Arterial Stiffness and Hypertension: A Review of Mechanism and Clinical Relevance
Bibliographic record
Abstract
Purpose of review: This review is intended to provide the background for a broad view of the influence of large-artery stiffness on the development of hypertension in aging, diabetes and end-stage renal disease. Recent findings: Arterial stiffness, particularly in aorta, is a major determinant of isolated systolic hypertension in the elderly. Studies have consistently shown that large-artery stiffness results in augmented amplitude of reflected pressure waves and their early return. This disturbed physiological phenomenon can alter the heart-vessel coupling and lead to increased cardiovascular risk. This review describes the structural, functional, environmental and genetic factors that influence arterial stiffness, wave reflection, and blood pressure. It also discusses non-invasive techniques to measure arterial stiffness and analyze arterial waveforms. The effects of various antihypertensive agents with respect to arterial stiffness and blood pressure reduction are examined. In addition, studies on non-pharmacologic interventions to modify large artery behavior are reviewed. Summary: Optimal clinical management of hypertension depends on better understanding of the contribution of vascular stiffness to hypertension. This information has significant implications for therapeutic decisions. Keywords: Arterial stiffness, wave reflections, aging, hypertension
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".