Discussion
One method for measuring kidney disease activity is by histopathology using the NIH-AI and NIH-CI to score kidney biopsy tissue.14 The aim of this study was to evaluate the use of high-throughput proteomics to identify sets of urine-based LN protein markers that correlate with kidney biopsy histopathology NIH-AI/NIH-CI scoring results for a cohort of patients with LN. The data presented here demonstrate that urine samples could be used to non-invasively track alterations in the kidney over time, as longitudinal biopsies are not routinely performed.
We identified eight urinary markers (with four each correlating to NIH-AI and NIH-CI scoring indices) and created two novel algorithms that predicted NIH-AI/NIH-CI scores with ≥88% specificity and 93% accuracy. Longitudinal predictions of NIH-AI and NIH-CI scores based on urinary protein expression suggested that patients with observed baseline NIH-AI scores of >8 were most sensitive to NIH-AI improvement over a period of 6–12 months. In contrast, score changes were predicted to be less likely in patients with baseline NIH-CI scores of >4. Our data suggest that, although formal clinical criteria that would instigate treatment have not yet been defined, it may be possible to use urinary protein markers that correlate with pathology findings to non-invasively evaluate kidney health and provide valuable information beyond what is provided through standard clinical laboratory tests.
The NIH-AI measures kidney disease activity/inflammation, whereas the NIH-CI is a measure of kidney damage.1 14 The biological plausibility of our predictive algorithms is supported by the recognised roles for the eight NIH-AI/NIH-CI-associated urine proteins. The four protein markers of our NIH-AI algorithm (ApoA-II, vWF, IL-1α and IGFBP2) have all independently been shown to play key roles in inflammation and LN disease activity.10 20–28 The top four identified urine NIH-CI protein markers (IL-6Rβ, KIM-1, DBH and fetuin A) have been implicated in kidney damage/injury.29–39 The NIH-CI canonical pathways identified (leucocyte adhesion/diapedesis,40 granulocyte function41 and the role of macrophages1 42) also represent key inflammatory activities that can contribute to kidney damage.1 42–44 Taken together, our large, unbiased proteomics screen identified inflammation-associated and damage-associated markers that correlated with NIH-AI/NIH-CI, supporting our methodological approach.
In addition to the eight urinary markers selected for the predictive algorithms, our wider screen provided important insights into LN and SLE immunopathology. Of note, many of the 96 analytes that were differentially expressed in patients with LN (compared with HDs) were also common to the SLE and biopsy control cohorts, with only 17 analytes specific to patients with LN. These results indicate the commonalities in inflammatory markers across patients with SLE and LN, as well as non-lupus kidney disease. Given the known, widespread upregulation of inflammatory markers in patients with SLE that eventually drive organ damage,45–48 our results show that kidney inflammation could be monitored using accessible urine screening prior to LN diagnosis.
Furthermore, of the 30 and 26 analytes in the narrowed analyte pools that strongly correlated with NIH-AI/NIH-CI histopathology scoring, respectively, many play key roles in SLE pathogenesis and treatment. For example, our screen identified BAFF and IFN-α, two immunomodulatory proteins whose activities are inhibited by monoclonal antibodies approved for the treatment of patients with SLE.43 44 Furthermore, the narrowed analyte pools also included multiple macrophage-associated markers (CD163,49 macrophage-stimulating factor, monocyte chemoattractant protein 1,50 metallopeptidase inhibitor 1 and IL-6Rβ51 52), supporting the importance of macrophage-driven inflammation in LN pathophysiology.52 53
As levels of macrophage-associated proteins correlated with kidney histopathology, and pathway analysis revealed macrophage and granulocyte pathways associated with NIH-AI/NIH-CI, we used IHC staining to evaluate neutrophil and macrophage infiltration in kidney biopsies. We found that infiltration of macrophages and neutrophils in the kidney was associated with NIH-AI scores of >8 vs ≤8. For example, the macrophage marker MMP-9 was upregulated in patients who had NIH-AI scores of >8 compared with those who had NIH-AI scores of ≤8. MMP-9 is released by macrophages in the kidney and is known to promote macrophage recruitment, supporting a role in driving the kidney inflammation captured by the NIH-AI.54 As such, our IHC findings directly support the urine proteomic data on the presence of these cell types in LN kidneys.
Notably, while macrophage infiltration was still detectable in kidneys scored as NIH-CI >4, there was less distinction between macrophage markers in kidneys scored as NIH-CI >4 vs ≤4 kidneys than in kidneys scored as NIH-AI >8 vs ≤8, whereas neutrophil infiltration was elevated in kidneys scored as NIH-CI >4 vs those scored as NIH-CI ≤4. Our results suggest that macrophage-driven inflammatory processes detectable earlier in the disease (measured by NIH-AI) have already induced some of the kidney damage measured by the NIH-CI. Unchecked, long-term neutrophil and macrophage activation and the subsequent release of proinflammatory cytokines contribute to the eventual chronic kidney damage in patients with LN.55 Together, these findings suggest that earlier detection of kidney inflammation could prevent long-term damage accrual.
Importantly, although we detected markers of immune cell infiltration and inflammation in the kidneys of patients scored as NIH-AI >8, our longitudinal predictions suggest that disease activity decreased over time in these patients, likely as a result of ongoing treatments. Notably, there was little change in NIH-CI over time, indicating the importance of early detection and treatment of inflammation.
Limitations of this study include that several clinical characteristics were unavailable for the SLE cohort, including SLEDAI-2K scores, urine protein concentration and antidouble-stranded DNA antibody measurements. The small cohort sizes and lack of a validation cohort were also limitations. Additionally, the LN cohort was predominantly composed of patients who were black, and it is unclear whether the same mechanisms are key in LN disease in patients of different races. Also, since patients were not standardised by treatment, observed variability could be the result of confounding factors that were not captured or measured. Further, the results were limited by the number of proteins assessed; that is, urine samples were not evaluated for all possible proteins in the human proteome. Additionally, patients with SLE can show abnormal urinalysis results from processes unrelated to LN. Unfortunately, total protein measurements were not performed on the urine samples from the control group of patients with SLE without LN. Therefore, the specificity of the urinary findings in the SLE group is not fully interpretable, and evaluation in a more thoroughly characterised SLE cohort should be conducted in future studies. Further limitations included the absence of longitudinal biopsies to confirm predicted results, and the determined coefficients of analytes in the algorithm will likely vary in other patient populations. Therefore, whereas this study assessed the utility of high-throughput proteomics measurements to generate predictive LN algorithms, further studies and external validation are needed to refine these novel models in larger independent patient populations. Although extremely logistically difficult, an ideal study would assess samples from inactive SLE, active SLE without active renal disease, LN and longitudinal samples from active SLE without active renal disease until LN is diagnosed.
The evaluation of multiple biomarkers is critical for developing a tool with robust clinical utility and for characterising the complex biological mechanisms in LN pathogenesis. Comprehensive urine screening assessments provide additional information beyond traditional clinical testing regarding kidney inflammatory activity and chronicity to improve our understanding of the overall kidney status. Urine proteomics and high-throughput multiplex approaches facilitate the discovery of key immune mediators involved in LN pathogenesis and enable monitoring of disease state. Ultimately, these strategies may lead to improved non-invasive monitoring approaches for patients with LN in an effort to inform treatment decisions and positively impact their quality of life.