Inhibitory Effects of Micronized Fenofibrate on Carotid Atherosclerosis in Patients with Essential Hypertension
Bibliographic record
Abstract
BACKGROUND: The coexistence of hypertension and dyslipidemia synergistically increases the risk of cardiovascular events. We investigated the effect of the lipid-lowering agent micronized fenofibrate on inhibition of carotid atherosclerosis in patients with essential hypertension and mild hyperlipidemia. METHODS: We measured serum lipid profiles and inflammatory markers on chemistry or immune analyzers and common or internal carotid intima-media thickness (IMT) and diameter (D) by ultrasonography. RESULTS: Patients receiving micronized fenofibrate for 24 months in addition to antihypertensive treatment had decreased concentrations of total cholesterol, LDL-cholesterol, triglyceride, apolipoprotein B100, oxidized LDL, high-sensitivity C-reactive protein, P-selectin, and cytokines. These patients had increased concentrations of HDL-cholesterol, apolipoprotein A-I, and nitric oxide. Common carotid artery IMT (CCAIMT) and internal carotid artery IMT (ICAIMT) remained unchanged during the 24-month intervention. Moreover, the mean CCAIMT/D ratio and ICAIMT/D ratio were significantly decreased in the fenofibrate intervention group. In contrast, CCAIMT/D and ICAIMT/D ratios were increased in the control group. The incidence rates of carotid artery plaque formation and stroke in the fenofibrate intervention group were significantly lower than those in the control group. CONCLUSION: The combination of antihypertensive agents with micronized fenofibrate can effectively prevent the progression of carotid atherosclerosis and reduce the incidence of stroke in patients with essential hypertension.
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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) | 0.000 | 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) | 0.000 | 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 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".