Amyloid‐β and islet amyloid pathologies link Alzheimer’s disease and type 2 diabetes in a transgenic model
Bibliographic record
Abstract
Alzheimer's disease (AD) and type 2 diabetes (T2D) present a significant risk to each other. AD and T2D are characterized by deposition of cerebral amyloid-β (Aβ) and pancreatic human islet amyloid polypeptide (hIAPP), respectively. We investigated the role of amyloidogenic proteins in the interplay between these diseases. A novel double transgenic mouse model combining T2D and AD was generated and characterized. AD-related amyloid transgenic mice coexpressing hIAPP displayed peripheral insulin resistance, hyperglycemia, and glucose intolerance. Aβ and IAPP amyloid co-deposition increased tau phosphorylation, and a reduction in pancreatic β-cell mass was detected in islets. Increased brain Aβ deposition and tau phosphorylation and reduced insulin levels and signaling were accompanied by extensive synaptic loss and decreased neuronal counts. Aβ immunization rescued the peripheral insulin resistance and hyperglycemia, suggesting a role for Aβ in T2D pathogenesis for individuals predisposed to AD. These findings demonstrate that Aβ and IAPP are key factors in the overlapping pathologies of AD and T2D.-Wijesekara, N., Ahrens, R., Sabale, M., Wu, L., Ha, K., Verdile, G., Fraser, P. E. Amyloid-β and islet amyloid pathologies link Alzheimer's disease and type 2 diabetes in a transgenic model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".