Effects of glucagon-like peptide-1 receptor agonists on psychiatric disorders: a systematic review
Bibliographic record
Abstract
Extant literature pertaining to the administration of glucagon-like peptide-1 receptor agonists (GLP-1RAs) for Alzheimer's disease, Parkinson's disease, major depressive disorder, bipolar disorder, substance-, alcohol- and nicotine-use disorders, suggests promising efficacy beyond the current FDA-approved indications (e.g., type 2 diabetes mellitus, obesity). The implicated brain regions of the aforementioned mental disorders contain glucagon-like peptide 1 (GLP-1) receptors associated with improving cognitive and behavioral functioning. Therefore, we aimed to systematically review the treatment effects of GLP-1RAs in various neurocognitive and psychiatric disorders. Online databases including PubMed, OVID, MEDLINE, Embase, PsycINFO and Google Scholar, were searched from inception until October 1, 2024. Additional studies were identified from the reference lists of the included articles. 22 studies were identified, with a total of 186,847 participants included. Results reported that GLP-1RAs meaningfully improved cognitive and affective functioning (e.g., memory), which in some cases was sustained beyond exposure to the agent. Separately, multiple epidemiological studies reported that GLP-1RAs have protective effects, with a suggestion of decrease in the incidence of mental disorders. These results provides the impetus for large, long-term, randomized controlled trials for GLP-1 RAs for the treatment of various mental disorders. This review is not registered in PROSPERO or any other registry.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".