Differentiating Psoriatic Arthritis From Psoriasis Without Psoriatic Arthritis Using Novel Serum Biomarkers
Bibliographic record
Abstract
OBJECTIVE: There is a high prevalence of undiagnosed psoriatic arthritis (PsA) in patients with psoriasis. Identifying soluble biomarkers for PsA will help in screening psoriasis patients for appropriate rheumatology referral. We therefore aimed to investigate whether serum levels of novel markers previously discovered by quantitative mass spectrometric analysis of synovial fluid and skin biopsies performs better than the C-reactive protein (CRP) level in differentiating PsA patients from those with psoriasis without PsA (PsC). METHODS: In this case-control study, serum samples were obtained from 100 subjects with PsA, 100 with PsC, and 100 healthy controls. Patients with PsA and PsC were group matched for age, sex, psoriasis duration, and Psoriasis Area and Severity Index and were not currently receiving biologic treatment. Using enzyme-linked immunosorbent assay, 4 high-priority markers (Mac-2-binding protein [M2BP], CD5-like protein [CD5L], myeloperoxidase [MPO], and integrin β5 [ITGβ5]), as well as previously established markers (matrix metalloproteinase 3 [MMP-3] and CRP level) were assayed. Data were analyzed using logistic regression. Receiver operating characteristic (ROC) curves were plotted. RESULTS: In comparisons to controls, CD5L, ITGβ5, M2BP, MPO, MMP-3, and CRP level were independently associated with PsA, while only CD5L, M2BP, and MPO were independently associated with PsC alone. In comparisons to PsC, ITGβ5, M2BP, and CRP level were independently associated with PsA. ROC analysis of this model shows an area under the curve (AUC) of 0.85 (95% confidence interval [95% CI] 0.80-0.90). The model that included CRP level alone had an AUC of 0.71 (95% CI 0.64-0.78). CONCLUSION: CD5L, ITGβ5, M2BP, MPO, MMP-3, and CRP level are markers for PsA. The combination of ITGβ5, M2BP, and CRP level differentiates PsA from PsC, and performs better than CRP level alone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".