Effect of Atorvastatin on Inflammation and Modification of Vascular Risk Factors in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of atorvastatin therapy on inflammation, disease activity, endothelial dysfunction, and arterial stiffness in patients with rheumatoid arthritis (RA). METHODS: This study included 30 patients with early RA, randomly divided into 2 groups. Group 1 (n = 15) received methotrexate (MTX; 0.2 mg/kg/week; mean (15.5 ± SD 1.3) plus prednisone (10 mg/day). Group 2 (n = 15) received MTX and prednisone with the same previous doses plus atorvastatin therapy (40 mg/day). Ten healthy individuals of similar age and sex served as controls. Disease activity, lipid profile, serum malondialdehyde (MDA), tumor necrosis factor-α (TNF-α), resistin, adiponectin, and brachial artery flow-mediated dilation (FMD) were measured before and after 6 months of treatment. RESULTS: Atorvastatin combined with MTX therapy significantly reduced serum total cholesterol, low-density lipoprotein cholesterol, and triglycerides, and increased high-density lipoprotein cholesterol (p < 0.001). Disease activity variables, serum MDA, TNF-α, resistin, adiponectin, and FMD were significantly improved by the drug combinations (p < 0.001). CONCLUSION: Atorvastatin therapy in patients with RA reduced disease activity and conventional and novel vascular risk factors that promote the atheromatous lesion. Therapy was also associated with concomitant improvement in endothelial function.
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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) | 0.001 | 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".