Abstract 319: Dalcetrapib-Mediated Modulation of Cholesteryl Ester Transfer Protein Activity Increases HDL-Cholesterol, Apolipoprotein A-I Levels and Cholesterol Efflux Capacity in Chow-Fed Rabbits
Bibliographic record
Abstract
Introduction: A potential approach to reduce CV risk is to increase HDL-C levels. This could be achieved by reducing cholesteryl ester transfer protein (CETP) activity. Dalcetrapib, which modulates CETP activity by changing its conformation and raises HDL-C without inhibiting CETP-induced pre-β-HDL formation in humans, was shown to decrease progression of atherosclerosis in rabbits. Hypothesis: Investigate the modifications of HDL particle size distribution and cholesterol efflux capacity of serum produced by dalcetrapib in normocholesterolemic rabbits. Methods: New Zealand white rabbits were treated with dalcetrapib (300 mg/kg as food admix) or placebo for 14 days. We evaluated CETP conformation and mass by ELISAs (including antibodies sensitive to conformational change), CETP activity by fluorescent lipid transfer, lipid profile and apoA-I distribution in HDL subclasses by 2D-non denaturing gradient gels (2D-NDGGE). Cholesterol efflux capacity of rabbit sera was determined after loading cells with 3 H-free cholesterol, using HepG2 hepatocytes to measure SR-BI-dependent efflux and by inducing ABCA1 or ABCG1 expression in BHK cells. Results: Dalcetrapib modified the conformation of rabbit CETP in vitro and in vivo and, after 14 days, this was associated with increased CETP mass (+50%, p<0.001) and reduced CETP activity (-86%, p<0.001). Total cholesterol was increased with dalcetrapib (+178%, p<0.001), due to a higher HDL-C level. In contrast, dalcetrapib reduced LDL-C and triglycerides by 41% (p<0.01) and 48% (p<0.001). Serum analysis by 2D-NDGGE showed that total rabbit apoA-I was increased 1.7- fold in animals treated with dalcetrapib. This was associated with an increase in large HDL but also in small α-migrating HDL with pre-β-HDL size. Cholesterol efflux assays showed that ABCA1-, ABCG1- and SR-BI-dependent efflux were all increased in dalcetrapib-treated rabbits (+24%, p=0.038; +21%, p=0.021; +44%, p<0.001). Conclusion: Modulation of CETP activity and conformation by dalcetrapib increases HDL-C and apoA-I levels and affects apoA-I distribution in HDL subclasses. These changes are associated with increased cholesterol efflux capacity, suggesting that HDL functionality is preserved in dalcetrapib-treated chow-fed rabbits.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".