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.

Comparison of predicting cardiovascular disease hospitalization using individual, ZIP code-derived, and machine learning model-predicted educational attainment in New York City.

BACKGROUND: Area-level social determinants of health (SDOH) based on patients' ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could be used to derive individual SDOH measures, specifically individual educational attainment, is unknown. METHODS: This is a retrospective study using data from the Mount Sinai BioMe Biobank. We included participants that completed a validated questionnaire on educational attainment and had home addresses in New York City. ZIP code-level education was derived from the American Community Survey matched for the participant's gender and race/ethnicity. We tested several algorithms to predict individual educational attainment from routinely collected clinical and demographic data. To evaluate how using different measures of educational attainment will impact model performance, we developed three distinct models for predicting cardiovascular (CVD) hospitalization. Educational attainment was imputed into models as either survey-derived, ZIP code-derived, or ML-predicted educational attainment. RESULTS: A total of 20,805 participants met inclusion criteria. Concordance between survey and ZIP code-derived education was 47%, while the concordance between survey and ML model-predicted education was 67%. A total of 13,715 patients from the cohort were included into our CVD hospitalization prediction models, of which 1,538 (11.2%) had a history of CVD hospitalization. The AUROC of the model predicting CVD hospitalization using survey-derived education was significantly higher than the model using ZIP code-level education (0.77 versus 0.72; p < 0.001) and the model using ML model-predicted education (0.77 versus 0.75; p < 0.001). The AUROC for the model using ML model-predicted education was also significantly higher than that using ZIP code-level education (p = 0.003). CONCLUSION: The concordance of survey and ZIP code-level educational attainment in NYC was low. As expected, the model utilizing survey-derived education achieved the highest performance. The model incorporating our ML model-predicted education outperformed the model relying on ZIP code-derived education. Implementing ML techniques can improve the accuracy of SDOH data and consequently increase the predictive performance of outcome models.

A Real-World Precision Medicine Program Including the KidneyIntelX Test Effectively Changes Management Decisions and Outcomes for Patients With Early-Stage Diabetic Kidney Disease.

INTRODUCTION/OBJECTIVE: The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to predict a patient's risk for a progressive decline in kidney function over 5 years. We report the 1-year pre- and post-test clinical impact on care management, eGFR slope, and A1C along with engagement of population health clinical pharmacists and patient coordinators to promote a program of sustainable kidney, metabolic, and cardiac health. METHODS: The KidneyIntelX in vitro prognostic test was previously validated for patients with type 2 diabetes and diabetic kidney disease (DKD) to predict kidney function decline within 5 years was introduced into the RWE study (NCT04802395) across the Health System as part of a population health chronic disease management program from [November 2020 to April 2023]. Pre- and post-test patients with a minimum of 12 months of follow-up post KidneyIntelX were assessed across all aspects of the program. RESULTS: A total of 5348 patients with DKD had a KidneyIntelX assay. The median age was 68 years old, 52% were female, 27% self-identified as Black, and 89% had hypertension. The median baseline eGFR was 62 ml/min/1.73 m2, urine albumin-creatinine ratio was 54 mg/g, and A1C was 7.3%. The KidneyIntelX risk level was low in 49%, intermediate in 40%, and high in 11% of cases. New prescriptions for SGLT2i, GLP-1 RA, or referral to a specialist were noted in 19%, 33%, and 43% among low-, intermediate-, and high-risk patients, respectively. The median A1C decreased from 8.2% pre-test to 7.5% post-test in the high-risk group (P < .001). UACR levels in the intermediate-risk patients with albuminuria were reduced by 20%, and in a subgroup treated with new scripts for SGLT2i, UACR levels were lowered by approximately 50%. The median eGFR slope improved from -7.08 ml/min/1.73 m2/year to -4.27 ml/min/1.73 m2/year in high-risk patients (P = .0003), -2.65 to -1.04 in intermediate risk, and -3.26 ml/min/1.73 m2/year to +0.45 ml/min/1.73 m2/year in patients with low-risk (P < .001). CONCLUSIONS: Deployment and risk stratification by KidneyIntelX was associated with an escalation in action taken to optimize cardio-kidney-metabolic health including medications and specialist referrals. Glycemic control and kidney function trajectories improved post-KidneyIntelX testing, with the greatest improvements observed in those scored as high-risk.

A Retrospective Cohort Study That Examined the Impact of Cannabis Consumption on Long-Term Kidney Outcomes.

Background: Cannabis consumption for recreational and medical use is increasing worldwide. However, the long-term effects on kidney health and disease are largely unknown. Materials and Methods: Post hoc analysis of cannabis use as a risk factor for kidney disease was performed using data from the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) study that enrolled hospitalized adults with and without acute kidney injury from four U.S. centers during 2009-2015. Associations between self-reported cannabis consumption and the categorical and continuous outcomes were determined using multivariable Cox regression and linear mixed models, respectively. Results: Over a mean follow-up of 4.5±1.8 years, 94 participants without chronic kidney disease (CKD) (estimated glomerular filtration rate [eGFR] >60 mL/min/1.73 m2) who consumed cannabis had similar rates of annual eGFR decline versus 889 nonconsumers (mean difference=-0.02 mL/min/1.73 m2/year, p=0.9) and incident CKD (≥25% reduction in eGFR compared with the 3-month post-hospitalization measured eGFR and achieving CKD stage 3 or higher) (adjusted hazard ratio [aHR]=1.2; 95% confidence interval [CI]=0.7-2.0). Nineteen participants with CKD (eGFR <60 mL/min/1.73 m2) who consumed cannabis had more rapid eGFR decline versus 597 nonconsumers (mean difference=-1.3 mL/min/1.73 m2/year; p=0.02) that was not independently associated with an increased risk of CKD progression (≥50% reduction in eGFR compared with the 3-month post-hospitalization eGFR, reaching CKD stage 5, or receiving kidney replacement therapy) (aHR=1.6; 95% CI=0.7-3.5). Cannabis consumption was not associated with the rate of change in urine albumin to creatinine ratio (UACR) over time among those with (p=0.7) or without CKD (p=0.4). Conclusions: Cannabis consumption did not adversely affect the kidney function of participants without CKD but was associated with a faster annual eGFR decline among participants with CKD. Cannabis consumption was not associated with changes in UACR over time, incident CKD, or progressive CKD regardless of baseline kidney function. Additional research is needed to investigate the kidney endocannabinoid system and the impact of cannabis use on kidney disease outcomes.

Urinary Biomarkers and Kidney Injury in VA NEPHRON-D: Phenotyping Acute Kidney Injury in Clinical Trials.

RATIONALE & OBJECTIVE: Urinary biomarkers of injury, inflammation, and repair may help phenotype acute kidney injury (AKI) observed in clinical trials. We evaluated the differences in biomarkers between participants randomized to monotherapy or to combination renin-angiotensin-aldosterone system (RAAS) blockade in VA NEPHRON-D, where an increased proportion of observed AKI was acknowledged in the combination arm. STUDY DESIGN: Longitudinal analysis. SETTING & PARTICIPANTS: A substudy of the VA NEPHRON-D trial. PREDICTOR: Primary exposure was the treatment arm (combination [RAAS inhibitor] vs monotherapy). AKI is used as a stratifying variable. OUTCOME: Urinary biomarkers, including albumin, EGF (epidermal growth factor), MCP-1 (monocyte chemoattractant protein-1), YKL-40 (chitinase 3-like protein 1), and KIM-1 (kidney injury molecule-1). ANALYTICAL APPROACH: Biomarkers measured at baseline and at 12 months in trial participants were compared between treatment groups and by AKI. AKI events occurring during hospitalization were predefined safety end points in the original trial. The results were included in a meta-analysis with other large chronic kidney disease trials to assess global trends in biomarker changes. RESULTS: In 707 participants followed for a median of 2.2 years, AKI incidence was higher in the combination (20.7%) versus the monotherapy group (12.7%; relative risk [RR], 1.64 [95% CI, 1.16-2.30]). Compared with the monotherapy arm, in the combination arm the urine biomarkers at 12 months were either unchanged (MCP-1: RR, -3% [95% CI, -13% to 9%], Padj=0.8; KIM-1: RR, -10% [95% CI, -20% to 1%], Padj=0.2; EGF, RR-7% [95% CI, -12% to-1%], Padj=0.08) or lower (albuminuria: RR, -24% [95% CI, -37% to-8%], Padj=0.02; YKL: RR, -40% to-44% [95% CI, -58% to-25%], Padj<0.001). Pooled meta-analysis demonstrated reduced albuminuria in the intervention arm across 3 trials and similar trajectories in other biomarkers. LIMITATIONS: Biomarker measurement was limited to 2 time points independent of AKI events. CONCLUSIONS: Despite the increased risk of serum creatinine-defined AKI, combination RAAS inhibitor therapy was associated with unchanged or decreased urinary biomarkers at 12 months. This suggests a possible role for kidney biomarkers to further characterize kidney injury in clinical trials. PLAIN-LANGUAGE SUMMARY: The VA NEPHRON-D trial investigated inhibition of the renin-angiotensin-aldosterone system (RAAS) hormonal axis on kidney outcomes in a large population of diabetic chronic kidney disease patients. The trial was stopped early due to increased events of serum creatinine-defined acute kidney injury in the combination therapy arm. Urine biomarkers can serve as an adjunct to serum creatinine in identifying kidney injury. We found that urinary biomarkers in the combination therapy group were not associated with a pattern of harm and damage to the kidney, despite the increased number of kidney injury events in that group. This suggests that serum creatinine alone may be insufficient for defining kidney injury and supports further exploration of how other biomarkers might improve identification of kidney injury in clinical trials.

Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection.

Background: Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored. Methods: In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells(PBMCs). We applied linear models accounting for technical and biological variability on RNA-Seq data accounting for false discovery rate (FDR) and compared functional enrichment and pathway results to a historical sepsis-AKI cohort. Finally, we evaluated the association of these signatures with long-term trends in kidney function. Results: Of 283 patients, 106 had AKI. After adjustment for sex, age, mechanical ventilation, and chronic kidney disease (CKD), we identified 2635 significant differential gene expressions at FDR<0.05. Top canonical pathways were EIF2 signaling, oxidative phosphorylation, mTOR signaling, and Th17 signaling, indicating mitochondrial dysfunction and endoplasmic reticulum (ER) stress. Comparison with sepsis associated AKI showed considerable overlap of key pathways (48.14%). Using follow-up estimated glomerular filtration rate (eGFR) measurements from 115 patients, we identified 164/2635 (6.2%) of the significantly differentiated genes associated with overall decrease in long-term kidney function. The strongest associations were 'autophagy', 'renal impairment via fibrosis', and 'cardiac structure and function'. Conclusions: We show that AKI in SARS-CoV2 is a multifactorial process with mitochondrial dysfunction driven by ER stress whereas long-term kidney function decline is associated with cardiac structure and function and immune dysregulation. Functional overlap with sepsis-AKI also highlights common signatures, indicating generalizability in therapeutic approaches. SIGNIFICANCE STATEMENT: Peripheral transcriptomic findings in acute and long-term kidney dysfunction after hospitalization for SARS-CoV2 infection are unclear. We evaluated peripheral blood molecular signatures in AKI from COVID-19 (COVID-AKI) and their association with long-term kidney dysfunction using the largest hospitalized cohort with transcriptomic data. Analysis of 283 hospitalized patients of whom 37% had AKI, highlighted the contribution of mitochondrial dysfunction driven by endoplasmic reticulum stress in the acute stages. Subsequently, long-term kidney function decline exhibits significant associations with markers of cardiac structure and function and immune mediated dysregulation. There were similar biomolecular signatures in other inflammatory states, such as sepsis. This enhances the potential for repurposing and generalizability in therapeutic approaches.

Structured Moderate Exercise and Biomarkers of Kidney Health in Sedentary Older Adults: The Lifestyle Interventions and Independence for Elders Randomized Clinical Trial.

Rationale & Objective: In the Lifestyle Interventions and Independence for Elders (LIFE) trial, a structured exercise intervention slowed kidney function decline in sedentary older adults. Biomarkers of kidney health could distinguish potential mechanisms for this beneficial effect. Study Design: Randomized controlled trial. Setting & Population: A total of 1,381 sedentary adults aged 70-89 years enrolled in the LIFE trial. Intervention: Structured, 2-year, moderate-intensity exercise intervention versus health education. Outcomes: Physical activity was measured by step count. Primary outcomes were changes in 14 serum and urine biomarkers of kidney health collected at baseline, year 1, and year 2. We determined the effect of randomization on changes in kidney measures and then evaluated observational associations of achieved activity on each measure. Results: Participants assigned to exercise walked on average 291 more steps per day than participants assigned to health education. The intervention was not significantly associated with changes in biomarkers of kidney health. In observational analyses, persons in the highest versus lowest quartile of activity (≥3,470 vs <1,568 steps/day) had significant improvement in urine albumin (mean, -0.22 mg albumin/g urine creatinine [interquartile range (IQR), -0.37 to -0.06]), alpha-1-microglobulin (-0.18 mg/L [-0.28 to -0.08]), trefoil factor-3 (-0.24 pg/mL [-0.35 to -0.13]), epidermal growth factor (0.19 pg/mL [0.06-0.32]), uromodulin (0.06 pg/mL [0.00-0.12]), interleukin 18 (-0.09 pg/mL [-0.15 to -0.03]), neutrophil gelatinase-associated lipocalin (-0.16 pg/mL [-0.24 to -0.07]), monocyte chemoattractant protein-1 (-0.25 pg/mL [-0.36 to -0.14]), clusterin (-0.16 pg/mL [-0.30 to -0.02]), serum tumor necrosis factor receptor-1 (-0.25 mg/dL [-0.39 to -0.11]) and tumor necrosis factor receptor-2 (-0.30 mg/dL [-0.44 to -0.16]). In sensitivity analyses, incremental changes in activity were most impactful on urine interleukin 18 and serum tumor necrosis factor-1. Limitations: The original study was not designed to assess the impact on kidney health. Non-white individuals and patients with advanced chronic kidney disease are underrepresented. Conclusions: Randomization to structured exercise did not improve kidney health at a group level. However, higher exercise was associated with concurrent improvements in biomarkers of glomerular injury, tubular function/repair, tubular injury, generalized inflammation, and tubulointerstitial repair/fibrosis. Plain-Language Summary: In the Lifestyle Interventions For Elders (LIFE) study, randomization to an exercise and physical activity intervention improved the slope of estimated glomerular filtration rate over 2 years compared with health education among older adults. In this study, we sought to determine whether there were specific biomarkers of kidney health that were affected by the exercise and physical activity intervention to investigate potential mechanisms for this positive impact on kidney decline. We found that randomization to the intervention did not improve any of the 14 measures of kidney tubule health. However, in observational analyses, higher activity was independently associated with improvements in several domains, especially tubular injury and generalized inflammation. These results help to clarify the impact of physical activity on kidney health.

Plasma Biomarkers and Incident CKD Among Individuals Without Diabetes.

Rationale & Objective: Biomarkers of kidney disease progression have been identified in individuals with diabetes and underlying chronic kidney disease (CKD). Whether or not these markers are associated with the development of CKD in a general population without diabetes or CKD is not well established. Study Design: Prospective observational cohort. Setting & Participants: In the Atherosclerosis Risk in Communities) study, 948 participants were studied. Exposures: The baseline plasma biomarkers of kidney injury molecule-1 (KIM-1), monocyte chemoattractant protein-1 (MCP-1), soluble urokinase plasminogen activator receptor (suPAR), tumor necrosis factor receptor 1 (TNFR-1), tumor necrosis factor receptor 2 (TNFR-2), and human cartilage glycoprotein-39 (YKL-40) measured in 1996-1998. Outcome: Incident CKD after 15 years of follow-up defined as ≥40% estimated glomerular filtration rate decline to <60 mL/min/1.73 m2 or dialysis dependence through United States Renal Data System linkage. Analytical Approach: Logistic regression and C statistics. Results: There were 523 cases of incident CKD. Compared with a random sample of 425 controls, there were greater odds of incident CKD per 2-fold higher concentration of KIM-1 (OR, 1.49; 95% CI, 1.25-1.78), suPAR (OR, 2.57; 95% CI, 1.74-3.84), TNFR-1 (OR, 2.20; 95% CI, 1.58-3.09), TNFR-2 (OR, 2.03; 95% CI, 1.37-3.04). After adjustment for all biomarkers, KIM-1 (OR, 1.42; 95% CI, 1.19-1.71), and suPAR (OR, 1.86; 95% CI, 1.18-2.92) remained associated with incident CKD. Compared with traditional risk factors, the addition of all 6 biomarkers improved the C statistic from 0.695-0.731 (P < 0.01) and using the observed risk of 12% for incident CKD, the predicted risk gradient changed from 5%-40% (for the 1st-5th quintile) to 4%-44%. Limitations: Biomarkers and creatinine were measured at one time point. Conclusions: Higher levels of KIM-1, suPAR, TNFR-1, and TNFR-2 were associated with higher odds of incident CKD among individuals without diabetes. Plain-Language Summary: For people with diabetes or kidney disease, several biomarkers have been shown to be associated with worsening kidney disease. Whether these biomarkers have prognostic significance in people without diabetes or kidney disease is less studied. Using the Atherosclerosis Risk in Communities study, we followed individuals without diabetes or kidney disease for an average of 15 years after biomarker measurement to see if these biomarkers were associated with the development of kidney disease. We found that elevated levels of KIM-1, suPAR, TNFR-1, and TNFR-2 were associated with the development of kidney disease. These biomarkers may help identify individuals who would benefit from interventions to prevent the development of kidney 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.

The ASSESS-AKI Study found urinary epidermal growth factor is associated with reduced risk of major adverse kidney events.

Biomarkers of tubular function such as epidermal growth factor (EGF) may improve prognostication of participants at highest risk for chronic kidney disease (CKD) after hospitalization. To examine this, we measured urinary EGF (uEGF) from samples collected in the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) Study, a multi-center, prospective, observational cohort of hospitalized participants with and without AKI. Cox proportional hazards regression was used to investigate the association of uEGF/Cr at hospitalization, three months post-discharge, and the change between these time points with major adverse kidney events (MAKE): CKD incidence, progression, or development of kidney failure. Clinical findings were paired with mechanistic studies comparing relative Egf expression in mouse models of kidney atrophy or repair after ischemia-reperfusion injury. MAKE was observed in 20% of 1,509 participants over 4.3 years of follow-up. Each 2-fold higher level of uEGF/Cr at three months was associated with decreased risk of MAKE (adjusted hazards ratio 0.46, 95% confidence interval: 0.39-0.55). Participants with the highest increase in uEGF/Cr from hospitalization to three-month follow-up had a lower risk of MAKE (adjusted hazards ratio 0.52; 95% confidence interval: 0.36-0.74) compared to those with the least change in uEGF/Cr. A model using uEGF/Cr at three months combined with clinical variables yielded moderate discrimination for MAKE (area under the curve 0.73; 95% confidence interval: 0.69-0.77) and strong discrimination for kidney failure at four years (area under the curve 0.96; 95% confidence interval: 0.92-1.00). Accelerated restoration of Egf expression in mice was seen in the model of adaptive repair after injury, compared to a model of progressive atrophy. Thus, urinary EGF/Cr may be a biomarker of distal tubular health, with higher concentrations and increased uEGF/Cr post-discharge independently associated with reduced risk of MAKE in hospitalized patients.

Patient-Specific Pharmacokinetics and Dasatinib Nephrotoxicity.

BACKGROUND: Dasatinib has been associated with nephrotoxicity. We sought to examine the incidence of proteinuria on dasatinib and determine potential risk factors that may increase dasatinib-associated glomerular injury. METHODS: We examined glomerular injury through urine albumin-creatinine ratio (UACR) in 82 patients with chronic myelogenous leukemia who were on tyrosine-kinase inhibitor therapy for at least 90 days. t tests were used to compare mean differences in UACR, while regression analysis was used to assess the effects of drug parameters on proteinuria development while on dasatinib. We assayed plasma dasatinib pharmacokinetics using tandem mass spectroscopy and further described a case study of a patient who experienced nephrotic-range proteinuria while on dasatinib. RESULTS: Participants treated with dasatinib ( n =32) had significantly higher UACR levels (median 28.0 mg/g; interquartile range, 11.5-119.5) than participants treated with other tyrosine-kinase inhibitors ( n =50; median 15.0 mg/g; interquartile range, 8.0-35.0; P < 0.001). In total, 10% of dasatinib users exhibited severely increased albuminuria (UACR >300 mg/g) versus zero in other tyrosine-kinase inhibitors. Average steady-state concentrations of dasatinib were positively correlated with UACR ( ρ =0.54, P = 0.03) and duration of treatment ( P = 0.003). There were no associations with elevated BP or other confounding factors. In the case study, kidney biopsy revealed global glomerular damage with diffuse foot process effacement that recovered on termination of dasatinib treatment. CONCLUSIONS: Exposure to dasatinib was associated with a significant chance of developing proteinuria compared with other similar tyrosine-kinase inhibitors. Dasatinib plasma concentration significantly correlated with higher risk of developing proteinuria while receiving dasatinib. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_09_08_CJN0000000000000219.mp3.

Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection.

BACKGROUND: Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS: Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS: We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS: Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Acute kidney injury (AKI) is a sudden, sometimes fatal, episode of kidney failure or damage. It is a known complication of COVID-19, albeit through unclear mechanisms. COVID-19 is also associated with kidney dysfunction in the long term, or chronic kidney disease (CKD). There is a need to better understand which patients with COVID-19 are at risk of AKI or CKD. We measure levels of several thousand proteins in the blood of hospitalized COVID-19 patients. We discover and validate sets of proteins associated with severe AKI and CKD in these patients. The markers identified suggest that kidney injury in COVID-19 patients involves damage to kidney cells that reabsorb fluid from urine and reduced blood flow to the heart, causing damage to heart muscles. Our findings might help clinicians to predict kidney injury in patients with COVID-19, and to understand its mechanisms.

Evaluation of Plasma Biomarkers to Predict Major Adverse Kidney Events in Hospitalized Patients With COVID-19.

RATIONALE & OBJECTIVE: Patients hospitalized with COVID-19 are at increased risk for major adverse kidney events (MAKE). We sought to identify plasma biomarkers predictive of MAKE in patients hospitalized with COVID-19. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: A total of 576 patients hospitalized with COVID-19 between March 2020 and January 2021 across 3 academic medical centers. EXPOSURE: Twenty-six plasma biomarkers of injury, inflammation, and repair from first available blood samples collected during hospitalization. OUTCOME: MAKE, defined as KDIGO stage 3 acute kidney injury (AKI), dialysis-requiring AKI, or mortality up to 60 days. ANALYTICAL APPROACH: Cox proportional hazards regression to associate biomarker level with MAKE. We additionally applied the least absolute shrinkage and selection operator (LASSO) and random forest regression for prediction modeling and estimated model discrimination with time-varying C index. RESULTS: The median length of stay for COVID-19 hospitalization was 9 (IQR, 5-16) days. In total, 95 patients (16%) experienced MAKE. Each 1 SD increase in soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 was significantly associated with an increased risk of MAKE (adjusted HR [AHR], 2.30 [95% CI, 1.86-2.85], and AHR, 2.26 [95% CI, 1.73-2.95], respectively). The C index of sTNFR1 alone was 0.80 (95% CI, 0.78-0.84), and the C index of sTNFR2 was 0.81 (95% CI, 0.77-0.84). LASSO and random forest regression modeling using all biomarkers yielded C indexes of 0.86 (95% CI, 0.83-0.89) and 0.84 (95% CI, 0.78-0.91), respectively. LIMITATIONS: No control group of hospitalized patients without COVID-19. CONCLUSIONS: We found that sTNFR1 and sTNFR2 are independently associated with MAKE in patients hospitalized with COVID-19 and can both also serve as predictors for adverse kidney outcomes. PLAIN-LANGUAGE SUMMARY: Patients hospitalized with COVID-19 are at increased risk for long-term adverse health outcomes, but not all patients suffer long-term kidney dysfunction. Identification of patients with COVID-19 who are at high risk for adverse kidney events may have important implications in terms of nephrology follow-up and patient counseling. In this study, we found that the plasma biomarkers soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 measured in hospitalized patients with COVID-19 were associated with a greater risk of adverse kidney outcomes. Along with clinical variables previously shown to predict adverse kidney events in patients with COVID-19, both sTNFR1 and sTNFR2 are also strong predictors of adverse kidney outcomes.