Urinary Plasminogen as a Marker of Disease Progression in Human Glomerular Disease.

RATIONALE & OBJECTIVE: Glomerular disorders have a highly variable clinical course, and biomarkers that reflect the molecular mechanisms underlying their progression are needed. Based on our previous work identifying plasminogen as a direct cause of podocyte injury, we designed this study to test the association between urine plasmin(ogen) (ie, plasmin and its precursor plasminogen) and end-stage kidney disease (ESKD). STUDY DESIGN: Multicenter cohort study. SETTING & PARTICIPANTS: 1,010 patients enrolled in the CureGN Cohort with biopsy-proven glomerular disease (focal segmental glomerulosclerosis, membranous nephropathy, and immunoglobulin A nephropathy). PREDICTORS: The main predictor was urine plasmin(ogen) at baseline. Levels were measured by an electrochemiluminescent immunoassay developed de novo. Traditional clinical and analytical characteristics were used for adjustment. The ratio of urine plasmin(ogen)/expected plasmin(ogen) was evaluated as a predictor in a separate model. OUTCOME: Progression to ESKD. ANALYTICAL APPROACH: Cox regression was used to examine the association between urinary plasmin(ogen) and time to ESKD. Urinary markers were log2 transformed to approximate normal distribution and normalized to urinary creatinine (Log2uPlasminogen/cr, Log2 urinary protein/cr [UPCR]). Expected plasmin(ogen) was calculated by multiple linear regression. RESULTS: Adjusted Log2uPlasminogen/cr was significantly associated with ESKD (HR per doubling Log2 uPlasminogen/cr 1.31 [95% CI, 1.22-1.40], P<0.001). Comparison of the predictive performance of the models including Log2 uPlasminogen/cr, Log2 UPCR, or both markers showed the plasmin(ogen) model superiority. The ratio of measured/expected urine plasmin(ogen) was independently associated with ESKD: HR, 0.41 (95% CI, 0.22-0.77) if ratio<0.8 and HR 2.42 (95% CI, 1.54-3.78) if ratio>1.1 (compared with ratio between 0.8 and 1.1). LIMITATIONS: Single plasmin(ogen) determination does not allow for the study of changes over time. The use of a cohort of mostly white patients and the restriction to patients with 3 glomerular disorders limits the external validity of our analysis. CONCLUSIONS: Urinary plasmin(ogen) and the ratio of measured/expected plasmin(ogen) are independently associated with ESKD in a cohort of patients with glomerular disease. Taken together with our previous experimental findings, urinary plasmin(ogen) could be a useful biomarker in prognostic decision making and a target for the development of novel therapies in patients with proteinuria and glomerular disease. PLAIN-LANGUAGE SUMMARY: Glomerular diseases are an important cause of morbidity and mortality in patients of all ages. Knowing the individual risk of progression to dialysis or transplantation would help to plan the follow-up and treatment of these patients. Our work studies the usefulness of urinary plasminogen as a marker of progression in this context, since previous studies indicate that plasminogen may be involved in the mechanisms responsible for the progression of these disorders. Our work in a sample of 1,010 patients with glomerular disease demonstrates that urinary plasminogen (as well as the ratio of measured to expected plasminogen) is associated with the risk of progression to end-stage kidney disease. Urine plasminogen exhibited good performance and, if further validated, could enable risk stratification for timely interventions in patients with proteinuria and glomerular disease.

Derivation and independent validation of kidneyintelX.dkd: A prognostic test for the assessment of diabetic kidney disease progression.

AIMS: To develop and validate an updated version of KidneyIntelX (kidneyintelX.dkd) to stratify patients for risk of progression of diabetic kidney disease (DKD) stages 1 to 3, to simplify the test for clinical adoption and support an application to the US Food and Drug Administration regulatory pathway. METHODS: We used plasma biomarkers and clinical data from the Penn Medicine Biobank (PMBB) for training, and independent cohorts (BioMe and CANVAS) for validation. The primary outcome was progressive decline in kidney function (PDKF), defined by a ≥40% sustained decline in estimated glomerular filtration rate or end-stage kidney disease within 5 years of follow-up. RESULTS: In 573 PMBB participants with DKD, 15.4% experienced PDKF over a median of 3.7 years. We trained a random forest model using biomarkers and clinical variables. Among 657 BioMe participants and 1197 CANVAS participants, 11.7% and 7.5%, respectively, experienced PDKF. Based on training cut-offs, 57%, 35% and 8% of BioMe participants, and 56%, 38% and 6% of CANVAS participants were classified as having low-, moderate- and high-risk levels, respectively. The cumulative incidence at these risk levels was 5.9%, 21.2% and 66.9% in BioMe and 6.7%, 13.1% and 59.6% in CANVAS. After clinical risk factor adjustment, the adjusted hazard ratios were 7.7 (95% confidence interval [CI] 3.0-19.6) and 3.7 (95% CI 2.0-6.8) in BioMe, and 5.4 (95% CI 2.5-11.9) and 2.3 (95% CI 1.4-3.9) in CANVAS, for high- versus low-risk and moderate- versus low-risk levels, respectively. CONCLUSIONS: Using two independent cohorts and a clinical trial population, we validated an updated KidneyIntelX test (named kidneyintelX.dkd), which significantly enhanced risk stratification in patients with DKD for PDKF, independently from known risk factors for progression.

Systematic Review and Meta-Analysis of Plasma and Urine Biomarkers for CKD Outcomes.

BACKGROUND: Sensitive and specific biomarkers are needed to provide better biologic insight into the risk of incident and progressive CKD. However, studies have been limited by sample size and design heterogeneity. METHODS: In this assessment of the prognostic value of preclinical plasma and urine biomarkers for CKD outcomes, we searched Embase (Ovid), MEDLINE ALL (Ovid), and Scopus up to November 30, 2020, for studies exploring the association between baseline kidney biomarkers and CKD outcomes (incident CKD, CKD progression, or incident ESKD). We used random-effects meta-analysis. RESULTS: After screening 26,456 abstracts and 352 full-text articles, we included 129 studies in the meta-analysis for the most frequently studied plasma biomarkers (TNFR1, FGF23, TNFR2, KIM-1, suPAR, and others) and urine biomarkers (KIM-1, NGAL, and others). For the most frequently studied plasma biomarkers, pooled RRs for CKD outcomes were 2.17 (95% confidence interval [95% CI], 1.91 to 2.47) for TNFR1 (31 studies); 1.21 (95% CI, 1.15 to 1.28) for FGF-23 (30 studies); 2.07 (95% CI, 1.82 to 2.34) for TNFR2 (23 studies); 1.51 (95% CI, 1.38 to 1.66) for KIM-1 (18 studies); and 1.42 (95% CI, 1.30 to 1.55) for suPAR (12 studies). For the most frequently studied urine biomarkers, pooled RRs were 1.10 (95% CI, 1.05 to 1.16) for KIM-1 (19 studies) and 1.12 (95% CI, 1.06 to 1.19) for NGAL (19 studies). CONCLUSIONS: Studies of preclinical biomarkers for CKD outcomes have considerable heterogeneity across study cohorts and designs, limiting comparisons of prognostic performance across studies. Plasma TNFR1, FGF23, TNFR2, KIM-1, and suPAR were among the most frequently investigated in the setting of CKD outcomes.

Clinical Utility of KidneyIntelX in Early Stages of Diabetic Kidney Disease in the CANVAS Trial.

INTRODUCTION: KidneyIntelX is a composite risk score, incorporating biomarkers and clinical variables for predicting progression of diabetic kidney disease (DKD). The utility of this score in the context of sodium glucose co-transporter 2 inhibitors and how changes in the risk score associate with future kidney outcomes are unknown. METHODS: We measured soluble tumor necrosis factor receptor (TNFR)-1, soluble TNFR-2, and kidney injury molecule 1 on banked samples from CANagliflozin cardioVascular Assessment Study (CANVAS) trial participants with baseline DKD (estimated glomerular filtration rate [eGFR] 30-59 mL/min/1.73 m2 or urine albumin-to-creatinine ratio [UACR] ≥30 mg/g) and generated KidneyIntelX risk scores at baseline and years 1, 3, and 6. We assessed the association of baseline and changes in KidneyIntelX with subsequent DKD progression (composite outcome of an eGFR decline of ≥5 mL/min/year [using the 6-week eGFR as the baseline in the canagliflozin group], ≥40% sustained decline in the eGFR, or kidney failure). RESULTS: We included 1,325 CANVAS participants with concurrent DKD and available baseline plasma samples (mean eGFR 65 mL/min/1.73 m2 and median UACR 56 mg/g). During a mean follow-up of 5.6 years, 131 participants (9.9%) experienced the composite kidney outcome. Using risk cutoffs from prior validation studies, KidneyIntelX stratified patients to low- (42%), intermediate- (44%), and high-risk (15%) strata with cumulative incidence for the outcome of 3%, 11%, and 26% (risk ratio 8.4; 95% confidence interval [CI]: 5.0, 14.2) for the high-risk versus low-risk groups. The differences in eGFR slopes for canagliflozin versus placebo were 0.66, 1.52, and 2.16 mL/min/1.73 m2 in low, intermediate, and high KidneyIntelX risk strata, respectively. KidneyIntelX risk scores declined by 5.4% (95% CI: -6.9, -3.9) in the canagliflozin arm at year 1 versus an increase of 6.3% (95% CI: 3.8, 8.7) in the placebo arm (p < 0.001). Changes in the KidneyIntelX score at year 1 were associated with future risk of the composite outcome (odds ratio per 10 unit decrease 0.80; 95% CI: 0.77, 0.83; p < 0.001) after accounting for the treatment arm, without evidence of effect modification by the baseline KidneyIntelX risk stratum or by the treatment arm. CONCLUSIONS: KidneyIntelX successfully risk-stratified a large multinational external cohort for progression of DKD, and greater numerical differences in the eGFR slope for canagliflozin versus placebo were observed in those with higher baseline KidneyIntelX scores. Canagliflozin treatment reduced KidneyIntelX risk scores over time and changes in the KidneyIntelX score from baseline to 1 year associated with future risk of DKD progression, independent of the baseline risk score and treatment arm.

Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease.

AIM: Predicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers. METHODS: This is an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net reclassification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of ≥5 ml/min per year, ≥40% sustained decline, or kidney failure within 5 years. RESULTS: In 1146 patients, the median age was 63 years, 51% were female, the baseline eGFR was 54 ml min-1 [1.73 m]-2, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol, follow-up was 4.3 years and 21% had the composite endpoint. On cross-validation in derivation (n = 686), KidneyIntelX had an AUC of 0.77 (95% CI 0.74, 0.79). In validation (n = 460), the AUC was 0.77 (95% CI 0.76, 0.79). By comparison, the AUC for the clinical model was 0.62 (95% CI 0.61, 0.63) in derivation and 0.61 (95% CI 0.60, 0.63) in validation. Using derivation cut-offs, KidneyIntelX stratified 46%, 37% and 17% of the validation cohort into low-, intermediate- and high-risk groups for the composite kidney endpoint, respectively. The PPV for progressive decline in kidney function in the high-risk group was 61% for KidneyIntelX vs 40% for the highest risk strata by KDIGO categorisation (p < 0.001). Only 10% of those scored as low risk by KidneyIntelX experienced progression (i.e., NPV of 90%). The NRIevent for the high-risk group was 41% (p < 0.05). CONCLUSIONS: KidneyIntelX improved prediction of kidney outcomes over KDIGO and clinical models in individuals with early stages of DKD.

Analytical validation of a multi-biomarker algorithmic test for prediction of progressive kidney function decline in patients with early-stage kidney disease.

BACKGROUND: The KidneyIntelX™ test applies a machine learning algorithm that incorporates plasma biomarkers and clinical variables to produce a composite risk score to predict a progressive decline in kidney function in patients with type 2 diabetes (T2D) and early-stage chronic kidney disease (CKD). The following studies describe the analytical validation of the KidneyIntelX assay including impact of observed methodologic variability on the composite risk score. METHODS: Analytical performance studies of sensitivity, precision, and linearity were performed on three biomarkers assayed in multiplexed format: kidney injury molecule-1 (KIM-1), soluble tumor necrosis factor receptor-1 (sTNFR-1) and soluble tumor necrosis factor receptor-2 (sTNFR-2) based on Clinical Laboratory Standards Institute (CLSI) guidelines. Analytical variability across twenty (20) experiments across multiple days, operators, and reagent lots was assessed to examine the impact on the reproducibility of the composite risk score. Analysis of cross-reactivity and interfering substances was also performed. RESULTS: Assays for KIM-1, sTNFR-1 and sTNFR-2 demonstrated acceptable sensitivity. Mean within-laboratory imprecision coefficient of variation (CV) was established as less than 9% across all assays in a multi-lot study. The linear range of the assays was determined as 12-5807 pg/mL, 969-23,806 pg/mL and 4256-68,087 pg/mL for KIM-1, sTNFR-1 and sTNFR-2, respectively. The average risk score CV% was less than 5%, with 98% concordance observed for assignment of risk categories. Cross-reactivity between critical assay components in a multiplexed format did not exceed 1.1%. CONCLUSIONS: The set of analytical validation studies demonstrated robust analytical performance across all three biomarkers contributing to the KidneyIntelX risk score, meeting or exceeding specifications established during characterization studies. Notably, reproducibility of the composite risk score demonstrated that expected analytical laboratory variation did not impact the assigned risk category, and therefore, the clinical validity of the reported results.

Plasminogenuria is associated with podocyte injury, edema, and kidney dysfunction in incident glomerular disease.

Urinary plasminogen/plasmin, or plasmin (ogen) uria, has been demonstrated in proteinuric patients and exposure of cultured podocytes to plasminogen results in injury via oxidative stress pathways. A causative role for plasmin (ogen) as a "second hit" in kidney disease progression has yet to have been demonstrated in vivo. Additionally, association between plasmin (ogen) uria and kidney function in glomerular diseases remains unclear. We performed comparative studies in a puromycin aminonucleoside (PAN) nephropathy rat model treated with amiloride, an inhibitor of plasminogen activation, and measured changes in plasmin (ogen) uria. In a glomerular disease biorepository cohort (n = 128), we measured time-of-biopsy albuminuria, proteinuria, and plasmin (ogen) uria for correlations with kidney outcomes. In cultured human podocytes, plasminogen treatment was associated with decreased focal adhesion marker expression with rescue by amiloride. Increased glomerular plasmin (ogen) was found in PAN rats and focal segmental glomerulosclerosis (FSGS) patients. PAN nephropathy was associated with increases in plasmin (ogen) uria and proteinuria. Amiloride was protective against PAN-induced glomerular injury, reducing CD36 scavenger receptor expression and oxidative stress. In patients, we found associations between plasmin (ogen) uria and edema status as well as eGFR. Our study demonstrates a role for plasmin (ogen)-induced podocyte injury in the PAN nephropathy model, with amiloride having podocyte-protective properties. In one of the largest glomerular disease cohorts to study plasminogen, we validated previous findings while suggesting a potentially novel relationship between plasmin (ogen) uria and estimated glomerular filtration rate (eGFR). Together, these findings suggest a role for plasmin (ogen) in mediating glomerular injury and as a viable targetable biomarker for podocyte-sparing treatments.

Hyperglycemia is Associated With Increased Mortality in Critically Ill Patients With COVID-19.

OBJECTIVE: To explore the relationship between hyperglycemia in the presence and absence of diabetes mellitus (DM) and adverse outcomes in critically ill patients with coronavirus disease 2019 (COVID-19). METHODS: The study included 133 patients with COVID-19 admitted to an intensive care unit (ICU) at an urban academic quaternary-care center between March 10 and April 8, 2020. Patients were categorized based on the presence or absence of DM and early-onset hyperglycemia (EHG), defined as a blood glucose >180 mg/dL during the first 2 days after ICU admission. The primary outcome was 14-day all-cause in-hospital mortality; also examined were 60-day all-cause in-hospital mortality and the levels of C-reactive protein, interleukin 6, procalcitonin, and lactate. RESULTS: Compared to non-DM patients without EHG, non-DM patients with EHG exhibited higher adjusted hazard ratios (HRs) for mortality at 14 days (HR 7.51, CI 1.70-33.24) and 60 days (HR 6.97, CI 1.86-26.13). Non-DM patients with EHG also featured higher levels of median C-reactive protein (306.3 mg/L, P = .036), procalcitonin (1.26 ng/mL, P = .028), and lactate (2.2 mmol/L, P = .023). CONCLUSION: Among critically ill COVID-19 patients, those without DM with EHG were at greatest risk of 14-day and 60-day in-hospital mortality. Our study was limited by its retrospective design and relatively small cohort. However, our results suggest the combination of elevated glucose and lactate may identify a specific cohort of individuals at high risk for mortality from COVID-19. Glucose testing and control are important in individuals with COVID-19, even those without preexisting diabetes.

Pre-clinical development of a cryopreservable megakaryocytic cell product capable of sustained platelet production in mice.

BACKGROUND: Platelet (PLT) transfusions are the most effective treatments for patients with thrombocytopenia. The growing demand for PLT transfusion products is compounded by a limited supply due to dependency on volunteer donors, a short shelf-life, risk of contaminating pathogens, and alloimmunization. This study provides preclinical evidence that a third-party, cryopreservable source of PLT-generating cells has the potential to complement presently available PLT transfusion products. STUDY DESIGN AND METHODS: CD34+ hematopoietic stem/progenitor cells derived from umbilical cord blood (UCB) units were used in a simple and efficient culture system to generate a cell product consisting of megakaryocytes (MKs) at different stages of development. The cultures thus generated were evaluated ex vivo and in vivo before and after cryopreservation. RESULTS: We generated a megakaryocytic cell product that can be cryopreserved without altering its phenotypical and functional capabilities. The infusion of such a product, either fresh or cryopreserved, into immune-deficient mice led to production of functional human PLTs which were observed within a week after infusion and persisted for 8 weeks, orders of magnitude longer than that observed after the infusion of traditional PLT transfusion products. The sustained human PLT engraftment was accompanied by a robust presence of human cells in the bone marrow (BM), spleen, and lungs of recipient mice. CONCLUSION: This is a proof-of-principle study demonstrating the creation of a cryopreservable megakaryocytic cell product which releases functional PLTs in vivo. Clinical development of such a product is currently being pursued for the treatment of thrombocytopenia in patients with hematological malignancies.

Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury.

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.