The expanding role of GLP-1 receptor agonists: a narrative review of current evidence and future directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it