Repurposing glucagon-like peptide-1 (GLP-1) receptor agonists for the treatment of depression: A systematic review of preclinical, observational and clinical investigations
Bibliographic record
Abstract
BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), currently used for metabolic conditions, have demonstrated potential antidepressant effects via neuromodulatory pathways. This systematic review aims to provide evidence on the antidepressant effects of GLP-1 RAs and elucidate their underlying mechanism of action. METHODS: We examined studies that investigated the effect of GLP-1 RAs on depressive symptoms. A comprehensive search was performed, and articles were retrieved from MEDLINE, PubMed, and PsychINFO. Both animal and human studies were included. RESULTS: 18 preclinical studies, 5 observational studies, and 3 clinical studies were included in our systematic review. Among the preclinical studies, 15 out of 18 (83 %) reported significant antidepressant-like effects, associated with enhanced neuroplasticity, reduced neuroinflammation, and neurotransmitter alterations. Observational studies indicated mixed results, with 4 out of 5 studies reporting reductions in depressive symptoms. However, only 1 of the 3 clinical trials showed statistically significant antidepressant effects. DISCUSSION: GLP-1 RAs show promise as treatment for depression through multiple neuromodulatory mechanisms. While there is strong preclinical evidence, observational results are mixed, and clinical findings are still preliminary. There is a need for short and long-term studies to establish whether GLP-1 RAs are capable of treating and/or preventing depressive symptoms and episodes in adults with major depressive disorder (MDD).
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".