Filling the void: a role for exercise-induced BDNF and brain amyloid precursor protein processing
Bibliographic record
Abstract
Inactivity, obesity, and insulin resistance are significant risk factors for the development of Alzheimer's disease (AD). Several studies have demonstrated that diet-induced obesity, inactivity, and insulin resistance exacerbate the neuropathological hallmarks of AD. The aggregation of β-amyloid peptides is one of these hallmarks. β-Site amyloid precursor protein-cleaving enzyme 1 (BACE1) is the rate-limiting enzyme in amyloid precursor protein (APP) processing, leading to β-amyloid peptide formation. Understanding how BACE1 content and activity are regulated is essential for establishing therapies aimed at reducing and/or slowing the progression of AD. Exercise training has been proven to reduce the risk of AD as well as decrease β-amyloid production and BACE1 content and/or activity. However, these long-term interventions also result in improvements in adiposity, circulating metabolites, glucose tolerance, and insulin sensitivity making it difficult to determine the direct effects of exercise on brain APP processing. This review highlights this large void in our knowledge and discusses our current understanding of the direct of effect of exercise on β-amyloid production. We have concentrated on the central role that brain-derived neurotrophic factor (BDNF) may play in mediating the direct effects of exercise on reducing brain BACE1 content and activity as well as β-amyloid production. Future studies should aim to generate a greater understanding of how obesity and exercise can directly alter APP processing and AD-related pathologies. This knowledge could provide evidence-based hypotheses for designing therapies to reduce the risk of AD and dementia.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".