<scp>L</scp>p‐<scp>PLA</scp>2 is associated with structural valve degeneration of bioprostheses
Bibliographic record
Abstract
OBJECTIVES: In this study, we sought to determine the metabolic markers associated with structural valve degeneration (SVD). BACKGROUND: Structural valve degeneration (SVD) is the major cause of bioprosthetic valve failure leading to bioprostheses (BPs) stenosis or regurgitation. We hypothesized that lipoprotein-associated phospholipase A2 (Lp-PLA2) is involved in the SVD of BPs. METHODS: We included 197 patients who underwent aortic valve replacement with a bioprosthetic valve and had echocardiographic follow-up to evaluate valve function. Moreover, explanted BPs (n = 39) were analysed by immunohistochemistry for the expression of Lp-PLA2. RESULTS: After a mean follow-up of 7·9 ±0·2 years, forty-one patients (21%) were identified as developing SVD. Patients with SVD had significantly higher plasma level of Lp-PLA2 mass (151·8 ± 9·2 ng/mL vs. 133·2 ± 3·4 ng/mL, P = 0·03) and activity (27·6 ± 0·9 nmol/min/mL vs. 25·0 ± 0·4 nmol/min/mL, P = 0·005). Multivariate analysis revealed that Lp-PLA2 activity (OR: 1·09, 95% CI: 1·01-1·18; P = 0·03) was the strongest independent predictor of SVD. Immunohistochemistry studies of explanted BP showed that 77% of explanted BPs had the expression of Lp-PLA2, which correlated with the density of macrophages (CD68), and ox-LDL levels in bioprosthetic tissues. CONCLUSIONS: Increased blood plasma activity of Lp-PLA2 is associated with higher prevalence of SVD. These findings open new avenues for the identification of patients at risk for SVD and for the development of pharmacotherapy aiming at the prevention of SVD.
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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 | 0.001 | 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) | 0.002 | 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".