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Van Roy N, Speeckaert MM. The Potential Use of Targeted Proteomics and Metabolomics for the Identification and Monitoring of Diabetic Kidney Disease. J Pers Med 2024; 14:1054. [PMID: 39452561 PMCID: PMC11508375 DOI: 10.3390/jpm14101054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2024] [Revised: 09/28/2024] [Accepted: 10/03/2024] [Indexed: 10/26/2024] Open
Abstract
Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus and is associated with a significantly worse prognosis compared to diabetic patients without kidney involvement, other microvascular complications, or non-diabetic chronic kidney disease, due to its higher risk of cardiovascular events, faster progression to end-stage kidney disease, and increased mortality. In clinical practice, diagnosis is based on estimated glomerular filtration rate (eGFR) and albuminuria. However, given the limitations of these diagnostic markers, novel biomarkers must be identified. Omics is a new field of study involving the comprehensive analysis of various types of biological data at the molecular level. In different fields, they have shown promising results in (early) detection of diseases, personalized medicine, therapeutic monitoring, and understanding pathogenesis. DKD is primarily utilized in scientific research and has not yet been implemented in routine clinical practice. The aim of this review is to provide an overview of currently available data on targeted omics. After an extensive literature search, 25 different (panels of) omics were withheld and analyzed. Both serum/plasma and urine proteomics and metabolomics have been described with varying degrees of evidence. For all omics, there is still a relative paucity of data from large, prospective, longitudinal cohorts, presumably because of the heterogeneity of DKD and the lack of patient selection in studies, the complexity of omics technologies, and various practical and ethical considerations (e.g., limited accessibility, cost, and privacy concerns).
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Affiliation(s)
- Nele Van Roy
- Department of Endocrinology, Ghent University Hospital, 9000 Ghent, Belgium;
| | - Marijn M. Speeckaert
- Department of Nephrology, Ghent University Hospital, 9000 Ghent, Belgium
- Research Foundation-Flanders (FWO), 1000 Brussels, Belgium
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Hu X, Chen S, Ye S, Chen W, Zhou Y. New insights into the role of immunity and inflammation in diabetic kidney disease in the omics era. Front Immunol 2024; 15:1342837. [PMID: 38487541 PMCID: PMC10937589 DOI: 10.3389/fimmu.2024.1342837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/19/2024] [Indexed: 03/17/2024] Open
Abstract
Diabetic kidney disease (DKD) is becoming the leading cause of chronic kidney disease, especially in the industrialized world. Despite mounting evidence has demonstrated that immunity and inflammation are highly involved in the pathogenesis and progression of DKD, the underlying mechanisms remain incompletely understood. Substantial molecules, signaling pathways, and cell types participate in DKD inflammation, by integrating into a complex regulatory network. Most of the studies have focused on individual components, without presenting their importance in the global or system-based processes, which largely hinders clinical translation. Besides, conventional technologies failed to monitor the different behaviors of resident renal cells and immune cells, making it difficult to understand their contributions to inflammation in DKD. Recently, the advancement of omics technologies including genomics, epigenomics, transcriptomics, proteomics, and metabolomics has revolutionized biomedical research, which allows an unbiased global analysis of changes in DNA, RNA, proteins, and metabolites in disease settings, even at single-cell and spatial resolutions. They help us to identify critical regulators of inflammation processes and provide an overview of cell heterogeneity in DKD. This review aims to summarize the application of multiple omics in the field of DKD and emphasize the latest evidence on the interplay of inflammation and DKD revealed by these technologies, which will provide new insights into the role of inflammation in the pathogenesis of DKD and lead to the development of novel therapeutic approaches and diagnostic biomarkers.
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Affiliation(s)
- Xinrong Hu
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China
| | - Sixiu Chen
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China
| | - Siyang Ye
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China
| | - Wei Chen
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China
| | - Yi Zhou
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China
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Lin L, Ren J, Wang C, Mei M, Zheng L, Yang J. A set of urinary peptides can predict early renal damage in primary hypertension. J Hypertens 2023; 41:1653-1660. [PMID: 37602482 DOI: 10.1097/hjh.0000000000003539] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
OBJECTIVES Renal diseases caused by primary hypertension (HTN) are often asymptomatic without sensitive markers for early diagnosis and prediction, easily progressing to severe and irreversible renal damage in patients with clinical manifestations. This study explored whether a set of urinary peptides could serve as a potential biomarker for early prediction of renal damage in HTN. METHODS Urinary peptides level of healthy individuals, HTN + normoalbuminuric and HTN + albuminuria patients were compared, and 22 baseline data including sex, age, renal function, hypertensive fundus lesions were collected. Patients diagnosed with HTN, albuminuria, and normal renal function were followed up. According to the follow-up results, the cut-off value of a set of urinary peptides in predicting hypertensive renal injury was calculated and analyzed in the high-risk and low-risk groups of HTN patients for its performance in detecting early hypertensive renal injury. RESULTS Among a sum of 319 participants, average urinary peptides level was significantly higher in patients with HTN than in normal individuals. A total of 147 HTN patients with normal albuminuria were followed up for a mean of 3.8 years. Thirty-five patients showed urinary albumin-to-creatinine ratio (uACR) at least 30 mg/g for three consecutive times. The receiver-operating characteristic (ROC) curve showed that the urinary peptides cut-off value for evaluating new-onset proteinuria in patients with HTN was 0.097. Based on this cut-off value, 39 and 108 patients were included in the high-risk and low-risk groups, respectively. Specifically, compared with patients in the low-risk group, those in the high-risk group showed significantly longer duration of HTN, higher proportions of hypertensive fundus lesions and at least 30 mg/g uACR, and higher levels of homocysteine (Hcy), cystatin C (CysC), beta-2 microglobulin (β2-MG), and uACR. 76.9% of high-risk patients had significantly higher new-onset proteinuria than the low-risk group. Correlation analysis demonstrated a positive correlation between urinary peptides and UACR ( r = 0.494, P < 0.001). The incidence of new-onset albuminuria was significantly higher in the high-risk group than in the low-risk group, as shown by Cox regression analysis. The areas under the curve of urinary peptides, Hcy, β2-MG and CysC were 0.925, 0.753, 0.796 and 0.769, respectively. CONCLUSION A set of urinary peptides is a predictor of new-onset proteinuria in patients with HTN, therefore, it can be used for diagnosing patients with early renal injury in patients with HTN, contributing to early prevention and treatment of hypertensive nephropathy.
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Affiliation(s)
- Lirong Lin
- Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (Gener Hospital)
| | - Jiangwen Ren
- Department of Nephrology, Rheumatism and Immunology, Jiulongpo District People's Hospital of Chongqing
| | - Chunxuan Wang
- Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (Gener Hospital)
| | - Mei Mei
- Department of Nephrology, Shapingba Hospital of Chongqing University, Chongqing, China
| | - Luquan Zheng
- Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (Gener Hospital)
| | - Jurong Yang
- Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (Gener Hospital)
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Lin L, Wang C, Ren J, Mei M, Zheng L, Yang J. A classifier based on 273 urinary peptides predicts early renal damage in primary hypertension. J Hypertens 2023; 41:1306-1312. [PMID: 37199562 PMCID: PMC10328506 DOI: 10.1097/hjh.0000000000003467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 04/20/2023] [Indexed: 05/19/2023]
Abstract
OBJECTIVES Renal diseases caused by primary hypertension (HTN) are often asymptomatic without sensitive markers for early diagnosis and prediction, easily progressing to severe and irreversible renal damage in patients with clinical manifestations. This study explored whether a classifier developed based on 273 urinary peptides (CKD273) could serve as a potential biomarker for early prediction of renal damage in HTN. METHODS Urinary CKD273 level of healthy individuals, HTN + normoalbuminuric and HTN + albuminuria patients were compared, and 22 baseline data including sex, age, renal function, and hypertensive fundus lesions were collected. Patients diagnosed with HTN, albuminuria, and normal renal function were followed up. According to the follow-up results, the cut-off value of CKD273 in predicting hypertensive renal injury was calculated and analyzed in the high-risk and low-risk groups of HTN patients for its performance in detecting early hypertensive renal injury. RESULTS Among a sum of 319 participants, average urinary CKD273 level was significantly higher in patients with HTN than in normal individuals. A total of 147 HTN patients with normal albuminuria were followed up for a mean of 3.8 years. Thirty-five patients showed urinary albumin-to-creatinine ratio (uACR) at least 30 mg/g for three consecutive times. The receiver-operating characteristic (ROC) curve showed that the urinary CKD273 cut-off value for evaluating new-onset proteinuria in patients with HTN was 0.097. Based on this cut-off value, 39 and 108 patients were included in the high-risk and low-risk groups, respectively. Specifically, compared with patients in the low-risk group, those in the high-risk group showed significantly longer duration of HTN, higher proportions of hypertensive fundus lesions and at least 30 mg/g uACR, and higher levels of homocysteine (Hcy), cystatin C (CysC), beta-2 microglobulin (β2-MG), and uACR. 76.9% of high-risk patients had significantly higher new-onset proteinuria than the low-risk group. Correlation analysis demonstrated a positive correlation between urinary CKD273 and UACR ( r = 0.494, P = 0.000). The incidence of new-onset albuminuria was significantly higher in the high-risk group than in the low-risk group, as shown by Cox regression analysis. The areas under the curve of CKD273, Hcy, β2-MG, and CysC were 0.925, 0.753, 0.796, and 0.769, respectively. CONCLUSION Urinary CKD273 is a predictor of new-onset proteinuria in patients with HTN, therefore, it can be used for diagnosing patients with early renal injury in patients with HTN, contributing to early prevention and treatment of hypertensive nephropathy.
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Affiliation(s)
- Lirong Lin
- Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (Gener Hospital)
| | - Chunxuan Wang
- Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (Gener Hospital)
| | - Jiangwen Ren
- Department of Nephrology, rheumatism and Immunology, Jiulongpo District People's Hospital of Chongqing
| | - Mei Mei
- Department of Nephrology, Shapingba Hospital of Chongqing University, Chongqing, China
| | - Luquan Zheng
- Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (Gener Hospital)
| | - Jurong Yang
- Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (Gener Hospital)
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Catanese L, Siwy J, Mischak H, Wendt R, Beige J, Rupprecht H. Recent Advances in Urinary Peptide and Proteomic Biomarkers in Chronic Kidney Disease: A Systematic Review. Int J Mol Sci 2023; 24:ijms24119156. [PMID: 37298105 DOI: 10.3390/ijms24119156] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Biomarker development, improvement, and clinical implementation in the context of kidney disease have been a central focus of biomedical research for decades. To this point, only serum creatinine and urinary albumin excretion are well-accepted biomarkers in kidney disease. With their known blind spot in the early stages of kidney impairment and their diagnostic limitations, there is a need for better and more specific biomarkers. With the rise in large-scale analyses of the thousands of peptides in serum or urine samples using mass spectrometry techniques, hopes for biomarker development are high. Advances in proteomic research have led to the discovery of an increasing amount of potential proteomic biomarkers and the identification of candidate biomarkers for clinical implementation in the context of kidney disease management. In this review that strictly follows the PRISMA guidelines, we focus on urinary peptide and especially peptidomic biomarkers emerging from recent research and underline the role of those with the highest potential for clinical implementation. The Web of Science database (all databases) was searched on 17 October 2022, using the search terms "marker *" OR biomarker * AND "renal disease" OR "kidney disease" AND "proteome *" OR "peptid *" AND "urin *". English, full-text, original articles on humans published within the last 5 years were included, which had been cited at least five times per year. Studies based on animal models, renal transplant studies, metabolite studies, studies on miRNA, and studies on exosomal vesicles were excluded, focusing on urinary peptide biomarkers. The described search led to the identification of 3668 articles and the application of inclusion and exclusion criteria, as well as abstract and consecutive full-text analyses of three independent authors to reach a final number of 62 studies for this manuscript. The 62 manuscripts encompassed eight established single peptide biomarkers and several proteomic classifiers, including CKD273 and IgAN237. This review provides a summary of the recent evidence on single peptide urinary biomarkers in CKD, while emphasizing the increasing role of proteomic biomarker research with new research on established and new proteomic biomarkers. Lessons learned from the last 5 years in this review might encourage future studies, hopefully resulting in the routine clinical applicability of new biomarkers.
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Affiliation(s)
- Lorenzo Catanese
- Department of Nephrology, Angiology and Rheumatology, Klinikum Bayreuth GmbH, 95447 Bayreuth, Germany
- Kuratorium for Dialysis and Transplantation (KfH), 95445 Bayreuth, Germany
- Medizincampus Oberfranken, Friedrich-Alexander-University Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Justyna Siwy
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany
| | | | - Ralph Wendt
- Department of Nephrology, St. Georg Hospital Leipzig, 04129 Leipzig, Germany
| | - Joachim Beige
- Department of Nephrology, St. Georg Hospital Leipzig, 04129 Leipzig, Germany
- Department of Internal Medicine II, Martin-Luther-University Halle/Wittenberg, 06108 Halle/Saale, Germany
- Kuratorium for Dialysis and Transplantation (KfH), 04129 Leipzig, Germany
| | - Harald Rupprecht
- Department of Nephrology, Angiology and Rheumatology, Klinikum Bayreuth GmbH, 95447 Bayreuth, Germany
- Kuratorium for Dialysis and Transplantation (KfH), 95445 Bayreuth, Germany
- Medizincampus Oberfranken, Friedrich-Alexander-University Erlangen-Nürnberg, 91054 Erlangen, Germany
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Wu Z, Lohmöller J, Kuhl C, Wehrle K, Jankowski J. Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk. Circ Res 2023; 132:1084-1100. [PMID: 37053282 DOI: 10.1161/circresaha.123.321765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.
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Affiliation(s)
- Zhuojun Wu
- Institute of Molecular Cardiovascular Research (Z.W., J.J.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
- Department of Radiology (C.K.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
| | - Johannes Lohmöller
- Medical Faculty, and Department of Computer Science, Communication and Distributed Systems (COMSYS) (J.L., K.W.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
| | - Christiane Kuhl
- Department of Radiology (C.K.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
| | - Klaus Wehrle
- Institute of Molecular Cardiovascular Research (Z.W., J.J.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
- Medical Faculty, and Department of Computer Science, Communication and Distributed Systems (COMSYS) (J.L., K.W.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
| | - Joachim Jankowski
- Institute of Molecular Cardiovascular Research (Z.W., J.J.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, The Netherlands (J.J.)
- Aachen-Maastricht Institute for Cardiorenal Disease (AMICARE), University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Germany (J.J.)
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Hellinger R, Sigurdsson A, Wu W, Romanova EV, Li L, Sweedler JV, Süssmuth RD, Gruber CW. Peptidomics. NATURE REVIEWS. METHODS PRIMERS 2023; 3:25. [PMID: 37250919 PMCID: PMC7614574 DOI: 10.1038/s43586-023-00205-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/09/2023] [Indexed: 05/31/2023]
Abstract
Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.
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Affiliation(s)
- Roland Hellinger
- Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Arnar Sigurdsson
- Institut für Chemie, Technische Universität Berlin, Berlin, Germany
| | - Wenxin Wu
- School of Pharmacy and Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Elena V Romanova
- Department of Chemistry, University of Illinois, Urbana, IL, USA
| | - Lingjun Li
- School of Pharmacy and Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Christian W Gruber
- Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
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Curovic VR, Eickhoff MK, Rönkkö T, Frimodt-Møller M, Hansen TW, Mischak H, Rossing P, Ahluwalia TS, Persson F. Dapagliflozin Improves the Urinary Proteomic Kidney-Risk Classifier CKD273 in Type 2 Diabetes with Albuminuria: A Randomized Clinical Trial. Diabetes Care 2022; 45:2662-2668. [PMID: 35998283 DOI: 10.2337/dc22-1157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 07/19/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To evaluate the effect of the sodium-glucose cotransporter 2 inhibitor dapagliflozin on the kidney-risk urinary proteomic classifier (CKD273) in persons with type 2 diabetes (T2D) and albuminuria. RESEARCH DESIGN AND METHODS In a double-blind, randomized, controlled, crossover trial, we assigned participants with T2D and urinary albumin to creatinine ratio (UACR) ≥30 mg/g to receive dapagliflozin or matching placebo added to guideline-recommended treatment (ClinicalTrial.gov identifier NCT02914691). Treatment periods lasted 12 weeks, when crossover to the opposing treatment occurred. The primary outcome was change in CKD273 score. Secondary outcomes included regression from high-risk to low-risk CKD273 pattern using the prespecified cutoff score of 0.154. The primary outcome was assessed using paired t test between end-to-end CKD273 scores after dapagliflozin and placebo treatment. The McNemar test was used to assess regression in risk category. RESULTS A total of 40 participants were randomized and 32 completed the trial with intact proteomic measurements. Twenty-eight (88%) were men, the baseline mean (SD) age was 63.0 (8.3) years, mean (SD) diabetes duration was 15.4 (4.5) years, mean HbA1c was 73 (14) mmol/mol (8.8% [1.3%]), and median (interquartile range) UACR was 154 (94, 329) mg/g. Dapagliflozin significantly lowered CKD273 score compared with placebo (-0.221; 95% CI -0.356, -0.087; P = 0.002). Fourteen participants exhibited a high-risk pattern after dapagliflozin treatment compared with 24 after participants placebo (P = 0.021). CONCLUSIONS Dapagliflozin added to renin-angiotensin system inhibition reduced the urinary proteomic classifier CKD273 in persons with T2D and albuminuria, paving the way for the further investigation of CKD273 as a modifiable kidney risk factor.
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Affiliation(s)
| | | | - Teemu Rönkkö
- Steno Diabetes Center Copenhagen, Herlev, Denmark
| | | | | | | | - Peter Rossing
- Steno Diabetes Center Copenhagen, Herlev, Denmark.,Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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Jung CY, Yoo TH. Novel biomarkers for diabetic kidney disease. Kidney Res Clin Pract 2022; 41:S46-S62. [DOI: 10.23876/j.krcp.22.084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/17/2022] [Indexed: 11/04/2022] Open
Abstract
Although diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in patients with diabetes mellitus; its prevalence has failed to decline over the past 30 years. To identify those at high risk of developing DKD and disease progression at an early stage, extensive research has been ongoing in the search for prognostic and surrogate endpoint biomarkers for DKD. Although biomarkers are not used routinely in clinical practice or prospective clinical trials, many biomarkers have been developed to improve the early identification and prognostication of patients with DKD. Novel biomarkers that capture one specific mechanism of the DKD disease process have been developed, and studies have evaluated the prognostic value of assay-based biomarkers either in small sets or in combinations involving multiple biomarkers. More recently, several studies have assessed the prognostic value of omics- based biomarkers that include proteomics, metabolomics, and transcriptomics. This review will first describe the biomarkers used in current practice and their limitations, and then summarize the current status of novel biomarkers for DKD with respect to assay- based protein biomarkers, proteomics, metabolomics, and transcriptomics.
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Provenzano M, Maritati F, Abenavoli C, Bini C, Corradetti V, La Manna G, Comai G. Precision Nephrology in Patients with Diabetes and Chronic Kidney Disease. Int J Mol Sci 2022; 23:5719. [PMID: 35628528 PMCID: PMC9144494 DOI: 10.3390/ijms23105719] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/17/2022] [Accepted: 05/19/2022] [Indexed: 02/04/2023] Open
Abstract
Diabetes is the leading cause of kidney failure and specifically, diabetic kidney disease (DKD) occurs in up to 30% of all diabetic patients. Kidney disease attributed to diabetes is a major contributor to the global burden of the disease in terms of clinical and socio-economic impact, not only because of the risk of progression to End-Stage Kidney Disease (ESKD), but also because of the associated increase in cardiovascular (CV) risk. Despite the introduction of novel treatments that allow us to reduce the risk of future outcomes, a striking residual cardiorenal risk has been reported. This risk is explained by both the heterogeneity of DKD and the individual variability in response to nephroprotective treatments. Strategies that have been proposed to improve DKD patient care are to develop novel biomarkers that classify with greater accuracy patients with respect to their future risk (prognostic) and biomarkers that are able to predict the response to nephroprotective treatment (predictive). In this review, we summarize the principal prognostic biomarkers of type 1 and type 2 diabetes and the novel markers that help clinicians to individualize treatments and the basis of the characteristics that predict an optimal response.
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Affiliation(s)
- Michele Provenzano
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS—Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, 40138 Bologna, Italy; (F.M.); (C.A.); (C.B.); (V.C.); (G.C.)
| | | | | | | | | | - Gaetano La Manna
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS—Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, 40138 Bologna, Italy; (F.M.); (C.A.); (C.B.); (V.C.); (G.C.)
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Jung CY, Yoo TH. Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease. Diabetes Metab J 2022; 46:181-197. [PMID: 35385633 PMCID: PMC8987689 DOI: 10.4093/dmj.2021.0329] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/14/2022] [Indexed: 12/15/2022] Open
Abstract
Although diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease eventually requiring chronic kidney replacement therapy, the prevalence of DKD has failed to decline over the past 30 years. In order to reduce disease prevalence, extensive research has been ongoing to improve prediction of DKD onset and progression. Although the most commonly used markers of DKD are albuminuria and estimated glomerular filtration rate, their limitations have encouraged researchers to search for novel biomarkers that could improve risk stratification. Considering that DKD is a complex disease process that involves several pathophysiologic mechanisms such as hyperglycemia induced inflammation, oxidative stress, tubular damage, eventually leading to kidney damage and fibrosis, many novel biomarkers that capture one specific mechanism of the disease have been developed. Moreover, the increasing use of high-throughput omic approaches to analyze biological samples that include proteomics, metabolomics, and transcriptomics has emerged as a strong tool in biomarker discovery. This review will first describe recent advances in the understanding of the pathophysiology of DKD, and second, describe the current clinical biomarkers for DKD, as well as the current status of multiple potential novel biomarkers with respect to protein biomarkers, proteomics, metabolomics, and transcriptomics.
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Affiliation(s)
- Chan-Young Jung
- Department of Internal Medicine and Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea
| | - Tae-Hyun Yoo
- Department of Internal Medicine and Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea
- Corresponding author: Tae-Hyun Yoo https://orcid.org/0000-0002-9183-4507 Department of Internal Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea E-mail:
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12
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Ceccarelli Ceccarelli D, Paleari R, Solerte B, Mosca A. Re-thinking diabetic nephropathy: Microalbuminuria is just a piece of the diagnostic puzzle. Clin Chim Acta 2021; 524:146-153. [PMID: 34767792 DOI: 10.1016/j.cca.2021.11.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 10/29/2021] [Accepted: 11/07/2021] [Indexed: 12/13/2022]
Abstract
The decline of the estimated glomerular filtration rate (eGFR) and the presence of albuminuria are the typical hallmarks of kidney disease arising as one of the most frequent diabetic complications over a long period of time, generally known as diabetic nephropathy or diabetes kidney disease (DKD). However, a decline in the renal function may occur in diabetic patients for other reasons unrelated to glycemic control, and this condition is known as non-diabetic kidney disease (NDKD). In this opinion paper we will review these conditions, and we outline the importance of other investigations, such as kidney biopsy and the measurement of novel biomarkers, in order to identify the disease progression early, and to allow a timely intervention. We will also focus on the actual limits of the quantitative measurements of albumin in urine, especially with regards to potential interferences due to the treatment of patients with statins.
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Affiliation(s)
| | - Renata Paleari
- Dip. di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università degli Studi di Milano, Milano, Italy
| | - Bruno Solerte
- Dip. di Medicina Interna e Terapia Medica, Università degli Studi di Pavia, Pavia, Italy
| | - Andrea Mosca
- Dip. di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università degli Studi di Milano, Milano, Italy.
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13
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Govender MA, Brandenburg JT, Fabian J, Ramsay M. The Use of 'Omics for Diagnosing and Predicting Progression of Chronic Kidney Disease: A Scoping Review. Front Genet 2021; 12:682929. [PMID: 34819944 PMCID: PMC8606569 DOI: 10.3389/fgene.2021.682929] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 10/18/2021] [Indexed: 12/19/2022] Open
Abstract
Globally, chronic kidney disease (CKD) contributes substantial morbidity and mortality. Recently, various 'omics platforms have provided insight into the molecular basis of kidney dysfunction. This scoping review is a synthesis of the current literature on the use of different 'omics platforms to identify biomarkers that could be used to detect early-stage CKD, predict disease progression, and identify pathways leading to CKD. This review includes 123 articles published from January 2007 to May 2021, following a structured selection process. The most common type of 'omic platform was proteomics, appearing in 55 of the studies and two of these included a metabolomics component. Most studies (n = 91) reported on CKD associated with diabetes mellitus. Thirteen studies that provided information on the biomarkers associated with CKD and explored potential pathways involved in CKD are discussed. The biomarkers that are associated with risk or early detection of CKD are SNPs in the MYH9/APOL1 and UMOD genes, the proteomic CKD273 biomarker panel and metabolite pantothenic acid. Pantothenic acid and the CKD273 biomarker panel were also involved in predicting CKD progression. Retinoic acid pathway genes, UMOD, and pantothenic acid provided insight into potential pathways leading to CKD. The biomarkers were mainly used to detect CKD and predict progression in high-income, European ancestry populations, highlighting the need for representative 'omics research in other populations with disparate socio-economic strata, including Africans, since disease etiologies may differ across ethnic groups. To assess the transferability of findings, it is essential to do research in diverse populations.
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Affiliation(s)
- Melanie A. Govender
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Jean-Tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - June Fabian
- Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michèle Ramsay
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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14
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Barutta F, Bellini S, Canepa S, Durazzo M, Gruden G. Novel biomarkers of diabetic kidney disease: current status and potential clinical application. Acta Diabetol 2021; 58:819-830. [PMID: 33528734 DOI: 10.1007/s00592-020-01656-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/09/2020] [Indexed: 12/12/2022]
Abstract
Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD). Although both albuminuria and glomerular filtration rate (GFR) are well-established diagnostic/prognostic biomarkers of DKD, they have important limitations. There is, thus, increasing quest to find novel biomarkers to identify the disease in an early stage and to improve risk stratification. In this review, we will outline the major pitfalls of currently available markers, describe promising novel biomarkers, and discuss their potential clinical relevance. In particular, we will focus on the importance of recent advancements in multi-omic technologies in the discovery of new DKD biomarkers. In addition, we will provide an update on new emerging approaches to explore renal function and structure, using functional tests and imaging.
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Affiliation(s)
- Federica Barutta
- Department of Medical Sciences, University of Turin, Turin, Italy.
| | - Stefania Bellini
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Silvia Canepa
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Marilena Durazzo
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Gabriella Gruden
- Department of Medical Sciences, University of Turin, Turin, Italy
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15
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Tye SC, Denig P, Heerspink HJL. Precision medicine approaches for diabetic kidney disease: opportunities and challenges. Nephrol Dial Transplant 2021; 36:3-9. [PMID: 34153985 PMCID: PMC8216727 DOI: 10.1093/ndt/gfab045] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Indexed: 12/27/2022] Open
Abstract
The prevalence of end-stage kidney disease (ESKD) continuously increases worldwide. The increasing prevalence parallels the growth in the number of people with diabetes, which is the leading cause of ESKD. Early diagnosis of chronic kidney disease (CKD) in patients with diabetes and appropriate intervention is important to delay the progression of kidney function decline and prevent ESKD. Rate of CKD progression and response to treatment varies among patients with diabetes, highlighting the need to tailor individual treatment. In this review, we describe recent advances and areas for future studies with respect to precision medicine in diabetic kidney disease (DKD). DKD is a multi-factorial disease that is subject in part to genetic heritability, but is also influenced by various exogenous mediators, such as environmental or dietary factors. Genetic testing so far has limited utility to facilitate early diagnosis, classify progression or evaluate response to therapy. Various biomarker-based approaches are currently explored to identify patients at high risk of ESKD and to facilitate decision-making for targeted therapy. These studies have led to discovery and validation of a couple of inflammatory proteins such as circulating tumour necrosis factor receptors, which are strong predictors of kidney disease progression. Moreover, risk and drug-response scores based on multiple biomarkers are developed to predict kidney disease progression and long-term drug efficacy. These findings, if implemented in clinical practice, will pave the way to move from a one-size-fits-all to a one-fit-for-everyone approach.
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Affiliation(s)
- Sok Cin Tye
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Petra Denig
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Hiddo J L Heerspink
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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16
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Fu J, Luo Y, Mou M, Zhang H, Tang J, Wang Y, Zhu F. Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection. Curr Drug Targets 2021; 21:34-54. [PMID: 31433754 DOI: 10.2174/1389450120666190821160207] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 07/17/2019] [Accepted: 07/24/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. OBJECTIVE The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. METHODS Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. RESULTS In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. CONCLUSION In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
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17
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Abstract
The traditional chronic kidney disease (CKD) biomarkers (eGFR based on serum creatinine, sex and age and albuminuria) cannot predict a patient's individual risk for developing progressive CKD. For this reason, it is necessary to identify novel CKD biomarkers that will be able to predict which patients are prone to develop progressive disease and discriminate between disease processes in different parts of the nephron (glomeruli or tubules). A good biomarker should change before or simultaneously with lesion development and its changes should correlate strongly with lesion development. Also, there should be a close relationship between severity of injury and amount of detectable biomarker and its levels should decrease with diminishing injury. Among the large number of molecules under investigation, we have reviewed the most promising ones: NGAL and KIM-1, MCP-1, MMP-9, clusterin, MMP-9, TIMP-1, Procollagen I alpha 1 and suPAR. All these, have been studied as biomarkers for prediction of CKD progression in cohorts of patients with chronic kidney disease of different stages and various aetiologies (proteinuric and non-proteinuric, glomerulonephritides, diabetic, hypertensive and polycystic kidney disease). There is evidence that these molecules could be useful as biomarkers for progressive chronic kidney disease, however, the available data are not enough to draw final conclusions. Further studies with large cohorts and long follow-up are required to identify appropriate biomarkers, that will be able to accurately and reliably define the risk for progressive chronic kidney disease.
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18
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Insights into predicting diabetic nephropathy using urinary biomarkers. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2020; 1868:140475. [DOI: 10.1016/j.bbapap.2020.140475] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 05/27/2020] [Accepted: 06/14/2020] [Indexed: 12/20/2022]
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19
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Provenzano M, De Nicola L, Pena MJ, Capitoli G, Garofalo C, Borrelli S, Gagliardi I, Antolini L, Andreucci M. Precision Nephrology Is a Non-Negligible State of Mind in Clinical Research: Remember the Past to Face the Future. Nephron Clin Pract 2020; 144:463-478. [PMID: 32810859 DOI: 10.1159/000508983] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 05/26/2020] [Indexed: 11/19/2022] Open
Abstract
CKD is a major public health problem. It is characterized by a multitude of risk factors that, when aggregated, can strongly modify outcome. While major risk factors, namely, albuminuria and low estimated glomerular filtration rate (eGFR) have been well analyzed, a large variability in disease progression still remains. This happens because (1) the weight of each risk factor varies between populations (general population or CKD cohort), countries, and single individuals and (2) response to nephroprotective drugs is so heterogeneous that a non-negligible part of patients maintains a high cardiorenal risk despite optimal treatment. Precision nephrology aims at individualizing cardiorenal prognosis and therapy. The purpose of this review is to focus on the risk stratification in different areas, such as clinical practice, population research, and interventional trials, and to describe the strategies used in observational or experimental studies to afford individual-level evidence. The future of precision nephrology is also addressed. Observational studies can in fact provide more adequate findings by collecting more information on risk factors and building risk prediction models that can be applied to each individual in a reliable fashion. Similarly, new clinical trial designs can reduce the individual variability in response to treatment and improve individual outcomes.
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Affiliation(s)
- Michele Provenzano
- Renal Unit, Department of Health Sciences, "Magna Graecia" University, Catanzaro, Italy,
| | - Luca De Nicola
- Renal Unit, Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli,", Naples, Italy
| | - Michelle J Pena
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, The Netherlands
| | - Giulia Capitoli
- Center of Biostatistics for Clinical Epidemiology, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Carlo Garofalo
- Renal Unit, Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli,", Naples, Italy
| | - Silvio Borrelli
- Renal Unit, Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli,", Naples, Italy
| | - Ida Gagliardi
- Renal Unit, Department of Health Sciences, "Magna Graecia" University, Catanzaro, Italy
| | - Laura Antolini
- Center of Biostatistics for Clinical Epidemiology, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Michele Andreucci
- Renal Unit, Department of Health Sciences, "Magna Graecia" University, Catanzaro, Italy
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20
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Provenzano M, Rotundo S, Chiodini P, Gagliardi I, Michael A, Angotti E, Borrelli S, Serra R, Foti D, De Sarro G, Andreucci M. Contribution of Predictive and Prognostic Biomarkers to Clinical Research on Chronic Kidney Disease. Int J Mol Sci 2020; 21:E5846. [PMID: 32823966 PMCID: PMC7461617 DOI: 10.3390/ijms21165846] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 08/09/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Chronic kidney disease (CKD), defined as the presence of albuminuria and/or reduction in estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2, is considered a growing public health problem, with its prevalence and incidence having almost doubled in the past three decades. The implementation of novel biomarkers in clinical practice is crucial, since it could allow earlier diagnosis and lead to an improvement in CKD outcomes. Nevertheless, a clear guidance on how to develop biomarkers in the setting of CKD is not yet available. The aim of this review is to report the framework for implementing biomarkers in observational and intervention studies. Biomarkers are classified as either prognostic or predictive; the first type is used to identify the likelihood of a patient to develop an endpoint regardless of treatment, whereas the second type is used to determine whether the patient is likely to benefit from a specific treatment. Many single assays and complex biomarkers were shown to improve the prediction of cardiovascular and kidney outcomes in CKD patients on top of the traditional risk factors. Biomarkers were also shown to improve clinical trial designs. Understanding the correct ways to validate and implement novel biomarkers in CKD will help to mitigate the global burden of CKD and to improve the individual prognosis of these high-risk patients.
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Affiliation(s)
- Michele Provenzano
- Renal Unit, Department of Health Sciences, “Magna Graecia” University of Catanzaro, I-88100 Catanzaro, Italy; (I.G.); (A.M.)
| | - Salvatore Rotundo
- Department of Health Sciences, “Magna Graecia” University of Catanzaro, I-88100 Catanzaro, Italy; (S.R.); (D.F.)
| | - Paolo Chiodini
- Medical Statistics Unit, University of Campania Luigi Vanvitelli, I-80138 Naples, Italy;
| | - Ida Gagliardi
- Renal Unit, Department of Health Sciences, “Magna Graecia” University of Catanzaro, I-88100 Catanzaro, Italy; (I.G.); (A.M.)
| | - Ashour Michael
- Renal Unit, Department of Health Sciences, “Magna Graecia” University of Catanzaro, I-88100 Catanzaro, Italy; (I.G.); (A.M.)
| | - Elvira Angotti
- Clinical Biochemistry Unit, Azienda Ospedaliera Universitaria Mater Domini Hospital, I-88100 Catanzaro, Italy;
| | - Silvio Borrelli
- Renal Unit, University of Campania “Luigi Vanvitelli”, I-80138 Naples, Italy;
| | - Raffaele Serra
- Interuniversity Center of Phlebolymphology (CIFL), “Magna Graecia” University of Catanzaro, I-88100 Catanzaro, Italy;
| | - Daniela Foti
- Department of Health Sciences, “Magna Graecia” University of Catanzaro, I-88100 Catanzaro, Italy; (S.R.); (D.F.)
| | - Giovambattista De Sarro
- Pharmacology Unit, Department of Health Sciences, School of Medicine, “Magna Graecia” University of Catanzaro, I-88100 Catanzaro, Italy;
| | - Michele Andreucci
- Renal Unit, Department of Health Sciences, “Magna Graecia” University of Catanzaro, I-88100 Catanzaro, Italy; (I.G.); (A.M.)
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21
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Reckoning the Dearth of Bioinformatics in the Arena of Diabetic Nephropathy (DN)—Need to Improvise. Processes (Basel) 2020. [DOI: 10.3390/pr8070808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Diabetic nephropathy (DN) is a recent rising concern amongst diabetics and diabetologist. Characterized by abnormal renal function and ending in total loss of kidney function, this is becoming a lurking danger for the ever increasing population of diabetics. This review touches upon the intensity of this complication and briefly reviews the role of bioinformatics in the area of diabetes. The advances made in the area of DN using proteomic approaches are presented. Compared to the enumerable inputs observed through the use of bioinformatics resources in the area of proteomics and even diabetes, the existing scenario of skeletal application of bioinformatics advances to DN is highlighted and the reasons behind this discussed. As this review highlights, almost none of the well-established tools that have brought breakthroughs in proteomic research have been applied into DN. Laborious, voluminous, cost expensive and time-consuming methodologies and advances in diagnostics and biomarker discovery promised through beckoning bioinformatics mechanistic approaches to improvise DN research and achieve breakthroughs. This review is expected to sensitize the researchers to fill in this gap, exploiting the available inputs from bioinformatics resources.
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22
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Wang D, Yang J, Fan J, Chen W, Nikolic‐Paterson DJ, Li J. Omics technologies for kidney disease research. Anat Rec (Hoboken) 2020; 303:2729-2742. [PMID: 32592293 DOI: 10.1002/ar.24413] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 12/31/2019] [Accepted: 02/17/2020] [Indexed: 12/24/2022]
Affiliation(s)
- Dan Wang
- Department of NephrologyThe First Affiliated Hospital, Sun Yat‐sen University Guangzhou China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province Guangzhou China
| | - Jiayi Yang
- Department of NephrologyThe First Affiliated Hospital, Sun Yat‐sen University Guangzhou China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province Guangzhou China
| | - Jinjin Fan
- Department of NephrologyThe First Affiliated Hospital, Sun Yat‐sen University Guangzhou China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province Guangzhou China
| | - Wei Chen
- Department of NephrologyThe First Affiliated Hospital, Sun Yat‐sen University Guangzhou China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province Guangzhou China
| | | | - Jinhua Li
- Department of NephrologyThe First Affiliated Hospital, Sun Yat‐sen University Guangzhou China
- Key Laboratory of Nephrology, National Health Commission and Guangdong Province Guangzhou China
- Shunde Women and Children Hospital, Guangdong Medical University Shunde Guangdong China
- The Second Clinical College, Guangdong Medical University Dongguan Guangdong China
- Department of Anatomy and Developmental BiologyMonash Biomedicine Discovery Institute, Monash University Clayton Victoria Australia
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23
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Omics research in diabetic kidney disease: new biomarker dimensions and new understandings? J Nephrol 2020; 33:931-948. [DOI: 10.1007/s40620-020-00759-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 05/23/2020] [Indexed: 12/14/2022]
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24
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Discovery of Urinary Proteomic Signature for Differential Diagnosis of Acute Appendicitis. BIOMED RESEARCH INTERNATIONAL 2020; 2020:3896263. [PMID: 32337245 PMCID: PMC7165319 DOI: 10.1155/2020/3896263] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 12/18/2019] [Indexed: 12/29/2022]
Abstract
Acute appendicitis is one of the most common acute abdomens, but the confident preoperative diagnosis is still a challenge. In order to profile noninvasive urinary biomarkers that could discriminate acute appendicitis from other acute abdomens, we carried out mass spectrometric experiments on urine samples from patients with different acute abdomens and evaluated diagnostic potential of urinary proteins with various machine-learning models. Firstly, outlier protein pools of acute appendicitis and controls were constructed using the discovery dataset (32 acute appendicitis and 41 control acute abdomens) against a reference set of 495 normal urine samples. Ten outlier proteins were then selected by feature selection algorithm and were applied in construction of machine-learning models using naïve Bayes, support vector machine, and random forest algorithms. The models were assessed in the discovery dataset by leave-one-out cross validation and were verified in the validation dataset (16 acute appendicitis and 45 control acute abdomens). Among the three models, random forest model achieved the best performance: the accuracy was 84.9% in the leave-one-out cross validation of discovery dataset and 83.6% (sensitivity: 81.2%, specificity: 84.4%) in the validation dataset. In conclusion, we developed a 10-protein diagnostic panel by the random forest model that was able to distinguish acute appendicitis from confusable acute abdomens with high specificity, which indicated the clinical application potential of noninvasive urinary markers in disease diagnosis.
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25
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Siwy J, Mischak H, Zürbig P. Proteomics and personalized medicine: a focus on kidney disease. Expert Rev Proteomics 2019; 16:773-782. [DOI: 10.1080/14789450.2019.1659138] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Justyna Siwy
- R & D, Mosaiques Diagnostics GmbH, Hannover, Germany
| | - Harald Mischak
- R & D, Mosaiques Diagnostics GmbH, Hannover, Germany
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Petra Zürbig
- R & D, Mosaiques Diagnostics GmbH, Hannover, Germany
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26
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Zürbig P, Siwy J, Mischak H. Emerging urine-based proteomic biomarkers as valuable tools in the management of chronic kidney disease. Expert Rev Mol Diagn 2019; 19:853-856. [DOI: 10.1080/14737159.2019.1657406] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
| | | | - Harald Mischak
- Mosaiques Diagnostics GmbH, Hannover, Germany
- Institute of Cardiovascular and Medical Sciences University of Glasgow, Glasgow, UK
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27
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Abstract
Proteome analysis has been applied in multiple studies in the context of chronic kidney disease, aiming at improving our knowledge on the molecular pathophysiology of the disease. The approach is generally based on the hypothesis that proteins are key in maintaining kidney function, and disease is a clinical consequence of a significant change of the protein level. Knowledge on critical proteins and their alteration in disease should in turn enable identification of ideal biomarkers that could guide patient management. In addition, all drugs currently employed target proteins. Hence, proteome analysis also promises to enable identifying the best suited therapeutic target, and, in combination with biomarkers, could be used as the rationale basis for personalized intervention. To assess the current status of proteome analysis in the context of CKD, we present the results of a systematic review, of up-to-date scientific research, and give an outlook on the developments that can be expected in near future. Based on the current literature, proteome analysis has already seen implementation in the management of CKD patients, and it is expected that this approach, also supported by the positive results generated to date, will see advanced high-throughput application.
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28
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Latosinska A, Siwy J, Mischak H, Frantzi M. Peptidomics and proteomics based on CE‐MS as a robust tool in clinical application: The past, the present, and the future. Electrophoresis 2019; 40:2294-2308. [DOI: 10.1002/elps.201900091] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 04/16/2019] [Accepted: 04/16/2019] [Indexed: 12/23/2022]
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Cañadas-Garre M, Anderson K, McGoldrick J, Maxwell AP, McKnight AJ. Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease. J Proteomics 2019; 193:93-122. [PMID: 30292816 DOI: 10.1016/j.jprot.2018.09.020] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 09/20/2018] [Accepted: 09/30/2018] [Indexed: 12/15/2022]
Abstract
Chronic kidney disease (CKD) is an aging-related disorder that represents a major global public health burden. Current biochemical biomarkers, such as serum creatinine and urinary albumin, have important limitations when used to identify the earliest indication of CKD or in tracking the progression to more advanced CKD. These issues underline the importance of finding and testing new molecular biomarkers that are capable of successfully meeting this clinical need. The measurement of changes in nature and/or levels of proteins and metabolites in biological samples from patients provide insights into pathophysiological processes. Proteomic and metabolomic techniques provide opportunities to record dynamic chemical signatures in patients over time. This review article presents an overview of the recent developments in the fields of metabolomics and proteomics in relation to CKD. Among the many different proteomic biomarkers proposed, there is particular interest in the CKD273 classifier, a urinary proteome biomarker reported to predict CKD progression and with implementation potential. Other individual non-invasive peptidomic biomarkers that are potentially relevant for CKD detection include type 1 collagen, uromodulin and mucin-1. Despite the limited sample sizes and variability of the metabolomics studies, some metabolites such as trimethylamine N-oxide, kynurenine and citrulline stand out as potential biomarkers in CKD.
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Affiliation(s)
- M Cañadas-Garre
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - K Anderson
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - J McGoldrick
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - A P Maxwell
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - A J McKnight
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
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Siwy J, Klein T, Rosler M, von Eynatten M. Urinary Proteomics as a Tool to Identify Kidney Responders to Dipeptidyl Peptidase-4 Inhibition: A Hypothesis-Generating Analysis from the MARLINA-T2D Trial. Proteomics Clin Appl 2019; 13:e1800144. [PMID: 30632692 DOI: 10.1002/prca.201800144] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 12/27/2018] [Indexed: 01/13/2023]
Abstract
PURPOSE Chronic kidney disease (CKD) is a serious complication of hyperglycemia and treatment options to slow its progression are scarce. Dipeptidyl peptidase-4 (DPP-4) inhibitors are common glucose-lowering drugs in type 2 diabetes (T2D). Among these, linagliptin has been suggested to exert kidney protective effects. It is investigated whether an effect of linagliptin on kidney function could be unmasked by characterizing the urinary proteome profile (UPP) in albuminuric T2D individuals. EXPERIMENTAL DESIGN Participants of the MARLINA-T2D trial (NCT01792518) are randomized 1:1 to receive either linagliptin 5 mg or placebo for 24 weeks. A previously developed proteome-based classifier, CKD273, is assessed. RESULTS Results confirm a significant correlation between CKD273 and clinical kidney parameters as well as with eGFR decline. Patient stratification using CKD273 at baseline, show a trend toward attenuation of renal function loss in high CKD-risk patients treated with linagliptin. Moreover, characterized are linagliptin affected peptides of which the majority contained a DPP-4 target sequence. CONCLUSIONS AND CLINICAL RELEVANCE CKD273 is a promising tool for identifying patients at high risk for CKD progression and may unmask a potential of linagliptin to slow progressive kidney function loss in high CKD-risk patients. UPP characterization reveals a significant impact of linagliptin on urinary peptides.
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Affiliation(s)
- Justyna Siwy
- mosaiques-diagnostics GmbH, Rotenburger Str. 20, 30659, Hannover, Germany
| | - Thomas Klein
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach an der Riß, Germany
| | - Marcel Rosler
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach an der Riß, Germany
| | - Maximilian von Eynatten
- Boehringer Ingelheim International GmbH. KG, Binger Str. 173, 55216, Ingelheim am Rhein, Germany
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Persson F, Rossing P. Urinary Proteomics and Precision Medicine for Chronic Kidney Disease: Current Status and Future Perspectives. Proteomics Clin Appl 2019; 13:e1800176. [DOI: 10.1002/prca.201800176] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Revised: 12/28/2018] [Indexed: 02/06/2023]
Affiliation(s)
- Frederik Persson
- Steno Diabetes Center Copenhagen Niels Steensensvej 1, DK‐2820 Gentofte Denmark
| | - Peter Rossing
- Steno Diabetes Center Copenhagen Niels Steensensvej 1, DK‐2820 Gentofte Denmark
- Institute of Clinical MedicineUniversity of Copenhagen Blegdamsvej 3B, DK‐2200 Copenhagen Denmark
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Oellgaard J, Gæde P, Persson F, Rossing P, Parving HH, Pedersen O. Application of urinary proteomics as possible risk predictor of renal and cardiovascular complications in patients with type 2-diabetes and microalbuminuria. J Diabetes Complications 2018; 32:1133-1140. [PMID: 30282584 DOI: 10.1016/j.jdiacomp.2018.09.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 09/13/2018] [Accepted: 09/18/2018] [Indexed: 01/30/2023]
Abstract
BACKGROUND Analyses of the urinary proteome have been proposed as a novel approach for early assessment of increased risk of renal- or cardiovascular disease. Here we investigate the potentials of various classifiers derived from urinary proteomics for prediction of renal and cardiovascular comorbidities in patients with type 2-diabetes. METHODS The study was a post hoc analysis of the randomized controlled Steno-2 trial comparing intensified multifactorial intervention to conventional treatment of type 2-diabetes and microalbuminuria. 151 diabetic patients with persistent microalbuminuria were included in year 1995 and followed for up to 19 years. For renal outcomes, two classifiers (CKD273 and a novel, GFR-based classifier) and for cardiovascular outcomes, three classifiers (CAD238, ACSP and ACSP75) were applied. Renal endpoints were progression to macroalbuminuria, impaired renal function (GFR < 45 ml/min/1.73 m2) or progression to end stage renal disease (ESRD) or death. Cardiovascular endpoints were coronary artery disease and a composite endpoint of incident death of cardiovascular disease, myocardial infarction or revascularization, stroke, amputation or peripheral revascularization. RESULTS CKD273 was not consistently associated with renal outcomes. The GFR-based classifier was associated with impaired renal function, but lost significance in extensively adjusted models. Both the ACSP75 and ACSP-scores, but not the CAD238-score were inversely associated (opposing the hypothesis) with cardiovascular endpoints. None of the classifiers improved prediction of any outcome on top of standard risk factors. CONCLUSIONS Risk-scores based upon urinary proteomics did not improve prediction of renal and cardiovascular endpoints on top of standard risk factors such as age and GFR during long-term (19 years) follow up in patients with type 2-diabetes and microalbuminuria.
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Affiliation(s)
- Jens Oellgaard
- Slagelse Hospital, Slagelse, Denmark; University of Southern Denmark, Odense, Denmark; Steno Diabetes Center, Gentofte, Denmark.
| | - Peter Gæde
- Slagelse Hospital, Slagelse, Denmark; University of Southern Denmark, Odense, Denmark.
| | | | - Peter Rossing
- Steno Diabetes Center, Gentofte, Denmark; University of Copenhagen, Denmark; Aarhus University, Aarhus, Denmark.
| | - Hans-Henrik Parving
- University of Copenhagen, Denmark; Department of Medical Endocrinology, Rigshospitalet, Denmark.
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Copenhagen, Denmark.
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Fenton RA. Proteomic approaches in kidney disease biomarker discovery. Am J Physiol Renal Physiol 2018; 315:F1817-F1821. [DOI: 10.1152/ajprenal.00421.2018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Biomarkers have the potential to greatly facilitate diagnosis and treatment of patients with various forms of kidney disease. State-of-the-art mass spectrometry-based methods possess the capability, on a proteome scale and in an unbiased manner, to detect alterations in protein abundances and/or posttranslational modifications in plasma, urine, or tissue. Such approaches can provide a large, unbiased database to facilitate identification of potential biomarkers. In the diagnosis of kidney diseases, urine is usually a more favorable specimen than plasma and kidney tissue due to its noninvasive collection and simplicity of processing. However, whether analysis of proteins in urine faithfully reflects their changes in the kidney tissue remains unclear. The use of proteomics to analyze kidney tissue samples collected during late-stage kidney diseases has also recently gathered pace. The goal of this minireview is to provide an overview of the proteomic technologies currently applied to studies of kidney and their limitations, present existing kidney and urine proteome databases, and highlight a few applications of such approaches in kidney disease biomarker discovery.
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Affiliation(s)
- Robert A. Fenton
- InterPrET Center, Department of Biomedicine, Aarhus University, Aarhus, Denmark
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Novel Urinary Biomarkers For Improved Prediction Of Progressive Egfr Loss In Early Chronic Kidney Disease Stages And In High Risk Individuals Without Chronic Kidney Disease. Sci Rep 2018; 8:15940. [PMID: 30374033 PMCID: PMC6206033 DOI: 10.1038/s41598-018-34386-8] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 10/15/2018] [Indexed: 12/22/2022] Open
Abstract
Chronic kidney disease is associated with increased risk of CKD progression and death. Therapeutic approaches to limit progression are limited. Developing tools for the early identification of those individuals most likely to progress will allow enriching clinical trials in high risk early CKD patients. The CKD273 classifier is a panel of 273 urinary peptides that enables early detection of CKD and prognosis of progression. We have generated urine capillary electrophoresis-mass spectrometry-based peptidomics CKD273 subclassifiers specific for CKD stages to allow the early identification of patients at high risk of CKD progression. In the validation cohort, the CKD273 subclassifiers outperformed albuminuria and CKD273 classifier for predicting rapid loss of eGFR in individuals with baseline eGFR > 60 ml/min/1.73 m2. In individuals with eGFR > 60 ml/min/1.73 m2 and albuminuria <30 mg/day, the CKD273 subclassifiers predicted rapid eGFR loss with AUC ranging from 0.797 (0.743-0.844) to 0.736 (0.689-0.780). The association between CKD273 subclassifiers and rapid progression remained significant after adjustment for age, sex, albuminuria, DM, baseline eGFR, and systolic blood pressure. Urinary peptidomics CKD273 subclassifiers outperformed albuminuria and CKD273 classifier for predicting the risk of rapid CKD progression in individuals with eGFR > 60 ml/min/1.73 m2. These CKD273 subclassifiers represented the earliest evidence of rapidly progressive CKD in non-albuminuric individuals with preserved renal function.
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Tofte N, Lindhardt M, Adamova K, Beige J, Beulens JWJ, Birkenfeld AL, Currie G, Delles C, Dimos I, Francová L, Frimodt-Møller M, Girman P, Göke R, Havrdova T, Kooy A, Mischak H, Navis G, Nijpels G, Noutsou M, Ortiz A, Parvanova A, Persson F, Ruggenenti PL, Rutters F, Rychlík I, Spasovski G, Speeckaert M, Trillini M, von der Leyen H, Rossing P. Characteristics of high- and low-risk individuals in the PRIORITY study: urinary proteomics and mineralocorticoid receptor antagonism for prevention of diabetic nephropathy in Type 2 diabetes. Diabet Med 2018; 35:1375-1382. [PMID: 29781558 DOI: 10.1111/dme.13669] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/10/2018] [Indexed: 12/13/2022]
Abstract
AIM To compare clinical baseline data in individuals with Type 2 diabetes and normoalbuminuria, who are at high or low risk of diabetic kidney disease based on the urinary proteomics classifier CKD273. METHODS We conducted a prospective, randomized, double-blind, placebo-controlled international multicentre clinical trial and observational study in participants with Type 2 diabetes and normoalbuminuria, stratified into high- or low-risk groups based on CKD273 score. Clinical baseline data for the whole cohort and stratified by risk groups are reported. The associations between CKD273 and traditional risk factors for diabetic kidney disease were evaluated using univariate and logistic regression analysis. RESULTS A total of 1777 participants from 15 centres were included, with 12.3% of these having a high-risk proteomic pattern. Participants in the high-risk group (n=218), were more likely to be men, were older, had longer diabetes duration, a lower estimated GFR and a higher urinary albumin:creatinine ratio than those in the low-risk group (n=1559, P<0.02). Numerical differences were small and univariate regression analyses showed weak associations (R2 < 0.04) of CKD273 with each baseline variable. In a logistic regression model including clinical variables known to be associated with diabetic kidney disease, estimated GFR, gender, log urinary albumin:creatinine ratio and use of renin-angiotensin system-blocking agents remained significant determinants of the CKD273 high-risk group: area under the curve 0.72 (95% CI 0.68-0.75; P<0.01). CONCLUSIONS In this population of individuals with Type 2 diabetes and normoalbuminuria, traditional diabetic kidney disease risk factors differed slightly between participants at high risk and those at low risk of diabetic kidney disease, based on CKD273. These data suggest that CKD273 may provide additional prognostic information over and above the variables routinely available in the clinic. Testing the added value will be subject to our ongoing study. (European Union Clinical Trials Register: EudraCT 2012-000452-34 and Clinicaltrials.gov: NCT02040441).
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Affiliation(s)
- N Tofte
- Steno Diabetes Centre Copenhagen, Gentofte, Denmark
| | - M Lindhardt
- Steno Diabetes Centre Copenhagen, Gentofte, Denmark
| | - K Adamova
- University Clinic of Endocrinology, Diabetes and Metabolic Disorders, Skopje, Macedonia
| | - J Beige
- Klinikum St. Georg, Nephrology and KfH Renal Unit, Leipzig, Martin-Luther University Halle, Wittenberg, Germany
| | - J W J Beulens
- Amsterdam Public Health Research Institute, VU University Medical Centre, Amsterdam, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - A L Birkenfeld
- Clinical Study Centre Metabolic Vascular Medicine, GWT TU-Dresden GmbH, Dresden, Germany
- Paul Langerhans Institute Dresden of the Helmholtz Centre Munich at University Hospital, and Faculty of Medicine, TU Dresden, Dresden, Germany
- German Centre for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - G Currie
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - C Delles
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - I Dimos
- Diabetespraxis, Leipzig, Germany
| | - L Francová
- 1st Department, Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | | | - P Girman
- Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - R Göke
- Diabetologische Schwerpunktpraxis, Diabetologen Hessen, Marburg, Germany
| | - T Havrdova
- Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - A Kooy
- Bethesda Diabetes Research Centre, Hoogeveen and University Medical Centre Groningen, Netherlands
| | - H Mischak
- Mosaiques Diagnostics, Hannover, Germany
| | - G Navis
- Division of Nephrology, Department of Internal Medicine, University Medical Centre Groningen, Groningen, Netherlands
| | - G Nijpels
- Department General Practice and Elderly Care, Amsterdam Public Health VU University Medical Centre, Amsterdam, The Netherlands
| | - M Noutsou
- Diabetes Centre and 2nd Department of Internal Medicine, National and Kapodistrian University of Athens, Hippokratio General Hospital, Athens, Greece
| | - A Ortiz
- Instituto de Investigacion Sanitaria de la Fundacion Jiménez Díaz UAM, Madrid, Spain
| | - A Parvanova
- Istituto di Richerche Farmacologiche Mario Negri, Bergamo, Italy
| | - F Persson
- Steno Diabetes Centre Copenhagen, Gentofte, Denmark
| | - P L Ruggenenti
- Istituto di Richerche Farmacologiche Mario Negri, Bergamo, Italy
| | - F Rutters
- Amsterdam Public Health Research Institute, VU University Medical Centre, Amsterdam, The Netherlands
| | - I Rychlík
- 1st Department, Charles University, Third Faculty of Medicine, Prague, Czech Republic
- Faculty Hospital Královské Vinohrady, Prague, Czech Republic
| | - G Spasovski
- Department of Nephrology, Cyril and Methodius University in Skopje, Skopje, Macedonia
| | - M Speeckaert
- Ghent University Hospital, Department of Nephrology, Ghent, Belgium
| | - M Trillini
- Istituto di Richerche Farmacologiche Mario Negri, Bergamo, Italy
| | | | - P Rossing
- Steno Diabetes Centre Copenhagen, Gentofte, Denmark
- University of Copenhagen, Copenhagen, Denmark
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Velez G, Tang PH, Cabral T, Cho GY, Machlab DA, Tsang SH, Bassuk AG, Mahajan VB. Personalized Proteomics for Precision Health: Identifying Biomarkers of Vitreoretinal Disease. Transl Vis Sci Technol 2018; 7:12. [PMID: 30271679 PMCID: PMC6159735 DOI: 10.1167/tvst.7.5.12] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 07/30/2018] [Indexed: 12/14/2022] Open
Abstract
Proteomic analysis is an attractive and powerful tool for characterizing the molecular profiles of diseased tissues, such as the vitreous. The complexity of data available for analysis ranges from single (e.g., enzyme-linked immunosorbent assay [ELISA]) to thousands (e.g., mass spectrometry) of proteins, and unlike genomic analysis, which is limited to denoting risk, proteomic methods take snapshots of a diseased vitreous to evaluate ongoing molecular processes in real time. The proteome of diseased ocular tissues was recently characterized, uncovering numerous biomarkers for vitreoretinal diseases and identifying protein targets for approved drugs, allowing for drug repositioning. These biomarkers merit more attention regarding their therapeutic potential and prospective validation, as well as their value as reproducible, sensitive, and specific diagnostic markers. TRANSLATIONAL RELEVANCE Personalized proteomics offers many advantages over alternative precision-health platforms for the diagnosis and treatment of vitreoretinal diseases, including identification of molecular constituents in the diseased tissue that can be targeted by available drugs.
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Affiliation(s)
- Gabriel Velez
- Omics Laboratory, Stanford University, Palo Alto, CA, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA
- Medical Scientist Training Program, University of Iowa, Iowa City, IA, USA
| | - Peter H. Tang
- Omics Laboratory, Stanford University, Palo Alto, CA, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Thiago Cabral
- Department of Specialized Medicine, CCS, Federal University of Espírito Santo (UFES), Vitória, Brazil
- Vision Center Unit, Ophthalmology, EBSERH, HUCAM-UFES, Vitória, Brazil
- Department of Ophthalmology, Federal University of São Paulo (UNIFESP), São Paulo, Brazil
| | - Galaxy Y. Cho
- Frank H. Netter MD School of Medicine, Quinnipiac University, North Haven, CT, USA
- Barbara and Donald Jonas Laboratory of Stem Cells and Regenerative Medicine and Bernard & Shirlee Brown Glaucoma Laboratory, Columbia University, New York, NY, USA
- Department of Ophthalmology, Columbia University, New York, NY, USA
| | - Daniel A. Machlab
- Omics Laboratory, Stanford University, Palo Alto, CA, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA
| | - Stephen H. Tsang
- Barbara and Donald Jonas Laboratory of Stem Cells and Regenerative Medicine and Bernard & Shirlee Brown Glaucoma Laboratory, Columbia University, New York, NY, USA
- Department of Ophthalmology, Columbia University, New York, NY, USA
- Department of Pathology & Cell Biology, College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | | | - Vinit B. Mahajan
- Omics Laboratory, Stanford University, Palo Alto, CA, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
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Gudehithlu KP, Hart PD, Vernik J, Sethupathi P, Dunea G, Arruda JAL, Singh AK. Peptiduria: a potential early predictor of diabetic kidney disease. Clin Exp Nephrol 2018; 23:56-64. [PMID: 30066159 DOI: 10.1007/s10157-018-1620-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 07/07/2018] [Indexed: 12/27/2022]
Abstract
BACKGROUND To protect the kidney effectively with medication in type 2 diabetics, it is crucial to identify such at-risk patients early for treatment. We investigated whether peptiduria precedes proteinuria (the earliest urinary marker in our model), and thereby serve as an early predictor of diabetic nephropathy. METHODS A longitudinal study was performed in a rat model of diabetic nephropathy. Peptides, defined as degradation products of proteins of < 13 kD size, were quantified by a previously validated method using a combination of Lowry and Biorad protein assays. Peptides in urine were also confirmed by chromatographically separating low molecular weight fractions from urine and quantifying albumin fragments in these fractions by enzyme immunoassay. Also, the mechanism of peptiduria was addressed by measuring acid phosphatase, a marker of lysosomal activity, in urine and on kidney sections (histochemically). RESULTS In rats with diabetic nephropathy, proteinuria occurred after 12 weeks of diabetes, while peptiduria occurred as early as 2 weeks after diabetes. Peptiduria was confirmed by showing that the chromatographically separated low molecular weight fractions of urine containing albumin fragments is in proportion to the level of peptiduria. The time course of peptiduria paralleled the increase in urinary acid phosphatase suggesting that the mechanism of early peptiduria could be due to upregulation of lysosomal enzyme activity in the tubules. CONCLUSIONS Our results showing that peptiduria precedes proteinuria in diabetic nephropathy provide a compelling rationale to perform a prospective human clinical trial to investigate whether peptiduria can serve as an early predictor of diabetic nephropathy.
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Affiliation(s)
- Krishnamurthy P Gudehithlu
- Division of Nephrology, John H. Stroger, Jr. Hospital of Cook County (JSH), 1900 West Polk Street, Suite 643, Chicago, IL, 60612, USA.
| | - Peter D Hart
- Division of Nephrology, John H. Stroger, Jr. Hospital of Cook County (JSH), 1900 West Polk Street, Suite 643, Chicago, IL, 60612, USA.,Department of Internal Medicine, Rush University Medical College, Chicago, IL, USA.,The Hektoen Institute of Medicine, Chicago, IL, USA
| | - Jane Vernik
- Division of Nephrology, John H. Stroger, Jr. Hospital of Cook County (JSH), 1900 West Polk Street, Suite 643, Chicago, IL, 60612, USA.,Department of Internal Medicine, Rush University Medical College, Chicago, IL, USA
| | | | - George Dunea
- Division of Nephrology, John H. Stroger, Jr. Hospital of Cook County (JSH), 1900 West Polk Street, Suite 643, Chicago, IL, 60612, USA.,Section of Nephrology, University of Illinois at Chicago, Chicago, IL, USA.,The Hektoen Institute of Medicine, Chicago, IL, USA
| | - Jose A L Arruda
- Division of Nephrology, John H. Stroger, Jr. Hospital of Cook County (JSH), 1900 West Polk Street, Suite 643, Chicago, IL, 60612, USA.,Section of Nephrology, University of Illinois at Chicago, Chicago, IL, USA.,The Hektoen Institute of Medicine, Chicago, IL, USA
| | - Ashok K Singh
- Division of Nephrology, John H. Stroger, Jr. Hospital of Cook County (JSH), 1900 West Polk Street, Suite 643, Chicago, IL, 60612, USA.,Section of Nephrology, University of Illinois at Chicago, Chicago, IL, USA.,The Hektoen Institute of Medicine, Chicago, IL, USA
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Frantzi M, Latosinska A, Kontostathi G, Mischak H. Clinical Proteomics: Closing the Gap from Discovery to Implementation. Proteomics 2018; 18:e1700463. [PMID: 29785737 DOI: 10.1002/pmic.201700463] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 05/10/2018] [Indexed: 12/15/2022]
Abstract
Clinical proteomics, the application of proteome analysis to serve a clinical purpose, represents a major field in the area of proteome research. Over 1000 manuscripts on this topic are published each year, with numbers continuously increasing. However, the anticipated outcome, the transformation of the reported findings into improvements in patient management, is not immediately evident. In this article, the value and validity of selected clinical proteomics findings are investigated, and it is assessed how far implementation has progressed. A main conclusion from this assessment is that to achieve implementation, well-powered clinical studies are required in the appropriate population, addressing a specific clinical need and with a clear context-of-use. Efforts toward implementation, to be feasible, must be supported by the key players in science: publishers and funders. The authors propose a change on objectives, from additional discovery studies toward studies aiming at validation of the plethora of potential biomarkers that have been described, to demonstrate practical value of clinical proteomics. All elements required, potential biomarkers, technologies, and bio-banked samples are available (based on today's literature), hence a change in focus from discovery toward validation and application is not only urgently necessary, but also possible based on resources available today.
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Affiliation(s)
- Maria Frantzi
- Mosaiques Diagnostics GmbH, Hannover, 30659, Germany
| | | | - Georgia Kontostathi
- Department of Biotechnology, Biomedical Research Foundation Academy of Athens, Athens, 11527, Greece
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39
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Siwy J, Zürbig P, Argiles A, Beige J, Haubitz M, Jankowski J, Julian BA, Linde PG, Marx D, Mischak H, Mullen W, Novak J, Ortiz A, Persson F, Pontillo C, Rossing P, Rupprecht H, Schanstra JP, Vlahou A, Vanholder R. Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis. Nephrol Dial Transplant 2018; 32:2079-2089. [PMID: 27984204 DOI: 10.1093/ndt/gfw337] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 08/10/2016] [Indexed: 12/11/2022] Open
Abstract
Background In spite of its invasive nature and risks, kidney biopsy is currently required for precise diagnosis of many chronic kidney diseases (CKDs). Here, we explored the hypothesis that analysis of the urinary proteome can discriminate different types of CKD irrespective of the underlying mechanism of disease. Methods We used data from the proteome analyses of 1180 urine samples from patients with different types of CKD, generated by capillary electrophoresis coupled to mass spectrometry. A set of 706 samples served as the discovery cohort, and 474 samples were used for independent validation. For each CKD type, peptide biomarkers were defined using statistical analysis adjusted for multiple testing. Potential biomarkers of statistical significance were combined in support vector machine (SVM)-based classifiers. Results For seven different types of CKD, several potential urinary biomarker peptides (ranging from 116 to 619 peptides) were defined and combined into SVM-based classifiers specific for each CKD. These classifiers were validated in an independent cohort and showed good to excellent accuracy for discrimination of one CKD type from the others (area under the receiver operating characteristic curve ranged from 0.77 to 0.95). Sequence analysis of the biomarkers provided further information that may clarify the underlying pathophysiology. Conclusions Our data indicate that urinary proteome analysis has the potential to identify various types of CKD defined by pathological assessment of renal biopsies and current clinical practice in general. Moreover, these approaches may provide information to model molecular changes per CKD.
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Affiliation(s)
| | | | | | - Joachim Beige
- KfH Renal Unit, Department Nephrology, Leipzig and Martin Luther University, Halle/Wittenberg, Germany
| | - Marion Haubitz
- Department of Nephrology, Klinikum Fulda gAG, Fulda, Germany
| | - Joachim Jankowski
- Institute for Molecular Cardiovascular Research, RWTH Aachen University Hospital, Aachen, Germany.,School for Cardiovascular Diseases (CARIM), University of Maastricht, Maastricht, The Netherlands
| | - Bruce A Julian
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - David Marx
- Department of Nephrology and Kidney Transplantation, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, Hanover, Germany.,BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - William Mullen
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Jan Novak
- University of Alabama at Birmingham, Birmingham, AL, USA
| | - Alberto Ortiz
- School of Medicine, Jimenez Diaz Foundation Institute for Health Research, Autonomous University of Madrid, Madrid, Spain
| | | | - Claudia Pontillo
- Mosaiques Diagnostics GmbH, Hanover, Germany.,Charite-Universitätsmedizin, Berlin, Germany
| | - Peter Rossing
- Steno Diabetes Center, Gentofte, Denmark.,Faculty of Health, University of Aarhus, Aarhus, Denmark.,Faculty of Health, University of Copenhagen, Copenhagen, Denmark
| | | | - Joost P Schanstra
- Institute of Cardiovascular and Metabolic Disease, French Institute of Health and Medical Research U1048, Toulouse, France.,Université Toulouse III Paul-Sabatier, Toulouse, France
| | - Antonia Vlahou
- Division of Biotechnology, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Raymond Vanholder
- Nephrology Section, Department of Internal Medicine, Ghent University Hospital, Ghent, Belgium
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40
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Pontillo C, Jacobs L, Staessen JA, Schanstra JP, Rossing P, Heerspink HJL, Siwy J, Mullen W, Vlahou A, Mischak H, Vanholder R, Zürbig P, Jankowski J. A urinary proteome-based classifier for the early detection of decline in glomerular filtration. Nephrol Dial Transplant 2018; 32:1510-1516. [PMID: 27387473 DOI: 10.1093/ndt/gfw239] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 05/02/2016] [Indexed: 12/13/2022] Open
Abstract
Background Chronic kidney disease (CKD) progression is currently assessed by a decline in estimated glomerular filtration rate (eGFR) and/or an increase in urinary albumin excretion (UAE). However, these markers are considered either to be late-stage markers or to have low sensitivity or specificity. In this study, we investigated the performance of the urinary proteome-based classifier CKD273, compared with UAE, in a number of different narrow ranges of CKD severity, with each range separated by an eGFR of 10 mL/min/1.73 m 2 . Methods A total of 2672 patients with different CKD stages were included in the study. Of these, 394 individuals displayed a decline in eGFR of >5 mL/min/1.73 m 2 /year (progressors) and the remaining individuals were considered non-progressors. For all samples, UAE values and CKD273 classification scores were obtained. To assess UAE values and CKD273 scores at different disease stages, the cohort was divided according to baseline eGFRs of ≥80, 70-79, 60-69, 50-59, 40-49, 30-39 and <29 mL/min/1.73 m 2 . In addition, areas under the curve for CKD273 and UAE were calculated. Results In early stage CKD, the urinary proteome-based classifier performed significantly better than UAE in detecting progressors. In contrast, UAE performed better in patients with late-stage CKD. No significant difference in performance was found between CKD273 and UAE in patients with moderately reduced renal function. Conclusions These results suggest that urinary peptides, as combined in the CKD273 classifier, allow the detection of progressive CKD at early stages, a point where therapeutic intervention is more likely to be effective. However, late-stage disease, where irreversible damage of the kidney is already present, is better detected by UAE.
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Affiliation(s)
- Claudia Pontillo
- Mosaiques Diagnostics, Hanover, Germany.,Charité-Universitatsmedizin, Berlin, Germany
| | - Lotte Jacobs
- Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Jan A Staessen
- Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.,R&D VitaK Group, Maastricht University, Maastricht, The Netherlands
| | - Joost P Schanstra
- Institute of Metabolic and Cardiovascular Diseases, Inserm U1048, Toulouse, France.,Université Toulouse III Paul-Sabatier, Toulouse, France
| | - Peter Rossing
- Steno Diabetes Center, Gentofte, Denmark.,University of Aarhus, Aarhus, Denmark.,Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Hiddo J L Heerspink
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | | | - Antonia Vlahou
- Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Harald Mischak
- Mosaiques Diagnostics, Hanover, Germany.,University of Glasgow, Glasgow, UK
| | - Ray Vanholder
- Nephrology Section, Department of Internal Medicine, Ghent University Hospital, Ghent, Belgium
| | | | - Joachim Jankowski
- Charité-Universitatsmedizin, Berlin, Germany.,Institute for Molecular Cardiovascular Research, University Hospital RWTH, Aachen, Germany.,Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, Maastricht, The Netherlands
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41
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Lindhardt M, Persson F, Zürbig P, Stalmach A, Mischak H, de Zeeuw D, Lambers Heerspink H, Klein R, Orchard T, Porta M, Fuller J, Bilous R, Chaturvedi N, Parving HH, Rossing P. Urinary proteomics predict onset of microalbuminuria in normoalbuminuric type 2 diabetic patients, a sub-study of the DIRECT-Protect 2 study. Nephrol Dial Transplant 2018; 32:1866-1873. [PMID: 27507891 DOI: 10.1093/ndt/gfw292] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 06/13/2016] [Indexed: 11/12/2022] Open
Abstract
Background Early prevention of diabetic nephropathy is not successful as early interventions have shown conflicting results, partly because of a lack of early and precise indicators of disease development. Urinary proteomics has shown promise in this regard and could identify those at high risk who might benefit from treatment. In this study we investigate its utility in a large type 2 diabetic cohort with normoalbuminuria. Methods We performed a post hoc analysis in the Diabetic Retinopathy Candesartan Trials (DIRECT-Protect 2 study), a multi centric randomized clinical controlled trial. Patients were allocated to candesartan or placebo, with the aim of slowing the progression of retinopathy. The secondary endpoint was development of persistent microalbuminuria (three of four samples). We used a previously defined chronic kidney disease risk score based on proteomic measurement of 273 urinary peptides (CKD273-classifier). A Cox regression model for the progression of albuminuria was developed and evaluated with integrated discrimination improvement (IDI), continuous net reclassification index (cNRI) and receiver operating characteristic curve statistics. Results Seven hundred and thirty-seven patients were analysed and 89 developed persistent microalbuminuria (12%) with a mean follow-up of 4.1 years. At baseline the CKD273-classifier predicted development of microalbuminuria during follow-up, independent of treatment (candesartan/placebo), age, gender, systolic blood pressure, urine albumin excretion rate, estimated glomerular filtration rate, HbA1c and diabetes duration, with hazard ratio 2.5 [95% confidence interval (CI) 1.4-4.3; P = 0.002] and area under the curve 0.79 (95% CI 0.75-0.84; P < 0.0001). The CKD273-classifier improved the risk prediction (relative IDI 14%, P = 0.002; cNRI 0.10, P = 0.043). Conclusions In this cohort of patients with type 2 diabetes and normoalbuminuria from a large intervention study, the CKD273-classifier was an independent predictor of microalbuminuria. This may help identify high-risk normoalbuminuric patients for preventive strategies for diabetic nephropathy.
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Affiliation(s)
| | | | | | | | - Harald Mischak
- Mosaiques Diagnostics GmbH, Hannover, Germany.,University of Glasgow, Glasgow, UK
| | - Dick de Zeeuw
- Department of Clinical Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Hiddo Lambers Heerspink
- Department of Clinical Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ronald Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Trevor Orchard
- Department of Epidemiology, Medicine & Pediatrics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Massimo Porta
- Department of Medical Sciences, University of Turin, Torino, Italy
| | - John Fuller
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Rudolf Bilous
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK.,South Tees NHS Trust, Middlesbrough, UK
| | - Nish Chaturvedi
- Institute of Cardiovascular Sciences, University College London, London, UK
| | - Hans-Henrik Parving
- Department of Medical Endocrinology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Peter Rossing
- Steno Diabetes Center, Gentofte, Denmark.,Faculty of Health Science, University of Aarhus, Aarhus, Denmark.,The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
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42
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Abstract
Diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in diabetes. The quest for both prognostic and surrogate endpoint biomarkers for advanced DKD and end-stage renal disease has received major investment and interest in recent years. However, at present no novel biomarkers are in routine use in the clinic or in trials. This review focuses on the current status of prognostic biomarkers. First, we emphasise that albuminuria and eGFR, with other routine clinical data, show at least modest prediction of future renal status if properly used. Indeed, a major limitation of many current biomarker studies is that they do not properly evaluate the marginal increase in prediction on top of these routinely available clinical data. Second, we emphasise that many of the candidate biomarkers for which there are numerous sporadic reports in the literature are tightly correlated with each other. Despite this, few studies have attempted to evaluate a wide range of biomarkers simultaneously to define the most useful among these correlated biomarkers. We also review the potential of high-dimensional panels of lipids, metabolites and proteins to advance the field, and point to some of the analytical and post-analytical challenges of taking initial studies using these and candidate approaches through to actual clinical biomarker use.
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Affiliation(s)
- Helen M Colhoun
- MRC Institute of Genetics & Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK.
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43
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Currie GE, von Scholten BJ, Mary S, Flores Guerrero JL, Lindhardt M, Reinhard H, Jacobsen PK, Mullen W, Parving HH, Mischak H, Rossing P, Delles C. Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria. Cardiovasc Diabetol 2018; 17:50. [PMID: 29625564 PMCID: PMC5889591 DOI: 10.1186/s12933-018-0697-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 04/02/2018] [Indexed: 01/01/2023] Open
Abstract
Background The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. Methods Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. Results CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. Conclusion A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers. Electronic supplementary material The online version of this article (10.1186/s12933-018-0697-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gemma E Currie
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK.
| | | | - Sheon Mary
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
| | - Jose-Luis Flores Guerrero
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
| | | | | | | | - William Mullen
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
| | | | - Harald Mischak
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK.,Mosaiques Diagnostics, Hanover, Germany
| | - Peter Rossing
- Steno Diabetes Center, Gentofte, Copenhagen, Denmark.,HEALTH, University of Aarhus, Aarhus, Denmark.,Institute for Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christian Delles
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
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44
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Chen L, Su W, Chen H, Chen DQ, Wang M, Guo Y, Zhao YY. Proteomics for Biomarker Identification and Clinical Application in Kidney Disease. Adv Clin Chem 2018; 85:91-113. [PMID: 29655463 DOI: 10.1016/bs.acc.2018.02.005] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Treatment effectiveness for kidney disease is limited by lack of accuracy, sensitivity, specificity of diagnostic, prognostic, and therapeutic biomarkers. The gold standard test renal biopsy along with serum creatinine and proteinuria is often necessary to establish a diagnosis, particularly in glomerular disease. Proteomics has become a powerful tool for novel biomarker discovery in kidney disease. Novel proteomics offer earlier and more accurate diagnosis of renal pathology than possible with traditional biomarkers such as serum creatinine and urine protein. In addition, proteomic biomarkers could also be useful to choose the most suitable therapeutic targets. This review focuses on the current status of proteomic biomarkers from animal models (5/6 nephrectomy, unilateral ureteral obstruction, and diabetic nephropathy) and human studies (chronic kidney disease, glomerular diseases, transplantation, dialysis, acute and drug-induced kidney injury) to assess relevant findings and clinical usefulness. Current issues and problems related to the discovery, validation, and clinical application of proteomic biomarkers are discussed. We also describe several proteomic strategies highlighting technologic advancements, specimen selection, data processing and analysis. This review might provide help in future proteomic studies to improve the diagnosis and management of kidney disease.
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Affiliation(s)
- Lin Chen
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Science, Northwest University, Xi'an, China
| | - Wei Su
- Baoji Central Hospital, Baoji, China
| | - Hua Chen
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Science, Northwest University, Xi'an, China
| | - Dan-Qian Chen
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Science, Northwest University, Xi'an, China
| | - Ming Wang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Science, Northwest University, Xi'an, China
| | - Yan Guo
- University of New Mexico, Comprehensive Cancer Center, Albuquerque, NM, United States
| | - Ying-Yong Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Life Science, Northwest University, Xi'an, China.
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45
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Brosius FC, Ju W. The Promise of Systems Biology for Diabetic Kidney Disease. Adv Chronic Kidney Dis 2018; 25:202-213. [PMID: 29580584 DOI: 10.1053/j.ackd.2017.10.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 10/19/2017] [Accepted: 10/23/2017] [Indexed: 12/21/2022]
Abstract
Diabetic kidney disease (DKD) has a complex and prolonged pathogenesis involving many cell types in the kidney as well as extrarenal factors. It is clinically silent for many years after the onset of diabetes and usually progresses over decades. Given this complexity, a comprehensive and unbiased molecular approach is best suited to help identify the most critical mechanisms responsible for progression of DKD and those most suited for targeted intervention. Systems biological investigations provide such an approach since they examine the entire network of molecular changes that occur in a disease process in a comprehensive way instead of focusing on a single abnormal molecule or pathway. Systems biological studies can also start with analysis of the disease in humans, not in animal or cell culture models that often poorly reproduce the changes in human DKD. Indeed, in the last decade, systems biological approaches have led to the identification of critical molecular abnormalities in DKD and have directly led to development of new biomarkers and potential treatments for DKD.
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46
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Adeniran A, Stainbrook S, Bostick JW, Tyo KEJ. Detection of a Peptide Biomarker by Engineered Yeast Receptors. ACS Synth Biol 2018; 7:696-705. [PMID: 29366326 PMCID: PMC5820653 DOI: 10.1021/acssynbio.7b00410] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Directed evolution of membrane receptors is challenging as the evolved receptor must not only accommodate a non-native ligand, but also maintain the ability to transduce the detection of the new ligand to any associated intracellular components. The G-protein coupled receptor (GPCR) superfamily is the largest group of membrane receptors. As members of the GPCR family detect a wide range of ligands, GPCRs are an incredibly useful starting point for directed evolution of user-defined analytical tools and diagnostics. The aim of this study was to determine if directed evolution of the yeast Ste2p GPCR, which natively detects the α-factor peptide, could yield a GPCR that detects Cystatin C, a human peptide biomarker. We demonstrate a generalizable approach for evolving Ste2p to detect peptide sequences. Because the target peptide differs significantly from α-factor, a single evolutionary step was infeasible. We turned to a substrate walking approach and evolved receptors for a series of chimeric intermediates with increasing similarity to the biomarker. We validate our previous model as a tool for designing optimal chimeric peptide steps. Finally, we demonstrate the clinical utility of yeast-based biosensors by showing specific activation by a C-terminally amidated Cystatin C peptide in commercially sourced human urine. To our knowledge, this is the first directed evolution of a peptide GPCR.
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Affiliation(s)
- Adebola Adeniran
- Department
of Chemical and Biological Engineering, ‡Interdisciplinary Biological Sciences
Graduate Program, Northwestern University, Evanston, Illinois
| | - Sarah Stainbrook
- Department
of Chemical and Biological Engineering, ‡Interdisciplinary Biological Sciences
Graduate Program, Northwestern University, Evanston, Illinois
| | - John W. Bostick
- Department
of Chemical and Biological Engineering, ‡Interdisciplinary Biological Sciences
Graduate Program, Northwestern University, Evanston, Illinois
| | - Keith E. J. Tyo
- Department
of Chemical and Biological Engineering, ‡Interdisciplinary Biological Sciences
Graduate Program, Northwestern University, Evanston, Illinois
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47
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Urinary proteomics using capillary electrophoresis coupled to mass spectrometry for diagnosis and prognosis in kidney diseases. Curr Opin Nephrol Hypertens 2018; 25:494-501. [PMID: 27584928 DOI: 10.1097/mnh.0000000000000278] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
PURPOSE OF REVIEW Urine is the most useful of body fluids for biomarker research. Therefore, we have focused on urinary proteomics, using capillary electrophoresis coupled to mass spectrometry, to investigate kidney diseases in recent years. RECENT FINDINGS Several urinary proteomics studies for the detection of various kidney diseases have indicated the potential of this approach aimed at diagnostic and prognostic assessment. Urinary protein biomarkers such as collagen fragments, serum albumin, α-1-antitrypsin, and uromodulin can help to explain the processes involved during disease progression. SUMMARY Urinary proteomics has been used in several studies in order to identify and validate biomarkers associated with different kidney diseases. These biomarkers, with improved sensitivity and specificity when compared with the current gold standards, provide a significant alternative for diagnosis and prognosis, as well as improving clinical decision-making.
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48
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Abstract
Approximately 20% to 40% of patients with type 1 or type 2 diabetes mellitus develop diabetic kidney disease. This is a clinical syndrome characterized by persistent albuminuria (> 300 mg/24 h, or > 300 mg/g creatinine), a relentless decline in glomerular filtration rate (GFR), raised arterial blood pressure, and enhanced cardiovascular morbidity and mortality. There is a characteristic histopathology. In classical diabetic nephropathy, the first clinical sign is moderately increased urine albumin excretion (microalbuminuria: 30-300 mg/24 h, or 30-300 mg/g creatinine; albuminuria grade A2). Untreated microalbuminuria will gradually worsen, reaching clinical proteinuria or severely increased albuminuria (albuminuria grade A3) over 5 to 15 years. The GFR then begins to decline, and without treatment, end-stage renal failure is likely to result in 5 to 7 years. Although albuminuria is the first sign of diabetic nephropathy, the first symptom is usually peripheral edema, which occurs at a very late stage. Regular, systematic screening for diabetic kidney disease is needed in order to identify patients at risk of or with presymptomatic diabetic kidney disease. Annual monitoring of urinary albumin-to-creatinine ratio, estimated GFR, and blood pressure is recommended. Several new biomarkers or profiles of biomarkers have been investigated to improve prognostic and diagnostic precision, but none have yet been implemented in routine clinical care. In the future such techniques may pave the way for personalized treatment.
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49
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Krochmal M, Schanstra JP, Mischak H. Urinary peptidomics in kidney disease and drug research. Expert Opin Drug Discov 2017; 13:259-268. [DOI: 10.1080/17460441.2018.1418320] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Magdalena Krochmal
- Department of Biotechnology, Biomedical Research Foundation Academy of Athens, Athens, Greece
- Mosaiques Diagnostics GmbH, Hannover, Germany
| | - Joost P Schanstra
- Institut of Cardiovascular and Metabolic Disease, Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Toulouse, France
- Université Toulouse III Paul-Sabatier, Toulouse, France
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, Hannover, Germany
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
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50
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Magalhães P, Pejchinovski M, Markoska K, Banasik M, Klinger M, Švec-Billá D, Rychlík I, Rroji M, Restivo A, Capasso G, Bob F, Schiller A, Ortiz A, Perez-Gomez MV, Cannata P, Sanchez-Niño MD, Naumovic R, Brkovic V, Polenakovic M, Mullen W, Vlahou A, Zürbig P, Pape L, Ferrario F, Denis C, Spasovski G, Mischak H, Schanstra JP. Association of kidney fibrosis with urinary peptides: a path towards non-invasive liquid biopsies? Sci Rep 2017; 7:16915. [PMID: 29208969 PMCID: PMC5717105 DOI: 10.1038/s41598-017-17083-w] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 11/20/2017] [Indexed: 12/16/2022] Open
Abstract
Chronic kidney disease (CKD) is a prevalent cause of morbidity and mortality worldwide. A hallmark of CKD progression is renal fibrosis characterized by excessive accumulation of extracellular matrix (ECM) proteins. In this study, we aimed to investigate the correlation of the urinary proteome classifier CKD273 and individual urinary peptides with the degree of fibrosis. In total, 42 kidney biopsies and urine samples were examined. The percentage of fibrosis per total tissue area was assessed in Masson trichrome stained kidney tissues. The urinary proteome was analysed by capillary electrophoresis coupled to mass spectrometry. CKD273 displayed a significant and positive correlation with the degree of fibrosis (Rho = 0.430, P = 0.0044), while the routinely used parameters (glomerular filtration rate, urine albumin-to-creatinine ratio and urine protein-to-creatinine ratio) did not (Rho = -0.222; -0.137; -0.070 and P = 0.16; 0.39; 0.66, respectively). We identified seven fibrosis-associated peptides displaying a significant and negative correlation with the degree of fibrosis. All peptides were collagen fragments, suggesting that these may be causally related to the observed accumulation of ECM in the kidneys. CKD273 and specific peptides are significantly associated with kidney fibrosis; such an association could not be detected by other biomarkers for CKD. These non-invasive fibrosis-related biomarkers can potentially be implemented in future trials.
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Affiliation(s)
- Pedro Magalhães
- Mosaiques Diagnostics GmbH, Hannover, Germany
- Department of Pediatric Nephrology, Hannover Medical School, Hannover, Germany
| | | | - Katerina Markoska
- Department of Nephrology, Medical Faculty, University of Skopje, Skopje, Macedonia
| | - Miroslaw Banasik
- Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Marian Klinger
- Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Dominika Švec-Billá
- 1st Department of Medicine, Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Ivan Rychlík
- 1st Department of Medicine, Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Merita Rroji
- Department of Nephrology, University Hospital Center "Mother Teresa", Tirana, Albania
| | - Arianna Restivo
- Department of Nephrology, University of Campania "Luigi Vanvitelli", Naples, Italy
| | | | - Flaviu Bob
- Department of Nephrology, 'Victor Babes' University of Medicine and Pharmacy, County Emergency Hospital, Timisoara, Romania
| | - Adalbert Schiller
- Department of Nephrology, 'Victor Babes' University of Medicine and Pharmacy, County Emergency Hospital, Timisoara, Romania
| | | | | | | | | | - Radomir Naumovic
- Clinic of Nephrology, Clinical Center of Serbia, Belgrade, Serbia
- School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Voin Brkovic
- Clinic of Nephrology, Clinical Center of Serbia, Belgrade, Serbia
- School of Medicine, University of Belgrade, Belgrade, Serbia
| | | | - William Mullen
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Antonia Vlahou
- Biotechnology Division, Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | | | - Lars Pape
- Department of Pediatric Nephrology, Hannover Medical School, Hannover, Germany
| | | | - Colette Denis
- Institut National de la Santé et de la Recherche Médicale (INSERM), Institute of Cardiovascular and Metabolic Disease, Toulouse, France
- Université Toulouse III Paul-Sabatier, Toulouse, France
| | - Goce Spasovski
- Department of Nephrology, Medical Faculty, University of Skopje, Skopje, Macedonia
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, Hannover, Germany
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Joost P Schanstra
- Institut National de la Santé et de la Recherche Médicale (INSERM), Institute of Cardiovascular and Metabolic Disease, Toulouse, France.
- Université Toulouse III Paul-Sabatier, Toulouse, France.
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