Serum Biomarkers for Prediction of Response to Methotrexate Monotherapy in Early Rheumatoid Arthritis: Results from the SWEFOT Trial
Bibliographic record
Abstract
OBJECTIVE: To investigate baseline levels of 12 serum biomarkers that constitute a multibiomarker disease activity test, as predictors of response to methotrexate (MTX) in patients with early rheumatoid arthritis (eRA). METHODS: In 298 patients from the Swedish Pharmacotherapy (SWEFOT) clinical trial, baseline serum levels of 12 proteins were analyzed for association with disease activity based on the 28-joint count Disease Activity Score (DAS28) after 3 months of MTX monotherapy using uni-/multivariate logistic regression. Primary outcome was low disease activity (LDA; DAS28 ≤ 3.2). RESULTS: Of 298 patients, 104 achieved LDA after 3 months on MTX. Four of the 12 biomarkers [C-reactive protein (CRP), leptin, tumor necrosis factor receptor I (TNF-RI), and vascular cell adhesion molecule 1 (VCAM-1)] significantly predicted LDA based on stepwise logistic regression analysis. Dichotomization of patients using receiver-operating characteristic curve analysis-based cutoffs for these biomarkers showed significantly higher proportions with LDA among patients with lower versus higher levels of CRP or leptin (40% vs 23%, p = 0.004, and 40% vs 25%, p = 0.011, respectively), as well as among those with higher versus lower levels of TNF-RI or VCAM-1 (43% vs 27%, p = 0.004, and 41% vs 25%, p = 0.004, respectively). Combined score based on these biomarkers, adjusted for known predictors of LDA (smoking, sex, and age), associated with decreased chance of LDA (adjusted OR 0.45, 95% CI 0.32-0.62). CONCLUSION: Low baseline levels of CRP and leptin, and high baseline levels of TNF-RI and VCAM-1 were associated with LDA after 3 months of MTX therapy in patients with eRA. Combination of these 4 biomarkers increased accuracy of prediction. [Trial registration number: NCT00764725].
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".