Abstract 104: Serum from Patients with Aortic Valvular Stenosis Shows Decreased Cholesterol Efflux Capacities Despite Similar High-Density Lipoprotein Cholesterol Levels
Bibliographic record
Abstract
Background: Studies have shown that low HDL-cholesterol levels may be associated with the progression of aortic valvular calcium and aortic valvular stenosis (AVS), but whether patients with AVS have impaired cholesterol efflux capacities is unknown. Methods and results: We have measured four parameters of cholesterol efflux capacity in apolipoprotein B-depleted serum samples from 48 patients with (aortic jet velocity ≥2.5 m/s, mean age = 72 ± 7 years and 72.7% men) and 51 patients without AVS (aortic jet velocity ≤ 1.7 m/s, mean age 71 ± 7 years and 70.6% men). Cholesterol efflux capacity was measured using J774 macrophages with and without stimulation of ABCA1 expression by cAMP (non-stimulated efflux, total efflux and ABCA1-mediated efflux), and HepG2 hepatocytes to measure SR-BI-mediated efflux. Mean HDL-cholesterol and apolipoprotein A-I levels as well as efflux are shown in the table for patients with vs. without AVS. The Pearson correlation coefficient between HDL-cholesterol levels and SR-B1-dependent efflux was 0.39 (p=0.007) in patients with AVS and 0.68 (<0.0001) in controls (P-value for the difference between the correlation coefficients obtained with Fisher’s test = 0.04). Conclusions: This study provides evidence that serum from patients with AVS may have impaired cholesterol efflux capacities, especially through the SR-B1 pathway. Table. Mean HDL-cholesterol and apolipoprotein A-I levels as well as non-stimulated-, total-, ABCA1-, and SR-B1-dependent cholesterol efflux obtained from patients’ serum with vs. without AVS. Data is shown as mean ± SD. Differences between categories were assessed using a Student unpaired t-test.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".