Cholesteryl Ester Transfer Protein Inhibition Reduces Major Adverse Cardiovascular Events by Lowering Apolipoprotein B Levels
Bibliographic record
Abstract
Cholesteryl ester transfer protein (CETP) facilitates the exchange of cholesteryl esters and triglycerides (TG) between high-density lipoprotein (HDL) particles and TG-rich, apolipoprotein (apo) B-containing particles. Initially, these compounds were developed to raise plasma HDL cholesterol (HDL-C) levels, a mechanism that was previously thought to lower the risk of atherosclerotic cardiovascular disease (ASCVD). More recently, the focus changed and the use of pharmacologic CETP inhibitors to reduce low-density lipoprotein cholesterol (LDL-C), non-HDL-C and apoB concentrations became supported by several lines of evidence from animal models, observational investigations, randomized controlled trials and Mendelian randomization studies. Furthermore, a cardiovascular outcome trial of anacetrapib demonstrated that CETP inhibition significantly reduced the risk of major coronary events in patients with ASCVD in a manner directly proportional to the substantial reduction in LDL-C and apoB. These data have dramatically shifted the attention on CETP away from raising HDL-C instead to lowering apoB-containing lipoproteins, which is relevant since the newest CETP inhibitor, obicetrapib, reduces LDL-C by up to 51% and apoB by up to 30% when taken in combination with a high-intensity statin. An ongoing cardiovascular outcome trial of obicetrapib in patients with ASCVD is expected to provide further evidence of the ability of CETP inhibitors to reduce major adverse cardiovascular events by lowering apoB. The purpose of the present review is to provide an up-to-date understanding of CETP inhibition and its relationship to ASCVD risk reduction.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".