Ezetimibe Monotherapy Reduces Serum Levels of Platelet-Activating Factor Acetylhydrolase in Patients With Dyslipidemia
Bibliographic record
Abstract
BACKGROUND: The combination of ezetimibe with statin therapy reduced cardiovascular events compared to statin monotherapy in IMPROVEIT study, and ezetimibe monotherapy attenuated atherosclerosis in basic study. We previously showed ezetimibe monotherapy was especially effective for metabolic syndrome (MetS) patients. We investigated the effects of ezetimibe monotherapy for high-density lipoprotein cholesterol (HDL-chol) function and platelet-activating factor acetylhydrolase (PAF-AH) activity. METHODS: Forty-two patients who initially received ezetimibe (10 mg/day) without statin treatment for 16 weeks from January 2009 to August 2011 were enrolled. Patients were divided into MetS and non-MetS groups, and serum levels of lipids, PAF-AH, and HDL-chol efflux capacity (HDL-CEC) at baseline and after 16 weeks of treatment were investigated. Serum PAF-AH, HDL-associated PAF-AH (HDL-PAF-AH), and LDL-associated PAF-AH (LDL-PAF-AH) were measured. RESULTS: , respectively. Total cholesterol and low-density lipoprotein cholesterol (LDL-chol) were significantly decreased by ezetimibe monotherapy. Serum PAF-AH and LDL-PAF-AH were significantly decreased by ezetimibe monotherapy, whereas HDL-PAF-AH and HDL-CEC were not. There was no difference in the results of PAF-AH and HDL-CEC between MetS and non-MetS groups. CONCLUSIONS: Ezetimibe monotherapy might prevent coronary heart disease (CHD) regardless of the presence of MetS, because PAF-AH was independent risk factor for CHD.
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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".