Serum Interleukin 6 Before and After Therapy with Tocilizumab Is a Principal Biomarker in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Biologic treatments including the humanized anti-interleukin 6 (anti-IL-6) receptor antibody tocilizumab (TCZ) provide therapeutic options for patients with rheumatoid arthritis (RA). We investigated useful biomarkers to predict the responsiveness to TCZ by measurement of serum proinflammatory cytokine concentrations. METHODS: Serum samples were collected from 61 patients with RA before biologic treatment and at 4 weeks after initial administration of either TCZ (n = 32) or infliximab (IFX; n = 29) and from 13 healthy serum donor controls. Disease Activity Score of 28 joints (DAS28) was determined at baseline and after treatment. RESULTS: Although IL-1β, IL-2, IL-6, IL-17A, IL-17F, interferon-α, and tumor necrosis factor-α (TNF-α) were all increased in sera from patients with RA compared with controls, only the IL-6 level was significantly correlated with DAS28 before treatment. The IL-6 level before treatment was positively correlated with DAS28 after TCZ treatment, and was significantly lower in TCZ-responsive patients (as judged by a post-treatment DAS28 < 3.2) than in TCZ-resistant patients (post-treatment DAS28 ≥ 3.2). DAS28 after TCZ was significantly lower than after administration of IFX in patients with low pretreatment IL-6 (< 51.5 pg/ml, the mean baseline value of IL-6 in all RA patients), but not in those with high pretreatment IL-6. These results indicate that low serum IL-6 is associated with a favorable response to TCZ. CONCLUSION: Although both TNF-α and IL-6 are major targets of therapeutic intervention in RA, baseline serum IL-6 but not baseline TNF-α level is a potential biomarker reflecting disease activity. Measurement of serum IL-6 in RA before treatment may be useful to estimate residual disease activity after TCZ treatment and to predict responsiveness to TCZ treatment.
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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 | 0.000 | 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 | 0.001 |
| 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 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".