Effect of Atorvastatin and Pioglitazone on Plasma Levels of Adhesion Molecules in Non-Diabetic Patients With Hypertension or Stable Angina or Both
Bibliographic record
Abstract
BACKGROUND: It was to study the effect of atorvastatin, pioglitazone and their combination on plasma levels of adhesion molecules in patients with hypertension or stable angina or both. METHODS: It was an open-label, randomized parallel-group study. Forty-five atorvastatin-naive patients with hypertension or stable angina or both, were randomized to receive either atorvastatin (19 patients; 10 mg OD for 12 weeks) or pioglitazone (26 patients; 30 mg OD for 12 weeks). Another group of 30 patients who were already on atorvastatin were put on add-on pioglitazone therapy (pioglitazone (15 mg OD) + atorvastatin (10 mg OD) for 12 weeks). Plasma high-sensitivity C-reactive protein (hsCRP), soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1 (sVCAM-1) levels were measured at baseline and after 12 weeks of therapy. RESULTS: Atorvastatin monotherapy significantly reduced plasma sICAM-1, but pioglitazone monotherapy did not produce any significant effect. Addition of pioglitazone in patients already receiving atorvastatin also significantly reduced plasma sICAM-1 level. However, there was no significant change in plasma hsCRP and sVCAM-1 levels in any of the groups after 12 weeks of therapy. CONCLUSION: There is therapeutic advantage of combining pioglitazone and atorvastatin on plasma sICAM-1 levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".