Urinary Biomarkers in Relapsing Antineutrophil Cytoplasmic Antibody-associated Vasculitis
Bibliographic record
Abstract
OBJECTIVE: Glomerulonephritis (GN) is common in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), but tools for early detection of renal involvement are imperfect. We investigated 4 urinary proteins as markers of active renal AAV: alpha-1 acid glycoprotein (AGP), kidney injury molecule-1 (KIM-1), monocyte chemoattractant protein-1 (MCP-1), and neutrophil gelatinase-associated lipocalin (NGAL). METHODS: Patients with active renal AAV (n = 20), active nonrenal AAV (n = 16), and AAV in longterm remission (n = 14) were identified within a longitudinal cohort. Urinary biomarker concentrations (by ELISA) were normalized for urine creatinine. Marker levels during active AAV were compared to baseline remission levels (from 1-4 visits) for each patient. Areas under receiver-operating characteristic curves (AUC), sensitivities, specificities, and likelihood ratios (LR) comparing disease states were calculated. RESULTS: Baseline biomarker levels varied among patients. All 4 markers increased during renal flares (p < 0.05). MCP-1 discriminated best between active renal disease and remission: a 1.3-fold increase in MCP-1 had 94% sensitivity and 89% specificity for active renal disease (AUC = 0.93, positive LR 8.5, negative LR 0.07). Increased MCP-1 also characterized 50% of apparently nonrenal flares. Change in AGP, KIM-1, or NGAL showed more modest ability to distinguish active renal disease from remission (AUC 0.71-0.75). Hematuria was noted in 83% of active renal episodes, but also 43% of nonrenal flares and 25% of remission samples. CONCLUSION: Either urinary MCP-1 is not specific for GN in AAV, or it identifies early GN not detected by standard assessment and thus has potential to improve care. A followup study with kidney biopsy as the gold standard is needed.
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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) | 0.001 | 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 | 0.001 |
| 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".