Candidate Biomarkers for Response to Treatment in Psoriatic Disease
Bibliographic record
Abstract
OBJECTIVE: To determine whether biologic therapy alters serum C-X-C motif chemokine ligand 10 (CXCL10), matrix metalloproteinase 3 (MMP3), S100 calcium-binding protein A8 (S100A8), acid phosphatase 5 (ACP5), and C-C motif chemokine ligand 2 (CCL2) levels in patients with psoriatic arthritis (PsA) and cutaneous psoriasis without arthritis (PsC), and whether baseline levels of these proteins predict response to treatment for PsA. METHODS: We included (1) patients with PsA taking tumor necrosis factor inhibitors (TNFi), interleukin 17 inhibitors (IL-17i), methotrexate (MTX), and those who were untreated with bDMARDs or csDMARDs; (2) patients with PsC taking bDMARDs; and (3) matched patients with PsC who were not treated with bDMARDs or csDMARDs. Serum samples at baseline and at the 3- to 6-month follow-up visit were retrieved from the biobank. Protein levels were quantified using a Luminex multiplex assay. We compared follow-up vs baseline protein levels within groups and change in levels between groups. For the predictive potential of the biomarkers, we developed logistic regression classification models. Response to treatment was defined as (1) achieving low disease activity or remission (according to the Disease Activity Index for Psoriatic Arthritis); (2) ≥ 75% reduction in Psoriasis Area and Severity Index; and (3) ≥ 50% reduction in actively inflamed joint count. RESULTS: < 0.05). There were no significant differences between treated or untreated patients with PsC. Baseline levels of CXCL10, MMP3, S100A8, and ACP5 had good predictive value (area under the curve > 0.80) for response to biologics in patients with PsA. CONCLUSION: Treatment with biologics and MTX affect serum CXCL10, MMP3, S100A8, ACP5, and CCL2 levels in patients with PsA. MMP3, S100A8, ACP5, and CXCL10 have potential use as serum biomarkers to predict response to treatment for 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".