Association of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and neurogenesis: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Glucagon-like peptide-1 (GLP-1) and glucagon-like peptide-1 receptor agonist (GLP-1 RA) administration has been associated with neuroproliferative effects and modulatory effects in neuronal pathways. Herein, we conducted a comprehensive synthesis of the effects of GLP-1 and GLP-1 RAs on neurogenesis. METHODS: We examined studies that investigate changes in neurogenesis mediated by GLP-1 and GLP-1 RA administration in both human and animal populations. Relevant articles were retrieved through OVID (MedLine, Embase, AMED, PsychINFO, JBI EBP Database), PubMed, and Web of Science from database inception to July 2nd. Primary studies investigating the role of GLP-1 and GLP-1 RAs on neurogenesis were included for analysis. RESULTS: GLP-1 and GLP-1 RAs (i.e. exenatide, geniposide, liraglutide, lixisenatide, and semaglutide), increased neurogenesis within the dentate gyrus, hippocampus, olfactory bulb, and the medial striatum in animal models. Additionally, GLP-1 and GLP-1 RAs were associated with modulating changes in multiple apoptotic pathways and upregulating survival pathways. DISCUSSION: GLP-1 and GLP-1 RAs are positively associated with neurogenesis. This effect may have translational implications insofar as disparate mental disorders that are characterised by neurogenesis defects (e.g. depressive disorders and neurocognitive disorders) may be benefitted by these agents.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".