Arterial stiffness, cognitive impairment and dementia: confounding factor or real risk?
Bibliographic record
Abstract
Large artery stiffness is a frequent condition that arises with ageing, and is accelerated by the presence of co-morbidities like hypertension, obesity and diabetes. Although epidemiological studies have indicated an association between arterial stiffness, cognitive impairment and dementia, the precise effects of stiff arteries on the brain remains obscure. This is because, in humans, arterial stiffness is often accompanied by other factors such as age, high blood pressure, atherosclerosis and inflammation, which could themselves damage the brain independently of stiffness. Therefore, the question remains: is arterial stiffness a true risk for cognitive decline? Or, is it a confounding factor? In this review, we provide an overview of arterial stiffness and its impact on brain function based on human and animal studies. We summarize the evidence linking arterial stiffness to cognitive dysfunction and dementia, and discuss the role of new animal models to better understand the mechanisms by which arterial stiffness affects the brain. We close with an overview of treatments to correct stiffness and discuss the challenges to translate them to real patient care. This article is part of the Special Issue "Vascular 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".