The expanding role of GLP-1 receptor agonists: a narrative review of current evidence and future directions
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have transformed obesity management, offering substantial weight loss and metabolic benefits. This review examines their expanding role, evaluating efficacy compared to alternative treatments, emerging indications, ongoing challenges, and future directions. Beyond obesity and type 2 diabetes, the therapeutic potential of GLP-1 RAs extends to a range of conditions such as cardiovascular disease, liver disease, neurodegenerative disease, and substance abuse disorders. While early concerns regarding pancreatic and thyroid cancer have been largely attenuated by recent evidence, issues such as gallbladder and biliary disorders, psychiatric safety, and perioperative aspiration risk require ongoing investigation. Additionally, observations of weight regain after treatment discontinuation and reductions in lean mass highlight the need for long-term, individualized strategies to sustain clinical benefits. The high cost and limited access to these medications raise critical policy and equity challenges. Future research must address these gaps, focusing on long-term safety, optimizing combination approaches, and evaluating the broader clinical and economic implications of widespread GLP-1 RA use. Funding: K.B.F. is supported by a William Dawson Scholar award from McGill University. T.M.P. is a Fond de Recherche du Québec-Santé (FRQS) research scholar. M.J.E. holds a James McGill Professor award from McGill University. The funding sources had no involvement in the conduct of this study, interpretation of results, or the preparation of this manuscript for publication.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".