State of the Science on Brain Insulin Resistance and Cognitive Decline Due to Alzheimer’s Disease
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) is common and increasing in prevalence worldwide, with devastating public health consequences. While peripheral insulin resistance is a key feature of most forms of T2DM and has been investigated for over a century, research on brain insulin resistance (BIR) has more recently been developed, including in the context of T2DM and non-diabetes states. Recent data support the presence of BIR in the aging brain, even in non-diabetes states, and found that BIR may be a feature in Alzheimer's disease (AD) and contributes to cognitive impairment. Further, therapies used to treat T2DM are now being investigated in the context of AD treatment and prevention, including insulin. In this review, we offer a definition of BIR, and present evidence for BIR in AD; we discuss the expression, function, and activation of the insulin receptor (INSR) in the brain; how BIR could develop; tools to study BIR; how BIR correlates with current AD hallmarks; and regional/cellular involvement of BIR. We close with a discussion on resilience to both BIR and AD, how current tools can be improved to better understand BIR, and future avenues for research. Overall, this review and position paper highlights BIR as a plausible therapeutic target for the prevention of cognitive decline and dementia due to 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 0.002 |
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".