E-selectin, Interleukin 18, Serum Amyloid A, and Matrix Metalloproteinase 9 Are Associated with Clinical Response to Golimumab plus Methotrexate in Patients with Active Rheumatoid Arthritis Despite Methotrexate Therapy
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of golimumab (human monoclonal antibody to tumor necrosis factor-alpha) plus methotrexate (MTX) on selected inflammatory biomarkers, and to determine if these effects predict clinical response in rheumatoid arthritis (RA). METHODS: Sera from adults with active RA despite MTX therapy, who received subcutaneous injections of placebo + MTX (MTX alone, n = 34) or golimumab 50 or 100 mg every 2 or 4 weeks + MTX (n = 137), were analyzed for levels of C-reactive protein (CRP), serum amyloid A (SAA), interleukin 18 (IL-18), E-selectin, matrix metalloproteinase 9 (MMP-9), and tissue inhibitor of matrix metalloproteinase 1 (TIMP-1). RESULTS: Golimumab + MTX treatment significantly decreased serum CRP, SAA, IL-18, E-selectin, TIMP-1, and MMP-9 levels (median percent changes of -4.1% to -74.3% across treatment groups) versus MTX alone (-5.8% to 9.7%) when first measured at Week 4; decreases were sustained through Week 16. Larger magnitudes of decrease in all biomarkers were observed for clinical responders versus nonresponders. For golimumab + MTX, regression analyses including all biomarkers and select clinical measures showed that reductions in levels of several markers (SAA, E-selectin, MMP-9) as early as Week 4 correlated significantly with improvement in swollen joint count (SJC) at Week 16, as did reductions in E-selectin with improvement in tender joint count at Week 16. After accounting for the biomarkers, however, treatment group was no longer significant for SJC. CONCLUSION: Significant decreases in several inflammatory biomarkers were associated with golimumab + MTX therapy. Decreases in serum levels of SAA, E-selectin, and MMP-9 at Week 4 may be useful in predicting clinical response at Week 16.
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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 | 0.002 |
| 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".