Abstract 17214: Differential Relationships Between Serum Cholesterol Efflux Capacities Measured From Three Cell Models and Coronary Artery Disease Status in the Montreal Heart Institute Biobank
Bibliographic record
Abstract
Recent clinical trials and Mendelian randomization studies suggest that raising HDL-cholesterol (HDL-C) concentration by itself is insufficient to lower cardiovascular (CV) risk, despite the established inverse relationship between HDL-C and cardiovascular risk. CV protection may derive from other characteristics of HDL. Such characteristics include the cholesterol efflux capacity of serum, the process by which HDL particles accept cholesterol from macrophages and other cell types. Recently, higher cholesterol efflux capacity was inversely associated with incident CV events over a >9 year period of follow-up, with a 67% risk reduction in the highest quartile of efflux. In our study, cholesterol efflux capacity was measured for 2000 patients from the Montreal Heart Institute Biobank as the ratio of pooled control serum. When comparing unadjusted cholesterol efflux values between 1000 controls and 1000 cases with previous myocardial infarction (MI), we observed significant decreases of efflux capacity in cases with J774 macrophages in basal and cAMP-stimulated conditions, with human HepG2 hepatocytes and with BHK cells expressing human ABCA1. In regression models of MI status against efflux variables, also adjusted for age, sex, HDL-C, triglycerides and statin use, the reduction in cholesterol efflux capacity in cases vs. controls remained highly significant for J774 cells in basal (p value = 5.8x10-11) and cAMP-stimulated conditions (p = 5.3x10-8), while the difference was lost with HepG2 cells (p=0.16) and was reversed for ABCA1-dependent efflux using BHK-ABCA1 cells (p=5.9x10-4). Thus, the relationship of cholesterol efflux capacity of serum HDL and cardiovascular status is heterogeneous, which suggest that the repertoire of cholesterol transporters expressed in cells and samples characteristics, such as the HDL proteome and lipidome, interact in a unique manner for each cell type. Future work will consist in identifying sources of such differences at the molecular level.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".