Glucagon-like peptide-1 (GLP-1) receptor agonists in inflammatory bowel disease: mechanisms, clinical implications, and therapeutic potential
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor agonists are increasingly recognized for their potential dual benefit in inflammatory bowel disease (IBD), offering metabolic advantages alongside emerging anti-inflammatory, immunomodulatory, and gut barrier-enhancing effects. Pre-clinical data demonstrate attenuation of inflammation, preservation of epithelial integrity, and modulation of the microbiome in colitis models. Early retrospective studies in patients with IBD suggest improved clinical outcomes, such as reduced hospitalization and surgery rates, particularly in those with obesity. Glucagon-like peptide-1 receptor agonists are already widely used for obesity and diabetes, including increasing self-administration by patients outside medical supervision. Their impact on drug absorption, safety in gastrointestinal disease, and interactions with existing IBD therapies require further exploration. This review synthesizes the mechanistic rationale, pre-clinical evidence, and clinical data to date, highlighting the potential utility and safety considerations of glucagon-like peptide-1 receptor agonists in IBD and emphasizes the need for robust prospective trials to ascertain their safety and efficacy in this patient population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".