Evaluation of Potential Serum Biomarkers of Disease Activity in Diverse Forms of Vasculitis
Bibliographic record
Abstract
OBJECTIVE: We evaluated potential circulating biomarkers of disease activity in giant cell arteritis (GCA), Takayasu arteritis (TA), polyarteritis nodosa (PAN), and eosinophilic granulomatosis with polyangiitis (EGPA). METHODS: A panel of 22 serum proteins was tested in patients enrolled in the Vasculitis Clinical Research Consortium Longitudinal Studies of GCA, TA, PAN, or EGPA. Mixed models were used for most analyses. A J48 classification tree method was used to find the most relevant markers to differentiate between active and inactive GCA. RESULTS: Tests were done on 418 samples from 152 patients (60 GCA, 29 TA, 26 PAN, 37 EGPA), during both active vasculitis and remission. In GCA, these showed significant (p < 0.05) differences between disease states: B cell-attracting chemokine 1 (BCA)-1/CXC motif ligand 13 (CXCL13), erythrocyte sedimentation rate (ESR), interferon-γ-induced protein 10/CXC motif chemokine 10, soluble interleukin 2 receptor α (sIL-2Rα), and tissue inhibitor of metalloproteinase-1 (TIMP-1). In EGPA, these showed significant increases during active disease: granulocyte colony-stimulating factor (G-CSF), granulocyte-macrophage-CSF, interleukin (IL)-6, IL-15, and sIL-2Rα. BCA-1/CXCL13 also showed such increases, but only after adjustment for treatment. In PAN, ESR and matrix metalloprotease (MMP)-3 showed significant differences between disease states. Differences in biomarker levels between diseases were significant for 11 markers and were more striking (all p < 0.01) than differences related to disease activity. A combination of lower values of TIMP-1, IL-6, interferon-γ, and MMP-3 correctly classified 87% of samples with inactive GCA. CONCLUSION: We identified novel biomarkers of disease activity in GCA and EGPA. Differences of biomarker levels between diseases, independent of disease activity, were more apparent than differences related to disease activity. Further studies are needed to determine whether these serum proteins have potential for clinical use in distinguishing active disease from remission or in predicting longer-term outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".