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INTRODUCTION: Preeclampsia increases the risk for future chronic kidney disease (CKD). Among those diagnosed with CKD, it is unclear whether a prior history of preeclampsia, or other complications in pregnancy, negatively impact kidney disease progression. In this longitudinal analysis, we assessed kidney disease progression among women with glomerular disease with and without a history of a complicated pregnancy. METHODS: Adult women enrolled in the Cure Glomerulonephropathy study (CureGN) were classified based on a history of a complicated pregnancy (defined by presence of worsening kidney function, proteinuria, or blood pressure; or a diagnosis of preeclampsia, eclampsia, or hemolysis, elevated liver enzymes, and low platelets [HELLP] syndrome), pregnancy without these complications, or no pregnancy history at CureGN enrollment. Linear mixed models were used to assess estimated glomerular filtration rate (eGFR) trajectories and urine protein-to-creatinine ratios (UPCRs) from enrollment. RESULTS: Over a median follow-up period of 36 months, the adjusted decline in eGFR was greater in women with a history of a complicated pregnancy compared to those with uncomplicated or no pregnancies (−1.96 [−2.67, −1.26] vs. −0.80 [−1.19, −0.42] and −0.64 [−1.17, −0.11] ml/min per 1.73 m(2) per year, P = 0.007). Proteinuria did not differ significantly over time. Among those with a complicated pregnancy history, eGFR slope did not differ by timing of first complicated pregnancy relative to glomerular disease diagnosis. CONCLUSIONS: A history of complicated pregnancy was associated with greater eGFR decline in the years following glomerulonephropathy (GN) diagnosis. A detailed obstetric history may inform counseling regarding disease progression in women with glomerular disease. Continued research is necessary to better understand pathophysiologic mechanisms by which complicated pregnancies contribute to glomerular disease progression.

BACKGROUND: Heterogeneity in disease course and treatment response among patients with MCD/FSGS necessitates a granular evaluation of kidney tissue features. This study aimed to identify histologic and ultrastructural descriptors of structural changes most predictive of clinical outcomes in the Nephrotic Syndrome Study Network (NEPTUNE). METHODS: Forty-eight histologic (37 glomerular, 9 tubulointerstitial, 2 vascular) and 20 ultrastructural descriptors were quantified by applying the NEPTUNE Digital Pathology Scoring System to NEPTUNE kidney biopsies. Outcomes included time from biopsy to disease progression, first complete remission of proteinuria, and treatment response. Relative importance of pathology and clinical predictors was obtained from random forest models, and predictive discrimination was assessed. RESULTS: Among 224 participants (34% Black, 24% Hispanic), model performance was excellent, with predictive discrimination of 0.9 for disease progression, 0.85 for complete remission, and 0.81 for treatment response. The most predictive descriptors of outcomes included both conventional—e.g., global sclerosis or segmental sclerosis and interstitial fibrosis/tubular atrophy—and novel features, including adhesion, interstitial foam cells, deflation, periglomerular fibrosis, mononuclear white blood cells, endothelial cell abnormalities, microvillous transformation, and acute tubular injury. CONCLUSIONS: The most predictive descriptors of clinical outcomes among MCD/FSGS patients reflected structural changes in multiple renal compartments. Reporting these descriptors should be standardized to guide prognostication of proteinuric glomerular diseases.

Introduction: Dysregulation of sphingolipid and cholesterol metabolism contributes to the pathogenesis of glomerular diseases (GDs). Apolipoprotein M (ApoM) promotes cholesterol efflux and modulates the bioactive sphingolipid sphingosine-1-phosphate (S1P). Glomerular ApoM expression is decreased in patients with focal segmental glomerulosclerosis (FSGS). We hypothesized that glomerular ApoM deficiency occurs in GD and that ApoM expression and plasma ApoM correlate with outcomes. Methods: Patients with GD from the Nephrotic Syndrome Study Network (NEPTUNE) were studied. We compared glomerular mRNA expression of ApoM (gApoM), sphingosine kinase 1 (SPHK1), and S1P receptors 1 to 5 (S1PR1–5) in patients (n = 84) and controls (n = 6). We used correlation analyses to determine associations between gApoM, baseline plasma ApoM (pApoM), and urine ApoM (uApoM/Cr). We used linear regression to determine whether gApoM, pApoM, and uApoM/Cr were associated with baseline estimated glomerular filtration rate (eGFR) and proteinuria. Using Cox models, we determined whether gApoM, pApoM, and uApoM/Cr were associated with complete remission (CR) and the composite of end-stage kidney disease (ESKD) or ≥40% eGFR decline. Results: gApoM was reduced (P < 0.01) and SPHK1 and S1PR1 to 5 expression was increased (P < 0.05) in patients versus controls, consistent with ApoM/S1P pathway modulation. gApoM positively correlated with pApoM in the overall cohort (r = 0.34, P < 0.01) and in the FSGS (r = 0.48, P < 0.05) and minimal change disease (MCD) (r = 0.75, P < 0.05) subgroups. Every unit decrease in gApoM and pApoM (log2) was associated with a 9.77 ml/min per 1.73 m2 (95% confidence interval [CI]: 3.96−15.57) and 13.26 ml/min per 1.73 m2 (95% CI: 3.57−22.96) lower baseline eGFR, respectively (P < 0.01). From Cox models adjusted for age, sex, or race, pApoM was a significant predictor of CR (hazard ratio [HR]: 1.85; 95% CI: 1.06–3.23). Conclusions: pApoM is a potential noninvasive biomarker of gApoM deficiency and strongly associates with clinical outcomes in GD.

Background: Primary nephrotic syndromes are rare diseases which impedes adequate sample size for observational patient-oriented research and clinical trial enrollment. A computable phenotype may be powerful in identifying patients with these diseases for research across multiple institutions. Methods: A comprehensive algorithm of inclusion and exclusion ICD-9 and ICD-10 codes to identify patients with primary nephrotic syndrome was developed. The algorithm was executed against the PCORnet® CDM at 3 institutions from Jan 1, 2009 to Jan 1, 2018, where a random selection of 50 cases and 50 non-cases (individuals not meeting case criteria seen within the same calendar year and within five years of age of a case) were reviewed by a nephrologist, for a total of 150 cases and 150 non-cases reviewed. The classification accuracy (sensitivity, specificity, positive and negative predictive value, F1 score) of the computable phenotype was determined. Results: The algorithm identified a total of 2,708 patients with nephrotic syndrome from 4,305,092 distinct patients in the CDM at all sites from 2009-2018. For all sites, the sensitivity, specificity, and area under the curve of the algorithm were 99% (95% CI: 97-99%), 79% (95% CI: 74-85%), and 0.9 (0.84-0.97), respectively. The most common causes of false positive classification were secondary FSGS (9/39) and lupus nephritis (9/39). Conclusion: This computable phenotype had good classification in identifying both children and adults with primary nephrotic syndrome utilizing only ICD-9 and ICD-10 codes, which are available across institutions in the United States. This may facilitate future screening and enrollment for research studies and enable comparative effectiveness research. Further refinements to the algorithm including use of laboratory data or addition of natural language processing may help better distinguish primary and secondary causes of nephrotic syndrome.

Kidney fibrosis constitutes the shared final pathway of nearly all chronic nephropathies, but biomarkers for the non-invasive assessment of kidney fibrosis are currently not available. To address this, we characterize five candidate biomarkers of kidney fibrosis: Cadherin-11 (CDH11), Sparc-related modular calcium binding protein-2 (SMOC2), Pigment epithelium-derived factor (PEDF), Matrix-Gla protein, and Thrombospondin-2. Gene expression profiles in single-cell and single-nucleus RNA-sequencing (sc/snRNA-seq) datasets from rodent models of fibrosis and human chronic kidney disease (CKD) were explored, and Luminex-based assays for each biomarker were developed. Plasma and urine biomarker levels were measured using independent prospective cohorts of CKD: the Boston Kidney Biopsy Cohort, a cohort of individuals with biopsy-confirmed semiquantitative assessment of kidney fibrosis, and the Seattle Kidney Study, a cohort of patients with common forms of CKD. Ordinal logistic regression and Cox proportional hazards regression models were used to test associations of biomarkers with interstitial fibrosis and tubular atrophy and progression to end-stage kidney disease and death, respectively. Sc/snRNA-seq data confirmed cell-specific expression of biomarker genes in fibroblasts. After multivariable adjustment, higher levels of plasma CDH11, SMOC2, and PEDF and urinary CDH11 and PEDF were significantly associated with increasing severity of interstitial fibrosis and tubular atrophy in the Boston Kidney Biopsy Cohort. In both cohorts, higher levels of plasma and urinary SMOC2 and urinary CDH11 were independently associated with progression to end-stage kidney disease. Higher levels of urinary PEDF associated with end-stage kidney disease in the Seattle Kidney Study, with a similar signal in the Boston Kidney Biopsy Cohort, although the latter narrowly missed statistical significance. Thus, we identified CDH11, SMOC2, and PEDF as promising non-invasive biomarkers of kidney fibrosis.

COVID-19 morbidity and mortality are increased in patients with diabetes and kidney disease via unknown mechanisms. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) for entry into host cells. Since ACE2 is a susceptibility factor for infection, we investigated how diabetic kidney disease and medications alter ACE2 receptor expression in kidneys. Single cell RNA profiling of healthy living donor and kidney biopsies from patients with diabetic kidney disease revealed ACE2 expression primarily in proximal tubular epithelial cells. This cell specific localization was confirmed by in situ hybridization. ACE2 expression levels were unaltered by exposures to renin angiotensin aldosterone system inhibitors in diabetic kidney disease. Bayesian integrative analysis of a large compendium of public-omics datasets identified molecular network modules induced in ACE2-expressing proximal tubular epithelial cells in diabetic kidney disease (searchable at hb.flatironinstitute.org/covid-kidney) that were linked to viral entry, immune activation, endomembrane reorganization, and RNA processing. The diabetic kidney disease ACE2-positive proximal tubular epithelial cell module overlapped with expression patterns seen in SARS-CoV-2 infected cells. Similar cellular programs were seen in ACE2-positive proximal tubular epithelial cells obtained from urine samples of 13hospitalized patients with COVID-19, suggesting a consistent ACE2-coregulated proximal tubular epithelial cell expression program that may interact with the SARS-CoV-2 infection processes. Thus SARS-CoV-2 receptor networks can seed further research into risk stratification and therapeutic strategies for COVID-19 related kidney damage., Graphical abstract

Neighborhoods are where we live, learn, work, pray, and play. Growing evidence indicates that neighborhoods are an important determinant of health. The built features of our neighborhoods, such as the ways in which the streets are designed and connected and the availability of green spaces and transit stops, as well as the social features, such as the trust among neighbors and the perceptions of safety, may influence health through multiple pathways, such as access to important resources, psychosocial stress, and health behaviors. In particular, the extant literature consistently documents an association between neighborhood features and renal-associated conditions, such as cardiovascular disease, hypertension, diabetes, and obesity. There is also some evidence suggesting an association between neighborhood poverty and ESKD. The link between neighborhood and earlier stages of CKD, however, has been less clear, with most studies documenting no association. It may be that the neighborhood measures used in previous studies do not capture features of the neighborhood important for earlier stages of disease development and progression. It may also be that our current biomarkers (e.g., eGFR) and urine protein are not able to pick up very early forms of renal damage because of the kidney’s overall high reserve capacity. This paper critically reviews the state of the literature on neighborhood and renal disease, with recommendations for neighborhood measures in future research. Neighborhoods are designed, built, and informed by policy, and thus, they are amenable to intervention, making them a potentially powerful way to improve renal health and reduce health inequalities at the population level.

Longitudinal biomarker data are often collected in studies, providing important information regarding the probability of an outcome of interest occurring at a future time. With many new and evolving technologies for biomarker discovery, the number of biomarker measurements available for analysis of disease progression has increased dramatically. A large amount of data provides a more complete picture of a patient's disease progression, potentially allowing us to make more accurate and reliable predictions, but the magnitude of available data introduces challenges to most statistical analysts. Existing approaches suffer immensely from the curse of dimensionality. In this article, we propose methods for making dynamic risk predictions using repeatedly measured biomarkers of a large dimension, including cases when the number of biomarkers is close to the sample size. The proposed methods are computationally simple, yet sufficiently flexible to capture complex relationships between longitudinal biomarkers and potentially censored events times. The proposed approaches are evaluated by extensive simulation studies and are further illustrated by an application to a data set from the Nephrotic Syndrome Study Network.

Rationale & Objective Surrogate outcomes for end-stage kidney disease often assume linear changes, which may not reflect true estimated glomerular filtration rate (eGFR) trajectories. This study’s objective was to characterize nonlinear eGFR trajectories in nephrotic syndrome. Study Design Observational cohort study. Setting & Participants Nephrotic Syndrome Study Network (NEPTUNE) is a multicenter study of adult and pediatric patients with proteinuria enrolled at clinically indicated kidney biopsy or initial presentation of disease (pediatric only). Predictors Patient demographic, clinical, and pathology variables at study enrollment and follow-up time. Outcome eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (patients ≥ 18 years old) or modified Chronic Kidney Disease in Children Study–Schwartz (patients < 18 years) formulas. The probability of nonlinearity (PNL) was calculated for individual eGFR trajectories. Analytical Approach Associations between predictors and PNL were assessed using multivariable linear regression. Results 453 patients with ≥3 eGFR measurements and 1 or more year of follow-up were included (median follow-up, 3.6 years). Median PNL was 0.052; 56% and 16% had PNL < 10% and >50%, respectively. In both adults and pediatric patients, higher baseline eGFR was associated with higher PNL, whereas longer follow-up time was associated with lower PNL. Higher urine protein-creatinine ratio and steroid use were also associated with higher PNL in adults. Higher percentages of tubular atrophy and foot-process effacement were associated with lower and higher PNLs, respectively, in adults. Limitations Relatively short follow-up time, inability to assess acute kidney injury events, and variable eGFR measurement frequency across patients. Conclusions Although increasing follow-up time resulted in more linear trajectories, nonlinear eGFR trajectories were common in this cohort. Future studies in nephrotic syndrome should consider novel outcomes that do not rely on linearity assumptions., Graphical abstract

Chronic kidney diseases (CKDs) are currently classified according to their clinical features, associated comorbidities and pattern of injury on biopsy. Even within a given classification, considerable variation exists in disease presentation, progression and response to therapy, highlighting heterogeneity in the underlying biological mechanisms. As a result, patients and clinicians experience uncertainty when considering optimal treatment approaches and risk projection. Technological advances now enable large-scale datasets, including DNA and RNA sequence data, proteomics and metabolomics data, to be captured from individuals and groups of patients along the genotype–phenotype continuum of CKD. The ability to combine these high-dimensional datasets, in which the number of variables exceeds the number of clinical outcome observations, using computational approaches such as machine learning, provides an opportunity to re-classify patients into molecularly defined subgroups that better reflect underlying disease mechanisms. Patients with CKD are uniquely poised to benefit from these integrative, multi-omics approaches since the kidney biopsy, blood and urine samples used to generate these different types of molecular data are frequently obtained during routine clinical care. The ultimate goal of developing an integrated molecular classification is to improve diagnostic classification, risk stratification and assignment of molecular, disease-specific therapies to improve the care of patients with CKD. Classification of kidney diseases according to their molecular mechanisms has potential to improve patient outcomes through the identification of targeted therapeutic approaches. This Review provides an overview of the ways in which omics and other data types can be integrated to enhance our understanding of the mechanisms underlying kidney function and failure.

Alport syndrome is an inherited disease characterized by progressive loss of kidney function. We aimed to evaluate the safety and efficacy of bardoxolone methyl in patients with Alport syndrome.We randomly assigned patients with Alport syndrome, ages 12-70 years and eGFR 30-90 ml/min per 1.73 mPatients randomized to bardoxolone methyl experienced preservation in eGFR relative to placebo at 48 and 100 weeks (between-group differences: 9.2 [97.5% confidence interval, 5.1 to 13.4;In adolescent and adult patients with Alport syndrome receiving standard of care, treatment with bardoxolone methyl resulted in preservation in eGFR relative to placebo after a 2-year study period; off-treatment results using all available data were not significantly different.A Phase 2/3 Trial of the Efficacy and Safety of Bardoxolone Methyl in Patients with Alport Syndrome - CARDINAL (CARDINAL), NCT03019185.

The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing.

Hyperfiltration is a state of high glomerular filtration rate (GFR) observed in early diabetes that damages glomeruli, resulting in an iterative process of increasing filtration load on fewer and fewer remaining functional glomeruli. To delineate underlying cellular mechanisms of damage associated with hyperfiltration, transcriptional profiles of kidney biopsies from Pima Indians with type 2 diabetes with or without early-stage diabetic kidney disease were grouped into two hyperfiltration categories based on annual iothalamate GFR measurements. Twenty-six participants with a peak GFR measurement within two years of biopsy were categorized as the hyperfiltration group, and 26 in whom biopsy preceded peak GFR by over two years were considered pre-hyperfiltration. The hyperfiltration group had higher hemoglobin A1c, higher urine albumin-to-creatinine ratio, increased glomerular basement membrane width and lower podocyte density compared to the pre-hyperfiltration group. A glomerular 1240-gene transcriptional signature identified in the hyperfiltration group was enriched for endothelial stress response signaling genes, including endothelin-1, tec-kinase and transforming growth factor-β1 pathways, with the majority of the transcripts mapped to endothelial and inflammatory cell clusters in kidney single cell transcriptional data. Thus, our analysis reveals molecular pathomechanisms associated with hyperfiltration in early diabetic kidney disease involving putative ligand-receptor pairs with downstream intracellular targets linked to cellular crosstalk between endothelial and mesangial cells.

An understanding of the ethical underpinnings of human subjects research that involves some risk to participants without anticipated direct clinical benefit-such as the kidney biopsy procedure as part of the Kidney Precision Medicine Project (KPMP)-requires a critical examination of the risks as well as the diverse set of countervailing potential benefits to participants. This kind of deliberation has been foundational to the development and conduct of the KPMP. Herein, we use illustrative features of this research paradigm to develop a more comprehensive conceptualization of the types of benefits that may be important to research participants, including respecting pluralistic values, supporting the opportunity to act altruistically, and enhancing benefits to a participant's community. This approach may serve as a model to help researchers, ethicists, and regulators to identify opportunities to better respect and support participants in future research that entails some risk to these participants as well as to improve the quality of research for people with kidney disease.

The current classification system for focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) does not fully capture the complex structural changes in kidney biopsies, nor the clinical and molecular heterogeneity of these diseases. The Nephrotic Syndrome Study Network (NEPTUNE) Digital Pathology Scoring System (NDPSS) was applied to 221 NEPTUNE FSGS/MCD digital kidney biopsies for glomerular scoring using 37 descriptors. The descriptor-based glomerular profiles were used to cluster patients with similar morphologic characteristics. Glomerular descriptors and patient clusters were assessed for association with time to proteinuria remission and disease progression by using adjusted Cox models, and eGFR measures over time by using linear mixed models. Messenger RNA from glomerular tissue was used to assess differentially expressed genes (DEG) between clusters and identify genes associated with individual descriptors driving cluster membership. Three clusters were identified: X (N=56), Y (N=68), and Z (N=97). Clusters Y and Z had higher probabilities of proteinuria remission (HR [95% CI]= 1.95 [0.99, 3.85] and 3.29 [1.52, 7.13], respectively), lower hazards of disease progression 0.22 [0.08, 0.57] and 0.11 [0.03, 0.45], respectively), and greater loss of eGFR over time compared with X. Cluster X had 1920 DEGs compared to Y+Z, which reflected activation of pathways of immune response and inflammation. Six individual descriptors driving the clusters individually correlated with clinical outcomes and gene expression. The NDPSS allows for characterization of FSGS/MCD patients into clinically and biologically relevant categories and uncovers histologic parameters associated with clinical outcomes and molecular signatures not included in current classification systems.TRANSLATIONAL STATEMENTFSGS and MCD are heterogeneous diseases that manifest with a variety of structural changes often not captured by conventional classification systems. This study shows that a detailed morphologic analysis and quantification of these changes allows for better representation of the structural abnormalities within each patient and for grouping patients with similar morphologic profiles into categories that are clinically and biologically relevant.

Hyperfiltration (HF) is a state of high glomerular filtration rate (GFR) observed in early diabetes that damages glomeruli, resulting in an iterative process of increasing filtration load on fewer and fewer remaining functional glomeruli. To delineate underlying cellular mechanisms of damage induced by HF, transcriptional profiles of kidney biopsies from Pima Indians with type 2 diabetes with or without early-stage diabetic kidney disease (DKD) were grouped into two HF categories based on annual iothalamate GFR measurements. Twenty-six participants with a peak GFR measurement within two years of biopsy were categorized as the “HF group”, and 26 in whom biopsy preceded peak GFR by >2 years were considered “pre-HF”. The HF group had higher hemoglobin A1c, higher urine albumin-to-creatinine ratio, increased glomerular basement membrane width and lower podocyte density compared to the pre-HF group. A glomerular 1240-gene transcriptional signature identified in the HF group was enriched for endothelial stress response signaling genes, including from endothelin-1, tec-kinase and TGF-β1 pathways, with the majority of the transcripts mapped to endothelial and inflammatory cell clusters in kidney single cell transcriptional data. This analysis reveals molecular pathomechanisms contributing to development of HF and early DKD and involving putative ligand-receptor pairs and downstream intracellular targets linked to cellular crosstalk between endothelial and mesangial cells.

BackgroundClassification of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies. This approach does not reflect underlying disease biology, limiting the ability to predict progression or treatment response.MethodsSystems biology approaches were used to categorize patients with minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) based on kidney biopsy tissue transcriptomics across three cohorts and assessed association with clinical outcomes. Patient-level tissue pathway activation scores were generated using differential gene expression. Then, functional enrichment and non-invasive urine biomarker candidates were identified. Biomarkers were validated in kidney organoid models and single nucleus RNA-seq (snRNAseq) from kidney biopsies.ResultsTranscriptome-based categorization identified three subgroups of patients with shared molecular signatures across independent North American, European and African cohorts. One subgroup demonstrated worse longterm outcomes (HR 5.2, p = 0.001) which persisted after adjusting for diagnosis and clinical measures (HR 3.8, p = 0.035) at time of biopsy. This subgroup’s molecular profile was largely (48%) driven by tissue necrosis factor (TNF) activation and could be predicted based on levels of TNF pathway urinary biomarkers TIMP-1 and MCP-1 and clinical features (correlation 0.63, p ConclusionsMolecular profiling identified a patient subgroup within nephrotic syndrome with poor outcome and kidney TNF pathway activation. Clinical trials using non-invasive biomarkers of pathway activation to target therapies are currently being evaluated.Significance StatementMechanistic, targeted therapies are urgently needed for patients with nephrotic syndrome. The inability to target an individual’s specific disease mechanism using currently used diagnostic parameters leads to potential treatment failure and toxicity risk. Patients with focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) were grouped by kidney tissue transcriptional profiles and a subgroup associated with poor outcomes defined. The segregation of the poor outcome group was driven by tumor necrosis factor (TNF) pathway activation and could be identified by urine biomarkers, MCP1 and TIMP1. Based on these findings, clinical trials utilizing non-invasive biomarkers of pathway activation to target therapies, improve response rates and facilitate personalized treatment in nephrotic syndrome have been initiated.

Background The rarity of pediatric glomerular disease makes it difficult to identify sufficient numbers of participants for clinical trials. This leaves limited data to guide improvements in care for these patients. Methods The authors developed and tested an electronic health record (EHR) algorithm to identify children with glomerular disease. We used EHR data from 231 patients with glomerular disorders at a single center to develop a computerized algorithm comprising diagnosis, kidney biopsy, and transplant procedure codes. The algorithm was tested using PEDSnet, a national network of eight children's hospitals with data on >6.5 million children. Patients with three or more nephrologist encounters (n=55,560) not meeting the computable phenotype definition of glomerular disease were defined as nonglomerular cases. A reviewer blinded to case status used a standardized form to review random samples of cases (n=800) and nonglomerular cases (n=798). Results The final algorithm consisted of two or more diagnosis codes from a qualifying list or one diagnosis code and a pretransplant biopsy. Performance characteristics among the population with three or more nephrology encounters were sensitivity, 96% (95% CI, 94% to 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%); negative predictive value, 97% (95% CI, 96% to 98%); and area under the receiver operating characteristics curve, 94% (95% CI, 93% to 95%). Requiring that the sum of nephrotic syndrome diagnosis codes exceed that of glomerulonephritis codes identified children with nephrotic syndrome or biopsy-based minimal change nephropathy, FSGS, or membranous nephropathy, with 94% sensitivity and 92% PPV. The algorithm identified 6657 children with glomerular disease across PEDSnet, ≥50% of whom were seen within 18 months. Conclusions The authors developed an EHR-based algorithm and demonstrated that it had excellent classification accuracy across PEDSnet. This tool may enable faster identification of cohorts of pediatric patients with glomerular disease for observational or prospective studies.

Background and objectives Identifying novel biomarkers is critical to advancing diagnosis and treatment of CKD, but relies heavily on the statistical methods used. Inappropriate methods can lead to both false positive and false negative associations between biomarkers and outcomes. This study assessed accuracy of methods using computer simulations and compared biomarker association estimates in the NEPhrotic syndrome sTUdy NEtwork (NEPTUNE), a prospective cohort study of patients with glomerular disease. Design, setting, participants, & measurements We compared three methods for analyzing repeatedly measured biomarkers in proportional hazards models: (1) time-invariant average, that averages values over all follow-up and uses the average as a baseline covariate, (2) time-varying last observation carried forward (LOCF), that assumes the covariate is unchanged until the next observed value, and (3) time-varying cumulative average, that updates the average using values at or before each measurement. Results Under both true mechanisms of LOCF and cumulative average, simulation results showed the time-invariant average method often gave extremely inaccurate results. When LOCF was the true association mechanism, the cumulative average method often gave overestimated association estimates that were further away from the null. When cumulative average was the true mechanism, LOCF always underestimated the associations, i.e., closer to the null. In NEPTUNE, compared with the LOCF or cumulative average methods, hazard ratios estimated from the time-invariant average method were always higher. Conclusions Different analytic methods resulted in markedly different results. Using the time-invariant average produces inaccurate association estimates, whereas other methods can estimate additive (cumulative average) or instantaneous (LOCF) associations depending on the hypothesized underlying association mechanism and research question.

Rationale & Objective - An elevated glomerular filtration rate (GFR), or renal hyperfiltration, may predispose individuals to subsequent rapid GFR decline in diabetes, obesity, and metabolic syndrome. Although this hypothesis is supported by results of experimental studies, the importance of hyperfiltration at the population level remains controversial. We investigated whether higher baseline GFR predicts a steeper decline in GFR. Study Design - Longitudinal cohort studies. Setting & Participants - 1,594 middle-aged Norwegians without diabetes (the Renal Iohexol Clearance Survey [RENIS]) and 319 Pima Indians (83% with type 2 diabetes). Predictor - Baseline measured GFR using exogenous clearance methods. Outcomes - Change in measured GFR over time. Analytical Approach - Linear mixed regression models fit to assess the correlation between the random intercept (reflecting baseline GFR) and random slope (change in GFR over time). Results - Mean baseline GFRs were 104.0 ± 20.1 (SD) and 149.4 ± 43.3 mL/min, and median follow-up durations were 5.6 (IQR, 5.2-6.0) and 9.1 (IQR, 4.0-15.0) years in the RENIS and Pima cohorts, respectively. Correlation between baseline GFR (random intercept) and slope of GFR decline was −0.31 (95% CI, −0.40 to −0.23) in the RENIS cohort and −0.41 (95% CI, −0.55 to −0.26) in the Pima cohort, adjusted for age, sex, height, and weight, suggesting that higher baseline GFRs were associated with steeper GFR decline rates. Limitations - Different methods for measuring GFR in the 2 cohorts. Renal hyperfiltration may not reflect higher single-nephron GFR. GFR decline is assumed to be linear, which may not match the actual pattern; observed correlations may arise from natural variation. Conclusions - Higher baseline GFR is associated with faster decline in GFR over time. If this relationship were causal, elevated GFR would represent a potentially modifiable risk factor for medium- to long-term GFR decline.

Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living kidney donor biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomics analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for ten years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.

Background High data quality is of crucial importance to the integrity of research projects. In the conduct of multi-center observational cohort studies with increasing types and quantities of data, maintaining data quality is challenging, with few published guidelines. Methods The Cure Glomerulonephropathy (CureGN) Network has established numerous quality control procedures to manage the 70 participating sites in the United States, Canada, and Europe. This effort is supported and guided by the activities of several committees, including Data Quality, Recruitment and Retention, and Central Review, that work in tandem with the Data Coordinating Center to monitor the study. We have implemented coordinator training and feedback channels, data queries of questionable or missing data, and developed performance metrics for recruitment, retention, visit completion, data entry, recording of patient-reported outcomes, collection, shipping and accessing of biological samples and pathology materials, and processing, cataloging and accessing genetic data and materials. Results We describe the development of data queries and site Report Cards, and their use in monitoring and encouraging excellence in site performance. We demonstrate improvements in data quality and completeness over 4 years after implementing these activities. We describe quality initiatives addressing specific challenges in collecting and cataloging whole slide images and other kidney pathology data, and novel methods of data quality assessment. Conclusions This paper reports the CureGN experience in optimizing data quality and underscores the importance of general and study-specific data quality initiatives to maintain excellence in the research measures of a multi-center observational study.

Introduction: Alport syndrome is a rare genetic disorder that affects as many as 60,000 persons in the USA and a total of 103,000 persons (200 pg/mL at baseline or with significant cardiovascular histories were excluded. Patients were randomized 1:1 to bardoxolone methyl or placebo, with stratification by baseline UACR. Results: A total of 371 patients were screened, and 157 patients were randomly assigned to receive bardoxolone methyl ( n = 77) or placebo ( n = 80). The average age at screening was 39.2 years, and 23 (15%) were

Rationale & Objective The current classification system for focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) does not fully capture the complex structural changes in kidney biopsies, nor the clinical and molecular heterogeneity of these diseases. Study Design Prospective observational cohort study. Setting & Participants N=221 MCD and FSGS patients enrolled in the Nephrotic Syndrome Study Network (NEPTUNE). Exposures The NEPTUNE Digital Pathology Scoring System (NDPSS) was applied to generate scores for 37 glomerular descriptors. Outcomes Time from biopsy to complete proteinuria remission, time from biopsy to kidney disease progression (40% eGFR decline or kidney failure), and eGFR over time. Analytical Approach Cluster analysis was used to group patients with similar morphologic characteristics. Glomerular descriptors and patient clusters were assessed for associations with outcomes using adjusted Cox models and linear mixed models. Messenger RNA from glomerular tissue was used to assess differentially expressed genes between clusters and identify genes associated with individual descriptors driving cluster membership. Results Three clusters were identified: X (N=56), Y (N=68), and Z (N=97). Clusters Y and Z had higher probabilities of proteinuria remission (HR [95% CI]= 1.95 [0.99, 3.85] and 3.29 [1.52, 7.13], respectively), lower hazards of disease progression 0.22 [0.08, 0.57] and 0.11 [0.03, 0.45], respectively), and lower loss of eGFR over time compared with X. Cluster X had 1920 differentially expressed genes compared to Y+Z, which reflected activation of pathways of immune response and inflammation. Six descriptors driving the clusters individually correlated with clinical outcomes and gene expression. Limitations Low prevalence of some descriptors and biopsy at a single time point. Conclusions The NDPSS allows for categorization of FSGS/MCD patients into clinically and biologically relevant subgroups, and uncovers histologic parameters associated with clinical outcomes and molecular signatures not included in current classification systems.

Vogt, Bruno/0000-0002-1548-6387; Dufek, Stephanie/0000-0002-6323-6673; Liu, Lili/0000-0002-2622-9669; Paczek, Leszek/0000-0003-0160-3009; Cui, Zhao/0000-0002-5837-1926; amoroso, antonio/0000-0002-9437-9407; Rosen, Raphael/0000-0003-1025-1965; Loos, Ruth/0000-0002-8532-5087; coenen, marieke/0000-0001-8796-2031; zanoni, francesca/0000-0001-9567-6713; CUCCA, Francesco/0000-0002-7414-1995; Ars, Elisabet/0000-0002-4118-4358; Hamilton, Patrick/0000-0001-6703-3745 WOS:000563559600001 PubMed ID: 32231244 Membranous Nephropathy (MN) is a rare autoimmune cause of kidney failure. Here we report a genome-wide association study (GWAS) for primary MN in 3,782 cases and 9,038 controls of East Asian and European ancestries. We discover two previously unreported loci, NFKB1 (rs230540, OR = 1.25, P = 3.4 x 10(-12)) and IRF4 (rs9405192, OR = 1.29, P = 1.4 x 10(-14)), fine-map the PLA2R1 locus (rs17831251, OR = 2.25, P = 4.7 x 10(-103)) and report ancestry-specific effects of three classical HLA alleles: DRB1*1501 in East Asians (OR = 3.81, P = 2.0 x 10(-49)), DQA1*0501 in Europeans (OR = 2.88, P = 5.7 x 10(-93)), and DRB1*0301 in both ethnicities (OR = 3.50, P = 9.2 x 10(-23) and OR = 3.39, P = 5.2 x 10(-82), respectively). GWAS loci explain 32% of disease risk in East Asians and 25% in Europeans, and correctly re-classify 20-37% of the cases in validation cohorts that are antibody-negative by the serum anti-PLA2R ELISA diagnostic test. Our findings highlight an unusual genetic architecture of MN, with four loci and their interactions accounting for nearly one-third of the disease risk. National Institute for Diabetes and Digestive Kidney Diseases (NIDDK) [RC2-DK116690, R01-DK105124, R01-DK097053, R01-DK108805]; National Institute on Minority Health and Health Disparities (NIMHD)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Minority Health & Health Disparities (NIMHD) [R01-MD009223]; Charles Woodson Clinical Research Fund; Nephrotic Syndrome Study Network Consortium (NEPTUNE) [U54-DK-083912]; Columbia University, Columbia Glomerular Center; Office of Rare Diseases Research, National Center for Advancing Translational Sciences (NCATS); National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Diabetes & Digestive & Kidney Diseases (NIDDK); University of MichiganUniversity of Michigan System; NephCure Kidney International; Halpin Foundation; National Key Research and Development Program of China [2016YFC0904100]; Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81621092]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81870460, 81570598]; Science and Technology Innovation Action Plan of Shanghai Science and Technology Committee [17441902200]; Shanghai Municipal Education Commission, Gaofeng, Clinical Medicine Grant [20152207]; Shanghai Jiao Tong University School of Medicine, Multi-Center Clinical Research Project [DLY201510]; International Cooperation and Exchange Projects of Shanghai Science and Technology Committee [14430721000]; Outstanding Young Scholar Award [81622009]; Shanghai Health and Family Planning Committee Hundred Talents Program [2018BR37]; Seoul National University Hospital Human Biobank, a member of the National Biobank of Korea - Ministry of Health and Welfare, Republic of Korea; MRCMedical Research Council UK (MRC) [MR/J010847/1]; Manchester Academic Health Science Centre [MAHSC 186/200]; Greater Manchester Local Clinical Research Network; Kidneys for Life Charity; David and Elaine Potter Charitable Foundation; St Peter's Trust for Kidney, Bladder and Prostate Research; Kids Kidney Research UKKidney Research UK (KRUK); Kidney Research UKKidney Research UK (KRUK); Italian Ministry of Health grantMinistry of Health, Italy [GR-2011-02350438]; Department of Excellence Grant 2018-2022 - Italian Ministry of Education for the Department of Medical Sciences of the University of Turin; Columbia University; Poznan University of Medical Sciences, Poland; German Ministry of Education and Research (BMBF)Federal Ministry of Education & Research (BMBF) [01ER0804]; KfH Foundation for Preventive Medicine; Bayer Pharma AG; German Research FoundationGerman Research Foundation (DFG) [CRC 1140, KO 3598/3-1, CRC 992]; European Research CouncilEuropean Research Council (ERC) [ERC-2012ADG_2012-0314, 322947]; 7th Framework Programme of the European Community contract [2012-305608]; National Research Agency grant MNaims [ANR-17-CE17-0012-01]; Dutch Kidney Foundation [OW08, KJPB11.021]; National Human Genome Research Institute (NHGRI)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Human Genome Research Institute (NHGRI); NIMHD We are grateful to all study participants across multiple nephrology centres worldwide for their contributions to this work. This work was supported by the following institutions, grants and funding agencies in the US: Columbia University, Columbia Glomerular Center, National Institute for Diabetes and Digestive Kidney Diseases (NIDDK) grants RC2-DK116690 (K.K., M.K.), R01-DK105124 (K.K.), R01-DK097053 (L.H.B., M.K.), R01-DK108805 (M.G.S.), and National Institute on Minority Health and Health Disparities (NIMHD) grant R01-MD009223 (K.K., A.G.G., A.B.). M.G.S. is additionally supported by the Charles Woodson Clinical Research Fund. The Nephrotic Syndrome Study Network Consortium (NEPTUNE), U54-DK-083912, is a part of the National Institutes of Health (NIH) Rare Disease Clinical Research Network (RDCRN), supported through a collaboration between the Office of Rare Diseases Research, National Center for Advancing Translational Sciences (NCATS) and the National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK). Additional funding and/or programmatic support for this project has also been provided by the University of Michigan, the NephCure Kidney International and the Halpin Foundation. The recruitment and analysis of the Chinese cohorts were supported by the National Key Research and Development Program of China (2016YFC0904100), Natural Science Foundation of China to the Innovation Research Group (81621092), National Natural Science Foundation of China (No. 81870460, 81570598), Science and Technology Innovation Action Plan of Shanghai Science and Technology Committee (No.17441902200), Shanghai Municipal Education Commission, Gaofeng, Clinical Medicine Grant (No.20152207), Shanghai Jiao Tong University School of Medicine, Multi-Center Clinical Research Project (No.DLY201510), International Cooperation and Exchange Projects of Shanghai Science and Technology Committee (No.14430721000), the Outstanding Young Scholar Award for Zhao Cui (No.81622009), and Shanghai Health and Family Planning Committee Hundred Talents Program for Jingyuan Xie (No.2018BR37). The recruitment of the Korean cohort was supported by the Seoul National University Hospital Human Biobank, a member of the National Biobank of Korea, financed by the Ministry of Health and Welfare, Republic of Korea. P.B. and P.H. acknowledge financial support from MRC project "Autoimmunity in Membranous Nephropathy", grant MR/J010847/1 which funded the sample collection from MN patients across the UK. P.B., P.H. and S.H. acknowledge support from Manchester Academic Health Science Centre (MAHSC 186/200), the Greater Manchester Local Clinical Research Network and Kidneys for Life Charity for supporting research in MN in Manchester. We are grateful to the MENTOR study (clinical trials no. NCT01180036), for contributing blood samples of trial participants. The UK cohort was supported in part by grants from the David and Elaine Potter Charitable Foundation (to S.H.P. and R.K.), St Peter's Trust for Kidney, Bladder and Prostate Research (to D.B., H.C.S., S.H.P. and R.K.), Kids Kidney Research UK and Kidney Research UK (to D.B. and R.K.). The Italian cohorts were supported by the Italian Ministry of Health grant GR-2011-02350438 (G.Z., S.G.) and the Department of Excellence Grant 2018-2022 funded by the Italian Ministry of Education for the Department of Medical Sciences of the University of Turin (A.A.).; The recruitment of Polish cases was sponsored by the Polish Kidney Genetics Network (POLYGENES), a collaborative effort between Columbia University and Poznan University of Medical Sciences, Poland. The full list of POLYGENES collaborators can be found in the Supplementary Materials. The GCKD (German Chronic Kidney Disease) study was funded by grants from the German Ministry of Education and Research (BMBF, No. 01ER0804) and the KfH Foundation for Preventive Medicine, with genotyping supported by Bayer Pharma AG. The list of GCKD investigators can be found in the Supplementary Materials. The work of M.W. and A.K. was funded by the CRC 1140 Initiative and by KO 3598/3-1 and CRC 992 (A.K.) of the German Research Foundation. The work of E.H. and R.A.K.S. was funded by the CRC 1192 from the German Research Foundation (Projects B1 and C1). P.R. is a recipient of European Research Council ERC-2012ADG_2012-0314 grant 322947, 7th Framework Programme of the European Community contract 2012-305608 (European Consortium for High-Throughput Research in Rare Kidney Diseases), and the National Research Agency grant MNaims (ANR-17-CE17-0012-01). The Dutch studies were supported by grants from the Dutch Kidney Foundation to JMH and JFW (Nierstichting Nederland grant OW08 and grant KJPB11.021). We would like to thank the Population Architecture Using Genomics and Epidemiology (PAGE) consortium, funded by the National Human Genome Research Institute (NHGRI) with co-funding from the NIMHD, for providing population controls for this study. For full acknowledgment of the PAGE consortium, please see Supplementary Materials. The funding sources were not involved in the study design, collection, analysis, and interpretation of data, writing of the report, or in the decision to submit the paper for publication.

BACKGROUND AND OBJECTIVES: IgA nephropathy is the most common primary glomerular disease in the world. Marked by mesangial inflammation and proliferation, it generally leads to progressive kidney fibrosis. As the Janus kinase signal transducer and activator of transcription pathway has been implicated as an important mediator of diabetic kidney disease and FSGS, detailed investigation of this pathway in IgA nephropathy was undertaken to establish the basis for targeting this pathway across glomerular diseases. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Well characterized patients with IgA nephropathy and controls were studied, allowing us to compare 77 patients with biopsy-proven IgA nephropathy with 45 healthy subjects. STAT phosphorylation was assessed in peripheral blood monocytes (PBMCs) by phosphoflow before and after cytokine stimulation. Kidney Janus kinase signal transducer and activator of transcription activity was studied by immunofluorescence and by transcriptomic studies. An STAT1 activity score was established using downstream transcriptional targets of pSTAT1 and associated with disease and clinical outcomes. RESULTS: We found PBMCs to have upregulated pSTAT production at baseline in patients with IgA nephropathy with a limited reserve to respond to cytokine stimulation compared with controls. Increased staining in glomerular mesangium and endothelium was seen for Jak-2 and pSTAT1 and in the tubulointerstitial for JAK2, pSTAT1, and pSTAT3. Activation of the Janus kinase signal transducer and activator of transcription pathway was further supported by increased pSTAT1 and pSTAT3 scores in glomerular and tubulointerstitial sections of the kidney (glomerular activation Z scores: 7.1 and 4.5, respectively; P values

BACKGROUND AND OBJECTIVES: Infections contribute to patient morbidity and mortality in glomerular disease. We sought to describe the incidence of, and identify risk factors for, infection-related acute care events among Cure Glomerulonephropathy Network (CureGN) study participants. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: CureGN is a prospective, multicenter, cohort study of children and adults with biopsy sample–proven minimal change disease, FSGS, membranous nephropathy, or IgA nephropathy/vasculitis. Risk factors for time to first infection-related acute care events (hospitalization or emergency department visit) were identified using multivariable Cox proportional hazards regression. RESULTS: Of 1741 participants (43% female, 41% 3.5 mg/mg), compared with serum albumin >2.5 g/dl and urinary protein-creatinine ratio ≤3.5 mg/mg, was associated with higher risk of time to first infection (adjusted hazard ratio, 2.49; 95% CI, 1.51 to 4.12). CONCLUSIONS: Among CureGN participants, infection-related acute care events were common and associated with younger age, corticosteroid exposure, and hypoalbuminemia with proteinuria.

With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.

BACKGROUND: The analysis and reporting of glomerular features ascertained by electron microscopy are limited to few parameters with minimal predictive value, despite some contributions to disease diagnoses. METHODS: We investigated the prognostic value of 12 electron microscopy histologic and ultrastructural changes (descriptors) from the Nephrotic Syndrome Study Network (NEPTUNE) Digital Pathology Scoring System. Study pathologists scored 12 descriptors in NEPTUNE renal biopsies from 242 patients with minimal change disease or FSGS, with duplicate readings to evaluate reproducibility. We performed consensus clustering of patients to identify unique electron microscopy profiles. For both individual descriptors and clusters, we used Cox regression models to assess associations with time from biopsy to proteinuria remission and time to a composite progression outcome (≥40% decline in eGFR, with eGFR0.60 for 12 out of 12 and seven out of 12 descriptors, respectively. Individual podocyte descriptors such as effacement and microvillous transformation were associated with complete remission, whereas endothelial cell and glomerular basement membrane abnormalities were associated with progression. We identified six descriptor-based clusters with distinct electron microscopy profiles and clinical outcomes. Patients in a cluster with more prominent foot process effacement and microvillous transformation had the highest rates of complete proteinuria remission, whereas patients in clusters with extensive loss of primary processes and endothelial cell damage had the highest rates of the composite progression outcome. CONCLUSIONS: Systematic analysis of electron microscopic findings reveals clusters of findings associated with either proteinuria remission or disease progression.

Context Testing reproducibility is critical for the development of methodologies for morphologic assessment. Our previous study using the descriptor-based Nephrotic Syndrome Study Network Digital Pathology Scoring System (NDPSS) on glomerular images revealed variable reproducibility. Objective To test reproducibility and feasibility of alternative scoring strategies for digital morphologic assessment of glomeruli and explore use of alternative agreement statistics. Design The original NDPSS was modified (NDPSS1 and NDPSS2) to evaluate (1) independent scoring of each individual biopsy level, (2) use of continuous measures, (3) groupings of individual descriptors into classes and subclasses prior to scoring, and (4) indication of pathologists' confidence/uncertainty for any given score. Three and 5 pathologists scored 157 and 79 glomeruli using the NDPSS1 and NDPSS2, respectively. Agreement was tested using conventional (Cohen κ) and alternative (Gwet agreement coefficient 1 [AC1]) agreement statistics and compared with previously published data (original NDPSS). Results Overall, pathologists' uncertainty was low, favoring application of the Gwet AC1. Greater agreement was achieved using the Gwet AC1 compared with the Cohen κ across all scoring methodologies. Mean (standard deviation) differences in agreement estimates using the NDPSS1 and NDPSS2 compared with the single-level original NDPSS were −0.09 (0.17) and −0.17 (0.17), respectively. Using the Gwet AC1, 79% of the original NDPSS descriptors had good or excellent agreement. Pathologist feedback indicated the NDPSS1 and NDPSS2 were time-consuming. Conclusions The NDPSS1 and NDPSS2 increased pathologists' scoring burden without improving reproducibility. Use of alternative agreement statistics was strongly supported. We suggest using the original NDPSS on whole slide images for glomerular morphology assessment and for guiding future automated technologies.

Hypertension and blood pressure variability (BPV; SD and average real variability) in primary proteinuric glomerulopathies are not well described. Data were from 433 participants in the NEPTUNE (Nephrotic Syndrome Study Network). Hypertensive BP status was defined as previous history of hypertension or BP ≥140/90 mm Hg for adults/≥95th percentile for children at baseline. BPV was measured in participants with ≥3 visits in the first year. Two-hundred ninety-six adults (43 years [interquartile range, 32–57.8 years], 61.5% male) and 147 children (11 years [interquartile range, 5–14 years], 57.8% male) were evaluated. At baseline, 64.8% of adults and 46.9% of children were hypertensive. Histological diagnosis was associated with hypertensive status in adults ( P =0.036). In adults, hypertensive status was associated with lower hazard of complete remission (hazard ratio, 0.36; 95% confidence interval, 0.19–0.68) and greater hazard of achieving the composite end point (end-stage renal disease or estimated glomerular filtration rate decline >40%; hazard ratio, 4.1; 95% confidence interval, 1.4–12). Greater systolic and diastolic SD and average real variability were also associated with greater hazard of reaching the composite end point in adults (all P P

Interstitial fibrosis (IF), tubular atrophy (TA) and interstitial inflammation (II) are known determinants of progression of renal disease. Standardized quantification of these features could add value to current classification of glomerulopathies.We studied 315 participants in the Nephrotic Syndrome Study Network (NEPTUNE) study, including biopsy-proven minimal change disease (MCD = 98), focal segmental glomerulosclerosis (FSGS = 121), membranous nephropathy (MN = 59) and IgA nephropathy (IgAN = 37). Cortical IF, TA and II were quantified (%) on digitized whole-slide biopsy images, by five pathologists with high inter-reader agreement (intra-class correlation coefficient0.8). Tubulointerstitial messenger RNA expression was measured in a subset of patients. Multivariable Cox proportional hazards models were fit to assess association of IF with the composite of 40% decline in estimated glomerular filtration rate (eGFR) and end-stage renal disease (ESRD) and separately as well, and with complete remission (CR) of proteinuria.IF was highly correlated with TA (P 0.001) and II (P 0.001). Median IF varied by diagnosis: FSGS 17, IgAN 21, MN 7, MCD 1 (P 0.001). IF was strongly correlated with baseline eGFR (P 0.001) and proteinuria (P = 0.002). After adjusting for clinical pathologic diagnosis, age, race, global glomerulosclerosis, baseline proteinuria, eGFR and medications, each 10% increase in IF was associated with a hazard ratio of 1.29 (P 0.03) for ESRD/40% eGFR decline, but was not significantly associated with CR. A total of 981 genes were significantly correlated with IF (|r|0.4, false discovery rate (FDR)0.01), including upstream regulators such as tumor necrosis factor, interferon gamma (IFN-gamma), and transforming growth factor beta 1 (TGF-B1), and signaling pathways for antigen presentation and hepatic fibrosis.The degree of IF is associated with risk of eGFR decline across different types of proteinuric glomerulopathy, correlates with inflammatory and fibrotic gene expression, and may have predictive value in assessing risk of progression.

Background The mechanisms leading to extracellular matrix (ECM) replacement of areas of glomerular capillaries in histologic variants of FSGS are unknown. This study used proteomics to test the hypothesis that glomerular ECM composition in collapsing FSGS (cFSGS) differs from that of other variants. Methods ECM proteins in glomeruli from biopsy specimens of patients with FSGS not otherwise specified (FSGS-NOS) or cFSGS and from normal controls were distinguished and quantified using mass spectrometry, verified and localized using immunohistochemistry (IHC) and confocal microscopy, and assessed for gene expression. The analysis also quantified urinary excretion of ECM proteins and peptides. Results Of 58 ECM proteins that differed in abundance between cFSGS and FSGS-NOS, 41 were more abundant in cFSGS and 17 in FSGS-NOS. IHC showed that glomerular tuft staining for cathepsin B, cathepsin C, and annexin A3 in cFSGS was significantly greater than in other FSGS variants, in minimal change disease, or in membranous nephropathy. Annexin A3 colocalized with cathepsin B and C, claudin-1, phosphorylated ERK1/2, and CD44, but not with synaptopodin, in parietal epithelial cells (PECs) infiltrating cFSGS glomeruli. Transcripts for cathepsins B and C were increased in FSGS glomeruli compared with normal controls, and urinary excretion of both cathepsins was significantly greater in cFSGS compared with FSGS-NOS. Urinary excretion of ECM-derived peptides was enhanced in cFSGS, although in silico analysis did not identify enhanced excretion of peptides derived from cathepsin B or C. Conclusions ECM differences suggest that glomerular sclerosis in cFSGS differs from that in other FSGS variants. Infiltration of activated PECs may disrupt ECM remodeling in cFSGS. These cells and their cathepsins may be therapeutic targets.

Nephrotic syndrome is classically categorized by the histopathology with examples including focal segmental glomerulosclerosis (FSGS) and minimal change disease. Pediatric patients are also classified by whether their nephrotic syndrome is sensitive to, dependent on, or resistant to steroids. However, this traditional classification system overlooks the frequent clinical conundrum when, for example, one patient with FSGS responds briskly to steroids, and another quickly progresses to end stage kidney disease despite therapy. Two patients may have similar histopathologic appearances on kidney biopsy but entirely different clinical characteristics, rates of progression, and treatment responses. Transcriptional regulation of gene activation and posttranscriptional processing of mRNA may drive the unique and heterogeneous phenotypes which are incompletely understood in kidney disease and are a recent focus of research. Gene expression profiles provide insight on active transcriptional programs in tissues, are being used to understand biologic mechanisms of progressive chronic kidney disease, and may help to identify patients with shared mechanisms of kidney damage. This mini-review discusses clinically relevant techniques of bulk tissue and single cell transcriptomics, as well as strengths and limitations of each methodology. Further, we summarize recent examples in kidney research achieved through transcriptomics. This review offers an outlook on the role of transcriptomics in an integrative systems biology model with the goal of defining unique disease subgroups, finding targets for drug development, and aligning the right drug with the right patient.