Role of Glucagon-Like Peptide-1 on Amyloid, Tau, and α-Synuclein: Target Engagement and Rationale for the Development in Neurodegenerative Disorders
Bibliographic record
Abstract
INTRODUCTION: Glucagon-like Peptide-1 (GLP-1) and Glucagon-Like Peptide-1 receptor agonist (GLP-1 RA) administration has been associated with neuroprotective effects in neurodegenerative disorders. We conducted a comprehensive synthesis of known effects of GLP-1 and GLP-1 RAs on the cognitive, cellular, and molecular changes in neurodegenerative diseases. METHODS: We examined preclinical and clinical paradigms that investigated changes in neurodegenerative disease pathology following administration of GLP-1 and GLP-1 RAs. Relevant articles were retrieved through OVID (MedLine, Embase, AMED, PsychINFO, JBI EBP Database), PubMed, and Web of Science from database inception to September 27th, 2024. Primary studies investigating the aforementioned changes following GLP-1 and GLP-1 RA administration were retrieved for analysis (n = 62). RESULTS: GLP-1 and GLP-1 RAs (i.e. dulaglutide, exenatide, liraglutide, lixisenatide, semaglutide, and tirzepatide) improved cognitive and motor function in neurodegenerative diseases in preclinical and clinical paradigms. Additionally, GLP-1 and GLP-1 RAs were associated with modulating changes in neuroinflammation, oxidative stress, and proliferative pathways. DISCUSSION: We observed that GLP-1 and GLP-1 RAs modulate molecular and cellular changes known to govern the phenomenology of neurodegenerative diseases. Future research should examine the interaction between signaling molecules, neuronal subpopulations, and cognitive effects affected by GLP-1 and GLP-1 RA administration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".