A novel GLP‐1/GIP/Gcg triagonist reduces cognitive deficits and pathology in the 3xTg mouse model of Alzheimer's disease
Bibliographic record
Abstract
Abstract Type 2 diabetes mellitus (T2DM) is an important risk factor for Alzheimer's disease (AD). Glucagon‐like peptide‐1 (GLP‐1) and glucose‐dependent insulinotropic polypeptide (GIP) have been identified to be effective in T2DM treatment and neuroprotection. In this study, we further explored the effects of a novel unimolecular GLP‐1/GIP/Gcg triagonist on the cognitive behavior and cerebral pathology in the 7‐month‐old triple transgenic mouse model of AD (3xTg‐AD), and investigated its possible electrophysiological and molecular mechanisms. After chronic administration of the GLP‐1/GIP/Gcg triagonist (10 nmol/kg bodyweight, once daily, i.p.) for 30 days, open field, Y maze and Morris water maze tests were performed, followed by in vivo electrophysiological recording, immunofluorescence and Western blotting experiments. We found that the chronic treatment with the triagonist could improve long‐term spatial memory of 3xTg‐AD mice in Morris water maze, as well as the working memory in Y maze task. The triagonist also alleviated the suppression of long‐term potentiation (LTP) in the CA1 region of hippocampus. In addition, the triagonist significantly reduced hippocampal pathological damages, including amyloid‐β (Aβ) and phosphorylated tau aggregates, and upregulated the expression levels of S133 p‐CREB, T286 p‐CAMKII and S9 p‐GSK3β in the hippocampus of the 3xTg‐AD mice. These results demonstrate for the first time that the novel GLP‐1/GIP/Gcg triagonist is efficacious in ameliorating cognitive deficits and pathological damages of 3xTg‐AD mice, suggesting that the triagonist might be potentially beneficial in the treatment of AD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".