Urinary markers differentially associate with kidney inflammatory activity and chronicity measures in patients with lupus nephritis

Abstract

Objective: Lupus nephritis (LN) is diagnosed by biopsy, but longitudinal monitoring assessment methods are needed. Here, in this preliminary and hypothesis-generating study, we evaluate the potential for using urine proteomics as a non-invasive method to monitor disease activity and damage. Urinary biomarkers were identified and used to develop two novel algorithms that were used to predict LN activity and chronicity.

Methods: Baseline urine samples were collected for four cohorts (healthy donors (HDs, n=18), LN (n=42), SLE (n=17) or non-LN kidney disease biopsy control (n=9)), and over 1 year for patients with LN (n=42). Baseline kidney biopsies were available for the LN (n=46) and biopsy control groups (n=9). High-throughput proteomics platforms were used to identify urinary analytes ≥1.5 SD from HD means, which were subjected to stepwise, univariate and multivariate logistic regression modelling to develop predictive algorithms for National Institutes of Health Activity Index (NIH-AI)/National Institutes of Health Chronicity Index (NIH-CI) scores. Kidney biopsies were analysed for macrophage and neutrophil markers using immunohistochemistry (IHC).

Results: In total, 112 urine analytes were identified from LN, SLE and biopsy control patients as both quantifiable and overexpressed compared with HDs. Regression analysis identified proteins associated with the NIH-AI (n=30) and NIH-CI (n=26), with four analytes common to both groups, demonstrating a difference in the mechanisms associated with NIH-AI and NIH-CI. Pathway analysis of the NIH-AI and NIH-CI analytes identified granulocyte-associated and macrophage-associated pathways, and the presence of these cells was confirmed by IHC in kidney biopsies. Four markers each for the NIH-AI and NIH-CI were identified and used in the predictive algorithms. The NIH-AI algorithm sensitivity and specificity were both 93% with a false-positive rate (FPR) of 7%. The NIH-CI algorithm sensitivity was 88%, specificity 96% and FPR 4%. The accuracy for both models was 93%.

Conclusions: Longitudinal predictions suggested that patients with baseline NIH-AI scores of ≥8 were most sensitive to improvement over 6-12 months. Viable approaches such as this may enable the use of urine samples to monitor LN over time.

Keywords: inflammation; lupus erythematosus, systemic; lupus nephritis.

Figure 1

Venn diagram of the number of creatinine-normalised urine analytes from the primary analyte pool that were significantly different with a ≥2-fold cutoff in patients with SLE, LN and biopsy controls relative to HDs (p value <0.05 by Wilcoxon test using Benjamini-Yekutieli false discovery rate adjustment). CLU, clusterin; DBH, dopamine beta-hydroxylase; EN-RAGE, extracellular newly identified receptor for advanced glycation end products binding protein; Fib-1C, fibulin-1C; FOLR3, folate receptor gamma; HD, healthy donor; HSP, heat shock protein; IFN-α, interferon alpha; IL-2Rα, interleukin-2 receptor alpha; IL-6R, interleukin-6 receptor; LN, lupus nephritis; LRG1, leucine-rich alpha-2-glycoprotein; MIP-1β, macrophage inflammatory protein-1 beta; MSP, macrophage-stimulating protein; RANTES, regulated on activation, normal T cell expressed and presumably secreted; SAP, serum amyloid P-component; ST2, suppression of tumorigenicity 2; TBG, thyroxine-binding globulin; TIMP-2, tissue inhibitor of metalloproteinases 2; VEGF, vascular endothelial growth factor.

Figure 2

ROC curves and confusion matrices for NIH-AI and NIH-CI predictive algorithms. (A) NIH-AI predictive algorithm ROC curve, (B) NIH-CI predictive algorithm ROC curve, (C) NIH-AI confusion matrix and (D) NIH-CI confusion matrix. AUC, area under the curve; CI, Chronicity Index; NIH-AI, National Institutes of Health Activity Index; NIH-CI, National Institutes of Health Chronicity Index; ROC, receiver operating characteristic.

Figure 3

NIH-AI and NIH-CI longitudinal predictions for patients with LN classified by index score. The solid lines represent median values over time. Dotted lines are the determined cut-off values using the predictive algorithm (NIH-AI cut-off=0.69, NIH-CI cut-off=0.5). (A) Predicted NIH-AI and (B) NIH-CI classifications using the predictive algorithm at various time points. CI, Chronicity Index; LN, lupus nephritis; NIH-AI, National Institutes of Health Activity Index; NIH-CI, National Institutes of Health Chronicity Index.

Figure 4

Kidney biopsy IHC staining results with NIH-AI score of >8 vs ≤8 from patients with LN versus biopsy controls. Macrophage-associated markers: CD163/MPO duplex stain, MMP-9; neutrophil-associated marker: neutrophil elastase. Healthy and fibrotic glomeruli were counted together. (A) Number of CD163-positive cells/mm². (B) Number of MPO-positive cells/mm². (C) Number of CD163/MPO dual-positive cells/mm². (D) Number of MMP-9-positive cells/mm². (E) Number of neutrophil elastase-positive cells/mm². *p<0.01, **p<0.05. CD, cluster of differentiation; IHC, immunohistochemistry; LN, lupus nephritis; MMP-9, matrix metalloproteinase-9; MPO, myeloperoxidase; NIH-AI, National Institutes of Health Activity Index.