Soluble Biomarkers Associated with Response to Treatment with Tumor Necrosis Factor Inhibitors in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To identify soluble biomarkers associated with response to therapy with tumor necrosis factor inhibitors (TNFi) in patients with psoriatic arthritis (PsA). METHODS: The study was conducted at a PsA clinic where patients are assessed every 6 months, and serum samples are collected and stored once a year at the time of clinical assessment. Forty patients with active PsA who gave serum samples prior to treatment with TNFi and after at least 3 months of therapy were identified. Patients were classified as TNFi responders if tender joint count was < 3, swollen joint count was 0, and Psoriasis Area and Severity Index score was < 4 at the time the second sample was collected. The following biomarkers were tested by ELISA: TNF superfamily 14, matrix metalloprotease-3 (MMP-3), receptor activator of nuclear factor kappa-B ligand, osteoprotegerin, cartilage oligomeric matrix protein (COMP), CPII, C2C and C1-2C, CS-846, and highly sensitive C-reactive protein. Paired t tests and logistic regression was used for statistical analyses. RESULTS: After a mean treatment duration of 11 months with TNFi (etanercept 28 patients, adalimumab 6, golimumab 4, infliximab 2), 29 patients were classified as TNFi responders. Baseline level of MMP-3 was independently associated with responder status (OR 1.067 for each 1-unit increase, p = 0.045). A reduction in MMP-3 levels with therapy increased the odds of achieving response (OR 1.213 for each 1-unit change, p = 0.030), whereas a reduction in COMP decreased the odds (OR 0.587, for each 100-unit increase, p = 0.039). None of the other biomarkers was associated with response. CONCLUSION: Baseline as well as reduction in serum MMP-3 and increase in serum COMP are independently associated with response to TNFi therapy in patients with PsA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".