Insulin resistance in Alzheimer’s disease –
Bibliographic record
Abstract
The search for risk factors that increase the risk of Alzheimer’s disease (AD) has converged on a cluster of disorders characterized by vascular, lipid, and metabolic abnormalities, such as cardiovascular disease, hypertension, and type 2 diabetes mellitus (T2DM). A common pathophysiology uniting these diseases is derangement of insulin metabolism, characterized by the inability of insulin to efficiently promote glucose uptake into muscle (insulin resistance), with concomitant peripheral insulin elevations (hyperinsulinemia). Although much attention has been paid to the metabolic consequences of insulin resistance, peripheral hyperinsulinemia has additional deleterious effects on systems that do not habituate to increased insulin. For example, as will be discussed, peripheral hyperinsulinemia has effects on inflammation and brain insulin levels that are of special relevance to the pathogenesis of AD. There are probably several pathways leading to the final common expression of AD pathology.1 Insulin resistance and peripheral hyperinsulinemia comprise one potential pathway, and as such do not apply to all AD patients. It is, however, a pathway with relevance to a rapidly growing segment of our population. Peripheral hyperinsulinemia and insulin resistance are mutually reinforcing (each can cause or exacerbate the other) and may result from a number of causes, including genetic vulnerability and/or environmental factors such as diet and inactivity. They are also increasingly common conditions, in part due to the complexity of insulin signaling pathways, and in part due to pervasive changes in diet and physical activity occurring at an unprecedented rate in Western societies. In this chapter, we discuss mechanisms through which insulin resistance and peripheral hyperinsulinemia may induce AD pathogenesis, and the manner in which greater understanding of these mechanisms may lead to the development of novel therapeutic strategies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".