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With urinary proteomics profiling (UPP) as exemplary omics technology, this review describes a workflow for the analysis of omics data in large study populations. The proposed workflow includes: (i) planning omics studies and sample size considerations; (ii) preparing the data for analysis; (iii) preprocessing the UPP data; (iv) the basic statistical steps required for data curation; (v) the selection of covariables; (vi) relating continuously distributed or categorical outcomes to a series of single markers (e.g., sequenced urinary peptide fragments identifying the parental proteins); (vii) showing the added diagnostic or prognostic value of the UPP markers over and beyond classical risk factors, and (viii) pathway analysis to identify targets for personalized intervention in disease prevention or treatment. Additionally, two short sections respectively address multiomics studies and machine learning. In conclusion, the analysis of adverse health outcomes in relation to omics biomarkers rests on the same statistical principle as any other data collected in large population or patient cohorts. The large number of biomarkers, which have to be considered simultaneously requires planning ahead how the study database will be structured and curated, imported in statistical software packages, analysis results will be triaged for clinical relevance, and presented., (© 2023 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd.)

Objective: Heart failure (HF) is characterised by collagen deposition. Urinary proteomic profiling (UPP) followed by peptide sequencing identifies parental proteins, for over 70% derived from collagens. This study aimed to refine understanding of the antifibrotic action of spironolactone., Methods: In this substudy (n=290) to the Heart 'Omics' in Ageing Study trial, patients were randomised to usual therapy combined or not with spironolactone 25-50 mg/day and followed for 9 months. The analysis included 1498 sequenced urinary peptides detectable in ≥30% of patients and carboxyterminal propeptide of procollagen I (PICP) and PICP/carboxyterminal telopeptide of collagen I (CITP) as serum biomarkers of COL1A1 synthesis. After rank normalisation of biomarker distributions, between-group differences in their changes were assessed by multivariable-adjusted mixed model analysis of variance. Correlations between the changes in urinary peptides and in serum PICP and PICP/CITP were compared between groups using Fisher's Z transform., Results: Multivariable-adjusted between-group differences in the urinary peptides with error 1 rate correction were limited to 27 collagen fragments, of which 16 were upregulated (7 COL1A1 fragments) on spironolactone and 11 downregulated (4 COL1A1 fragments). Over 9 months of follow-up, spironolactone decreased serum PICP from 81 (IQR 66-95) to 75 (61-90) µg/L and PICP/CITP from 22 (17-28) to 18 (13-26), whereas no changes occurred in the control group, resulting in a difference (spironolactone minus control) expressed in standardised units of -0.321 (95% CI 0.0007). Spironolactone did not affect the correlations between changes in urinary COL1A1 fragments and in PICP or the PICP/CITP ratio., Conclusions: Spironolactone decreased serum markers of collagen synthesis and predominantly downregulated urinary collagen-derived peptides, but upregulated others. The interpretation of these opposite UPP trends might be due to shrinking the body-wide pool of collagens, explaining downregulation, while some degree of collagen synthesis must be maintained to sustain vital organ functions, explaining upregulation. Combining urinary and serum fibrosis markers opens new avenues for the understanding of the action of antifibrotic drugs., Trial Registration Number: NCT02556450., Competing Interests: Competing interests: JS and AL are employees of Mosaiques-Diagnostics, Hanover, Germany. HM is the co-founder and co-owner of Mosaiques-Diagnostics. The other authors declare no conflict of interest., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Catheter-based renal denervation (RDN) reduces blood pressure in hypertension. Urinary peptides are associated with cardiovascular and renal disease and provide prognostic information. We aimed to investigate the effect of RDN on urinary peptide-based classifiers associated with chronic kidney and heart disease and to identify urinary peptides affected by RDN., Methods: This single-arm, single-center study included patients undergoing catheter-based RDN. Urine samples were collected before and 24 months after RDN and were analyzed using capillary electrophoresis coupled with mass spectrometry. Predefined urinary peptide-based classifiers for chronic kidney disease (CKD273), coronary artery disease (CAD238), and heart failure (HF1) were applied., Results: This study included 48 patients (33% female) with uncontrolled hypertension. At 24 months after RDN, systolic blood pressure (165±17 versus 148±20 mm Hg; P <0.0001), diastolic blood pressure (90±17 versus 81±13 mm Hg; P <0.0001), and mean arterial pressure (115±15 versus 103±13 mm Hg; P <0.0001) decreased significantly. A total of 103 urinary peptides from 37 different proteins, mostly collagens, altered following RDN. CAD238, a 238 coronary artery-specific polypeptide-based classifier, significantly improved following RDN (Cohen's d , -0.632; P =0.0001). The classification scores of HF1 ( P =0.8295) and CKD273 ( P =0.6293) did not change significantly., Conclusions: RDN beneficially affected urinary peptides associated with coronary artery disease., Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01888315., Competing Interests: Disclosures L. Lauder reports speaker honoraria from AstraZeneca, Medtronic, Pfizer, and ReCor Medical. J. Siwy and E. Mavrogeorgis are employees of the Mosaiques Diagnostics GmbH. I.E. Emrich has received speaker honoraria/consulting fees from Pharmacosmos, Astellas, Bristol Myers Squibb, AstraZeneca, and Novartis. M. Böhm is supported by the Deutsche Forschungsgemeinschaft (German Research Foundation; TTR 219, project No. 322900939) and reports personal fees from Abbott, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Cytokinetics, Edwards, Medtronic, Novartis, ReCor Medical, Servier, and Vifor during the conduct of the study. H. Mischak is cofounder and a shareholder of Mosaiques Diagnostics GmbH. F. Mahfoud is supported by Deutsche Gesellschaft für Kardiologie, Deutsche Forschungsgemeinschaft (SFB TRR219, project ID 322900939), and Deutsche Herzstiftung. His institution (Saarland University) has received scientific support from Ablative Solutions, Medtronic, and ReCor Medical, and speaker honoraria/consulting fees from Ablative Solutions, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Inari, Medtronic, Merck, ReCor Medical, Servier, and Terumo. The other authors report no conflicts.

Background: The risk of diabetic kidney disease (DKD) progression is significant despite treatment with renin-angiotensin system (RAS) blocking agents. Current clinical tools cannot predict whether or not patients will respond to treatment with RAS inhibitors (RASi). We aimed to investigate whether proteome analysis could identify urinary peptides as biomarkers that could predict the response to angiotensin-converting enzyme inhibitor and angiotensin-receptor blockers treatment to avoid DKD progression. Furthermore, we investigated the comparability of the estimated glomerular filtration rate (eGFR), calculated using four different GFR equations, for DKD progression., Methods: We evaluated urine samples from a discovery cohort of 199 diabetic patients treated with RASi. DKD progression was defined based on eGFR percentage slope results between visits (∼1 year) and for the entire period (∼3 years) based on the eGFR values of each GFR equation. Urine samples were analysed using capillary electrophoresis-coupled mass spectrometry. Statistical analysis was performed between the uncontrolled (patients who did not respond to RASi treatment) and controlled kidney function groups (patients who responded to the RASi treatment). Peptides were combined in a support vector machine-based model. The area under the receiver operating characteristic curve was used to evaluate the risk prediction models in two independent validation cohorts treated with RASi., Results: The classification of patients into uncontrolled and controlled kidney function varies depending on the GFR equation used, despite the same sample set. We identified 227 peptides showing nominal significant difference and consistent fold changes between uncontrolled and controlled patients in at least three methods of eGFR calculation. These included fragments of collagens, alpha-1-antitrypsin, antithrombin-III, CD99 antigen and uromodulin. A model based on 189 of 227 peptides (DKDp189) showed a significant prediction of non-response to the treatment/DKD progression in two independent cohorts., Conclusions: The DKDp189 model demonstrates potential as a predictive tool for guiding treatment with RASi in diabetic patients., (© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)

Introduction: Preterm infants are at risk for a variety of somatic and neurological disorders. In recent years, biofluid proteomics has emerged as a potential diagnostic tool for biomarker analysis. The aim of this study was to determine gestational age (GA)-related patterns of the urinary peptidome in preterm infants for researching potential novel prognostic biomarkers., Methods: We performed urinary peptidomics in longitudinal samples of 24 preterm (mean GA weeks 28 + 1 [24+1-31 + 6]) and 27 term born controls (mean GA weeks 39 + 2 [37+0-41 + 1]) using capillary electrophoresis combined with mass spectrometry (CE-MS). Peptides were sequenced using CE-MS/MS or LC-MS/MS analysis and were deposited, matched, and annotated in a Microsoft SQL database for statistical analysis. We compared their abundance in urine of preterm and term born infants and performed a validation analysis as well as correlations to GA and clinical risk scores., Results: Our results confirmed significant differences in the abundance of peptides and the hypothesis of age-dependent urinary peptidome changes in preterm and term infants. In preterm infants, SLC38A10 (solute carrier family 38 member 10) is one of the most abundant peptides. Combined urinary peptides correlated with clinical risk scores (p &lt; 0.05)., Conclusion: This is the first study reporting GA-related urinary peptidome changes of preterm infants detected by CE-MS and a modulation of the peptidome with GA. Further research is required to locate peptidome clusters correlated with specific clinical complications and long-term outcome. This may identify preterm infants at higher risk for adverse outcome who would benefit from early intervention., (© 2024 S. Karger AG, Basel.)

Urinary peptides gained significant attention as potential biomarkers especially in the context of kidney and cardiovascular disease. In this manuscript the recent literature since 2015 on urinary peptide investigation in human kidney and cardiovascular disease is reviewed. The technology most commonly used in this context is capillary electrophoresis coupled mass spectrometry, in part owed to the large database available and the well-defined dataspace. Several studies based on over 1000 subjects are reported in the recent past, especially examining CKD273, a classifier for assessment of chronic kidney disease based on 273 urine peptides. Interestingly, the most abundant urinary peptides are generally collagen fragments, which may have gone undetected for some time as they are typically modified via proline hydroxylation. The data available suggest that urinary peptides specifically depict inflammation and fibrosis, and may serve as a non-invasive tool to assess fibrosis, which appears to be a key driver in kidney and cardiovascular disease. The recent successful completion of the first urinary peptide guided intervention trial, PRIORITY, is expected to further spur clinical application of urinary peptidomics, aiming especially at early detection of chronic diseases, prediction of progression, and prognosis of drug response., (© 2020 Wiley-VCH GmbH.)

Background: Liver fibrosis is a consequence of chronic inflammation and is associated with protein changes within the hepatocytes structure. In this study, we aimed to investigate if this is reflected by the urinary proteome and can be explored to diagnose liver fibrosis in patients with chronic liver disease., Methods: In a multicentre combined cross-sectional and prospective diagnostic test validation study, 129 patients with varying degrees of liver fibrosis and 223 controls without liver fibrosis were recruited. Additionally, 41 patients with no liver, but kidney fibrosis were included to evaluate interference with expressions of kidney fibrosis. Urinary low molecular weight proteome was analysed by capillary electrophoresis coupled to mass spectrometry (CE-MS) and a support vector machine marker model was established by integration of peptide markers for liver fibrosis., Findings: CE-MS enabled identification of 50 urinary peptides associated with liver fibrosis. When combined into a classifier, LivFib-50, it separated patients with liver fibrosis (N = 31) from non-liver disease controls (N = 123) in cross-sectional diagnostic phase II evaluation with an area under the curve (AUC) of 0.94 (95% confidence intervals (CI): 0.89-0.97, p<0.0001). When adjusted for age, LivFib-50 demonstrated an AUC of 0.94 (95% CI: 0.89-0.97, p<0.0001) in chronic liver disease patients with (N = 19) or without (N = 17) liver fibrosis progression. In this prospective diagnostic phase III validation set, age-adjusted LivFib-50 showed 84.2% sensitivity (95% CI: 60.4-96.6) and 82.4% specificity (95% CI: 56.6-96.2) for detection of liver fibrosis. The sequence-identified peptides are mainly fragments of collagen chains, uromodulin and Na/K-transporting ATPase subunit γ. We also identified ten putative proteolytic cleavage sites, eight were specific for matrix metallopeptidases and two for cathepsins., Interpretation: In liver fibrosis, urinary peptides profiling offers potential diagnostic markers and leads to discovery of proteolytic sites that could be targets for developing anti-fibrotic therapy., Competing Interests: Declaration of Competing Interest Harald Mischak is founder and co-owner of Mosaiques Diagnostics GmbH, which developed the CE-MS technology. Jochen Metzger, Agnieszka Latosinska and Martin Pejchinovski are employees of Mosaiques Diagnostics GmbH. All other authors declare no conflict of interest., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

Rationale: The urinary proteome reflects molecular drivers of disease., Objectives: To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality., Methods: In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses., Measurements and Main Results: In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708-0.798) and 0.688 (0.656-0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00-2.91) for ACM128 (+ 1 SD), 1.24 (1.16-1.32) for the Charlson Comorbidity Index (+ 1 point), and ≥ 1.19 (P ≤ 0.022) for other biomarkers (+ 1 SD). ACM128 improved (P ≤ 0.0001) IDI (≥ + 0.50), NRI (≥ + 53.7), and AUC (≥ + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis., Conclusions: The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.

Introduction : Inter-individual variability in response to drug treatment has induced an increased demand for decisions via personalize medicine. Also, the contribution of proteomics to the era of personalized medicine would seem to be vital in improving therapeutic outcomes. Areas covered : We review validated biomarkers discovered by proteomics techniques and their use in personalized medicine with the focus on kidney diseases. We discuss this topic with a special emphasis on recent publications and relevant initiatives and depict some limitations that remain for personalized medicine. Expert opinion : The development of highly accurate biomarkers is essential for optimizing the management of kidney diseases. Various biomarkers of kidney diseases have been identified using proteomic techniques. However, only a few of these biomarkers showed the potential to be used in clinical practice concerning personalized medicine. Therefore, it becomes evident that the combination of multiple biomarkers confers higher accuracy and the ability to depict complex pathophysiological conditions, a prerequisite for personalized treatment. CKD273, a multimarker panel for early CKD detection may serve as a first example for personalized medicine in nephrology. Based on this successful example, proteomics is expected to develop into the key technology to guide personalized intervention.

Background Identifying the mechanistic pathways potentially associated with incident heart failure (HF) may provide a basis for novel preventive strategies. Methods and Results To identify proteomic biomarkers and the potential underlying mechanistic pathways that may be associated with incident HF defined as the first hospitalization for HF, a nested-matched case-control design was used with cases (incident HF) and controls (without HF) selected from 3 cohorts (>20 000 individuals). Controls were matched on cohort, follow-up time, age, and sex. Two independent sample sets (a discovery set with 286 cases and 591 controls and a replication set with 276 cases and 280 controls) were used to discover and replicate the findings. Two hundred fifty-two circulating proteins in the plasma were studied. Adjusting for the matching variables age, sex, and follow-up time (and correcting for multiplicity of tests), 89 proteins were found to be associated with incident HF in the discovery phase, of which 38 were also associated with incident HF in the replication phase. These 38 proteins pointed to 4 main network clusters underlying incident HF: (1) inflammation and apoptosis, indicated by the expression of the TNF (tumor necrosis factor)-family members; (2) extracellular matrix remodeling, angiogenesis and growth, indicated by the expression of proteins associated with collagen metabolism, endothelial function, and vascular homeostasis; (3) blood pressure regulation, indicated by the expression of natriuretic peptides and proteins related to the renin-angiotensin-aldosterone system; and (4) metabolism, associated with cholesterol and atherosclerosis. Conclusions Clusters of biomarkers associated with mechanistic pathways leading to HF were identified linking inflammation, apoptosis, vascular function, matrix remodeling, blood pressure control, and metabolism. These findings provide important insight on the pathophysiological mechanisms leading to HF. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02556450.

Renal Cysts and Diabetes Syndrome (RCAD) is an autosomal dominant disorder caused by mutations in the HNF1B gene encoding for the transcriptional factor hepatocyte nuclear factor-1B. RCAD is characterized as a multi-organ disease, with a broad spectrum of symptoms including kidney abnormalities (renal cysts, renal hypodysplasia, single kidney, horseshoe kidneys, hydronephrosis), early-onset diabetes mellitus, abnormal liver function, pancreatic hypoplasia and genital tract malformations. In the present study, using capillary electrophoresis coupled to mass spectrometry (CE-MS), we investigated the urinary proteome of a pediatric cohort of RCAD patients and different controls to identify peptide biomarkers and obtain further insights into the pathophysiology of this disorder. As a result, 146 peptides were found to be associated with RCAD in 22 pediatric patients when compared to 22 healthy age-matched controls. A classifier based on these peptides was generated and further tested on an independent cohort, clearly discriminating RCAD patients from different groups of controls. This study demonstrates that the urinary proteome of pediatric RCAD patients differs from autosomal dominant polycystic kidney disease (PKD1, PKD2), congenital nephrotic syndrome (NPHS1, NPHS2, NPHS4, NPHS9) as well as from chronic kidney disease conditions, suggesting differences between the pathophysiology behind these disorders.

Chronic antibody-mediated rejection (cABMR) is the main cause of long-term renal graft loss. Late-stage diagnosis is made by detecting donor-specific antibodies (DSA) in blood combined with typical histomorphological lesions in renal allografts. There is a need for noninvasive biomarkers for cABMR that might permit screening and earlier diagnosis. In a case control study of 24 pediatric renal transplant recipients, urine samples were analyzed using capillary electrophoresis and mass spectrometry. Patients were matched with 36 pediatric renal transplant patients without cABMR. Statistical analysis used the nonparametric Wilcoxon test to identify 79 significant biomarkers, which were combined to a support vector machine-based classifier. After validation in an independent test cohort of eight pediatric patients with and 12 without cABMR, the area under the receiver operating characteristic (ROC) curve (AUC) for detection of cABMR was 0.92 (95% CI 0.71-0.99) with a sensitivity of 100% (95% CI 63-100%) and a specificity of 75% (95% CI 43-95%). Combining this classifier with the urinary proteomic marker CKD273 improved the detection of patients with cABMR with misclassification in only 2/20 of the patients. These data indicate that a biomarker pattern derived from urinary proteomics allows the detection of cABMR in pediatric renal transplant recipients with high sensitivity and moderate specificity., (© 2018 Steunstichting ESOT.)

Introduction : Biomarkers are expected to improve the management of cancer patients by enabling early detection and prediction of therapeutic response. Proteins reflect a molecular phenotype, have high potential as biomarkers, and also are key targets for intervention. Given the ease of collection and proximity to certain tumors, the urinary proteome is a rich source of biomarkers and several proteins have been already implemented. Areas covered : We examined the literature on urine proteins and proteome analysis in oncology from reports published during the last 5 years to generate an overview on the status of urine protein and peptide biomarkers, with emphasis on their actual clinical value. Expert commentary : A few studies report on biomarkers that are ready to be implemented in patient management, among others in bladder cancer and cholangiocarcinoma. These reports are based on multi-marker approaches. A high number of biomarkers, though, has been described in studies with low statistical power. In fact, several of them have been consistently reported across different studies. The latter should be the focus of attention and be tested in properly designed confirmatory and ultimately, prospective investigations. It is expected that multi-marker classifiers for a specific context-of-use, will be the preferred path toward clinical implementation.

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 m 2 . In individuals with eGFR > 60 ml/min/1.73 m 2 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 m 2 . These CKD273 subclassifiers represented the earliest evidence of rapidly progressive CKD in non-albuminuric individuals with preserved renal function.

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., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: An improvement in the glomerular filtration rate (GFR) of chronic kidney disease patients has been an underestimated clinical outcome. Although this may be considered as an unexpected disease course, it may provide some insights into possible mechanisms underlying disease remission and/or regression. Therefore, our aim was to identify urinary peptide biomarkers associated with an improvement in estimated GFR (eGFR) over time and to improve patient stratification., Methods: Capillary electrophoresis coupled with mass spectrometry (CE-MS) was employed to evaluate the urine peptidome of patients with different types of renal diseases. In total, 376 patients with a slope/year between -1.5% and +1.5% were designated as non-progressors or stable, while 177 patients with a > 5% slope/year were designated as patients with an improved eGFR for state-of-art biomarker discovery and validation., Results: We detected 384 significant peptide fragments by comparing the CE-MS data of the stable patients and those with improved renal function in our development cohort. Of these 384, a set of 141 peptides with available amino acid sequence information were used to generate a support vector machine-based classification panel. The biomarker panel was applied to our validation cohort, achieving a moderate area under the curve (AUC) value of 0.85 (81% sensitivity and 81% specificity). The majority of the peptides (78%) from the diagnostic panel arose from different types of collagen., Conclusions: We have developed a panel of urinary peptide markers able to discriminate those patients predisposed to improve their kidney function over time and possibly be treated with more specific or less aggressive therapy.

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., (© The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: The excessive accumulation of extracellular matrix (ECM) in the renal tubulointerstitium is a key component of chronic renal damage in lupus nephritis (LN) and a critical determinant of the disease progression to renal failure. Detection of fibrosis requires renal biopsy and is therefore limited by high risks associated with an invasive procedure. This study explores whether a unique LN urinary peptidome can be identified and whether LN-specific alteration reflects the underlying fibrogenic process of altered ECM turnover., Method: Urinary peptides were analyzed for 36 LN and 35 nonrenal systemic lupus erythematosus (SLE) subjects and 58 healthy volunteers (HVs)., Results: In total, 70 collagen and 230 noncollagen peptides were significantly changed between LN and nonrenal SLE and between LN and HV and defined as 'LN peptides'; 14 proteases associated with observed LN collagen peptides were identified and activities in 9 proteases were significantly different between LN and nonrenal SLE; 28 collagen peptides were correlated with at least one parameter of clinical renal dysfunction or histolopathology., Conclusion: Urinary peptidomic alterations likely reflect pathogenic pathways involving ECM turnover in LN kidneys and potentially could be developed as biomarkers to monitor renal disease progression., (© The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

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., (© The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Introduction: Although multiple efforts have been initiated to shed light into the molecular mechanisms underlying cardiovascular disease, it still remains one of the major causes of death worldwide. Proteomic approaches are unequivocally powerful tools that may provide deeper understanding into the molecular mechanisms associated with cardiovascular disease and improve its management. Areas covered: Cardiovascular proteomics is an emerging field and significant progress has been made during the past few years with the aim of defining novel candidate biomarkers and obtaining insight into molecular pathophysiology. To summarize the recent progress in the field, a literature search was conducted in PubMed and Web of Science. As a result, 704 studies from PubMed and 320 studies from Web of Science were retrieved. Findings from original research articles using proteomics technologies for the discovery of biomarkers for cardiovascular disease in human are summarized in this review. Expert commentary: Proteins associated with cardiovascular disease represent pathways in inflammation, wound healing and coagulation, proteolysis and extracellular matrix organization, handling of cholesterol and LDL. Future research in the field should target to increase proteome coverage as well as integrate proteomics with other omics data to facilitate both drug development as well as clinical implementation of findings.

Introduction: Renal tract malformations (RTMs) are congenital anomalies of the kidneys and urinary tract, which are the major cause of end-stage renal disease in children. Using immunoassay-based approaches (ELISA, western blot), individual urinary proteins including transforming growth factor β, tumor necrosis factor and monocyte attractant proteins 1 were found to be associated to RTMs. However, only mass spectrometry (MS) based methods leading to the identification of panels of protein-based markers composed of fragments of the extracellular matrix allowed the prediction of progression of RTMs and its complications. Areas covered: In this review, we summarized relevant studies identified in "Pubmed" using the keywords "urinary biomarkers" and "proteomics" and "renal tract malformations" or "hydronephrosis" or "ureteropelvic junction obstruction" or "posterior urethral valves" or "vesicoureteral reflux". These publications represent studies on potential protein-based biomarkers, either individually or combined in panels, of RTMs in human and animal models. Expert commentary: Successful use in the clinic of these protein-based biomarkers will need to involve larger scale studies to reach sufficient power. Improved performance will potentially come from combining immunoassay- and MS-based markers.

The quest for the ideal therapeutic target in chronic kidney disease (CKD) has been riddled with many obstacles stemming from the molecular complexity of the disease and its co-morbidities. Recent advances in omics technologies and the resulting amount of available data encompassing genomics, proteomics, peptidomics, transcriptomics and metabolomics has created an opportunity for integrating omics datasets to build a comprehensive and dynamic model of the molecular changes in CKD for the purpose of biomarker and drug discovery. This article reviews relevant concepts in omics data integration using systems biology, a mathematical modelling method that globally describes a biological system on the basis of its modules and the functional connections that govern their behaviour. The review describes key databases and bioinformatics tools, as well as the challenges and limitations of the current state of the art, along with practical application to CKD therapeutic target discovery. Moreover, it describes how systems biology and visualization tools can be used to generate clinically relevant molecular models with the capability to identify specific disease pathways, recognize key events in disease development and track disease progression., (© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Nephropathy is among the most frequent complications of diabetes and the leading cause of end-stage renal disease. Despite the success of novel drugs in animal models, the majority of the subsequent clinical trials employing those drugs targeting diabetic nephropathy failed. This lack of translational value may in part be due to an inadequate comparability of human disease and animal models that often capture only a few aspects of disease. Here we overcome this limitation by developing a multimolecular noninvasive humanized readout of diabetic nephropathy based on urinary peptidomics. The disease-modified urinary peptides of 2 type 2 diabetic nephropathy mouse models were identified and compared with previously validated urinary peptide markers of diabetic nephropathy in humans to generate a classifier composed of 21 ortholog peptides. This classifier predicted the response to disease and treatment with inhibitors of the renin-angiotensin system in mice. The humanized classifier was significantly correlated with glomerular lesions. Using a human type 2 diabetic validation cohort of 207 patients, the classifier also distinguished between patients with and without diabetic nephropathy, and their response to renin-angiotensin system inhibition. Thus, a combination of multiple molecular features common to both human and murine disease could provide a significant change in translational drug discovery research in type 2 diabetic nephropathy., (Copyright © 2016 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

Objectives: Differentiation of pancreatic cancer (PCA) from chronic pancreatitis (CP) is challenging. We searched for peptide markers in urine to develop a diagnostic peptide marker model., Methods: Capillary electrophoresis-mass spectrometry was used to search for peptides in urine of patients with PCA (n = 39) or CP (n = 41). Statistical different peptides were included in a peptide multimarker model. Peptide markers were sequence identified and validated by immunoassay and immunohistochemistry (IHC)., Results: Applied to a validation cohort of 54 patients with PCA and 52 patients with CP, the peptide model correctly classified 47 patients with PCA and 44 patients with CP (area under the curve, 0.93; 87% sensitivity; 85% specificity). All 5 patients with PCA with concomitant CP were classified positive. Urine proteome analysis outperformed carbohydrate antigen 19-9 (area under the curve, 0.84) by a 15% increase in sensitivity at the same specificity. From 99 healthy subjects, only four were misclassified. Fetuin-A was the most prominent peptide marker source for PCA as verified by immunoassay and IHC. In silico protease mapping of the peptide markers' terminal sequences pointed to increased meprin-A activity in PCA, which in IHC was associated with neoangiogenesis., Conclusions: Urinary proteome analysis differentiates PCA from CP and may serve as PCA screening tool.

Background: Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF) may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF., Methods and Results: Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS) to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFrEF patients and 29 age- and sex-matched individuals without HFrEF resulted in identification of 103 peptides that were significantly differentially excreted in HFrEF. These 103 peptides were used to establish the support vector machine-based HFrEF classifier HFrEF103. In a subsequent validation cohort, HFrEF103 very accurately (area under the curve, AUC = 0.972) discriminated between HFrEF patients (N = 94, sensitivity = 93.6%) and control individuals with and without impaired renal function and hypertension (N = 552, specificity = 92.9%). Interestingly, HFrEF103 showed low sensitivity (12.6%) in individuals with diastolic left ventricular dysfunction (N = 176). The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin., Conclusion: CE-MS based urine proteome analysis served as a sensitive tool to determine a vast array of HFrEF-related urinary peptide biomarkers which might help improving our understanding and diagnosis of heart failure.

Introduction: Diabetes mellitus affects 9% of the European population and accounts for 15% of healthcare expenditure, in particular, due to excess costs related to complications. Clinical trials aiming for earlier prevention of diabetic nephropathy by renin angiotensin system blocking treatment in normoalbumuric patients have given mixed results. This might reflect that the large fraction of normoalbuminuric patients are not at risk of progression, thereby reducing power in previous studies. A specific risk classifier based on urinary proteomics (chronic kidney disease (CKD)273) has been shown to identify normoalbuminuric diabetic patients who later progressed to overt kidney disease, and may hold the potential for selection of high-risk patients for early intervention. Combining the ability of CKD273 to identify patients at highest risk of progression with prescription of preventive aldosterone blockade only to this high-risk population will increase power. We aim to confirm performance of CKD273 in a prospective multicentre clinical trial and test the ability of spironolactone to delay progression of early diabetic nephropathy., Methods and Analysis: Investigator-initiated, prospective multicentre clinical trial, with randomised double-masked placebo-controlled intervention and a prospective observational study. We aim to include 3280 type 2 diabetic participants with normoalbuminuria. The CKD273 classifier will be assessed in all participants. Participants with high-risk pattern are randomised to treatment with spironolactone 25 mg once daily, or placebo, whereas, those with low-risk pattern will be observed without intervention other than standard of care. Treatment or observational period is 3 years.The primary endpoint is development of confirmed microalbuminuria in 2 of 3 first morning voids urine samples., Ethics and Dissemination: The study will be conducted under International Conference on Harmonisation - Good clinical practice (ICH-GCP) requirements, ethical principles of Declaration of Helsinki and national laws. This first new biomarker-directed intervention trial aiming at primary prevention of diabetic nephropathy may pave the way for personalised medicine approaches in treatment of diabetes complications., Trial Registration Number: NCT02040441; Pre-results., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Background: The reduced glomerular filtration rate in the advanced stages of chronic kidney disease (CKD) leads to plasma accumulation of uraemic retention solutes including proteins. It has been hypothesized that these changes may, at least in part, be responsible for CKD-associated morbidity and mortality. However, most studies focused on the role of individual proteins, while a holistic, large-scale, integrative approach may generate significant additional insight., Methods: In a discovery study, we analysed the plasma proteome of patients with stage 2-3 CKD (n = 14) and stage 5 CKD with haemodialysis (HD) (n = 15), using high-resolution LC-MS/MS analysis. Selected results were validated in a cohort of 40 patients with different CKD stages with or without HD, using ELISA., Results: Of a total of 2054 detected proteins, 127 displayed lower, while 206 displayed higher abundance in the plasma of patients on HD. Molecular pathway analysis confirmed the modification of known processes involved in CKD complications, including decreased haemostasis and increased inflammation, complement activation and vascular damage. In addition, we identified the plasma increase during CKD progression of lysozyme C and leucine-rich alpha-2 glycoprotein, two proteins related to vascular damage and heart failure. High level of leucine-rich alpha-2 glycoprotein was associated with higher mortality in stage 5 CKD patients on HD., Conclusions: This study provides for the first time a comprehensive assessment of CKD plasma proteome, contributing to new knowledge and potential markers of CKD. These results will serve as a basis for future studies investigating the relevance of these molecules in CKD associated morbidity and mortality., (© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Body fluid protein-based biomarkers carry the hope of improving patient management in diabetes by enabling more accurate and earlier detection of diabetic kidney disease (DKD), but also of defining the most suitable therapeutic targets. We present the data on some of the best studied individual protein markers in body fluids and conclude that their potential in clinical application for assessing DKD is moderate. Proteome-based approaches aiming at the identification of panels of body fluid biomarkers might be a valid alternative. We discuss the past (first) clinical proteomics studies in DKD, stressing their drawbacks but also the lessons that could be learned from them, as well as the more recent studies that have a potential for actual clinical implementation. We also highlight relevant issues and current problems associated with clinical proteomics from discovery towards application, and give suggestions for solutions that may help guiding proteomic studies, thereby removing some of the current hurdles for implementation of potentially beneficial results., (© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Diabetic kidney disease occurs in ∼ 25-40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification. In this review, we first focus on the classical panel of albuminuria and estimated glomerular filtration rate as the primary clinical predictors of renal disease and then move our attention to novel biomarkers, primarily concentrating on assay-based multiple/panel biomarkers, proteomics biomarkers and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple biomarker panels are thus required. There is also a paucity of studies that assess the effect of treatments on novel biomarker panels and determine whether initial treatment-induced changes in novel biomarkers predict changes in long-term renal outcomes. Such studies can not only improve our healthcare but also our understanding of the mechanisms of actions of existing and novel drugs and may yield biomarkers that can be used to monitor drug response. We conclude that this will be an area to focus research on in the future., (© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Proteome analysis of complex biological samples for biomarker identification remains challenging, among others due to the extended range of protein concentrations. High-abundance proteins like albumin or IgG of plasma and urine, may interfere with the detection of potential disease biomarkers. Currently, several options are available for the depletion of abundant proteins in plasma. However, the applicability of these methods in urine has not been thoroughly investigated. In this study, we compared different, commercially available immunodepletion and ion-exchange based approaches on urine samples from both healthy subjects and CKD patients, for their reproducibility and efficiency in protein depletion. A starting urine volume of 500 μL was used to simulate conditions of a multi-institutional biomarker discovery study. All depletion approaches showed satisfactory reproducibility (n=5) in protein identification as well as protein abundance. Comparison of the depletion efficiency between the unfractionated and fractionated samples and the different depletion strategies, showed efficient depletion in all cases, with the exception of the ion-exchange kit. The depletion efficiency was found slightly higher in normal than in CKD samples and normal samples yielded more protein identifications than CKD samples when using both initial as well as corresponding depleted fractions. Along these lines, decrease in the amount of albumin and other targets as applicable, following depletion, was observed. Nevertheless, these depletion strategies did not yield a higher number of identifications in neither the urine from normal nor CKD patients. Collectively, when analyzing urine in the context of CKD biomarker identification, no added value of depletion strategies can be observed and analysis of unfractionated starting urine appears to be preferable.

Biomarkers hold the promise of significantly improving health care by enabling prognosis and diagnosis with improved accuracy, and at earlier points in time. Previous results have indicated that single biomarkers are not suitable to describe complex diseases such as kidney disease. Here we provide an update on the progress of urinary proteomics-based studies and strategies to develop biomarker-based classifiers that tolerate instability and inconsistency of individual biomarkers. The examples focus on two major fields in nephrology: chronic kidney disease in the adult population and obstructive nephropathies in the pediatric population. When employed adequately, urinary proteomics demonstrates a clear value in kidney disease, indicating that the current status quo ruling for decades now could be changed by applying modern "omics" approaches. However, while research is able to deliver these useful tools for patient management, the issues associated with implementation are not yet solved. Active engagement of the relevant clinical professional societies, as well as patient's organizations, might help to implement these omics approaches that have shown a clear benefit for the patient.

CE-MS is applied in clinical proteomics for both the identification of biomarkers of disease and assessment of biomarkers in clinical diagnosis. The analysis is reproducible, fast, and requires only small sample volumes. However, successful CE-MS analysis depends on several critical steps that can be consolidated as follows: (i) proper sample preparation and fractionation, (ii) application of suitable capillary coating and appropriate CE-MS interfaces, to ensure the reproducibility and stability of the analysis, and (iii) an optimized clinical and statistical study design to increase the chances for obtaining clinically relevant results. In this review, we cover all these aspects, and present several examples of the application of CE-MS in clinical proteomics., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

In this article, the benefits of urinary proteomics in comparison with kidney biopsy are discussed. The majority of urinary proteins are generated by the kidney, hence the urinary proteome holds substantial information on the kidney, and assessment of the urinary proteome could be considered a 'liquid biopsy'. The main question is how well the information contained in the urinary proteome can be assessed today, if it is ready to be routinely used, and what are the advantages and possible disadvantages in comparison with current standards. Since chronic kidney disease (CKD) is by far the largest area in nephrology based on the number of patients affected, the focus of this article is on CKD. Substantial progress was made in the last decade in urinary proteomics, and today we have comparable urinary proteome datasets of tens of thousands of subjects available. Clinical proteomics studies in CKD including close to, or even exceeding, 1000 subjects have recently been published, demonstrating a benefit over the current state-of-the-art in diagnosis and especially prognosis. The first large multicentric randomized controlled intervention trial aiming at preventing CKD by employing urinary proteomics-guided intervention has been initiated recently. These data provide ample evidence for the utility and value of urinary proteomics in nephrology. A further consideration is that the purpose of the biopsy, be it 'liquid' or 'solid', is to guide intervention. However, essentially all drug targets are proteins, not microscopic structures. Therefore, obtaining information on the proteome to guide intervention appears to be the most appropriate approach. Presenting more detailed evidence, I argue that urinary proteome analysis can, in most cases, be employed to guide therapeutic intervention, can be repeated multiple times as it is without any direct risk or discomfort and can be considered as a liquid biopsy., (© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: We investigate whether the urinary proteome refines the diagnosis of renal dysfunction, which affects over 10% of the adult population., Methods: We measured serum creatinine, estimated glomerular filtration rate (eGFR) and 24-h albuminuria in 797 people randomly recruited from a population. We applied capillary electrophoresis coupled with mass spectrometry to measure multi-dimensional urinary proteomic classifiers developed for renal dysfunction (CKD273) or left ventricular dysfunction (HF1 and HF2). Renal function was followed up in 621 participants and the incidence of cardiovascular events in the whole study population., Results: In multivariable-adjusted cross-sectional analyses, higher biomarker levels analysed separately or combined by principal component analysis into a single factor (SF), correlated (P ≤ 0.010) with worse renal function. Over 4.8 years, higher HF1 and SF predicted (P ≤ 0.014) lowering of eGFR; higher HF2 predicted (P ≤ 0.049) increase in serum creatinine and decrease eGFR. HF1, HF2 and SF predicted progression from CKD Stages 2 or ≤2 to Stage ≥3, with risk estimates for a 1-SD increment in the urinary biomarkers ranging from 38 to 71% (P ≤ 0.039). HF1, HF2 and SF yielded a net reclassification improvement of 31-51% (P ≤ 0.029). Over 6.1 years, 47 cardiovascular events occurred. HF2 and SF, independent of baseline eGFR, 24-h albuminuria and other covariables were significant predictors of cardiovascular complications with risk estimates for 1-SD increases ranging from 32 to 41% (P ≤ 0.047)., Conclusions: The urinary proteome refines the diagnosis of existing or progressing renal dysfunction and predicts cardiovascular complications., (© The Author 2014. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Early diagnosis and treatment of rheumatoid arthritis are associated with improved outcomes but current diagnostic tools such as rheumatoid factor or anti-citrullinated protein antibodies have shown limited sensitivity. In this pilot study we set out to establish a panel of urinary biomarkers associated with rheumatoid arthritis using capillary electrophoresis coupled to mass spectrometry. We compared the urinary proteome of 33 participants of the Scottish Early Rheumatoid Arthritis inception cohort study with 30 healthy controls and identified 292 potential rheumatoid arthritis-specific peptides. Amongst them, 39 were used to create a classifier model using support vector machine algorithms. Specific peptidic fragments were differentially excreted between groups; fragments of protein S100-A9 and gelsolin were less abundant in rheumatoid arthritis while fragments of uromodulin, complement C3 and fibrinogen were all increasingly excreted. The model generated was subsequently tested in an independent test-set of 31 samples. The classifier demonstrated a sensitivity of 88% and a specificity of 93% in diagnosing the condition, with an area under the receiver operating characteristic curve of 0.93 (p<0.0001). These preliminary results suggest that urinary biomarkers could be useful in the early diagnosis of rheumatoid arthritis. Further studies are currently being undertaken in larger cohorts of patients with rheumatoid arthritis and other athridities to assess the potential of the urinary peptide based classifier in the early detection of rheumatoid arthritis.

The success of clinical proteome analysis should be assessed based on the clinical impact following implementation of findings. Although there have been several technological advancements in mass spectrometry in the last years, these have not resulted in similar advancements in clinical proteomics. In addition, application of proteomic biomarkers in clinical diagnostics and practical improvement in the disease management is extremely rare. In this review, we discuss the relevant issues associated with identification of robust biomarkers of clinical value. Urine appears to be an ideal source of biomarkers, for theoretical, methodological, and practical reasons. Therefore, this review is focused on the search for biomarkers in urine within the last decade. Urine can be used for non-invasive assessment of a variety of diseases including those affecting the urogenital tract and also other pathologies such as cardiovascular disease or appendicitis. We also discuss the importance of data validation, an essential step in translating biomarkers into the clinical practice. Furthermore, we examine several examples of apparently successful proteomic biomarker discovery studies and their implications for disease diagnosis, prognosis, and therapy evaluation. We also discuss some current challenges in this field and reflect on future research prospects. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge., (© 2013.)

Introduction: Renal cell carcinoma (RCC) is often accompanied by non-specific symptoms. The increase of incidentally discovered small renal masses also presents a diagnostic dilemma. This study investigates whether RCC-specific peptides with diagnostic potential can be detected in urine and whether a combination of such peptides could form a urinary screening tool., Materials and Methods: For the discovery of RCC-specific biomarkers, we have employed CE-MS to analyze urine samples from patients with RCC (N=40) compared to non-diseased controls (N=68)., Results and Discussion: 86 peptides were found to be specifically associated to RCC, of which sequence could be obtained for 40. A classifier based on these peptides was evaluated in an independent set of 76 samples, resulting in 80% sensitivity and 87% specificity. The specificity of the marker panel was further validated in a historical dataset of 1077 samples including age-matched controls (N=218), patients with related cancer types and renal diseases (N=859). In silico protease prediction based on the cleavage sites of differentially excreted peptides, suggested modified activity of certain proteases including cathepsins, ADAMTS and kallikreins some of which were previously found to be associated to RCC., Conclusions: RCC can be detected with high accuracy based on specific urinary peptides., Biological Significance: Clear cell renal cell carcinoma (RCC) has the highest incidence among the renal malignancies, often presenting non-specific or no symptoms at all. Moreover, with no diagnostic marker being available so far, almost 30% of the patients are diagnosed with metastatic disease and 30-40% of the patients initially diagnosed with localized tumor relapse. These facts introduce the clinical need of early diagnosis. This study is focused on the investigation of a marker model based on urinary peptides, as a tool for the detection of RCC in selected patients at risk. Upon evaluation of the marker model in an independent blinded set of 76 samples, 80% sensitivity and 87% specificity were reported. An additional dataset of 1077 samples was subsequently employed for further evaluation of the specificity of the classifier., (Copyright © 2013 Elsevier B.V. All rights reserved.)

The urinary proteome is the focus of many studies due to the ease of urine collection and the relative proteome stability. Systems biology allows the combination of multiple omics studies, forming a link between proteomics, metabolomics, genomics and transcriptomics. In-depth data interpretation is achieved by bioinformatics analysis of -omics data sets. It is expected that the contribution of systems biology to the study of the urinary proteome will offer novel insights. The main focus of this review is on technical aspects of proteomics studies, available tools for systems biology analysis and the application of urinary proteomics in clinical studies and systems biology.

Aims/hypothesis: Microalbuminuria is considered the first clinical sign of kidney dysfunction and is associated with a poor renal and cardiovascular prognosis in type 2 diabetes. Detection of patients who are prone to develop micro- or macroalbuminuria may represent an effective strategy to start or optimise therapeutic intervention. Here we assessed the value of a urinary proteomic-based risk score (classifier) in predicting the development and progression of microalbuminuria., Methods: We conducted a prospective case-control study. Cases (n = 44) and controls (n = 44) were selected from the PREVEND (Prevention of Renal and Vascular End-stage Disease) study and from the Steno Diabetes Center (Gentofte, Denmark). Cases were defined by transition from normo- to microalbuminuria or from micro- to macroalbuminuria over a follow-up of 3 years. Controls with no transitions in albuminuria were pair-matched for age, sex and albuminuria status. A model for the progression of albuminuria was built using a proteomic classifier based on 273 urinary peptides., Results: The proteomic classifier was independently associated with transition to micro- or macroalbuminuria (OR 1.35 [95% CI 1.02, 1.79], p = 0.035). The classifier predicted the development and progression of albuminuria on top of albuminuria and estimated GFR (eGFR, area under the receiver operating characteristic [ROC] curve increase of 0.03, p = 0.002; integrated discrimination index [IDI]: 0.105, p = 0.002). Fragments of collagen and α-2-HS-glycoprotein showed significantly different expression between cases and controls., Conclusions/interpretation: Although limited by the relatively small sample size, these results suggest that analysis of a urinary biomarker set enables early renal risk assessment in patients with diabetes. Further work is required to confirm the role of urinary proteomics in the prevention of renal failure in diabetes.

Background: Diagnosis and curative treatment of cholangiocarcinoma (CC) often comes too late due to the lack of reliable tumour markers especially in patients with primary sclerosing cholangitis (PSC). The authors recently introduced bile proteomic analysis for CC diagnosis. Nevertheless, bile collection depends on invasive endoscopic retrograde cholangiography. The authors therefore evaluated urine proteomic analysis for non-invasive CC diagnosis., Methods: Using capillary electrophoresis mass spectrometry the authors established a CC-specific peptide marker model based on the distribution of 42 peptides in 14 CC, 13 PSC and 14 benign biliary disorder (BBD) patients., Results: In cross-sectional validation of 123 patients, the urine peptide marker model correctly classified 35 of 42 CC patients and 64 of 81 PSC and BBD patients with an area under the curve value of 0.87 (95% CI 0.80 to 0.92, p=0.0001, 83% sensitivity, 79% specificity). Evaluation of 101 normal controls resulted in 86% specificity. All 10 patients with CC on top of PSC were correctly classified. The majority of sequence-identified peptides are fragments of interstitial collagens with some of them also detected in blood indicating their extra-renal origin. Immunostaining of liver sections for matrix metallopeptidase 1 indicated increased activity of the interstitial collagenase in liver epithelial cells of CC patients., Conclusion: The urine test differentiates CC from PSC and other BBD and may provide a new diagnostic non-invasive tool for PSC surveillance and CC detection.

Treatment options for autosomal dominant polycystic kidney disease (ADPKD) will likely become available in the near future, hence reliable diagnostic and prognostic biomarkers for the disease are strongly needed. Here, we aimed to define urinary proteomic patterns in ADPKD patients, which aid diagnosis and risk stratification. By capillary electrophoresis online coupled to mass spectrometry (CE-MS), we compared the urinary peptidome of 41 ADPKD patients to 189 healthy controls and identified 657 peptides with significantly altered excretion, of which 209 could be sequenced using tandem mass spectrometry. A support-vector-machine based diagnostic biomarker model based on the 142 most consistent peptide markers achieved a diagnostic sensitivity of 84.5% and specificity of 94.2% in an independent validation cohort, consisting of 251 ADPKD patients from five different centers and 86 healthy controls. The proteomic alterations in ADPKD included, but were not limited to markers previously associated with acute kidney injury (AKI). The diagnostic biomarker model was highly specific for ADPKD when tested in a cohort consisting of 481 patients with a variety of renal and extrarenal diseases, including AKI. Similar to ultrasound, sensitivity and specificity of the diagnostic score depended on patient age and genotype. We were furthermore able to identify biomarkers for disease severity and progression. A proteomic severity score was developed to predict height adjusted total kidney volume (htTKV) based on proteomic analysis of 134 ADPKD patients and showed a correlation of r = 0.415 (p<0.0001) with htTKV in an independent validation cohort consisting of 158 ADPKD patients. In conclusion, the performance of peptidomic biomarker scores is superior to any other biochemical markers of ADPKD and the proteomic biomarker patterns are a promising tool for prognostic evaluation of ADPKD.

Non-invasive detection of diseases, based on urinary proteomics, is becoming an increasingly important area of research, especially in the area of chronic kidney disease (CKD). Different platforms have been used in independent studies, mostly capillary-electrophoresis coupled ESI-MS (CE-MS), liquid chromatography coupled mass spectrometry, and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We have compared the performance of CE-MS with MALDI-MS in detecting CKD, based on a cohort of 137 urine samples (62 cases and 75 controls). Data cross-talk between the two platforms was established for the comparison of detected biomarkers. The results demonstrate superior performance of the CE-MS approach in terms of peptide resolution and obtained disease prediction accuracy rates. However, the data also demonstrate the ability of the MALDI-MS approach to separate CKD patients from controls, at slightly reduced accuracy, but expected reduced cost and time. As a consequence, a practical approach can be foreseen where MALDI-MS is employed as an inexpensive, fast, and robust screening tool to detect probable CKD. In a second step, high resolution CE-MS could be used in those patients only that scored negative for CKD in the MALDI-MS analysis, reducing costs and time of such a program., (Copyright © 2012 Elsevier B.V. All rights reserved.)

While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare., (© 2012 The Authors. European Journal of Clinical Investigation © 2012 Stichting European Society for Clinical Investigation Journal Foundation.)

One of the aims in the field of proteomics is the identification of a protein or polypeptide, or a range of these compounds, that could provide pre-symptomatic indication of the onset of a disease. A number of analytical techniques have been employed to try and achieve this end. These techniques have been applied to the complete range of body fluids and tissues that are readily available from clinical studies. Of these sample sources, the urinary low molecular weight peptidome has been shown to reflect changes in the health status of the individual. The alterations that occur in the polypeptide make up of urine, which reflect changes in biological status, are known as biomarkers. To be able to determine these changes no single technique has emerged that can cope with detecting the large number of peptides present and quantifying them over the wide concentration range they exist in. In this investigation, we made use of a single reflectron time of flight (RTOF)-MS analyser to which we first connected a CE system and then a nanoflow HPLC. Two pooled male and female standard urine samples were compared on these systems. Both techniques had similar results in terms of number of peptides detected and the mass range the peptides were detected over. The major differences in terms of biomarker research were the ability in CE to calibrate the migration time of the peptides to allow comparison between samples. In addition, CE was shown not to suffer from carry over from previous samples as was seen in the LC analysis., (Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Acute kidney injury (AKI) comprises several syndromes that are associated with a sudden decrease in renal function. AKI is a common condition especially among critically ill patients. It is typically multifactorial and of great prognostic significance. The incidence of AKI has increased while the associated mortality rate has remained unchanged over the last years. Recent definitions of AKI, namely the Risk, Injury, Failure, Loss of renal function and End-stage kidney disease (RIFLE) classifycation or the Acute Kidney Injury Network (AKIN) criteria, incorporate serum creatinine and urine output as the principal markers to define and detect AKI. However, elevated serum creatinine or oliguria were demonstrated to detect AKI at late stages of renal injury when preventive strategies may be less effective. Therefore, there has recently been a great scientific interest in obtainng valuable markers for early AKI detection. In the last 5 years numerous new markers such as neutrophil-gelatinase associated lipo-calin, interleukin-18, cystatin C and kidney injury molecule 1 in the urine and/or serum have been studied and proposed as early detection markers of AKI. Persistently, these markers performed well in initial pilot trials. However, these promising results could often not be confirmed in later, larger multicentre trials and limitation of these biomarkers in the early diagnosis of renal injury were discovered. Furthermore, as AKI is multi-factorial and heterogeneous in origin, it seems likely that not one single marker but a panel of biomarkers will be required to detect all subtypes of AKI early during their evolution. This has initiated proteomic studies to develop panels of biomarkers which may facilitate early detection of AKI. The present review will focus on the most important clinical studies evaluating the ability of single AKI biomarkers and on those in clinical proteomics that attempted to establish panels of biomarkers in urine for early and accurate AKI diagnosis and prognosis.

Representative animal models for diabetes-associated vascular complications are extremely relevant in assessing potential therapeutic drugs. While several rodent models for type 2 diabetes (T2D) are available, their relevance in recapitulating renal and cardiovascular features of diabetes in man is not entirely clear. Here we evaluate at the molecular level the similarity between Zucker diabetic fatty (ZDF) rats, as a model of T2D-associated vascular complications, and human disease by urinary proteome analysis. Urine analysis of ZDF rats at early and late stages of disease compared to age- matched LEAN rats identified 180 peptides as potentially associated with diabetes complications. Overlaps with human chronic kidney disease (CKD) and cardiovascular disease (CVD) biomarkers were observed, corresponding to proteins marking kidney damage (eg albumin, alpha-1 antitrypsin) or related to disease development (collagen). Concordance in regulation of these peptides in rats versus humans was more pronounced in the CVD compared to the CKD panels. In addition, disease-associated predicted protease activities in ZDF rats showed higher similarities to the predicted activities in human CVD. Based on urinary peptidomic analysis, the ZDF rat model displays similarity to human CVD but might not be the most appropriate model to display human CKD on a molecular level.

Polyphenol rich diets have been associated with a reduced risk of cardiovascular disease. We examined the effect of a polyphenol rich (P-R) drink on biomarkers assessed by urinary proteomics. Thirty nine middle aged and overweight subjects were randomized to P-R drink (n = 20) or placebo (n = 19) in addition to their normal diet. After two weeks urine samples were obtained for assessment of the urinary proteome using capillary electrophoresis coupled to a mass spectrometer. A total of 93 polypeptides were found to be candidates for differential distribution with a nominal p-value <0.05, though these differences did not reach significance when multiple testing was accounted for. Sequences were determined in 19 of these demonstrating that they originate from alpha-1 antitrypsin, collagens, fibrinogen alpha and IgG kappa. Levels of 27 polypeptides were greater than 4-fold different between the two groups. Of these, 7 were previously found to be part of a coronary artery disease (CAD) specific urinary biomarker pattern. Their direction of expression was closer to the healthy state in the P-R drink group and closer to CAD state in the placebo group. Our data suggest that the P-R drink may have beneficial effects on urinary biomarkers of CAD. The data encourage the planning of future prospective studies, aimed at investigating significant effects of polyphenol rich dietary products.