What does the future hold for cholesteryl ester transfer protein inhibition?
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article summarizes the latest studies relevant to cholesteryl ester transfer protein (CETP) inhibition and cardiovascular risk and proposes a series of patient populations that might eventually derive benefits from CETP inhibition. RECENT FINDINGS: Results of recently published genetic epidemiology studies have helped shape our understanding of the association between lipoprotein-lipid levels and cardiovascular disease risk. These studies have confirmed the proatherogenic role of apolipoprotein B-containing lipoproteins and triglycerides and renewed our interest for lipoprotein(a) as a significant and causal predictor of cardiovascular risk. The association between HDL cholesterol levels and cardiovascular risk, albeit strong and consistent, is unlikely to be of causative nature, at least according to genetic epidemiology. However, a handful of intriguing studies have highlighted a predictive role for HDL cholesterol efflux capacities in predicting cardiovascular risk independently of HDL cholesterol levels. Potent CETP inhibitors, currently under investigation, significantly decrease apolipoprotein B-containing lipoproteins and lipoprotein(a) and increase both HDL cholesterol levels and HDL cholesterol efflux capacities. SUMMARY: Two phase 3 cardiovascular outcomes trials testing the hypothesis that CETP inhibition will reduce cardiovascular outcomes in high-risk patients are well underway. The future of CETP inhibition will depend on the outcomes of these trials.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".