Cholesteryl Ester Transfer Protein Inhibitors and Cardiovascular Outcomes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Cholesteryl ester transfer protein (CETP) inhibitors increase serum high-density lipoprotein cholesterol (HDL-c) concentration; however, their impact on cardiovascular outcomes is not clear. This systematic review examines the effect of CETP inhibitors on serum lipid profiles, cardiovascular events, and all-cause mortality. METHODS: We searched MEDLINE, Embase, and the Cochrane Library of Clinical Trials for placebo-controlled randomized controlled trials (RCTs) that examined the effect of a CETP inhibitor (dalcetrapib, anacetrapib, evacetrapib, or TA-8995) on all-cause mortality, major adverse cardiovascular events (MACE), or the components of MACE at ≥6 months. Data were pooled using random-effects models. RESULTS: A total of 11 RCTs (n = 62,431) were included in our systematic review; 4 examined dalcetrapib (n = 16,612), 6 anacetrapib (n = 33,682), and 1 evacetrapib (n = 12,092). Compared to dalcetrapib, ana-cetrapib and evacetrapib were more efficacious at raising HDL-c levels (∼100-130 vs. ∼30%). Anacetrapib and evacetrapib also decreased low-density lipoprotein cholesterol (LDL-c) by approximately 30% while dalcetrapib did not affect the LDL-c level. Overall, CETP inhibitors were not associated with the incidence of MACE (pooled relative risk [RR]: 0.97; 95% confidence interval [CI]: 0.91-1.04). CETP inhibitors may decrease the risks of nonfatal myocardial infarction (MI) (RR: 0.93; 95% CI: 0.87-1.00) and cardiovascular death (RR: 0.92; 95% CI: 0.83-1.01), though these trends did not reach statistical significance. CONCLUSIONS: CETP inhibitors are not associated with an increased risk of MACE or all-cause mortality. There is a trend towards small reductions in nonfatal MI and cardiovascular death, though the clinical im-portance of such reductions is likely modest.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.032 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".