IntroductionIn the setting of early acute kidney injury (AKI), no test has been shown to definitively predict the progression to more severe stages.MethodsWe investigated the ability of a furosemide stress test (FST) (one-time dose of 1.0 or 1.5 mg/kg depending on prior furosemide-exposure) to predict the development of AKIN Stage-III in 2 cohorts of critically ill subjects with early AKI. Cohort 1 was a retrospective cohort who received a FST in the setting of AKI in critically ill patients as part of Southern AKI Network. Cohort 2 was a prospective multicenter group of critically ill patients who received their FST in the setting of early AKI.ResultsWe studied 77 subjects; 23 from cohort 1 and 54 from cohort 2; 25 (32.4%) met the primary endpoint of progression to AKIN-III. Subjects with progressive AKI had significantly lower urine output following FST in each of the first 6 hours (p<0.001). The area under the receiver operator characteristic curves for the total urine output over the first 2 hours following FST to predict progression to AKIN-III was 0.87 (p = 0.001). The ideal-cutoff for predicting AKI progression during the first 2 hours following FST was a urine volume of less than 200mls(100ml/hr) with a sensitivity of 87.1% and specificity 84.1%.ConclusionsThe FST in subjects with early AKI serves as a novel assessment of tubular function with robust predictive capacity to identify those patients with severe and progressive AKI. Future studies to validate these findings are warranted.

AKI is a common clinical condition associated with a number of adverse outcomes. More timely diagnosis would allow for earlier intervention and could improve patient outcomes. The goal of early identification of AKI has been the primary impetus for AKI biomarker research, and has led to the discovery of numerous novel biomarkers. However, in addition to facilitating more timely intervention, AKI biomarkers can provide valuable insight into the molecular mechanisms of this complex and heterogeneous disease. Furthermore, AKI biomarkers could also function as molecular phenotyping tools that could be used to direct clinical intervention. This review highlights the major studies that have characterized the diagnostic and prognostic predictive power of these biomarkers. The mechanistic relevance of neutrophil gelatinase-associated lipocalin, kidney injury molecule 1, IL-18, livertype fatty acid-binding protein, angiotensinogen, tissue inhibitor of metalloproteinase-2, and IGF-binding protein 7 to the pathogenesis and pathobiology of AKI is discussed, putting these biomarkers in the context of the progressive phases of AKI. A biomarker-integrated model of AKI is proposed, which summarizes the current state of knowledge regarding the roles of these biomarkers and the molecular and cellular biology of AKI.

Combining two differential isolation techniques magnified protein identification from human urine. Proteomic analysis of urinary proteins is a promising tool to study renal physiology and pathophysiology and to determine biomarkers of renal disease.

Controversy exists as to whether minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) represent different diseases or are manifestations within the same disease spectrum. Urinary excretion of CD80 (also known as B7.1) is elevated in patients with MCD and hence we tested whether urinary CD80 excretion might distinguish between patients with MCD from those with FSGS. Urinary CD80 was measured in 17 patients with biopsy-proven MCD and 22 with proven FSGS using a commercially available enzyme-linked immunosorbent assay and its molecular size determined by western blot analysis. A significant increase in urinary CD80, normalized to urinary creatinine, was found in patients with MCD in relapse compared to those in remission or those with FSGS. No significant differences were seen when CD80 urinary excretion from MCD patients in remission were compared to those with FSGS. In seven of eight MCD patients in relapse, CD80 was found in glomeruli by immunohistochemical analysis of their biopsy specimen. No CD80 was found in glomeruli of two patients with FSGS and another MCD patient in remission. Thus, our study supports the hypothesis that MCD and FSGS represent two different diseases rather than a continuum of one disease. Urinary CD80 excretion may be a useful marker to differentiate between MCD and FSGS.

Clinicians have access to limited tools that predict which patients with early AKI will progress to more severe stages. In early AKI, urine output after a furosemide stress test (FST), which involves intravenous administration of furosemide (1.0 or 1.5 mg/kg), can predict the development of stage 3 AKI. We measured several AKI biomarkers in our previously published cohort of 77 patients with early AKI who received an FST and evaluated the ability of FST urine output and biomarkers to predict the development of stage 3 AKI (n=25 [32.5%]), receipt of RRT (n=11 [14.2%]), or inpatient mortality (n=16 [20.7%]). With an area under the curve (AUC)6SEM of 0.8760.09 (P,0.0001), 2-hour urine output after FST was significantly better than each urinary biomarker tested in predicting progression to stage 3 (P,0.05). FST urine output was the only biomarker to significantly predict RRT (0.8660.08; P=0.001). Regardless of the end point, combining FST urine output with individual biomarkers using logistic regression did not significantly improve risk stratification (DAUC, P.0.10 for all). When FST urine output was assessed in patients with increased biomarker levels, the AUC for progression to stage 3 improved to 0.9060.06 and the AUC for receipt of RRT improved to 0.9160.08. Overall, in the setting of early AKI, FST urine output outperformed biochemical biomarkers for prediction of progressive AKI, need for RRT, and inpatient mortality. Using a FST in patients with increased biomarker levels improves risk stratification, although further research is needed.

Patients with membranous nephropathy have an increased risk of malignancy compared to the general population, but the target antigen for malignancy-associated membranous nephropathy is unknown. To explore this, we utilized mass spectrometry for antigen discovery in malignancy-associated membranous nephropathy examining immune complexes eluted from frozen kidney biopsy tissue using protein G bead immunoglobulin capture. Antigen discovery was performed comparing cases of membranous nephropathy of unknown and known type. Mass spectrophotometric analysis revealed that nerve epidermal growth factor-like 1 (NELL1) immune complexes were uniquely present within the biopsy tissue in membranous nephropathy. Additional NELL1-positive cases were subsequently identified by immunofluorescence. In a consecutive series, 3.8% of PLA2R-and THSD7A-negative cases were NELL1-positive. These NELL1-positive cases had segmental to incomplete IgG capillary loop staining (93.4%) and dominant or codominant IgG1-subclass staining (95.5%). The mean age of patients with NELL1-positive membranous nephropathy was 66.8 years, with a slight male predominance (58.2%) and 33% had concurrent malignancy. Compared with PLA2R-and THSD7A-positive cases of membranous nephropathy, there was a greater proportion of cases with malignancies in the NELL1-associated group. Thus, NELL1associated membranous nephropathy has a unique histopathology characterized by incomplete capillary loop staining, IgG1-predominance, and is more often associated with malignancy than other known types of membranous nephropathy.

Diagnosis of the type of glomerular disease that causes the nephrotic syndrome is necessary for appropriate treatment and typically requires a renal biopsy. The goal of this study was to identify candidate protein biomarkers to diagnose glomerular diseases. Proteomic methods and informatic analysis were used to identify patterns of urine proteins that are characteristic of the diseases. Urine proteins were separated by two-dimensional electrophoresis in 32 patients with FSGS, lupus nephritis, membranous nephropathy, or diabetic nephropathy. Protein abundances from 16 patients were used to train an artificial neural network to create a prediction algorithm. The remaining 16 patients were used as an external validation set to test the accuracy of the prediction algorithm. In the validation set, the model predicted the presence of the diseases with sensitivities between 75 and 86% and specificities from 92 to 67%. The probability of obtaining these results in the novel set by chance is 5 ؋ 10 ؊8 . Twenty-one gel spots were most important for the differentiation of the diseases. The spots were cut from the gel, and 20 were identified by mass spectrometry as charge forms of 11 plasma proteins: Orosomucoid, transferrin, ␣-1 microglobulin, zinc ␣-2 glycoprotein, ␣-1 antitrypsin, complement factor B, haptoglobin, transthyretin, plasma retinol binding protein, albumin, and hemopexin. These data show that diseases that cause nephrotic syndrome change glomerular protein permeability in characteristic patterns. The fingerprint of urine protein charge forms identifies the glomerular disease. The identified proteins are candidate biomarkers that can be tested in assays that are more amenable to clinical testing.

The gut microbiome is composed of a diverse population of bacteria that have beneficial and adverse effects on human health. The microbiome has recently gained attention and is increasingly noted to play a significant role in health and a number of disease states. Increasing urea concentration during chronic kidney disease (CKD) leads to alterations in the intestinal flora that can increase production of gut-derived toxins and alter the intestinal epithelial barrier. These changes can lead to an acceleration of the process of kidney injury. A number of strategies have been proposed to interrupt this pathway of injury in CKD. The purpose of this review is to summarize the role of the gut microbiome in CKD, tools used to study this microbial population, and attempts to alter its composition for therapeutic purposes.