An anti-diabetes agent protects the mouse brain from defective insulin signaling caused by Alzheimer’s disease–associated Aβ oligomers
Bibliographic record
Abstract
Defective brain insulin signaling has been suggested to contribute to the cognitive deficits in patients with Alzheimer's disease (AD). Although a connection between AD and diabetes has been suggested, a major unknown is the mechanism(s) by which insulin resistance in the brain arises in individuals with AD. Here, we show that serine phosphorylation of IRS-1 (IRS-1pSer) is common to both diseases. Brain tissue from humans with AD had elevated levels of IRS-1pSer and activated JNK, analogous to what occurs in peripheral tissue in patients with diabetes. We found that amyloid-β peptide (Aβ) oligomers, synaptotoxins that accumulate in the brains of AD patients, activated the JNK/TNF-α pathway, induced IRS-1 phosphorylation at multiple serine residues, and inhibited physiological IRS-1pTyr in mature cultured hippocampal neurons. Impaired IRS-1 signaling was also present in the hippocampi of Tg mice with a brain condition that models AD. Importantly, intracerebroventricular injection of Aβ oligomers triggered hippocampal IRS-1pSer and JNK activation in cynomolgus monkeys. The oligomer-induced neuronal pathologies observed in vitro, including impaired axonal transport, were prevented by exposure to exendin-4 (exenatide), an anti-diabetes agent. In Tg mice, exendin-4 decreased levels of hippocampal IRS-1pSer and activated JNK and improved behavioral measures of cognition. By establishing molecular links between the dysregulated insulin signaling in AD and diabetes, our results open avenues for the investigation of new therapeutics in 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".