Evaluation of Links Between High-Density Lipoprotein Genetics, Functionality, and Aortic Valve Stenosis Risk in Humans
Bibliographic record
Abstract
OBJECTIVE: Studies have shown that high-density lipoprotein (HDL)-raising compounds induce regression of aortic valve stenosis (AVS) in animal models. However, whether patients with AVS have an impaired HDL metabolism is unknown. APPROACH AND RESULTS: A total of 1435 single nucleotide polymorphisms in genes associated with HDL cholesterol levels (in or around GALNT2, LPL, ABCA1, APOA5, SCARB1, LIPC, CETP, LCAT, LIPG, APOC4, and PLTP) were genotyped in 382 patients with echocardiography-confirmed AVS (aortic jet velocity ≥2.5 m/s) and 401 controls. After control for multiple testing, none of the genetic variants showed a positive association with case/control status (adjusted P≥0.05 for all single nucleotide polymorphisms tested). In a subsample of this cohort, HDL cholesterol levels, apolipoprotein AI levels, lecithin-cholesterol acyltransferase activity, pre-β-HDL, HDL size, and 4 parameters of cholesterol efflux capacity were measured in apolipoprotein B-depleted serum samples from 86 patients with and 86 patients without AVS. Cholesterol efflux capacity was measured using J774 macrophages with and without stimulation of ATP-binding cassette A-1 expression by cAMP, and HepG2 hepatocytes for scavenger receptor class B type 1-mediated efflux. None of these parameters were different between cases and controls. However, compared with patients without coronary artery disease, sera from patients with coronary artery disease had lower HDL cholesterol levels, scavenger receptor class B type 1-mediated efflux, and HDL size (P≤0.003), independently of the presence or absence of AVS. CONCLUSIONS: Results of the present study suggest that, based on HDL genetics and HDL functionality, HDL metabolism does not seem to predict the risk of AVS. Because of our limited sample size, additional studies are needed to confirm these findings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".