Cerebrovascular insulin receptors are defective in Alzheimer’s disease
Bibliographic record
Abstract
Central response to insulin is suspected to be defective in Alzheimer's disease. As most insulin is secreted in the bloodstream by the pancreas, its capacity to regulate brain functions must, at least partly, be mediated through the cerebral vasculature. However, how insulin interacts with the blood-brain barrier and whether alterations of this interaction could contribute to Alzheimer's disease pathophysiology both remain poorly defined. Here, we show that human and murine cerebral insulin receptors (INSRs), particularly the long isoform INSRα-B, are concentrated in microvessels rather than in the parenchyma. Vascular concentrations of INSRα-B were lower in the parietal cortex of subjects diagnosed with Alzheimer's disease, positively correlating with cognitive scores, leading to a shift towards a higher INSRα-A/B ratio, consistent with cerebrovascular insulin resistance in the Alzheimer's disease brain. Vascular INSRα was inversely correlated with amyloid-β plaques and β-site APP cleaving enzyme 1, but positively correlated with insulin-degrading enzyme, neprilysin and P-glycoprotein. Using brain cerebral intracarotid perfusion, we found that the transport rate of insulin across the blood-brain barrier remained very low (<0.03 µl/g·s) and was not inhibited by an insulin receptor antagonist. However, intracarotid perfusion of insulin induced the phosphorylation of INSRβ that was restricted to microvessels. Such an activation of vascular insulin receptor was blunted in 3xTg-AD mice, suggesting that Alzheimer's disease neuropathology induces insulin resistance at the level of the blood-brain barrier. Overall, the present data in post-mortem Alzheimer's disease brains and an animal model of Alzheimer's disease indicate that defects in the insulin receptor localized at the blood-brain barrier strongly contribute to brain insulin resistance in Alzheimer's disease, in association with β-amyloid pathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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".