Angiotensin II Receptor Blockers Are Associated With Reduced Valvular Fibrosis in Women With Aortic Stenosis
Bibliographic record
Abstract
BACKGROUND: Angiotensin receptor blockers (ARBs) may slow down the progression of aortic stenosis (AS) through their antifibrotic effect. Women present more valvular fibrosis than men, so ARBs may have more effect in females. Our aim was to assess the impact of ARBs on the remodelling of the aortic valve in men and women. METHODS: We included patients who had an aortic valve replacement with or without coronary bypass grafting from 2006 to 2013. Patients with missing echocardiographic or histologic data were excluded. Warren-Yong and fibrosis scores of the explanted valves were performed. Patients were divided into 4 phenotypes according to their Warren-Yong and fibrosis scores: mild calcification/fibrosis, severe calcification/fibrosis group, predominant fibrosis group, predominant calcification group. RESULTS: Among the 1321 included patients, the vast majority (89%) has severe AS. Patients in the predominant fibrosis group, compared with the predominant calcium group, were more often female (39% vs 31%; P = 0.008) with bicuspid valves (44% vs 34%; P = 0.002), and less often used ARBs (25% vs 30%; P = 0.046). Female sex was independently associated with being in the predominant fibrosis group (odds ratio 1.45, 95% confidence interval 1.08-1.95; P = 0.01), with a significant interaction between female sex and ARBs. Women taking ARBs compared with women not taking ARBs had significantly lower fibrosis scores (P < 0.001). This difference was not seen in men. CONCLUSIONS: In this large series of patients with moderate-severe AS, among the women there was a negative association between intake of ARBs and valvular fibrosis. Thus, the possible effects of ARBs may be sex specific, with a larger therapeutic role in women.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".