The diabetes drug liraglutide reverses cognitive impairment in mice and attenuates insulin receptor and synaptic pathology in a non‐human primate model of Alzheimer's disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a devastating neurological disorder that still lacks an effective treatment, and this has stimulated an intense pursuit of disease-modifying therapeutics. Given the increasingly recognized link between AD and defective brain insulin signaling, we investigated the actions of liraglutide, a glucagon-like peptide-1 (GLP-1) analog marketed for treatment of type 2 diabetes, in experimental models of AD. Insulin receptor pathology is an important feature of AD brains that impairs the neuroprotective actions of central insulin signaling. Here, we show that liraglutide prevented the loss of brain insulin receptors and synapses, and reversed memory impairment induced by AD-linked amyloid-β oligomers (AβOs) in mice. Using hippocampal neuronal cultures, we determined that the mechanism of neuroprotection by liraglutide involves activation of the PKA signaling pathway. Infusion of AβOs into the lateral cerebral ventricle of non-human primates (NHPs) led to marked loss of insulin receptors and synapses in brain regions related to memory. Systemic treatment of NHPs with liraglutide provided partial protection, decreasing AD-related insulin receptor, synaptic, and tau pathology in specific brain regions. Synapse damage and elimination are amongst the earliest known pathological changes and the best correlates of memory impairment in AD. The results illuminate mechanisms of neuroprotection by liraglutide, and indicate that GLP-1 receptor activation may be harnessed to protect brain insulin receptors and synapses in AD. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".