Elevated Proportion of Small, Dense Low-Density Lipoprotein Particles and Lower Adiponectin Blood Levels Predict Early Structural Valve Degeneration of Bioprostheses
Bibliographic record
Abstract
OBJECTIVES: Long-term durability of bioprosthetic heart valves (BPs) are limited by structural valve degeneration (SVD) leading to stenosis and/or regurgitation. In this study, we sought to determine the metabolic markers associated with SVD. METHODS: In a cohort of 220 patients with an aortic BP (mean follow-up of 2.5 ± 1.2 years), we compared the metabolic and blood lipid profile including the levels of adiponectin and the proportion of small, dense low-density lipoprotein (LDL) particles (%LDL(<)(255Å)) in individuals developing echocardiographic evidence of early BP hemodynamic dysfunction with subjects having no features of BP dysfunction. RESULTS: Patients developing BP dysfunction (n = 69; 31.3%) had a tendency of higher triglyceride levels. Moreover, patients with BP dysfunction had an increased proportion of %LDL(<)(255Å). In multivariate linear regression analysis, after adjustment for age, gender, BP size and hypertension, the %LDL(<)(255Å) (p = 0.04) was significantly associated with BP dysfunction. In addition, patients with an elevated level of %LDL(<)(255Å) along with a decreased plasma adiponectin level were at a very high risk of developing early BP hemodynamic dysfunction (OR = 2.54, p = 0.04). CONCLUSION: BP dysfunction is significantly associated with an increased proportion of small, dense LDL.
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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) | 0.001 | 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".