Beneficial Effects of High-Density Lipoproteins on Acquired von Willebrand Syndrome in Aortic Valve Stenosis
Bibliographic record
Abstract
Background Infusions of apolipoprotein A-I (apoA-I), the major protein component of high-density lipoproteins (HDL), result in aortic valve stenosis (AVS) regression in experimental models. Severe AVS can be complicated by acquired von Willebrand syndrome, a haemorrhagic disorder associated with loss of high-molecular-weight von Willebrand factor (vWF) multimers (HMWM), the latter being a consequence of increased shear stress and enhanced vWF-cleaving protease (ADAMTS-13) activity. Although antithrombotic actions of HDL have been described, its effects on ADAMTS-13 and vWF in AVS are unknown. Methods and Results We assessed ADAMTS-13 activity in plasma derived from a rabbit model of AVS (n = 29) as well as in plasma collected from 64 patients with severe AVS (age 65.0 ± 10.4 years, 44 males) undergoing aortic valve replacement (AVR). In both human and rabbit AVS plasma, ADAMTS-13 activity was higher than that in controls (p < 0.05). Accordingly, AVS patients had less HMWM than controls (66.3 ± 27.2% vs. 97.2 ± 24.1%, p < 0.0001). Both ADAMTS-13 activity and HMWM correlated significantly with aortic transvalvular gradients, thereby showing opposing correlations (r = 0.3, p = 0.018 and r = −0.4, p = 0.003, respectively). Administration of an apoA-I mimetic peptide reduced ADAMTS-13 activity in AVS rabbits as compared with the placebo group (2.0 ± 0.5 RFU/sec vs. 3.8 ± 0.4 RFU/sec, p < 0.05). Similarly, a negative correlation was found between ADAMTS-13 activity and HDL cholesterol levels in patients with AVS (r = −0.3, p = 0.045). Conclusion Our data indicate that HDL levels are associated with reduced ADAMTS-13 activity and increased HMWM. HDL-based therapies may reduce the haematologic abnormalities of the acquired von Willebrand syndrome in AVS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".