Abstract 17151: Very Low Serum Cholesterol Efflux Capacity is a Positive Predictor of Cardiovascular Risk, Independent of HDL-Cholesterol, Apolipoprotein A-I and HDL Particle Concentrations in the Montreal Heart Institute Biobank Cohort
Bibliographic record
Abstract
Plasma levels of high-density lipoprotein cholesterol (HDL-C) are inversely associated with cardiovascular (CV) diseases. However, CV protection may derive from other characteristics of HDL, such as the cholesterol efflux capacity of serum HDL, the process by which HDL particles accept cholesterol from macrophages and other cell types. Cholesterol efflux capacity is inversely associated with CV risk and incident CV events. Our aim was to estimate CV risk in subjects with very low cholesterol efflux capacity and its relationship with HDL-related measures. To this end, cholesterol efflux capacity was measured with J774 macrophages in the basal state and after cAMP-stimulation and expressed as the ratio of control serum, for 1000 cases with previous myocardial infarction (MI) and 1000 healthy controls from the Montreal Heart Institute Biobank. Subjects with extreme efflux capacity were categorized as th percentile or >90 th percentile for each efflux variable. HDL particle concentration (HDL-P) was obtained from the NMR Lipoprofile, while the plasma LCAT and MPO concentrations were obtained by ELISA. Unadjusted odds ratios (OR) for MI were highly significant for efflux capacity th percentile with J774 cells in basal (OR=4.58, p th percentile was unaffected by individually adjusting for HDL-C (OR=4.86, p=0.0001), apoA-I (OR=3.76, p=0.0011), HDL-P (OR=3.23, p=0.0033), LCAT mass (OR=4.17, p th percentile with the J774 macrophage model have greatly increased CV risk, which is independent of both HDL mass and concentration and which reflects HDL dysfunction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".