The molecular mechanisms underlying the reduction of LDL apoB-100 by ezetimibe plus simvastatin
Bibliographic record
Abstract
The combination of ezetimibe, an inhibitor of Niemann-Pick C1-like 1 protein (NPC1L1), and an HMG-CoA reductase inhibitor decreases cholesterol absorption and synthesis. In clinical trials, ezetimibe plus simvastatin produces greater LDL-cholesterol reductions than does monotherapy. The molecular mechanism for this enhanced efficacy has not been defined. Apolipoprotein B-100 (apoB-100) kinetics were determined in miniature pigs treated with ezetimibe (0.1 mg/kg/day), ezetimibe plus simvastatin (10 mg/kg/day), or placebo (n = 7/group). Ezetimibe decreased cholesterol absorption (-79%) and plasma phytosterols (-91%), which were not affected further by simvastatin. Ezetimibe increased plasma lathosterol (+65%), which was prevented by addition of simvastatin. The combination decreased total cholesterol (-35%) and LDL-cholesterol (-47%). VLDL apoB pool size decreased 26%, due to a 35% decrease in VLDL apoB production. LDL apoB pool size decreased 34% due to an 81% increase in the fractional catabolic rate, both of which were significantly greater than monotherapy. Combination treatment decreased hepatic microsomal cholesterol (-29%) and cholesteryl ester (-65%) and increased LDL receptor (LDLR) expression by 240%. The combination increased NPC1L1 expression in liver and intestine, consistent with increased SREBP2 expression. Ezetimibe plus simvastatin decreases VLDL and LDL apoB-100 concentrations through reduced VLDL production and upregulation of LDLR-mediated LDL clearance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".