Intranasal Insulin as a Treatment for Alzheimer’s Disease: A Review of Basic Research and Clinical Evidence
Bibliographic record
Abstract
Research in animals and humans has associated Alzheimer's disease (AD) with decreased cerebrospinal fluid levels of insulin in combination with decreased insulin sensitivity (insulin resistance) in the brain. This phenomenon is accompanied by attenuated receptor expression of insulin and insulin-like growth factor, enhanced serine phosphorylation of insulin receptor substrate-1, and impaired transport of insulin across the blood-brain barrier. Moreover, clinical trials have demonstrated that intranasal insulin improves both memory performance and metabolic integrity of the brain in patients suffering from AD or its prodrome, mild cognitive impairment. These results, in conjunction with the finding that insulin mitigates hippocampal synapse vulnerability to beta amyloid, a peptide thought to be causative in the development of AD, provide a strong rationale for hypothesizing that pharmacological strategies bolstering brain insulin signaling, such as intranasal administration of insulin, could have significant potential in the treatment and prevention of AD. With this view in mind, the review at hand will present molecular mechanisms potentially underlying the memory-enhancing and neuroprotective effects of intranasal insulin. Then, we will discuss the results of intranasal insulin studies that have demonstrated that enhancing brain insulin signaling improves memory and learning processes in both cognitively healthy and impaired humans. Finally, we will provide an overview of neuroimaging studies indicating that disturbances in insulin metabolism--such as insulin resistance in obesity, type 2 diabetes and AD--and altered brain responses to insulin are linked to decreased cerebral volume and especially to hippocampal atrophy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".