How do heart disease and stroke become risk factors for Alzheimer's disease?
Bibliographic record
Abstract
BACKGROUND: Heart disease and stroke are two of the major leading causes of death and disability in the world. Mainly affecting the elderly population, heart disease and stroke are important risk factors for Alzheimer's disease (AD). METHODS: This review examines the evidence linking chronic brain hypoperfusion (CBH) produced by several types of heart disease and stroke on the development of AD. RESULTS: The evidence indicates a strong association between such risk factors as coronary artery bypass surgery (CABG), atrial fibrillation, aortic/mitral valve damage, hypertension, hypotension, congestive heart failure, cerebrovascular-carotid atherosclerosis, and transient ischemic attacks in producing CBH. In people whose cerebral perfusion is already diminished by their advanced age, further cerebral blood flow reductions from heart-brain vascular-related risk factors, seemingly increases the probability of AD. The evidence also suggests that a neuronal energy crisis brought on by a relentless CBH is responsible for protein synthesis defects that later result in the classic AD neurodegenerative lesions such as the formation of excess beta-amyloid plaques and neurofibrillary tangles. CONCLUSIONS: Knowledge of how heart disease and stroke can progress to AD should provide a better understanding of the physiopathology characteristic of AD and also target more precise therapy in preventing, controlling or reversing this 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".