The Link Between Diabetes, Glucose Control, and Alzheimer's Disease and Neurodegenerative Diseases
Bibliographic record
Abstract
Largely as a result of increases in life expectancy in most countries, as well as changes in lifestyle over the last few decades, chronic diseases such as Type 2 diabetes (T2D) and dementia are increasing in prevalence. A wealth of evidence indicates a strong link between T2D and the development of neurodegenerative diseases such as Alzheimer's disease (AD). Although the precise mechanisms remain unclear, there is now strong evidence T2D and AD have many aspects in common, and that T2D can exacerbate neurodegenerative processes. Brain atrophy, reduced cerebral glucose metabolism, and central nervous system (CNS) insulin resistance are features of both AD and T2D. The T2D phenotype (glucose dyshomeostasis, insulin resistance, impaired insulin signalling) also promotes AD pathology, namely the accumulation of Aβ and hyperphosphorylated tau, and can induce other aspects of neuronal degeneration including inflammatory and oxidative processes. This chapter discusses the evidence and potential underlying mechanisms that link these two chronic diseases of ageing. The potential of common treatments for the management of diabetes as therapies for AD are also discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".