Dalcetrapib and anacetrapib differently impact HDL structure and function in rabbits and monkeys
Bibliographic record
Abstract
Inhibition of cholesteryl ester transfer protein (CETP) increases HDL cholesterol (HDL-C) levels. However, the circulating CETP level varies and the impact of its inhibition in species with high CETP levels on HDL structure and function remains poorly characterized. This study investigated the effects of dalcetrapib and anacetrapib, the two CETP inhibitors (CETPis) currently being tested in large clinical outcome trials, on HDL particle subclass distribution and cholesterol efflux capacity of serum in rabbits and monkeys. New Zealand White rabbits and vervet monkeys received dalcetrapib and anacetrapib. In rabbits, CETPis increased HDL-C, raised small and large α-migrating HDL, and increased ABCA1-induced cholesterol efflux. In vervet monkeys, although anacetrapib produced similar results, dalcetrapib caused opposite effects because the LDL-C level was increased by 42% and HDL-C decreased by 48% ( P < 0.01). The levels of α- and preβ-HDL were reduced by 16% ( P < 0.001) and 69% ( P < 0.01), resulting in a decrease of the serum cholesterol efflux capacity. CETPis modulate the plasma levels of mature and small HDL in vivo and consequently the cholesterol efflux capacity. The opposite effects of dalcetrapib in different species indicate that its impact on HDL metabolism could vary greatly according to the metabolic environment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".