Effect of Ezetimibe on the In Vivo Kinetics of ApoB-48 and ApoB-100 in Men With Primary Hypercholesterolemia
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of ezetimibe, a selective inhibitor of intestinal cholesterol absorption, on the in vivo kinetics of apolipoproteins (apo) B-48 and B-100 in humans. METHODS AND RESULTS: Kinetics of triglyceride-rich lipoprotein (TRL) apoB-48 and very-low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and low-density lipoprotein (LDL) apoB-100 labeled with a stable isotope were assessed at baseline and at the end of 8 weeks of treatment with 10 mg/d of ezetimibe in 8 men with moderate primary hypercholesterolemia. Data were fit to a multicompartmental model using SAAMII to calculate fractional catabolic rate (FCR) and production rate (PR). Ezetimibe significantly decreased total and LDL cholesterol concentrations by -14.5% and -22.0% (P=0.004), respectively, with no significant change in plasma triglyceride and high-density lipoprotein (HDL) cholesterol levels. Ezetimibe had no significant effect on TRL apoB-48 kinetics and pool size (PS). However, VLDL and IDL apoB-100 FCRs were significantly increased (+31.2%, P=0.02 and +20.8%, P=0.04, respectively) with a concomitant elevation of VLDL apoB-100 PR (+20.9%, P=0.04). Furthermore, LDL apoB-100 PS was significantly reduced by -23.2% (P=0.004), caused by a significant increase in FCR of this lipoprotein fraction (+24.0%, P=0.04). CONCLUSIONS: These results indicate that reduction of plasma LDL cholesterol concentration after treatment with ezetimibe is associated with an increase in FCR of apoB-100-containing lipoproteins.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".