Dataset Information

Quantification of Urinary Protein Biomarkers of Autosomal Dominant Polycystic Kidney Disease by Parallel Reaction Monitoring.

ABSTRACT: PURPOSE:Autosomal dominant polycystic kidney disease (ADPKD) is a life-long disease in which the genes responsible are known, but the pathogenesis of cyst formation and cyst growth are not understood. Cyst growth ultimately leads to end-stage renal failure in most patients. Analysis of the urinary proteome offers the potential to identify proteins that indicate the presence of cysts (and thus provides diagnosis) as well as the rates of cyst growth (providing prognostic information). EXPERIMENTAL DESIGN:A scheduled parallel reaction monitoring (sPRM) assay is performed on urine samples from 14 patients and 18 normal controls. For relative quantification, stable isotope-labeled synthetic peptides are spiked in the urinary protein digests prior to data collection. The data are subsequently normalized to creatinine and protein concentration in the respective urine samples to control for variations in water intake between individuals. RESULTS:Out of the 143 urinary proteins targeted for sPRM assay, 69 proteins are observed to be significantly dysregulated in ADPKD. The dysregulated proteins are used to cluster ADPKD patients into those who are more or less similar to normal controls. CONCLUSIONS AND CLINICAL RELEVANCE:This study shows that sPRM is a promising approach to rapidly screen large numbers of proteins in urine in order to provide earlier diagnosis and potentially better understand the pathogenesis of ADPKD development and progression.

SUBMITTER: Rauniyar N

PROVIDER: S-EPMC6736530 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Quantification of Urinary Protein Biomarkers of Autosomal Dominant Polycystic Kidney Disease by Parallel Reaction Monitoring.

Rauniyar Navin N Yu Xiaoqing X Cantley Jennifer J Voss Edward Z EZ Belcher Justin J Colangelo Christopher M CM Stone Kathryn L KL Dahl Neera N Parikh Chirag C Lam TuKiet T TT Cantley Lloyd G LG

Proteomics. Clinical applications 20180502 5

<h4>Purpose</h4>Autosomal dominant polycystic kidney disease (ADPKD) is a life-long disease in which the genes responsible are known, but the pathogenesis of cyst formation and cyst growth are not understood. Cyst growth ultimately leads to end-stage renal failure in most patients. Analysis of the urinary proteome offers the potential to identify proteins that indicate the presence of cysts (and thus provides diagnosis) as well as the rates of cyst growth (providing prognostic information).<h4>E ...[more]

PMID: 29573172

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Project description:BACKGROUND:Markers currently used to predict the likelihood of rapid disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD) are expensive and time consuming to assess and often have limited sensitivity. New, easy-to-measure markers are therefore needed that alone or in combination with conventional risk markers can predict the rate of disease progression. In the present study, we investigated the ability of tubular damage and inflammation markers to predict kidney function decline. METHODS:At baseline, albumin, immunoglobulin G, kidney injury molecule 1, ?2 microglobulin (?2MG), heart-type fatty acid-binding protein, neutrophil gelatinase-associated lipocalin, and monocyte chemotactic protein-1 -(MCP-1) were measured in 24-h urine samples of patients participating in a study investigating the therapeutic efficacy of lanreotide in ADPKD. Individual change in estimated glomerular filtration rate (eGFR) during follow-up was calculated using mixed-model analysis taking into account 13 -eGFRs (chronic kidney disease EPIdemiology) per patient. Logistic regression analysis was used to select urinary biomarkers that had the best association with rapidly progressive disease. The predictive value of these selected urinary biomarkers was compared to other risk scores using C-statistics. RESULTS:Included were 302 patients of whom 53.3% were female, with an average age of 48 ± 7 years, eGFR of 52 ± 12 mL/min/1.73 m2, and a height-adjusted total kidney volume (htTKV) of 1,082 (736-1,669) mL/m. At baseline, all urinary damage and inflammation markers were associated with baseline eGFR, also after adjustment for age, sex and baseline htTKV. For longitudinal analyses only patients randomized to standard care were considered (n = 152). A stepwise backward analysis revealed that ?2MG and MCP-1 showed the strongest association with rapidly progressive disease. A urinary biomarker score was created by summing the ranking of tertiles of ?2MG and MCP-1 excretion. The predictive value of this urinary biomarker score was higher compared to that of the Mayo htTKV classification (area under the curve [AUC] 0.73 [0.64-0.82] vs. 0.61 [0.51-0.71], p = 0.04) and comparable to that of the predicting renal outcomes in -ADPKD score (AUC 0.73 [0.64-0.82] vs. 0.65 [0.55-0.75], p = 0.18). In a second independent cohort with better kidney function, similar results were found for the urinary biomarker score. CONCLUSION:Measurement of urinary ?2MG and MCP-1 excretion allows selection of ADPKD patients with rapidly progressive disease, with a predictive value comparable to or even higher than that of TKV or PKD mutation. Easy and inexpensive to measure urinary markers therefore hold promise to help predict prognosis in ADPKD.

Project description:Introduction:The variable disease course of autosomal dominant polycystic kidney disease (ADPKD) makes it important to develop biomarkers that can predict disease progression, from a patient perspective and to select patients for renoprotective treatment. We therefore investigated whether easy-to-measure urinary biomarkers are associated with disease progression and have additional value over that of conventional risk markers. Methods:At baseline, inflammatory, glomerular, and tubular damage markers were measured in 24-hour urine collections (albumin, IgG, kidney injury molecule-1 (KIM-1), N-acetyl-?-d-glucosaminidase (NAG), ?2 microglobulin (?2MG), heart-type fatty acid binding protein (HFABP), macrophage migration inhibitory factor (MIF), neutrophil gelatinase-associated lipocalin (NGAL), and monocyte chemotactic protein-1 (MCP-1). Disease progression was expressed as annual change in estimated glomerular filtration rate (eGFR, Chronic Kidney Disease EPIdemiology equation), measured glomerular filtation rate (mGFR, using 125I-iothalamate), or height-adjusted total kidney volume (htTKV). Multivariable linear regression was used to assess associations of these markers independent of conventional risk markers. Results:A total of 104 ADPKD patients were included (40 ± 11 years, 39% female, eGFR 77 ± 30, mGFR 79 ± 30 ml/min per 1.73 m2 and htTKV 852 [510-1244] ml/m). In particular, ?2MG and MCP-1 were associated with annual change in eGFR, and remained associated after adjustment for conventional risk markers (standardized ? = -0.35, P = 0.001, and standardized ? = -0.29, P = 0.009, respectively). Adding ?2MG and MCP-1 to a model containing conventional risk markers that explained annual change in eGFR significantly increased the performance of the model (final R2 = 0.152 vs. 0.292, P = 0.001). Essentially similar results were obtained when only patients with an eGFR ? 60 ml/min per 1.73 m2 were selected, or when change in mGFR was studied. Associations with change in htTKV were less strong. Conclusion:Urinary ?2MG and MCP-1 excretion were both associated with GFR decline in ADPKD, and had added value beyond that of conventional risk markers. These markers therefore have the potential to serve as predictive tools for clinical practice.

Dataset Information

Quantification of Urinary Protein Biomarkers of Autosomal Dominant Polycystic Kidney Disease by Parallel Reaction Monitoring.

Publications

Quantification of Urinary Protein Biomarkers of Autosomal Dominant Polycystic Kidney Disease by Parallel Reaction Monitoring.

Similar Datasets

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