The Role of Insulin, Insulin Growth Factor, and Insulin‐Degrading Enzyme in Brain Aging and Alzheimer′s Disease
Bibliographic record
Abstract
Most brain insulin comes from the pancreas and is taken up by the brain by what appears to be a receptor-based carrier. Type 2 diabetes animal models associated with insulin resistance show reduced insulin brain uptake and content. Recent data point to changes in the insulin receptor cascade in obesity-related insulin resistance, suggesting that brain insulin receptors also become less sensitive to insulin, which could reduce synaptic plasticity. Insulin transport to the brain is reduced in aging and in some animal models of type 2 diabetes; brain insulin resistance may be present as well. Studies examining the effect of the hyperinsulinic clamp or intranasal insulin on cognitive function have found a small but consistent improvement in memory and changes in brain neuroelectric parameters in evoked brain potentials consistent with improved attention or memory processing. These effects appear to be due to raised brain insulin levels. Peripheral levels of Insulin Growth Factor-1 (IGF-I) are associated with glucose regulation and influence glucose disposal. There is some indication that reduced sensitivity to insulin or IGF-I in the brain, as observed in aging, obesity, and diabetes, decreases the clearance of Abeta amyloid. Such a decrease involves the insulin receptor cascade and can also increase amyloid toxicity. Insulin and IGF-I may modulate brain levels of insulin degrading enzyme, which would also lead to an accumulation of Abeta amyloid.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".