High Glucose Promotes Aβ Production by Inhibiting APP Degradation
Bibliographic record
Abstract
Abnormal deposition of neuriticplaques is the uniqueneuropathological hallmark of Alzheimer's disease (AD).Amyloid β protein (Aβ), the major component of plaques, is generated from sequential cleavage of amyloidβ precursor protein (APP) by β-secretase and γ-secretase complex. Patients with diabetes mellitus (DM), characterized by chronic hyperglycemia,have increased risk of AD development.However, the role of high blood glucose in APP processing and Aβ generation remains elusive. In this study, we investigated the effect of high glucose on APP metabolism and Aβ generation in cultured human cells. We found that high glucose treatment significantly increased APP protein level in both neuronal-like and non-neuronal cells, and promoted Aβ generation. Furthermore, we found that high glucose-induced increase of APP level was not due to enhancement of APP gene transcription but resulted from inhibition of APP protein degradation. Taken together, our data indicated that hyperglycemia could promote AD pathogenesis by inhibiting APP degradation and enhancing Aβ production. More importantly, the elevation of APP level and Aβ generation by high glucose was caused by reduction of APP turnover rate.Thus,our study provides a molecular mechanism of increased risk of developing AD in patients withDMand suggests thatglycemic control might be potentially beneficial for reducing the incidence of AD in diabetic patients and delaying the AD progression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".