045. EVALUATION OF NOVEL SERUM BIOMARKERS OF DISEASE ACTIVITY IN GIANT CELL ARTERITIS, TAKAYASU’S ARTERITIS, POLYARTERITIS NODOSA, AND EOSINOPHILIC GRANULOMATOSIS WITH POLYANGIITIS
Bibliographic record
Abstract
Background: Better biomarkers are needed for clinical assessment of vasculitis. This study assessed potential circulating biomarkers of disease activity in giant cell arteritis (GCA), Takayasu’s arteritis (TAK), polyarteritis nodosa (PAN) and eosinophilic granulomatosis with polyangiitis (EGPA, Churg-Strauss). Methods: A panel of 22 serum proteins was tested in patients enrolled in longitudinal cohorts of patients with GCA, TAK, PAN, or EGPA. Biomarker data were ln-transformed when appropriate to reduce skewing. Mixed models were used for most analyses, with biomarker level as the dependent variable. Correlation coefficients were also calculated. A J48 classification tree method was used to find the most relevant markers to differentiate between active and inactive GCA. Results: 418 samples from 152 patients (60 GCA, 29 TAK, 26 PAN, 37 EGPA) were tested. Most patients were on treatment. In GCA, BCA-1/CXCL13, ESR, IP-10/CXCL10, sIL-2R, and TIMP-1 showed significant (P < 0.05) differences during active disease with or without adjustment for treatment. In EGPA, BCA-1/CXCL13, G-CSF, GM-CSF, IL-6, IL-15, and sIL-2R were higher in active disease (Table 1). In PAN, ESR and MMP-3 were higher in active disease, and no significant markers were identified in TAK. The correlations of ESR or CRP with the experimental markers were all ≤ r = 0.25. Differences in marker levels between diseases were significant in mixed models for 11 markers, after adjustment for disease activity or treatment, and were more striking (all P < 0.01) than differences related to disease activity or treatment: BCA-1/CXCL13, CRP, ESR, G-CSF, GM-CSF, IL-6, IL-8, IL-18BP, IP-10/CXCL10, MMP-3, and sIL-2R. Using a classification tree, a combination of TIMP-1, IL-6, INF-ɣ, and MMP-3 correctly classified 87% of patients with inactive GCA, but the method did not identify a combination of markers that correctly classified more than 50% of patients with active GCA. Biomarkers associated with active GCA, PAN, or EGPA Mixed effects models included marker concentration as the dependent variable, disease activity as a dichotomous independent variable, the patient as the random effect, with (“Meds”) or without current use of prednisone and other immunosuppressive drugs as two additional dichotomous variables. Numbers indicate beta-coefficients associating an increase (if > 0) or decrease (if < 0) in marker concentration with active disease, with 95% confidence intervals in parentheses, and P-values. All marker values except ESR were ln-transformed. Therefore, the beta-coefficient for ESR represents absolute change (mm/hr), whereas for other markers, the beta-coefficient multiplied by 2.72 represents fold-change. Only analyses with P < 0.05 are shown. CRP was also one of the markers in this study, with no P < 0.05. Conclusion: This study identified several biomarkers of disease activity in GCA and EGPA, with few significant markers shared between diseases. Levels of 11 markers differed between diseases, after adjusting for disease activity and treatment. Further studies are needed to confirm these findings and better understand potential clinical uses of these markers. Disclosures: Dr. Merkel reports receiving funds for the following activities: Consulting: AbbVie, AstraZeneca, Biogen, Boeringher-Ingelheim, Bristol-Myers Squibb, Celgene, ChemoCentryx, Genentech/Roche, Genzyme/Sanofi, GlaxoSmithKline, InflaRx, Insmed, Jannsen, Kiniksa; Research Support: AstraZeneca, Boeringher-Ingelheim, Bristol-Myers Squibb, Celgene, ChemoCentryx, Genentech/Roche, GlaxoSmithKline, Kypha, TerumoBCT; Royalties: UpToDate. This work was sponsored by the Vasculitis Clinical Research Consortium and received support from the National Institutes of Health: U54 AR057319, RC1 AR 058303, P60 AR047785, and N01 AI15416.
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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 | 0.000 | 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".