Impact of pulse pressure on cerebrovascular events leading to age-related cognitive decline
Bibliographic record
Abstract
Aging is a modern concept: human life expectancy has more than doubled in less than 150 yr in Western countries. Longer life span, however, reveals age-related diseases, including cerebrovascular diseases. The vascular system is a prime target of aging: the "wear and tear" of large elastic arteries exposed to a lifelong pulsatile pressure causes arterial stiffening by fragmentation of elastin fibers and replacement by stiffer collagen. This arterial stiffening increases in return the amplitude of the pulse pressure (PP), its wave penetrating deeper into the microcirculation of low-resistance, high-flow organs such as the brain. Several studies have associated peripheral arterial stiffness responsible for the sustained increase in PP, with brain microvascular diseases such as cerebral small vessel disease, cortical gray matter thinning, white matter atrophy, and cognitive dysfunction in older individuals and prematurely in hypertensive and diabetic patients. The rarefaction of white matter is also associated with middle cerebral artery pulsatility that is strongly dependent on PP and artery stiffness. PP and brain damage are likely associated, but the sequence of mechanistic events has not been established. Elevated PP promotes endothelial dysfunction that may slowly develop in parallel with the accumulation of proinflammatory senescent cells and oxidative stress, generating cerebrovascular damage and remodeling, as well as brain structural changes. Here, we review data suggesting that age-related increased peripheral artery stiffness may promote the penetration of a high PP to cerebral microvessels, likely causing functional, structural, metabolic, and hemodynamic alterations that could ultimately promote neuronal dysfunction and cognitive decline.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".