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 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".