. OAC was controlled if they were classified as “controlled” by RDAC and used <3 SABA inhalers. Logistic regressions were undertaken to compare asthma outcomes. GLP1 patients were found to have better outcomes for both RDAC and OAC measures. Controlling for baseline measures the OR were 2.1 (1.9-2.4) and 2.1 (1.8-2.5) respectively. Weight was found to decrease for both groups, though greater in the GLP1 population. GLP1 patients had lower, non-significant, number of exacerbations 0.52 (0.50-0.54) vs 0.54 (0.53-0.55). Obese asthmatics are a group with higher morbidity, this study shows use of GLP1 provides substantially improvements in asthma control. Whether this is related to weight loss, diabetes control or an anti-inflammatory effect requires further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.026 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".