Brief Report: CXCL10 Is a Possible Biomarker for the Development of Psoriatic Arthritis Among Patients With Psoriasis
Bibliographic record
Abstract
OBJECTIVE: Biomarkers that can predict the development of psoriatic arthritis (PsA) in patients with psoriasis would be useful in clinical practice. The aim of this study was to assess whether CXCL10 could be a predictive biomarker of PsA prior to its onset. METHODS: Psoriasis patients without arthritis were followed up prospectively and assessed annually for development of PsA by a rheumatologist. Patients in whom PsA developed were designated as converters, while those in whom PsA did not develop were termed nonconverters. Baseline serum concentrations of CXCL10 were measured by Luminex assay in 46 converters and 45 nonconverters. RESULTS: The level of CXCL10 was significantly higher in converters (median 493 pg/ml [interquartile range (IQR) 356-984]) than in nonconverters (median 371 pg/ml [IQR 263-578]; P = 0.005). In contrast, C-reactive protein (CRP) levels were not significantly different between converters and nonconverters at baseline. CXCL10 was associated with conversion status after adjustment for age, sex, duration of psoriasis, and duration of follow-up (odds ratio 1.3, 95% confidence interval 1.1-1.5, P = 0.004). In a subset of converters, the CXCL10 level was significantly higher at baseline (median 927.4 pg/ml [IQR 547.6-1,243]) than after PsA diagnosis (491.5 pg/ml [IQR 323.2-607]; P < 0.0001), while CRP levels were lower at baseline (26.6 μg/ml [IQR 16.37-62.75]) than after PsA diagnosis (36.1 μg/ml [IQR 14.74-101.7]; P = 0.003). CXCL10 gene expression was increased 17.3-fold in synovial fluid (SF) compared with blood from PsA patients (P = 0.01) and 44.3-fold in the SF of PsA patients compared with the SF of patients with gout (P = 0.001). CONCLUSION: CXCL10 may be involved in PsA pathogenesis and is a candidate predictive biomarker for PsA in patients with psoriasis.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".