Effects of Ezetimibe Add-on to Statin Therapy on Adipokine Production in Patients With Metabolic Syndrome and Stable Vascular Disease
Bibliographic record
Abstract
The aim of this study was to determine whether the addition of ezetimibe to ongoing statin therapy in patients with atherosclerosis and metabolic syndrome would favorably affect levels of inflammatory markers and adipokines. Individuals with the metabolic syndrome exhibit higher levels of inflammatory biomarkers and adipokines, which have been implicated in the pathobiology of cardiovascular risk. The impact of the addition of ezetimibe to statin therapy on these proinflammatory mediators is unclear. Fifty patients with metabolic syndrome and concomitant vascular disease receiving stable statin monotherapy, with low-density lipoprotein cholesterol (LDL-C) levels >77.4 mg/dL (>2.0 mM), were treated with ezetimibe 10 mg per day for 12 weeks. The primary study end point was the % change in adiponectin levels from baseline to 12 weeks. Secondary study end points included % changes in the levels of other circulating inflammatory markers, adipokines, and plasma lipids. The addition of ezetimibe to statin therapy resulted in a significant reduction in total cholesterol and LDL-C and the ratio of total cholesterol to high-density lipoprotein cholesterol. However, ezetimibe add-on treatment had no effect on the primary outcome of plasma adiponectin or on any of the secondary outcomes, including leptin, hsCRP, tumor necrosis factor-α, or interleukin-6 concentrations. These observations remained unchanged after adjusting for body mass index and for background statin used. The addition of ezetimibe to stable statin therapy in patients with vascular disease and metabolic syndrome, who were not at guideline recommended LDL-C levels, did not alter adipokine levels after 12 weeks. Short-term add-on with ezetimibe may not be associated with additional inflammatory benefits beyond improvements in cholesterol homeostasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".