Capsaicin consumption reduces brain amyloid-beta generation and attenuates Alzheimer’s disease-type pathology and cognitive deficits in APP/PS1 mice
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common cause of age-related dementia and is currently incurable. The failures of current clinical trials and the establishment of modifiable risk factors have shifted the AD intervention from treatment to prevention in the at-risk population. Previous studies suggest that there is a geographic overlap between AD incidence and spicy food consumption. We previously reported that capsaicin-rich diet consumption was associated with better cognition and lower serum Amyloid-beta (Aβ) levels in people aged 40 years and over. In the present study, we found that intake of capsaicin, the pungent ingredient in chili peppers, reduced brain Aβ burden and rescued cognitive decline in APP/PS1 mice. Our in vivo and in vitro studies revealed that capsaicin shifted Amyloid precursor protein (APP) processing towards α-cleavage and precluded Aβ generation by promoting the maturation of a disintegrin and metalloproteinase 10 (ADAM10). We also found that capsaicin alleviated other AD-type pathologies, such as tau hyperphosphorylation, neuroinflammation and neurodegeneration. The present study suggests that capsaicin is a potential therapeutic candidate for AD and warrants clinical trials on chili peppers or capsaicin as dietary supplementation for the prevention and treatment of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".