Serum MMP-3 Level as a Biomarker for Monitoring and Predicting Response to Etanercept Treatment in Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: To investigate whether level of serum matrix metalloproteinase-3 (MMP-3) can serve as a biomarker for monitoring and predicting response to etanercept treatment in patients with ankylosing spondylitis (AS) in daily clinical practice. METHODS: Ninety-two consecutive AS outpatients with active disease who started etanercept treatment were included in this longitudinal observational study. Clinical data were collected prospectively at baseline and after 3 and 12 months of treatment. At the same timepoints, serum MMP-3 levels were measured retrospectively by ELISA. RESULTS: Since baseline serum MMP-3 levels were significantly higher in male compared to female patients with AS, data analysis was split for gender. Changes in serum MMP-3 levels after etanercept treatment correlated positively with changes in clinical assessments of disease activity and physical function in both male and female patients. Receiver operating characteristic analysis in male patients showed that baseline serum MMP-3 levels had poor accuracy (AUC < 0.7) to discriminate between Assessments in Ankylosing Spondylitis 20 (ASAS20) or ASAS40 responders and nonresponders after 3 or 12 months of treatment. The accuracy of change in serum MMP-3 levels from baseline to 3 months in predicting response after 3 or 12 months of treatment was poor for ASAS40 (AUC < 0.7) or moderate for ASAS20 (AUC = 0.752 and 0.744, respectively), and was not superior to the accuracy of change in the currently used objective biomarkers, erythrocyte sedimentation rate and C-reactive protein. CONCLUSION: Although significant changes in serum MMP-3 levels were found after etanercept treatment, data analysis indicates that serum MMP-3 levels are not very useful for monitoring and predicting response to etanercept treatment in patients with AS in daily clinical practice.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".