NOVEL URINARY BIOMARKER MODEL FOR DIFFERENTIATING LUPUS NEPHRITIS FROM ANCA-ASSOCIATED VASCULITIS
Bibliographic record
Abstract
PV126 / #511 Poster Topic: AS15 - Lupus Nephritis-Clinical Background/Purpose Urinary complement activation products (uCAP) and soluble CD163 (usCD163) are promising biomarkers that reflect active renal inflammation in lupus nephritis (LN). However, these urinary proteins alone are not specific to LN since they can be elevated in other disorders including ANCA-associated vasculitis (AAV). The goal of this study was to develop a model that can accurately differentiate between LN vs. AAV using a combination of uCAP, usCD163, and urine protein-creatinine ratio (UPCR) levels. Methods We included patients with renal biopsy-confirmed cases of LN (n=12) and AAV (n=9) enrolled in the Biobank for Molecular Classification of Kidney Disease (BMCKD) as well as healthy controls (n=10). Their urine samples were collected anytime from 14 days prerenal biopsy to 238 days post-renal biopsy. Each urine sample was tested for 3 different uCAP: C3a and C5a using the U-PLEX sandwich immunoassay (BD Biosciences, Franklin Lakes, United States) and sC5b9 using enzyme-linked immunosorbent assay (ELISA) (QuidelOrtho. San Diego, United States). usCD163 was tested using a commercial ELISA (Euroimmun, Luebeck, Germany) normalized to urine creatinine. UPCR levels were tested via conventional clinical methodologies. We compared 6 logistic regression models for LN vs. AAV prediction, each calculating the area under the receiver operating characteristic curve (AUC) utilizing: 1) C3a, 2) C5a, 3) sC5b9, 4) usCD163, 5) UPCR, and 6) all 5 urinary biomarkers. Results The mean levels of the 3 uCAPs, usCD163, and UPCR for LN, AAV, and healthy controls, are shown in Figure 1A-E. Among these urinary markers, mean usCD163 was significantly higher in LN (mean difference 776.80 ng/mmol, 95% CI 23.45 – 1530.15) and AAV (mean difference 502.34 ng/mmol, 95% CI 84.19 – 920.48) compared to healthy controls. UPCR was also significantly elevated among LN (mean difference 188.18 mg/mmol, 95% CI 54.80-321.55) and AAV (mean difference 93.0 mg/mmol, 95% CI 28.15-157.84) compared to controls. There were no differences among the uCAP biomarkers for LN/AAV compared to controls. When comparing LN and AAV, there were no significant differences in mean levels of any urinary biomarkers. Models 1-6 for differentiating LN vs. AAV yielded the following AUCs: C3a 0.62 (95% CI 0.34-0.90), C5a 0.56 (95% CI 0.29-0.84), sC5b9 0.56 (95% CI 0.30-0.83), usCD163 0.52 (95% CI 0.29-0.84), UPCR 0.67 (95% CI 0.42-0.92), and combined 0.79 (95% CI 0.57-1.00). (Figure 2). Figure 1. Mean concentration (95% confidence interval) of urinary biomarkers among patients with lupus nephritis (LN), ANCA-associated vasculitis (AAV), and healthy controls. A. C3a. B.C5a. C. sC5b9, D. usCD163 creatinine ratio, E. Urine protein-creatinine ratio (UPCR). * denotes p <0.05, and **p<0.01. Figure 2. Receiver operator characteristic (ROC) curves of individual urinary biomarkers and a combined model of all 5 urinary biomarkers for the differentiation of lupus nephritis (LN) and ANCA-associated vasculitis (AAV). Area under the curve (AUC) was based on logistic regression predicting LN vs. AAV for each urinary biomarker and the combination of all 5 biomarkers. Conclusions In this preliminary study, we demonstrated that both LN and AAV had higher concentrations of usCD163 compared to controls, but it was unable to differentiate between the 2 diseases. We developed a diagnostic model that combined uCAP, usCD163, and UPCR biomarkers that could differentiate between LN and AAV with an AUC of 79%. A study of larger disease and control cohorts to validate our model is underway. Acknowledgment: We would like to thank the Biobank for the Molecular Classification of Kidney Disease for supporting this work.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".