Rationale and design of the Nephrotic Syndrome Study Network (NEPTUNE) Match in glomerular diseases: designing the right trial for the right patient, today.

Glomerular diseases are classified using a descriptive taxonomy that is not reflective of the heterogeneous underlying molecular drivers. This limits not only diagnostic and therapeutic patient management, but also impacts clinical trials evaluating targeted interventions. The Nephrotic Syndrome Study Network (NEPTUNE) is poised to address these challenges. The study has enrolled >850 pediatric and adult patients with proteinuric glomerular diseases who have contributed to deep clinical, histologic, genetic, and molecular profiles linked to long-term outcomes. The NEPTUNE Knowledge Network, comprising combined, multiscalar data sets, captures each participant's molecular disease processes at the time of kidney biopsy. In this editorial, we describe the design and implementation of NEPTUNE Match, which bridges a basic science discovery pipeline with targeted clinical trials. Noninvasive biomarkers have been developed for real-time pathway analyses. A Molecular Nephrology Board reviews the pathway maps together with clinical, laboratory, and histopathologic data assembled for each patient to compile a Match report that estimates the fit between the specific molecular disease pathway(s) identified in an individual patient and proposed clinical trials. The NEPTUNE Match report is communicated using established protocols to the patient and the attending nephrologist for use in their selection of available clinical trials. NEPTUNE Match represents the first application of precision medicine in nephrology with the aim of developing targeted therapies and providing the right medication for each patient with primary glomerular disease.

Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

Sodium glucose co-transporter 2 inhibition increases epidermal growth factor expression and improves outcomes in patients with type 2 diabetes.

Underlying molecular mechanisms of the kidney protective effects of sodium glucose co-transporter 2 (SGLT2) inhibitors are not fully elucidated. Therefore, we studied the association between urinary epidermal growth factor (uEGF), a mitogenic factor involved in kidney repair, and kidney outcomes in patients with type 2 diabetes (T2D). The underlying molecular mechanisms of the SGLT2 inhibitor canagliflozin on EGF using single-cell RNA sequencing from kidney tissue were examined. Urinary EGF-to-creatinine ratio (uEGF/Cr) was measured in 3521 CANagliflozin cardioVascular Assessment Study (CANVAS) participants at baseline and week 52. Associations of uEGF/Cr with kidney outcome were assessed using multivariable-adjusted Cox regression models. Single-cell RNA sequencing was performed using protocol kidney biopsy tissue from ten young patients with T2D on SGLT2i, six patients with T2D on standard care only, and six healthy controls (HCs). In CANVAS, each doubling in baseline uEGF/Cr was associated with a 12% (95% confidence interval 1-22) decreased risk of kidney outcome. uEGF/Cr decreased after 52 weeks with placebo and remained stable with canagliflozin (between-group difference +7.3% (2.0-12.8). In young persons with T2D, EGF mRNA was primarily expressed in the thick ascending loop of Henle. Expression in biopsies from T2D without SGLT2i was significantly lower compared to HCs, whereas treatment with SGLT2i increased EGF levels closer to the healthy state. In young persons with T2D without SGLT2i, endothelin-1 emerged as a key regulator of the EGF co-expression network. SGLT2i treatment was associated with a shift towards normal EGF expression. Thus, decreased uEGF represents increased risk of kidney disease progression in patients with T2D. Canagliflozin increased kidney tissue expression of EGF and was associated with a downstream signaling cascade linked to tubular repair and reversal of tubular injury.

Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis.

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.

Insulin resistance, kidney outcomes and effects of the endothelin receptor antagonist atrasentan in patients with type 2 diabetes and chronic kidney disease.

BACKGROUND: Insulin resistance (IR) is a pathophysiologic hallmark of type 2 diabetes and associated with the presence of chronic kidney disease (CKD). Experimental studies suggest that endothelin-1 increases IR. We assessed the association between IR and cardio-renal outcomes and the effect of the selective endothelin receptor antagonist atrasentan on IR in patients with type 2 diabetes and CKD. METHODS: We used data from the RADAR and SONAR trials that recruited participants with type 2 diabetes and CKD [eGFR 25-75 mL/min/1.73 m², urine albumin-to-creatinine ratio of 300-5000 mg/g]. IR was calculated using the homeostatic model assessment (HOMA-IR). The association between HOMA-IR and the pre-specified cardio-renal outcomes was assessed using multivariable Cox proportional hazards regression, and effects of atrasentan on HOMA-IR by a linear mixed effect model. RESULTS: In the SONAR trial, each log-unit increase in HOMA-IR was associated with an increased risk of the composite cardio-renal outcome [hazard ratio 1.32 (95%CI 1.09,1.60; p = 0.004)], kidney outcome [hazard ratio 1.30 (95%CI 1.00,1.68; p-value = 0.048)], and the kidney or all-cause mortality outcome [hazard ratio 1.25 (95%CI 1.01,1.55; p-value = 0.037)]. After 12 weeks treatment in the RADAR trial (N = 123), atrasentan 0.75 mg/day and 1.25 mg/day compared to placebo reduced HOMA-IR by 19.1 (95%CI -17.4, 44.3) and 26.7% (95%CI -6.4, 49.5), respectively. In the SONAR trial (N = 1914), atrasentan 0.75 mg/day compared to placebo reduced HOMA-IR by 9.6% (95%CI 0.6, 17.9). CONCLUSIONS: More severe IR is associated with increased risk of cardio-renal outcomes. The endothelin receptor antagonist atrasentan reduced IR. TRIAL REGISTRATION: RADAR trial (Reducing Residual Albuminuria in Subjects With Diabetes and Nephropathy With AtRasentan): NCT01356849. SONAR trial (The Study Of Diabetic Nephropathy With AtRasentan) NCT01858532.

Urinary epidermal growth factor predicts complete remission of proteinuria in Chinese children with IgA nephropathy.

BACKGROUND: This study investigated the association between urinary epidermal growth factor (EGF) and complete remission (CR) of proteinuria in children with IgA nephropathy (IgAN). METHODS: We included 108 patients from the Registry of IgA Nephropathy in Chinese Children. The urinary EGF at the baseline and follow-up were measured and normalized by urine creatinine (expressed as uEGF/Cr). The person-specific uEGF/Cr slopes were estimated using linear mixed-effects models for the subset of patients with longitudinal data of uEGF/Cr. Cox models were used to analyze the associations of baseline uEGF/Cr and uEGF/Cr slope with CR of proteinuria. RESULTS: Patients with high baseline uEGF/Cr were more likely to achieve CR of proteinuria (adjusted HR 2.24, 95% CI: 1.05-4.79). The addition of high baseline uEGF/Cr on the traditional parameters significantly improved the model fit for predicting CR of proteinuria. In the subset of patients with longitudinal data of uEGF/Cr, high uEGF/Cr slope was associated with a higher likelihood of CR of proteinuria (adjusted HR 4.03, 95% CI: 1.02-15.88). CONCLUSIONS: Urinary EGF may be a useful noninvasive biomarker for predicting and monitoring CR of proteinuria in children with IgAN. IMPACT: High levels of baseline uEGF/Cr (>21.45 ng/mg) could serve as an independent predictor for CR of proteinuria. The addition of baseline uEGF/Cr on the traditional clinical pathological parameters significantly improved the fitting ability for the prediction of CR of proteinuria. Longitudinal data of uEGF/Cr were also independently associated with CR of proteinuria. Our study provides evidence that urinary EGF may be a useful noninvasive biomarker in the prediction of CR of proteinuria as well as monitoring therapeutic response, thus guiding treatment strategies in clinical practice for children with IgAN.

Intraindividual variations of urinary biomarkers in hospitalized children with glomerular diseases: a prospective observational study.

This study aimed to assess the intraindividual variations of urinary biomarkers in hospitalized children with glomerular diseases. Hospitalized children with glomerular diseases participated in the study. For each patient, an overnight (9:00 p.m.-7:00 a.m.) urine was collected, followed by a 24-h urine (classified into four distinct periods: morning 7:00 a.m.-12:00 p.m., afternoon 12:00 p.m.-4:00 p.m., evening 4:00 p.m.-9:00 p.m., and overnight 9:00 p.m.-7:00 a.m.). The concentrations of protein, albumin, N-acetyl-beta-D-glucosaminidase, and epidermal growth factor (EGF) were measured and normalized by three correction factors (creatinine, osmolality, or specific gravity, respectively). Additionally, the 2nd overnight urine sample was grouped into different aliquots according to centrifugation, additives, storage temperature, or delayed processing. Twenty (14 boys, 6 girls) children were enrolled, with an average age of 11.3 years. Among the three correction factors, creatinine-normalized biomarkers provided the best agreements among different periods over 24 h. There were significant diurnal variations during 24 h in the concentrations of urinary protein, albumin, N-acetyl-beta-D-glucosaminidase, and EGF (p = 0.001, p = 0.003, p = 0.003, and p = 0.003, respectively). Evening urine overestimated 24-h urinary protein and albumin, while overnight urine underestimated 24-h urinary albumin. Urinary EGF showed low variability within a day or between the 2 days (coefficients of variation 10.2% and 10.6%, respectively) and excellent agreements (intraclass correlation coefficients > 0.9) with 24-h urinary concentration. Furthermore, urinary EGF was not affected by centrifugation, additives, storage temperature, or delayed processing of urine samples (all p > 0.05).  Conclusion: Given the diurnal variations of urinary biomarkers, urine samples should be collected during the same time period in clinical practice if possible. The results also extend the evidence for urinary EGF as a relatively stable biomarker applied in the future clinical practice. What is Known: • Urinary biomarkers have been widely used or discussed in making diagnoses and therapy regimens and estimating the prognosis of pediatric glomerular diseases. It remains unclear whether their levels would be affected by the time of sample collection, processing methods, and storage conditions in hospitalized children with glomerular diseases. What is New: • The levels of both commonly used biomarkers and novel biomarkers exhibited diurnal variations in hospitalized children with glomerular diseases. • Our results extend the evidence for urinary EGF as a relatively stable biomarker applied in the future clinical practice.

A role for NPY-NPY2R signaling in albuminuric kidney disease.

Albuminuria is an independent risk factor for the progression to end-stage kidney failure, cardiovascular morbidity, and premature death. As such, discovering signaling pathways that modulate albuminuria is desirable. Here, we studied the transcriptomes of podocytes, key cells in the prevention of albuminuria, under diabetic conditions. We found that Neuropeptide Y (NPY) was significantly down-regulated in insulin-resistant vs. insulin-sensitive mouse podocytes and in human glomeruli of patients with early and late-stage diabetic nephropathy, as well as other nondiabetic glomerular diseases. This contrasts with the increased plasma and urinary levels of NPY that are observed in such conditions. Studying NPY-knockout mice, we found that NPY deficiency in vivo surprisingly reduced the level of albuminuria and podocyte injury in models of both diabetic and nondiabetic kidney disease. In vitro, podocyte NPY signaling occurred via the NPY2 receptor (NPY2R), stimulating PI3K, MAPK, and NFAT activation. Additional unbiased proteomic analysis revealed that glomerular NPY-NPY2R signaling predicted nephrotoxicity, modulated RNA processing, and inhibited cell migration. Furthermore, pharmacologically inhibiting the NPY2R in vivo significantly reduced albuminuria in adriamycin-treated glomerulosclerotic mice. Our findings suggest a pathogenic role of excessive NPY-NPY2R signaling in the glomerulus and that inhibiting NPY-NPY2R signaling in albuminuric kidney disease has therapeutic potential.

The Michigan O'Brien Kidney Research Center: transforming translational kidney research through systems biology.

Research on kidney diseases is being transformed by the rapid expansion and innovations in omics technologies. The analysis, integration, and interpretation of big data, however, have been an impediment to the growing interest in applying these technologies to understand kidney function and failure. Targeting this urgent need, the University of Michigan O'Brien Kidney Translational Core Center (MKTC) and its Administrative Core established the Applied Systems Biology Core. The Core provides need-based support for the global kidney community centered on enabling incorporation of systems biology approaches by creating web-based, user-friendly analytic and visualization tools, like Nephroseq and Nephrocell, guiding with experimental design, and processing, analysis, and integration of large data sets. The enrichment core supports systems biology education and dissemination through workshops, seminars, and individualized training sessions. Meanwhile, the Pilot and Feasibility Program of the MKTC provides pilot funding to both early-career and established investigators new to the field, to integrate a systems biology approach into their research projects. The relevance and value of the portfolio of training and services offered by MKTC are reflected in the expanding community of young investigators, collaborators, and users accessing resources and engaging in systems biology-based kidney research, thereby motivating MKTC to persevere in its mission to serve the kidney research community by enabling access to state-of-the-art data sets, tools, technologies, expertise, and learning opportunities for transformative basic, translational, and clinical studies that will usher in solutions to improve the lives of people impacted by kidney disease.

Phospholipase Cε insufficiency causes ascending aortic aneurysm and dissection.

Phospholipase Cε (PLCε) is a phospholipase C isoform with a wide range of physiological functions. It has been implicated in aortic valve disorders, but its role in frequently associated aortic disease remains unclear. To determine the role of PLCε in thoracic aortic aneurysm and dissection (TAAD) we used PLCε-deficient mice, which develop aortic valve insufficiency and exhibit aortic dilation of the ascending thoracic aorta and arch without histopathological evidence of injury. Fourteen days of infusion of Plce1+/+ and Plce1-/- mice with angiotensin II (ANG II), which induces aortic dilation and dissection, led to sudden death secondary to ascending aortic dissection in 43% of Plce1-/- versus 5% of Plce1+/+ mice (P < 0.05). Medial degeneration and TAAD were detected in 80% of Plce1-/- compared with 10% of Plce1+/+ mice (P < 0.05) after 4 days of ANG II. Treatment with ANG II markedly increased PLCε expression within the ascending aortic adventitia. Total RNA sequencing demonstrated marked upregulation of inflammatory and fibrotic pathways mediated by interleukin-1ß, interleukin-6, and tumor necrosis factor-α. In silico analysis of whole exome sequences of 258 patients with type A dissection identified 5 patients with nonsynonymous PLCE1 variants. Our data suggest that PLCε deficiency plays a role in the development of TAAD and aortic insufficiency.NEW & NOTEWORTHY We describe a novel phenotype by which PLCε deficiency predisposes to aortic valve insufficiency and ascending aortic aneurysm, dissection, and sudden death in the setting of ANG II-mediated hypertension. We demonstrate PLCE1 variants in patients with type A aortic dissection and aortic insufficiency, suggesting that PLCE1 may also play a role in human aortic disease. This finding is of very high significance because it has not been previously demonstrated that PLCε directly mediates aortic dissection.

IGFBP-1 expression is reduced in human type 2 diabetic glomeruli and modulates ß1-integrin/FAK signalling in human podocytes.

AIMS/HYPOTHESIS: Podocyte loss or injury is one of the earliest features observed in the pathogenesis of diabetic kidney disease (DKD), which is the leading cause of end-stage renal failure worldwide. Dysfunction in the IGF axis, including in IGF binding proteins (IGFBPs), is associated with DKD, particularly in the early stages of disease progression. The aim of this study was to investigate the potential roles of IGFBPs in the development of type 2 DKD, focusing on podocytes. METHODS: IGFBP expression was analysed in the Pima DKD cohort, alongside data from the Nephroseq database, and in ex vivo human glomeruli. Conditionally immortalised human podocytes and glomerular endothelial cells were studied in vitro, where IGFBP-1 expression was analysed using quantitative PCR and ELISAs. Cell responses to IGFBPs were investigated using migration, cell survival and adhesion assays; electrical cell-substrate impedance sensing; western blotting; and high-content automated imaging. RESULTS: Data from the Pima DKD cohort and from the Nephroseq database demonstrated a significant reduction in glomerular IGFBP-1 in the early stages of human type 2 DKD. In the glomerulus, IGFBP-1 was predominantly expressed in podocytes and controlled by phosphoinositide 3-kinase (PI3K)-forkhead box O1 (FoxO1) activity. In vitro, IGFBP-1 signalled to podocytes via ß1-integrins, resulting in increased phosphorylation of focal-adhesion kinase (FAK), increasing podocyte motility, adhesion, electrical resistance across the adhesive cell layer and cell viability. CONCLUSIONS/INTERPRETATION: This work identifies a novel role for IGFBP-1 in the regulation of podocyte function and that the glomerular expression of IGFBP-1 is reduced in the early stages of type 2 DKD, via reduced FoxO1 activity. Thus, we hypothesise that strategies to maintain glomerular IGFBP-1 levels may be beneficial in maintaining podocyte function early in DKD.

Annexin A1 alleviates kidney injury by promoting the resolution of inflammation in diabetic nephropathy.

Since failed resolution of inflammation is a major contributor to the progression of diabetic nephropathy, identifying endogenously generated molecules that promote the physiological resolution of inflammation may be a promising therapeutic approach for this disease. Annexin A1 (ANXA1), as an endogenous mediator, plays an important role in resolving inflammation. Whether ANXA1 could affect established diabetic nephropathy through modulating inflammatory states remains largely unknown. In the current study, we found that in patients with diabetic nephropathy, the levels of ANXA1 were upregulated in kidneys, and correlated with kidney function as well as kidney outcomes. Therefore, the role of endogenous ANXA1 in mouse models of diabetic nephropathy was further evaluated. ANXA1 deficiency exacerbated kidney injuries, exhibiting more severe albuminuria, mesangial matrix expansion, tubulointerstitial lesions, kidney inflammation and fibrosis in high fat diet/streptozotocin-induced-diabetic mice. Consistently, ANXA1 overexpression ameliorated kidney injuries in mice with diabetic nephropathy. Additionally, we found Ac2-26 (an ANXA1 mimetic peptide) had therapeutic potential for alleviating kidney injuries in db/db mice and diabetic Anxa1 knockout mice. Mechanistic studies demonstrated that intracellular ANXA1 bound to the transcription factor NF-κB p65 subunit, inhibiting its activation thereby modulating the inflammatory state. Thus, our data indicate that ANXA1 may be a promising therapeutic approach to treating and reversing diabetic nephropathy.

Urinary excretion of epidermal growth factor and rapid loss of kidney function.

BACKGROUND: Lower urinary excretion of the kidney tubule-specific biomarker epidermal growth factor (uEGF) is associated with increased risk of renal function [glomerular filtration rate (GFR)] loss in diabetes and in patients with established chronic kidney disease (CKD). We investigated whether uEGF is associated with rapid GFR decline or incident CKD in the general population. METHODS: Subjects without CKD or diabetes were recruited from the general population in Tromso, Norway [Renal Iohexol Clearance Survey (RENIS); N = 1249] and Groningen, the Netherlands [Prevention of REnal and Vascular END-stage disease (PREVEND); N = 4534], with a median follow-up of 5.6 and 7.4 years, respectively. GFR was measured by iohexol clearance in the RENIS and estimated using the CKD Epidemiology Collaboration creatinine-cystatin C equation in the PREVEND study. Rapid GFR decline was defined as an annual GFR loss >3.0 mL/min/1.73 m2 and in sensitivity analyses as subjects with the 10% steepest GFR slope within each cohort. RESULTS: Lower baseline uEGF excretion was associated with rapid GFR loss in both cohorts {RENIS, odds ratio [OR] per 1 µg/mmol lower uEGF 1.42 [95% confidence interval (CI) 1.06-1.91], P = 0.02; PREVEND, OR 1.29 [95% CI 1.10-1.53], P < 0.01}, adjusted for baseline GFR, albumin:creatinine ratio and conventional CKD risk factors. Similar results were obtained using the outcome of the 10% steepest GFR slope in each cohort. Lower uEGF levels were associated with incident CKD in the combined analysis of both cohorts. CONCLUSIONS: Lower uEGF levels are associated with increased risk of rapid GFR loss and incident CKD in the general population. This finding, together with previous findings in CKD and high-risk populations, supports that uEGF may serve as a broadly applicable biomarker representing the tubular component of the current glomerulus-centric clinical risk assessment system.

Machine-learning-based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials data.

AIM: To predict end-stage renal disease (ESRD) in patients with type 2 diabetes by using machine-learning models with multiple baseline demographic and clinical characteristics. MATERIALS AND METHODS: In total, 11 789 patients with type 2 diabetes and nephropathy from three clinical trials, RENAAL (n = 1513), IDNT (n = 1715) and ALTITUDE (n = 8561), were used in this study. Eighteen baseline demographic and clinical characteristics were used as predictors to train machine-learning models to predict ESRD (doubling of serum creatinine and/or ESRD). We used the area under the receiver operator curve (AUC) to assess the prediction performance of models and compared this with traditional Cox proportional hazard regression and kidney failure risk equation models. RESULTS: The feed forward neural network model predicted ESRD with an AUC of 0.82 (0.76-0.87), 0.81 (0.75-0.86) and 0.84 (0.79-0.90) in the RENAAL, IDNT and ALTITUDE trials, respectively. The feed forward neural network model selected urinary albumin to creatinine ratio, serum albumin, uric acid and serum creatinine as important predictors and obtained a state-of-the-art performance for predicting long-term ESRD. CONCLUSIONS: Despite large inter-patient variability, non-linear machine-learning models can be used to predict long-term ESRD in patients with type 2 diabetes and nephropathy using baseline demographic and clinical characteristics. The proposed method has the potential to create accurate and multiple outcome prediction automated models to identify high-risk patients who could benefit from therapy in clinical practice.

A metabolomics-based molecular pathway analysis of how the sodium-glucose co-transporter-2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes.

AIM: To investigate which metabolic pathways are targeted by the sodium-glucose co-transporter-2 inhibitor dapagliflozin to explore the molecular processes involved in its renal protective effects. METHODS: An unbiased mass spectrometry plasma metabolomics assay was performed on baseline and follow-up (week 12) samples from the EFFECT II trial in patients with type 2 diabetes with non-alcoholic fatty liver disease receiving dapagliflozin 10 mg/day (n = 19) or placebo (n = 6). Transcriptomic signatures from tubular compartments were identified from kidney biopsies collected from patients with diabetic kidney disease (DKD) (n = 17) and healthy controls (n = 30) from the European Renal cDNA Biobank. Serum metabolites that significantly changed after 12 weeks of dapagliflozin were mapped to a metabolite-protein interaction network. These proteins were then linked with intra-renal transcripts that were associated with DKD or estimated glomerular filtration rate (eGFR). The impacted metabolites and their protein-coding transcripts were analysed for enriched pathways. RESULTS: Of all measured (n = 812) metabolites, 108 changed (P < 0.05) during dapagliflozin treatment and 74 could be linked to 367 unique proteins/genes. Intra-renal mRNA expression analysis of the genes encoding the metabolite-associated proteins using kidney biopsies resulted in 105 genes that were significantly associated with eGFR in patients with DKD, and 135 genes that were differentially expressed between patients with DKD and controls. The combination of metabolites and transcripts identified four enriched pathways that were affected by dapagliflozin and associated with eGFR: glycine degradation (mitochondrial function), TCA cycle II (energy metabolism), L-carnitine biosynthesis (energy metabolism) and superpathway of citrulline metabolism (nitric oxide synthase and endothelial function). CONCLUSION: The observed molecular pathways targeted by dapagliflozin and associated with DKD suggest that modifying molecular processes related to energy metabolism, mitochondrial function and endothelial function may contribute to its renal protective effect.

Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo-observation approach.

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.

Urinary prostaglandin E2 is a biomarker of early adaptive hyperfiltration in solitary functioning kidney.

INTRODUCTION: Hyperfiltration is a major contributor to progression of chronic kidney disease (CKD) in diabetes, obesity and in individuals with solitary functioning kidney (SFK). We have proposed hyperfiltration-induced injury as a continuum of overlapping glomerular changes caused by increased biomechanical forces namely, fluid flow shear stress (FFSS) and tensile stress. We have shown that FFSS is elevated in animals with SFK and, it upregulates prostaglandin E2 (PGE2), cyclooxygenase-2 and PGE2 receptor EP2 in cultured podocytes and in uninephrectomized mice. We conceptualized urinary PGE2 as a biomarker of early effects of hyperfiltration-induced injury preceding microalbuminuria in individuals with SFK. We studied children with SFK to validate our hypothesis. METHODS: Urine samples from children with SFK and controls were analyzed for PGE2, albumin (glomerular injury biomarker) and epidermal growth factor (EGF, tubular injury biomarker). Age, gender, and Z-scores for height, weight, BMI, and blood pressure were obtained. RESULTS: Children with SFK were comparable to controls except for lower BMI Z-scores. The median values were elevated in SFK compared to control for urine PGE2 [9.1 (n = 57) vs. 5.7 (n = 72), p = 0.009] ng/mgCr and albumin [7.6 (n = 40) vs. 7.0 (n = 41), p = 0.085] µg/mgCr, but not for EGF [20098 (n = 44) vs. 18637 (n = 44), p = 0.746] pg/mgCr. Significant increase in urinary PGE2 (p = 0.024) and albumin (p = 0.019) but not EGF (p = 0.412) was observed using additional regression modeling. These three urinary analytes were independent of each other. CONCLUSION: Increased urinary PGE2 from elevated SNGFR and consequently increased FFSS during early stage of CKD precedes overt microalbuminuria and is a biomarker for early hyperfiltration-induced injury in individuals with SFK.

LRG1 Promotes Diabetic Kidney Disease Progression by Enhancing TGF-ß-Induced Angiogenesis.

BACKGROUND: Glomerular endothelial dysfunction and neoangiogenesis have long been implicated in the pathogenesis of diabetic kidney disease (DKD). However, the specific molecular pathways contributing to these processes in the early stages of DKD are not well understood. Our recent transcriptomic profiling of glomerular endothelial cells identified a number of proangiogenic genes that were upregulated in diabetic mice, including leucine-rich α-2-glycoprotein 1 (LRG1). LRG1 was previously shown to promote neovascularization in mouse models of ocular disease by potentiating endothelial TGF-ß/activin receptor-like kinase 1 (ALK1) signaling. However, LRG1's role in the kidney, particularly in the setting of DKD, has been unclear. METHODS: We analyzed expression of LRG1 mRNA in glomeruli of diabetic kidneys and assessed its localization by RNA in situ hybridization. We examined the effects of genetic ablation of Lrg1 on DKD progression in unilaterally nephrectomized, streptozotocin-induced diabetic mice at 12 and 20 weeks after diabetes induction. We also assessed whether plasma LRG1 was associated with renal outcome in patients with type 2 diabetes. RESULTS: LRG1 localized predominantly to glomerular endothelial cells, and its expression was elevated in the diabetic kidneys. LRG1 ablation markedly attenuated diabetes-induced glomerular angiogenesis, podocyte loss, and the development of diabetic glomerulopathy. These improvements were associated with reduced ALK1-Smad1/5/8 activation in glomeruli of diabetic mice. Moreover, increased plasma LRG1 was associated with worse renal outcome in patients with type 2 diabetes. CONCLUSIONS: These findings identify LRG1 as a potential novel pathogenic mediator of diabetic glomerular neoangiogenesis and a risk factor in DKD progression.

Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes.

Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m2, urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor.