The urinary proteomics classifier chronic kidney disease 273 predicts cardiovascular outcome in patients with chronic kidney disease.

BACKGROUND: The urinary proteomic classifier chronic kidney disease 273 (CKD273) is predictive for the development and progression of chronic kidney disease (CKD) and/or albuminuria in type 2 diabetes. This study evaluates its role in the prediction of cardiovascular (CV) events in patients with CKD Stages G1-G5. METHODS: We applied the CKD273 classifier in a cohort of 451 patients with CKD Stages G1-G5 followed prospectively for a median of 5.5 years. Primary endpoints were all-cause mortality, CV mortality and the composite of non-fatal and fatal CV events (CVEs). RESULTS: In multivariate Cox regression models adjusting for age, sex, prevalent diabetes and CV history, the CKD273 classifier at baseline was significantly associated with total mortality and time to fatal or non-fatal CVE, but not CV mortality. Because of a significant interaction between CKD273 and CV history (P = 0.018) and CKD stages (P = 0.002), a stratified analysis was performed. In the fully adjusted models, CKD273 classifier was a strong and independent predictor of fatal or non-fatal CVE only in the subgroup of patients with CKD Stages G1-G3b and without a history of CV disease. In those patients, the highest tertile of CKD273 was associated with a >10-fold increased risk as compared with the lowest tertile. CONCLUSIONS: The urinary CKD273 classifier provides additional independent information regarding the CV risk in patients with early CKD stage and a blank CV history. Determination of CKD273 scores on a random urine sample may improve the efficacy of intensified surveillance and preventive strategies by selecting patients who potentially will benefit most from early risk management.

Urinary peptide biomarker panel associated with an improvement in estimated glomerular filtration rate in chronic kidney disease patients.

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.

A urinary proteome-based classifier for the early detection of decline in glomerular filtration.

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.

Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis.

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.

α-1-Antitrypsin detected by MALDI imaging in the study of glomerulonephritis: Its relevance in chronic kidney disease progression.

Idiopathic glomerulonephritis (GN), such as membranous glomerulonephritis, focal segmental glomerulosclerosis (FSGS), and IgA nephropathy (IgAN), represent the most frequent primary glomerular kidney diseases (GKDs) worldwide. Although the renal biopsy currently remains the gold standard for the routine diagnosis of idiopathic GN, the invasiveness and diagnostic difficulty related with this procedure highlight the strong need for new diagnostic and prognostic biomarkers to be translated into less invasive diagnostic tools. MALDI-MS imaging MALDI-MSI was applied to fresh-frozen bioptic renal tissue from patients with a histological diagnosis of FSGS (n = 6), IgAN, (n = 6) and membranous glomerulonephritis (n = 7), and from controls (n = 4) in order to detect specific molecular signatures of primary glomerulonephritis. MALDI-MSI was able to generate molecular signatures capable to distinguish between normal kidney and pathological GN, with specific signals (m/z 4025, 4048, and 4963) representing potential indicators of chronic kidney disease development. Moreover, specific disease-related signatures (m/z 4025 and 4048 for FSGS, m/z 4963 and 5072 for IgAN) were detected. Of these signals, m/z 4048 was identified as α-1-antitrypsin and was shown to be localized to the podocytes within sclerotic glomeruli by immunohistochemistry. α-1-Antitrypsin could be one of the markers of podocyte stress that is correlated with the development of FSGS due to both an excessive loss and a hypertrophy of podocytes.

Urinary proteomics and molecular determinants of chronic kidney disease: possible link to proteases.

Chronic kidney disease (CKD) is the gradual decrease in renal function. Currently available biomarkers are effective only in detecting late stage CKD. Biomarkers of early stage CKD and prognostic biomarkers are required. We review the major findings in urinary proteomics in CKD during the last five years. Significant progress has been made and today urinary proteomics is applied in large randomized trials, and in patient management. Many of the biomarkers indicate altered protease activity. We therefore also review the literature on proteases associated with renal function loss. We anticipate in silico prediction tools of protease activity and additional system biology studies may contribute to biomarker discovery and elucidate the role of proteases in CKD development and progression. These approaches will enable the deciphering of the molecular pathophysiology of CKD, and hence definition of the most appropriate therapeutic targets in the future. Together with stable biomarker panels available today, this will significantly improve patient management.

Publisher Correction: 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.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

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.

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.

Comparison of Urine and Plasma Peptidome Indicates Selectivity in Renal Peptide Handling.

PURPOSE: Urine is considered to be produced predominantly as a result of plasma filtration in the kidney. However, the origin of the native peptides present in urine has never been investigated in detail. Therefore, the authors aimed to obtain a first insight into the origin of urinary peptides based on a side-by-side comprehensive analysis of the plasma and urine peptidome. METHODS: Twenty-two matched urine and plasma samples are analyzed for their peptidome using capillary electrophoresis coupled to mass spectrometry (CE-MS; for relative quantification) and CE or LC coupled to tandem mass spectrometry (CE- or LC-MS/MS; for peptide identification). The overlap and association of abundance of the different peptides present in these two body fluids are evaluated. RESULTS: The authors are able to identify 561 plasma and 1461 urinary endogenous peptides. Only 90 peptides are detectable in both urine and plasma. No significant correlation is found when comparing the abundance of these common peptides, with the exception of collagen fragments. This observation is also supported when comparing published peptidome data from both plasma and urine. CONCLUSIONS AND CLINICAL RELEVANCE: Most of the plasma peptides are not detectable in urine, possibly due to tubular reabsorption. The majority of urinary peptides may in fact originate in the kidney. The notable exception is collagen fragments, which indicates potential selective exclusion of these peptides from tubular reabsorption. Experimental verification of this hypothesis is warranted.

Urinary peptide-based classifier CKD273: towards clinical application in chronic kidney disease.

Capillary electrophoresis coupled with mass spectrometry (CE-MS) has been used as a platform for discovery and validation of urinary peptides associated with chronic kidney disease (CKD). CKD affects ∼ 10% of the population, with high associated costs for treatments. A urinary proteome-based classifier (CKD273) has been discovered and validated in cross-sectional and longitudinal studies to assess and predict the progression of CKD. It has been implemented in studies employing cohorts of > 1000 patients. CKD273 is commercially available as an in vitro diagnostic test for early detection of CKD and is currently being used for patient stratification in a multicentre randomized clinical trial (PRIORITY). The validity of the CKD273 classifier has recently been evaluated applying the Oxford Evidence-Based Medicine and Southampton Oxford Retrieval Team guidelines and a letter of support for CKD273 was issued by the US Food and Drug Administration. In this article we review the current evidence published on CKD273 and the challenges associated with implementation. Definition of a possible surrogate early endpoint combined with CKD273 as a biomarker for patient stratification currently appears as the most promising strategy to enable the development of effective drugs to be used at an early time point when intervention can still be effective.

Prediction of acute coronary syndromes by urinary proteome analysis.

Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.273 ± 0.048 (P < 0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.

Prediction of Chronic Kidney Disease Stage 3 by CKD273, a Urinary Proteomic Biomarker.

INTRODUCTION: CKD273 is a urinary biomarker, which in advanced chronic kidney disease predicts further deterioration. We investigated whether CKD273 can also predict a decline of estimated glomerular filtration rate (eGFR) to <60 ml/min per 1.73 m2. METHODS: In analyses of 2087 individuals from 6 cohorts (46.4% women; 73.5% with diabetes; mean age, 46.1 years; eGFR ≥ 60 ml/min per 1.73 m2, 100%; urinary albumin excretion rate [UAE] ≥20 µg/min, 6.2%), we accounted for cohort, sex, age, mean arterial pressure, diabetes, and eGFR at baseline and expressed associations per 1-SD increment in urinary biomarkers. RESULTS: Over 5 (median) follow-up visits, eGFR decreased more with higher baseline CKD273 than UAE (1.64 vs. 0.82 ml/min per 1.73 m2; P < 0.0001). Over 4.6 years (median), 390 participants experienced a first renal endpoint (eGFR decrease by ≥10 to <60 ml/min per 1.73 m2), and 172 experienced an endpoint sustained over follow-up. The risk of a first and sustained renal endpoint increased with UAE (hazard ratio ≥ 1.23; P ≤ 0.043) and CKD273 (≥ 1.20; P ≤ 0.031). UAE (≥20 µg/min) and CKD273 (≥0.154) thresholds yielded sensitivities of 30% and 33% and specificities of 82% and 83% (P ≤ 0.0001 for difference between UAE and CKD273 in proportion of correctly classified individuals). As continuous markers, CKD273 (P = 0.039), but not UAE (P = 0.065), increased the integrated discrimination improvement, while both UAE and CKD273 improved the net reclassification index (P ≤ 0.0003), except for UAE per threshold (P = 0.086). DISCUSSION: In conclusion, while accounting for baseline eGFR, albuminuria, and covariables, CKD273 adds to the prediction of stage 3 chronic kidney disease, at which point intervention remains an achievable therapeutic target.

PeptiCKDdb-peptide- and protein-centric database for the investigation of genesis and progression of chronic kidney disease.

The peptiCKDdb is a publicly available database platform dedicated to support research in the field of chronic kidney disease (CKD) through identification of novel biomarkers and molecular features of this complex pathology. PeptiCKDdb collects peptidomics and proteomics datasets manually extracted from published studies related to CKD. Datasets from peptidomics or proteomics, human case/control studies on CKD and kidney or urine profiling were included. Data from 114 publications (studies of body fluids and kidney tissue: 26 peptidomics and 76 proteomics manuscripts on human CKD, and 12 focusing on healthy proteome profiling) are currently deposited and the content is quarterly updated. Extracted datasets include information about the experimental setup, clinical study design, discovery-validation sample sizes and list of differentially expressed proteins (P-value < 0.05). A dedicated interactive web interface, equipped with multiparametric search engine, data export and visualization tools, enables easy browsing of the data and comprehensive analysis. In conclusion, this repository might serve as a source of data for integrative analysis or a knowledgebase for scientists seeking confirmation of their findings and as such, is expected to facilitate the modeling of molecular mechanisms underlying CKD and identification of biologically relevant biomarkers.Database URL: www.peptickddb.com.

Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy in TYpe 2 diabetic patients with normoalbuminuria (PRIORITY): essential study design and rationale of a randomised clinical multicentre trial.

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.

Diastolic Left Ventricular Function in Relation to Urinary and Serum Collagen Biomarkers in a General Population.

Current knowledge on the pathogenesis of diastolic heart failure predominantly rests on case-control studies involving symptomatic patients with preserved ejection fraction and relying on invasive diagnostic procedures including endomyocardial biopsy. Our objective was to gain insight in serum and urinary biomarkers reflecting collagen turnover and associated with asymptomatic diastolic LV dysfunction. We randomly recruited 782 Flemish (51.3% women; 50.5 years). We assessed diastolic LV function from the early and late diastolic peak velocities of the transmitral blood flow and of the mitral annulus. By sequencing urinary peptides, we identified 70 urinary collagen fragments. In serum, we measured carboxyterminal propeptide of procollagen type 1 (PICP) as marker of collagen I synthesis and tissue inhibitor of matrix metalloproteinase type 1 (TIMP-1), an inhibitor of collagen-degrading enzymes. In multivariable-adjusted analyses with Bonferroni correction, we expressed effect sizes per 1-SD in urinary collagen I (uCI) or collagen III (uCIII) fragments. In relation to uCI fragments, e' decreased by 0.183 cm/s (95% confidence interval, 0.017 to 0.350; p = 0.025), whereas E/e' increased by 0.210 (0.067 to 0.353; p = 0.0012). E/e' decreased with uCIII by 0.168 (0.021 to 0.316; p = 0.018). Based on age-specific echocardiographic criteria, 182 participants (23.3%) had subclinical diastolic LV dysfunction. Partial least squares discriminant analysis contrasting normal vs. diastolic LV dysfunction confirmed the aforementioned associations with the uCI and uCIII fragments. PICP and TIMP-1 increased in relation to uCI (p<0.0001), whereas these serum markers decreased with uCIII (p≤0.0006). Diastolic LV dysfunction was associated with higher levels of TIMP-1 (653 vs. 696 ng/mL; p = 0.013). In a general population, the non-invasively assessed diastolic LV function correlated inversely with uCI and serum markers of collagen I deposition, but positively with uCIII. These observations generalise previous studies in patients to randomly recruited people, in whom diastolic LV function ranged from normal to subclinical impairment, but did not encompass overt diastolic heart failure.

CE-MS-based proteomics in biomarker discovery and clinical application.

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.