Vascular Fibrosis in Aging and Hypertension: Molecular Mechanisms and Clinical Implications
Bibliographic record
Abstract
Aging is the primary risk factor underlying hypertension and incident cardiovascular disease. With aging, the vasculature undergoes structural and functional changes characterized by endothelial dysfunction, wall thickening, reduced distensibility, and arterial stiffening. Vascular stiffness results from fibrosis and extracellular matrix (ECM) remodelling, processes that are associated with aging and are amplified by hypertension. Some recently characterized molecular mechanisms underlying these processes include increased expression and activation of matrix metalloproteinases, activation of transforming growth factor-β1/SMAD signalling, upregulation of galectin-3, and activation of proinflammatory and profibrotic signalling pathways. These events can be induced by vasoactive agents, such as angiotensin II, endothelin-1, and aldosterone, which are increased in the vasculature during aging and hypertension. Complex interplay between the "aging process" and prohypertensive factors results in accelerated vascular remodelling and fibrosis and increased arterial stiffness, which is typically observed in hypertension. Because the vascular phenotype in a young hypertensive individual resembles that of an elderly otherwise healthy individual, the notion of "early" or "premature" vascular aging is now often used to describe hypertension-associated vascular disease. We review the vascular phenotype in aging and hypertension, focusing on arterial stiffness and vascular remodelling. We also highlight the clinical implications of these processes and discuss some novel molecular mechanisms of fibrosis and ECM reorganization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".