Etanercept Improves Lipid Profile and Oxidative Stress Measures in Patients with Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of 1-year treatment with the anti-tumor necrosis factor-α (TNF-α) drug etanercept on lipid profile and oxidative stress in children and adolescents with juvenile idiopathic arthritis (JIA). METHODS: Thirty children with JIA (22 females; mean age 12.3 ± SD 5.7 yrs), all eligible for anti-TNF-α treatment, were assessed at baseline and after 6- and 12-month treatment with etanercept. Disease activity was determined using the Juvenile Arthritis Disease Activity Score (JADAS). Blood samples were drawn to measure the acute-phase reactants C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), lipids, and the proinflammatory cytokines TNF-α, interleukin-1β (IL-1β), IL-6 and interferon-γ. To measure the oxidative stress marker 8-iso-prostaglandin F2α, 24-h urine samples were collected. RESULTS: Inflammatory indicators (CRP and ESR) and JADAS scores improved significantly after 1 year of etanercept treatment (all p < 0.001). Proinflammatory cytokines showed significant reduction during the study period (all p < 0.001). Similar reductions were detected in total cholesterol (p < 0.001), low-density lipoprotein cholesterol (p = 0.04), and triglycerides (p < 0.001), whereas no significant change was found in high-density lipoprotein cholesterol. No side effects were observed during the treatment period. CONCLUSION: This study shows for the first time that anti-TNF-α therapy for JIA is associated not only with a beneficial effect on clinical disease activity and inflammatory indexes, but also with improved lipid profile and oxidative stress. These findings suggest that TNF-α blockers might reduce atherosclerotic risk in children with JIA.
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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 | 0.001 | 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 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".