Circulating Angiopoietin-2 as a Biomarker in ANCA-Associated Vasculitis
Bibliographic record
Abstract
The endothelial-specific Angiopoietin-Tie2 ligand-receptor system is an important regulator of endothelial activation. Binding of angiopoietin-2 (Ang-2) to Tie2 receptor renders the endothelial barrier responsive to pro-inflammatory cytokines. We previously showed that circulating Ang-2 correlated with disease severity in a small cohort of critically ill patients with anti-neutrophil cytoplasmic antibody (ANCA)-associated glomerulonephritis. The current study reassessed Ang-2 as a biomarker of disease activity and relapse in AAV. Circulating Ang-2 was measured in 162 patients with severe AAV (BVAS/WG≥3, with or without glomerulonephritis) in a clinical trial. Ang-2 levels during active AAV were compared to levels in the same patients during remission (BVAS/WG = 0). Levels in clinical subsets of AAV were compared, and association with future disease course was assessed. Ang-2 levels were elevated in severe disease (median 3.0 ng/ml, interquartile range 1.9-4.4) compared to healthy controls (1.2, 0.9-1.5). However, they did not reliably decline with successful treatment (median 2.6 ng/ml, interquartile range 1.9-3.8, median change -0.1). Ang-2 correlated weakly with BVAS/WG score (r = 0.17), moderately with markers of systemic inflammation (r = 0.25-0.41), and inversely with renal function (r = -0.36). Levels were higher in patients with glomerulonephritis, but levels adjusted for renal dysfunction were no different in patients with or without glomerulonephritis. Levels were higher in patients with newly diagnosed AAV and lower in patients in whom treatment had recently been started. Ang-2 levels during active disease did not predict response to treatment, and Ang-2 levels in remission did not predict time to flare. Thus, Ang-2 appears to have limited practical value in AAV as a biomarker of disease activity at time of measurement or for predicting future activity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".