NOVEL URINARY BIOMARKER MODEL FOR DIFFERENTIATING LUPUS NEPHRITIS FROM ANCA-ASSOCIATED VASCULITIS
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».