Urinary proteome analysis enables assessment of renoprotective treatment in type 2 diabetic patients with microalbuminuria

Angiotensin-converting-enzyme inhibitors and angiotensin receptor blockers for preventing the progression of diabetic kidney disease

BACKGROUND

Guidelines suggest that adults with diabetes and kidney disease receive treatment with angiotensin-converting-enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARB). This is an update of a Cochrane review published in 2006.

OBJECTIVES

We compared the efficacy and safety of ACEi and ARB therapy (either as monotherapy or in combination) on cardiovascular and kidney outcomes in adults with diabetes and kidney disease.

SEARCH METHODS

We searched the Cochrane Kidney and Transplants Register of Studies to 17 March 2024 through contact with the Information Specialist using search terms relevant to this review. Studies in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE, conference proceedings, the International Clinical Trials Registry Platform (ICTRP) Search Portal, and ClinicalTrials.gov.

SELECTION CRITERIA

We included studies evaluating ACEi or ARB alone or in combination, compared to each other, placebo or no treatment in people with diabetes and kidney disease.

DATA COLLECTION AND ANALYSIS

Two authors independently assessed the risk of bias and extracted data. Summary estimates of effect were obtained using a random-effects model, and results were expressed as risk ratios (RR) and their 95% confidence intervals (CI) for dichotomous outcomes and mean difference (MD) or standardised mean difference (SMD) and 95% CI for continuous outcomes. Confidence in the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.

MAIN RESULTS

One hundred and nine studies (28,341 randomised participants) were eligible for inclusion. Overall, the risk of bias was high. Compared to placebo or no treatment, ACEi may make little or no difference to all-cause death (24 studies, 7413 participants: RR 0.91, 95% CI 0.73 to 1.15; I2 = 23%; low certainty) and with similar withdrawals from treatment (7 studies, 5306 participants: RR 1.03, 95% CI 0.90 to 1.19; I2 = 0%; low certainty). ACEi may prevent kidney failure (8 studies, 6643 participants: RR 0.61, 95% CI 0.39 to 0.94; I2 = 0%; low certainty). Compared to placebo or no treatment, ARB may make little or no difference to all-cause death (11 studies, 4260 participants: RR 0.99, 95% CI 0.85 to 1.16; I2 = 0%; low certainty). ARB have uncertain effects on withdrawal from treatment (3 studies, 721 participants: RR 0.85, 95% CI 0.58 to 1.26; I2 = 2%; low certainty) and cardiovascular death (6 studies, 878 participants: RR 3.36, 95% CI 0.93 to 12.07; low certainty). ARB may prevent kidney failure (3 studies, 3227 participants: RR 0.82, 95% CI 0.72 to 0.94; I2 = 0%; low certainty), doubling of serum creatinine (SCr) (4 studies, 3280 participants: RR 0.84, 95% CI 0.72 to 0.97; I2 = 32%; low certainty), and the progression from microalbuminuria to macroalbuminuria (5 studies, 815 participants: RR 0.44, 95% CI 0.23 to 0.85; I2 = 74%; low certainty). Compared to ACEi, ARB had uncertain effects on all-cause death (15 studies, 1739 participants: RR 1.13, 95% CI 0.68 to 1.88; I2 = 0%; low certainty), withdrawal from treatment (6 studies, 612 participants: RR 0.91, 95% CI 0.65 to 1.28; I2 = 0%; low certainty), cardiovascular death (13 studies, 1606 participants: RR 1.15, 95% CI 0.45 to 2.98; I2 = 0%; low certainty), kidney failure (3 studies, 837 participants: RR 0.56, 95% CI 0.29 to 1.07; I2 = 0%; low certainty), and doubling of SCr (2 studies, 767 participants: RR 0.88, 95% CI 0.52 to 1.48; I2 = 0%; low certainty). Compared to ACEi plus ARB, ACEi alone has uncertain effects on all-cause death (6 studies, 1166 participants: RR 1.08, 95% CI 0.49 to 2.40; I2 = 20%; low certainty), withdrawal from treatment (2 studies, 172 participants: RR 0.78, 95% CI 0.33 to 1.86; I2 = 0%; low certainty), cardiovascular death (4 studies, 994 participants: RR 3.02, 95% CI 0.61 to 14.85; low certainty), kidney failure (3 studies, 880 participants: RR 1.36, 95% CI 0.79 to 2.32; I2 = 0%; low certainty), and doubling of SCr (2 studies, 813 participants: RR 1.14, 95% CI 0.70 to 1.85; I2 = 0%; low certainty). Compared to ACEi plus ARB, ARB alone has uncertain effects on all-cause death (7 studies, 2607 participants: RR 1.02, 95% CI 0.76 to 1.37; I2 = 0%; low certainty), withdrawn from treatment (3 studies, 1615 participants: RR 0.81, 95% CI 0.53 to 1.24; I2 = 0%; low certainty), cardiovascular death (4 studies, 992 participants: RR 3.03, 95% CI 0.62 to 14.93; low certainty), kidney failure (4 studies, 2321 participants: RR 1.15, 95% CI 0.67 to 1.95; I2 = 29%; low certainty), and doubling of SCr (3 studies, 2252 participants: RR 1.18, 95% CI 0.85 to 1.64; I2 = 0%; low certainty). Comparative effects of different ACEi or ARB and low-dose versus high-dose ARB were rarely evaluated. No study compared different doses of ACEi. Adverse events of ACEi and ARB were rarely reported.

AUTHORS' CONCLUSIONS

ACEi or ARB may make little or no difference to all-cause and cardiovascular death compared to placebo or no treatment in people with diabetes and kidney disease but may prevent kidney failure. ARB may prevent the doubling of SCr and the progression from microalbuminuria to macroalbuminuria compared with a placebo or no treatment. Despite the international guidelines suggesting not combining ACEi and ARB treatment, the effects of ACEi or ARB monotherapy compared to dual therapy have not been adequately assessed. The limited data availability and the low quality of the included studies prevented the assessment of the benefits and harms of ACEi or ARB in people with diabetes and kidney disease. Low and very low certainty evidence indicates that it is possible that further studies might provide different results.

Integration of Urinary Peptidome and Fecal Microbiome to Explore Patient Clustering in Chronic Kidney Disease

Millions of people worldwide currently suffer from chronic kidney disease (CKD), requiring kidney replacement therapy at the end stage. Endeavors to better understand CKD pathophysiology from an omics perspective have revealed major molecular players in several sample sources. Focusing on non-invasive sources, gut microbial communities appear to be disturbed in CKD, while numerous human urinary peptides are also dysregulated. Nevertheless, studies often focus on isolated omics techniques, thus potentially missing the complementary pathophysiological information that multidisciplinary approaches could provide. To this end, human urinary peptidome was analyzed and integrated with clinical and fecal microbiome (16S sequencing) data collected from 110 Non-CKD or CKD individuals (Early, Moderate, or Advanced CKD stage) that were not undergoing dialysis. Participants were visualized in a three-dimensional space using different combinations of clinical and molecular data. The most impactful clinical variables to discriminate patient groups in the reduced dataspace were, among others, serum urea, haemoglobin, total blood protein, urinary albumin, urinary erythrocytes, blood pressure, cholesterol measures, body mass index, Bristol stool score, and smoking; relevant variables were also microbial taxa, including Roseburia, Butyricicoccus, Flavonifractor, Burkholderiales, Holdemania, Synergistaceae, Enterorhabdus, and Senegalimassilia; urinary peptidome fragments were predominantly derived from proteins of collagen origin; among the non-collagen parental proteins were FXYD2, MGP, FGA, APOA1, and CD99. The urinary peptidome appeared to capture substantial variation in the CKD context. Integrating clinical and molecular data contributed to an improved cohort separation compared to clinical data alone, indicating, once again, the added value of this combined information in clinical practice.

New insights into the role of immunity and inflammation in diabetic kidney disease in the omics era

Diabetic kidney disease (DKD) is becoming the leading cause of chronic kidney disease, especially in the industrialized world. Despite mounting evidence has demonstrated that immunity and inflammation are highly involved in the pathogenesis and progression of DKD, the underlying mechanisms remain incompletely understood. Substantial molecules, signaling pathways, and cell types participate in DKD inflammation, by integrating into a complex regulatory network. Most of the studies have focused on individual components, without presenting their importance in the global or system-based processes, which largely hinders clinical translation. Besides, conventional technologies failed to monitor the different behaviors of resident renal cells and immune cells, making it difficult to understand their contributions to inflammation in DKD. Recently, the advancement of omics technologies including genomics, epigenomics, transcriptomics, proteomics, and metabolomics has revolutionized biomedical research, which allows an unbiased global analysis of changes in DNA, RNA, proteins, and metabolites in disease settings, even at single-cell and spatial resolutions. They help us to identify critical regulators of inflammation processes and provide an overview of cell heterogeneity in DKD. This review aims to summarize the application of multiple omics in the field of DKD and emphasize the latest evidence on the interplay of inflammation and DKD revealed by these technologies, which will provide new insights into the role of inflammation in the pathogenesis of DKD and lead to the development of novel therapeutic approaches and diagnostic biomarkers.

Exploratory Study Analyzing the Urinary Peptidome of T2DM Patients Suggests Changes in ECM but Also Inflammatory and Metabolic Pathways Following GLP-1R Agonist Treatment

Type II diabetes mellitus (T2DM) accounts for approximately 90% of all diabetes mellitus cases in the world. Glucagon-like peptide-1 receptor (GLP-1R) agonists have established an increased capability to target directly or indirectly six core defects associated with T2DM, while the underlying molecular mechanisms of these pharmacological effects are not fully known. This exploratory study was conducted to analyze the effect of treatment with GLP-1R agonists on the urinary peptidome of T2DM patients. Urine samples of thirty-two T2DM patients from the PROVALID study ("A Prospective Cohort Study in Patients with T2DM for Validation of Biomarkers") collected pre- and post-treatment with GLP-1R agonist drugs were analyzed by CE-MS. In total, 70 urinary peptides were significantly affected by GLP-1R agonist treatment, generated from 26 different proteins. The downregulation of MMP proteases, based on the concordant downregulation of urinary collagen peptides, was highlighted. Treatment also resulted in the downregulation of peptides from SERPINA1, APOC3, CD99, CPSF6, CRNN, SERPINA6, HBA2, MB, VGF, PIGR, and TTR, many of which were previously found to be associated with increased insulin resistance and inflammation. The findings indicate potential molecular mechanisms of GLP-1R agonists in the context of the management of T2DM and the prevention or delaying of the progression of its associated diseases.

Recent Advances in Urinary Peptide and Proteomic Biomarkers in Chronic Kidney Disease: A Systematic Review

Biomarker development, improvement, and clinical implementation in the context of kidney disease have been a central focus of biomedical research for decades. To this point, only serum creatinine and urinary albumin excretion are well-accepted biomarkers in kidney disease. With their known blind spot in the early stages of kidney impairment and their diagnostic limitations, there is a need for better and more specific biomarkers. With the rise in large-scale analyses of the thousands of peptides in serum or urine samples using mass spectrometry techniques, hopes for biomarker development are high. Advances in proteomic research have led to the discovery of an increasing amount of potential proteomic biomarkers and the identification of candidate biomarkers for clinical implementation in the context of kidney disease management. In this review that strictly follows the PRISMA guidelines, we focus on urinary peptide and especially peptidomic biomarkers emerging from recent research and underline the role of those with the highest potential for clinical implementation. The Web of Science database (all databases) was searched on 17 October 2022, using the search terms "marker *" OR biomarker * AND "renal disease" OR "kidney disease" AND "proteome *" OR "peptid *" AND "urin *". English, full-text, original articles on humans published within the last 5 years were included, which had been cited at least five times per year. Studies based on animal models, renal transplant studies, metabolite studies, studies on miRNA, and studies on exosomal vesicles were excluded, focusing on urinary peptide biomarkers. The described search led to the identification of 3668 articles and the application of inclusion and exclusion criteria, as well as abstract and consecutive full-text analyses of three independent authors to reach a final number of 62 studies for this manuscript. The 62 manuscripts encompassed eight established single peptide biomarkers and several proteomic classifiers, including CKD273 and IgAN237. This review provides a summary of the recent evidence on single peptide urinary biomarkers in CKD, while emphasizing the increasing role of proteomic biomarker research with new research on established and new proteomic biomarkers. Lessons learned from the last 5 years in this review might encourage future studies, hopefully resulting in the routine clinical applicability of new biomarkers.

Dapagliflozin Improves the Urinary Proteomic Kidney-Risk Classifier CKD273 in Type 2 Diabetes with Albuminuria: A Randomized Clinical Trial

OBJECTIVE

To evaluate the effect of the sodium-glucose cotransporter 2 inhibitor dapagliflozin on the kidney-risk urinary proteomic classifier (CKD273) in persons with type 2 diabetes (T2D) and albuminuria.

RESEARCH DESIGN AND METHODS

In a double-blind, randomized, controlled, crossover trial, we assigned participants with T2D and urinary albumin to creatinine ratio (UACR) ≥30 mg/g to receive dapagliflozin or matching placebo added to guideline-recommended treatment (ClinicalTrial.gov identifier NCT02914691). Treatment periods lasted 12 weeks, when crossover to the opposing treatment occurred. The primary outcome was change in CKD273 score. Secondary outcomes included regression from high-risk to low-risk CKD273 pattern using the prespecified cutoff score of 0.154. The primary outcome was assessed using paired t test between end-to-end CKD273 scores after dapagliflozin and placebo treatment. The McNemar test was used to assess regression in risk category.

RESULTS

A total of 40 participants were randomized and 32 completed the trial with intact proteomic measurements. Twenty-eight (88%) were men, the baseline mean (SD) age was 63.0 (8.3) years, mean (SD) diabetes duration was 15.4 (4.5) years, mean HbA1c was 73 (14) mmol/mol (8.8% [1.3%]), and median (interquartile range) UACR was 154 (94, 329) mg/g. Dapagliflozin significantly lowered CKD273 score compared with placebo (-0.221; 95% CI -0.356, -0.087; P = 0.002). Fourteen participants exhibited a high-risk pattern after dapagliflozin treatment compared with 24 after participants placebo (P = 0.021).

CONCLUSIONS

Dapagliflozin added to renin-angiotensin system inhibition reduced the urinary proteomic classifier CKD273 in persons with T2D and albuminuria, paving the way for the further investigation of CKD273 as a modifiable kidney risk factor.

Synergic Renoprotective Effects of Combined ASC Therapy with RAAS Blockade in Experimental Advanced CKD

Global prevalence of chronic kidney disease (CKD) has increased considerably in the recent decades. Overactivity of the renin-angiotensin-aldosterone system (RAAS), associated to renal inflammation and fibrosis, contributes to its evolution. The treatments currently employed to control CKD progression are limited and mainly based on the pharmacological inhibition of RAAS, associated with diuretics and immunosuppressive drugs. However, this conservative management promotes only partial deceleration of CKD evolution and does not completely avoid the progression of the disease and the loss of renal function, which motivates the medical and scientific community to investigate new therapeutic approaches to detain renal inflammation/fibrosis and CKD progression. Recent studies have shown the application of mesenchymal stem cells (mSC) to exert beneficial effects on the renal tissue of animals submitted to experimental models of CKD. In this context, the aim of the present study was to evaluate the effects of subcapsular application of adipose tissue-derived mSC (ASC) in rats submitted to the 5/6 renal ablation model, 15 days after the establishment of CKD, when the nephropathy was already severe. We also verify whether ASC associated to Losartan would promote greater renoprotection when compared to the respective monotherapies. Animals were followed until 30 days of CKD, when body weight, systolic blood pressure, biochemical, histological, immunohistochemical, and gene expression analysis were performed. The combination of ASC and Losartan was more effective than Losartan monotherapy in reducing systolic blood pressure and glomerulosclerosis and also promoted the complete normalization of proteinuria and albuminuria, a significant reduction in renal interstitial macrophage infiltration and downregulation of renal IL-6 gene expression. The beneficial effects of ACS are possibly due to the immunomodulatory and anti-inflammatory role of factors secreted by these cells, modulating the local immune response. Although studies are still required, our results demonstrated that a subcapsular inoculation of ASC, associated with the administration of Losartan, exerted additional renoprotective effect in rats submitted to a severe model of established CKD, when compared to Losartan monotherapy, thus suggesting ASC may be a potential adjuvant to RAAS-blockade therapy currently employed in the conservative management of CKD.

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The ‘omics’ techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.

The Use of 'Omics for Diagnosing and Predicting Progression of Chronic Kidney Disease: A Scoping Review

Globally, chronic kidney disease (CKD) contributes substantial morbidity and mortality. Recently, various ‘omics platforms have provided insight into the molecular basis of kidney dysfunction. This scoping review is a synthesis of the current literature on the use of different ‘omics platforms to identify biomarkers that could be used to detect early-stage CKD, predict disease progression, and identify pathways leading to CKD. This review includes 123 articles published from January 2007 to May 2021, following a structured selection process. The most common type of ‘omic platform was proteomics, appearing in 55 of the studies and two of these included a metabolomics component. Most studies (n = 91) reported on CKD associated with diabetes mellitus. Thirteen studies that provided information on the biomarkers associated with CKD and explored potential pathways involved in CKD are discussed. The biomarkers that are associated with risk or early detection of CKD are SNPs in the MYH9/APOL1 and UMOD genes, the proteomic CKD273 biomarker panel and metabolite pantothenic acid. Pantothenic acid and the CKD273 biomarker panel were also involved in predicting CKD progression. Retinoic acid pathway genes, UMOD, and pantothenic acid provided insight into potential pathways leading to CKD. The biomarkers were mainly used to detect CKD and predict progression in high-income, European ancestry populations, highlighting the need for representative ‘omics research in other populations with disparate socio-economic strata, including Africans, since disease etiologies may differ across ethnic groups. To assess the transferability of findings, it is essential to do research in diverse populations.

Urinary peptidome and diabetic retinopathy in the DIRECT-Protect 1 and 2 trials

BACKGROUND

Given the association of diabetic retinopathy (DR) and kidney disease, we investigated the urinary peptidome to presence and deterioration of DR in a post hoc analysis of trials investigating the effect of candesartan on progression of DR in type 1 and type 2 diabetes, respectively.

METHODS

Baseline urinary peptidomic analysis was performed on a random selection of 783 and 792 subjects in two randomized controlled trials, DIRECT-Protect 1 and 2, respectively. End points were two-step (RET2) and three-step (RET3) change in Early Treatment of Diabetic Retinopathy Study protocol (ETDRS) defined level. Peptide levels were correlated to baseline EDTRS level in a discovery set of 2/3 of the participants from DIRECT-Protect 1. The identified peptides were then validated cross-sectionally in the remaining 1/3 from DIRECT-Protect 1. Thereafter, peptides identified in the discovery set were assessed in the entire DIRECT-Protect 1 and 2 cohorts and significant peptides were tested longitudinally.

RESULTS

Follow-up ranged 4.0-4.7 years. 24 peptides were associated with baseline DR in the discovery set. COL3A1 (seq: NTG~) and COL4A1 (seq: DGA~) were associated with baseline DR in the validation set (Rho: -.223, p < 0.001 and Rho: -.141, p = 0.024). Neither was significantly associated with end points. Assessing the 24 identified peptides in the entire cohorts, several collagen peptides were associated with baseline DR and end points; however, there was no overlap across diabetes types.

CONCLUSIONS

We identified several urinary peptides (mainly collagen) associated with the presence of DR, however they could not be conclusively associated with worsening of DR.

Targeting the progression of chronic kidney disease

Chronic kidney disease (CKD) is a devastating condition that is reaching epidemic levels owing to the increasing prevalence of diabetes mellitus, hypertension and obesity, as well as ageing of the population. Regardless of the underlying aetiology, CKD is slowly progressive and leads to irreversible nephron loss, end-stage renal disease and/or premature death. Factors that contribute to CKD progression include parenchymal cell loss, chronic inflammation, fibrosis and reduced regenerative capacity of the kidney. Current therapies have limited effectiveness and only delay disease progression, underscoring the need to develop novel therapeutic approaches to either stop or reverse progression. Preclinical studies have identified several approaches that reduce fibrosis in experimental models, including targeting cytokines, transcription factors, developmental and signalling pathways and epigenetic modulators, particularly microRNAs. Some of these nephroprotective strategies are now being tested in clinical trials. Lessons learned from the failure of clinical studies of transforming growth factor β1 (TGFβ1) blockade underscore the need for alternative approaches to CKD therapy, as strategies that target a single pathogenic process may result in unexpected negative effects on simultaneously occurring processes. Additional promising avenues include preventing tubular cell injury and anti-fibrotic therapies that target activated myofibroblasts, the main collagen-producing cells.

Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease

The aim of this study was to establish a peptidomic profile based on LC-MS/MS and random forest (RF) algorithm to distinguish the urinary peptidomic scenario of type 2 diabetes mellitus (T2DM) patients with different stages of diabetic kidney disease (DKD). Urine from 60 T2DM patients was collected: 22 normal (stage A1), 18 moderately increased (stage A2) and 20 severely increased (stage A3) albuminuria. A total of 1080 naturally occurring peptides were detected, which resulted in the identification of a total of 100 proteins, irrespective of the patients’ renal status. The classification accuracy showed that the most severe DKD (A3) presented a distinct urinary peptidomic pattern. Estimates for peptide importance assessed during RF model training included multiple fragments of collagen and alpha-1 antitrypsin, previously associated to DKD. Proteasix tool predicted 48 proteases potentially involved in the generation of the 60 most important peptides identified in the urine of DM patients, including metallopeptidases, cathepsins, and calpains. Collectively, our study lightened some biomarkers possibly involved in the pathogenic mechanisms of DKD, suggesting that peptidomics is a valuable tool for identifying the molecular mechanisms underpinning the disease and thus novel therapeutic targets.

Urinary Peptidomic Biomarkers in Kidney Diseases

In order to effectively develop personalized medicine for kidney diseases we urgently need to develop highly accurate biomarkers for use in the clinic, since current biomarkers of kidney damage (changes in serum creatinine and/or urine albumin excretion) apply to a later stage of disease, lack accuracy, and are not connected with molecular pathophysiology. Analysis of urine peptide content (urinary peptidomics) has emerged as one of the most attractive areas in disease biomarker discovery. Urinary peptidome analysis allows the detection of short and long-term physiological or pathological changes occurring within the kidney. Urinary peptidomics has been applied extensively for several years now in renal patients, and may greatly improve kidney disease management by supporting earlier and more accurate detection, prognostic assessment, and prediction of response to treatment. It also promises better understanding of kidney disease pathophysiology, and has been proposed as a “liquid biopsy” to discriminate various types of renal disorders. Furthermore, proteins being the major drug targets, peptidome analysis may allow one to evaluate the effects of therapies at the protein signaling pathway level. We here review the most recent findings on urinary peptidomics in the setting of the most common kidney diseases.

Predicting albuminuria response to spironolactone treatment with urinary proteomics in patients with type 2 diabetes and hypertension

Background

The mineralocorticoid receptor antagonist spironolactone significantly reduces albuminuria in patients with diabetes. Prior studies have shown large between-patient variability in albuminuria treatment response. We previously developed and validated a urinary proteomic classifier that predicts onset and progression of chronic kidney disease. Here, we tested whether the proteomic classifier based on 273 urinary peptides (CKD273) predicts albuminuria response to spironolactone treatment.

Methods

We performed a post hoc analysis in a double-blind randomized clinical trial with allocation to either spironolactone 12.5-50 mg/day (n = 57) or placebo (n = 54) for 16 weeks. Patients were diagnosed with type 2 diabetes and resistant hypertension. Treatment was an adjunct to renin-angiotensin system inhibition. Primary endpoint was the percentage change in urine albumin to creatinine ratio (UACR). Capillary electrophoresis mass spectrometry was used to quantify urinary peptides at baseline. The previously validated combination of 273 known urinary peptides was used as proteomic classifier.

Results

Spironolactone reduced UACR relative to placebo by 50%, although with a large between-patient variability in UACR response (5th to 95th percentile, 7 to 312%). An interaction was detected between CKD273 and treatment assignment (β = -1.09, P = 0.026). Higher values of CKD273 at baseline were associated with a larger reduction in UACR in the spironolactone group (β = -0.70, P = 0.049), but not in the placebo group (β = 0.39, P = 0.25). Stratified in tertiles of baseline CKD273, reduction in UACR was greater in the highest tertile, 63% (95% confidence interval: 35-79%), as compared with the two other tertiles combined, 16% (-17 to 40%) (P = 0.011).

Conclusions

A urinary proteomics classifier can be used to identify individuals with type 2 diabetes who are more likely to show an albuminuria-lowering response to spironolactone treatment. These results suggest that urinary proteomics may be a valuable tool to tailor therapy, but confirmation in a larger clinical trial is required.

Clinical Proteomics in Kidney Disease: From Discovery to Clinical Application

Proteome analysis has been applied in multiple studies in the context of chronic kidney disease, aiming at improving our knowledge on the molecular pathophysiology of the disease. The approach is generally based on the hypothesis that proteins are key in maintaining kidney function, and disease is a clinical consequence of a significant change of the protein level. Knowledge on critical proteins and their alteration in disease should in turn enable identification of ideal biomarkers that could guide patient management. In addition, all drugs currently employed target proteins. Hence, proteome analysis also promises to enable identifying the best suited therapeutic target, and, in combination with biomarkers, could be used as the rationale basis for personalized intervention. To assess the current status of proteome analysis in the context of CKD, we present the results of a systematic review, of up-to-date scientific research, and give an outlook on the developments that can be expected in near future. Based on the current literature, proteome analysis has already seen implementation in the management of CKD patients, and it is expected that this approach, also supported by the positive results generated to date, will see advanced high-throughput application.

Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease

Chronic kidney disease (CKD) is an aging-related disorder that represents a major global public health burden. Current biochemical biomarkers, such as serum creatinine and urinary albumin, have important limitations when used to identify the earliest indication of CKD or in tracking the progression to more advanced CKD. These issues underline the importance of finding and testing new molecular biomarkers that are capable of successfully meeting this clinical need. The measurement of changes in nature and/or levels of proteins and metabolites in biological samples from patients provide insights into pathophysiological processes. Proteomic and metabolomic techniques provide opportunities to record dynamic chemical signatures in patients over time. This review article presents an overview of the recent developments in the fields of metabolomics and proteomics in relation to CKD. Among the many different proteomic biomarkers proposed, there is particular interest in the CKD273 classifier, a urinary proteome biomarker reported to predict CKD progression and with implementation potential. Other individual non-invasive peptidomic biomarkers that are potentially relevant for CKD detection include type 1 collagen, uromodulin and mucin-1. Despite the limited sample sizes and variability of the metabolomics studies, some metabolites such as trimethylamine N-oxide, kynurenine and citrulline stand out as potential biomarkers in CKD.

Urinary Proteomics as a Tool to Identify Kidney Responders to Dipeptidyl Peptidase-4 Inhibition: A Hypothesis-Generating Analysis from the MARLINA-T2D Trial

PURPOSE

Chronic kidney disease (CKD) is a serious complication of hyperglycemia and treatment options to slow its progression are scarce. Dipeptidyl peptidase-4 (DPP-4) inhibitors are common glucose-lowering drugs in type 2 diabetes (T2D). Among these, linagliptin has been suggested to exert kidney protective effects. It is investigated whether an effect of linagliptin on kidney function could be unmasked by characterizing the urinary proteome profile (UPP) in albuminuric T2D individuals.

EXPERIMENTAL DESIGN

Participants of the MARLINA-T2D trial (NCT01792518) are randomized 1:1 to receive either linagliptin 5 mg or placebo for 24 weeks. A previously developed proteome-based classifier, CKD273, is assessed.

RESULTS

Results confirm a significant correlation between CKD273 and clinical kidney parameters as well as with eGFR decline. Patient stratification using CKD273 at baseline, show a trend toward attenuation of renal function loss in high CKD-risk patients treated with linagliptin. Moreover, characterized are linagliptin affected peptides of which the majority contained a DPP-4 target sequence.

CONCLUSIONS AND CLINICAL RELEVANCE

CKD273 is a promising tool for identifying patients at high risk for CKD progression and may unmask a potential of linagliptin to slow progressive kidney function loss in high CKD-risk patients. UPP characterization reveals a significant impact of linagliptin on urinary peptides.

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.

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.

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Background

The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown.

Methods

Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years.

Results

CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model.

Conclusion

A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0697-9) contains supplementary material, which is available to authorized users.

Urinary proteomics using capillary electrophoresis coupled to mass spectrometry for diagnosis and prognosis in kidney diseases

PURPOSE OF REVIEW

Urine is the most useful of body fluids for biomarker research. Therefore, we have focused on urinary proteomics, using capillary electrophoresis coupled to mass spectrometry, to investigate kidney diseases in recent years.

RECENT FINDINGS

Several urinary proteomics studies for the detection of various kidney diseases have indicated the potential of this approach aimed at diagnostic and prognostic assessment. Urinary protein biomarkers such as collagen fragments, serum albumin, α-1-antitrypsin, and uromodulin can help to explain the processes involved during disease progression.

SUMMARY

Urinary proteomics has been used in several studies in order to identify and validate biomarkers associated with different kidney diseases. These biomarkers, with improved sensitivity and specificity when compared with the current gold standards, provide a significant alternative for diagnosis and prognosis, as well as improving clinical decision-making.

Proteomics for Biomarker Identification and Clinical Application in Kidney Disease

Treatment effectiveness for kidney disease is limited by lack of accuracy, sensitivity, specificity of diagnostic, prognostic, and therapeutic biomarkers. The gold standard test renal biopsy along with serum creatinine and proteinuria is often necessary to establish a diagnosis, particularly in glomerular disease. Proteomics has become a powerful tool for novel biomarker discovery in kidney disease. Novel proteomics offer earlier and more accurate diagnosis of renal pathology than possible with traditional biomarkers such as serum creatinine and urine protein. In addition, proteomic biomarkers could also be useful to choose the most suitable therapeutic targets. This review focuses on the current status of proteomic biomarkers from animal models (5/6 nephrectomy, unilateral ureteral obstruction, and diabetic nephropathy) and human studies (chronic kidney disease, glomerular diseases, transplantation, dialysis, acute and drug-induced kidney injury) to assess relevant findings and clinical usefulness. Current issues and problems related to the discovery, validation, and clinical application of proteomic biomarkers are discussed. We also describe several proteomic strategies highlighting technologic advancements, specimen selection, data processing and analysis. This review might provide help in future proteomic studies to improve the diagnosis and management of kidney disease.

The effect of sodium/glucose cotransporter 2 (SGLT2) inhibition on the urinary proteome

Treatment with empagliflozin, an inhibitor of the sodium/glucose cotransporter 2 (SGLT2), is associated with slower progression of diabetic kidney disease. In this analysis, we explored the hypothesis that empagliflozin may have an impact on urinary peptides associated with chronic kidney disease (CKD). In this post-hoc, exploratory analysis, we investigated urine samples obtained from 40 patients with uncomplicated type 1 diabetes (T1D) before and after treatment with empagliflozin for 8 weeks to for significant post-therapy changes in urinary peptides. We further assessed the association of these changes with CKD in an independent cohort, and with a previously established urinary proteomic panel, termed CKD273. 107 individual peptides significantly changed after treatment. The majority of the empagliflozin-induced changes were in the direction of “CKD absent” when compare to patients with CKD and controls. A classifier consisting of these 107 peptides scored significantly different in controls, in comparison to CKD patients. However, empagliflozin did not impact the CKD273 classifier. Our data indicate that empagliflozin induces multiple significant changes in the urinary proteomic markers such as mucin and clusterin. The relationship between empagliflozin-induced proteomic changes and clinical outcomes merits further investigation.

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.

Urinary Proteomics for Diagnosis and Monitoring of Diabetic Nephropathy

The last decade has seen a surge in publications describing novel biomarkers for early detection of diabetic nephropathy (DN), but as yet none have outperformed albuminuria in well-designed prospective studies. This is partially attributable to our incomplete understanding of the many complex interrelated mechanisms underlying DN development, a heterogeneous process unlikely to be captured by a single biomarker. Proteomics offers the advantage of simultaneously analysing the entire protein content of a biological sample, and the technique has gained attention as a potential tool for a more accurate diagnosis of disease at an earlier stage as well as a means by which to unravel the pathogenesis of complex diseases such as DN using an untargeted approach. This review will discuss the potential of proteomics as both a clinical and research tool, evaluating exploratory work in animal models as well as diagnostic potential in human subjects.

Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease

The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice.

Pro: urine proteomics as a liquid kidney biopsy: no more kidney punctures!

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.

Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes

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.

Body fluid peptide and protein signatures in diabetic kidney diseases

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.

Role of IGFBP7 in Diabetic Nephropathy: TGF-β1 Induces IGFBP7 via Smad2/4 in Human Renal Proximal Tubular Epithelial Cells

Tubular injury is one of the important determinants of progressive renal failure in diabetic nephropathy (DN), and TGF-β1 has been implicated in the pathogenesis of tubulointerstitial disease that characterizes proteinuric renal disease. The aim of this study was to identify novel therapeutic target molecules that play a role in the tubule damage of DN. We used an LC-MS/MS-based proteomic technique and human renal proximal epithelial cells (HRPTECs). Urine samples from Japanese patients with type 2 diabetes (n = 46) were used to quantify the candidate protein. Several proteins in HRPTECs in cultured media were observed to be driven by TGF-β1, one of which was 33-kDa IGFBP7, which is a member of IGFBP family. TGF-β1 up-regulated the expressions of IGFBP7 mRNA and protein in a dose- and time-dependent fashion via Smad2 and 4, but not MAPK pathways in HRPTECs. In addition, the knockdown of IGFBP7 restored the TGF-β1-induced epithelial to mesenchymal transition (EMT). In the immunohistochemical analysis, IGFBP7 was localized to the cytoplasm of tubular cells but not that of glomerular cells in diabetic kidney. Urinary IGFBP7 levels were significantly higher in the patients with macroalbuminuria and were correlated with age (r = 0.308, p = 0.037), eGFR (r = −0.376, p = 0.01), urinary β₂-microglobulin (r = 0.385, p = 0.008), and urinary N-acetyl-beta-D-glucosaminidase (NAG) (r = 0.502, p = 0.000). A multivariate regression analysis identified urinary NAG and age as determinants associated with urinary IGFBP7 levels. In conclusion, our data suggest that TGF-β1 enhances IGFBP7 via Smad2/4 pathways, and that IGFBP7 might be involved in the TGF-β1-induced tubular injury in DN.

Implementation of CE-MS-identified proteome-based biomarker panels in drug development and patient management

The recent advancements in clinical proteomics enabled identification of biomarker panels for a large range of diseases. A number of CE-MS-identified biomarker panels were verified and implemented in clinical studies. Despite multiple challenges, accumulating evidence supports the value and the need for proteome-based biomarker panels. In this perspective, we provide an overview of clinical studies indicating the added value of CE-MS biomarker panels over traditional diagnostics and monitoring methods. We outline apparent advantages of applying novel proteomic biomarker panels for disease diagnosis, prognosis, staging, drug development and patient management. Facing the plethora of benefits associated with the use of CE-MS biomarker panels, we envision their implementation into the medical practice in the near future.

Identification of urinary proteins potentially associated with diabetic kidney disease

Diabetic nephropathy (DN) is the most common cause of chronic kidney disease. Although several parameters are used to evaluate renal damage, in many instances, there is no pathological change until damage is already advanced. Mass spectrometry-based proteomics is a novel tool to identify newer diagnostic markers. To identify urinary proteins associated with renal complications in diabetes, we collected urine samples from 10 type 2 diabetes patients each with normoalbuminuria, micro- and macro-albuminuria and compared their urinary proteome with that of 10 healthy individuals. Urinary proteins were concentrated, depleted of albumin and five other abundant plasma proteins and in-gel trypsin digested after prefractionation on sodium dodecyl sulfate polyacrylamide gel electrophoresis. The peptides were analyzed using a nanoflow reverse phase liquid chromatography system coupled to linear trap quadrupole-Orbitrap mass spectrometer. We identified large number of proteins in each group, of which many were exclusively present in individual patient groups. A total of 53 proteins were common in all patients but were absent in the controls. The majority of the proteins were functionally binding, biologically involved in metabolic processes, and showed enrichment of alternative complement and blood coagulation pathways. In addition to identifying reported proteins such as α2-HS-glycoprotein and Vitamin D binding protein, we detected novel proteins such as CD59, extracellular matrix protein 1 (ECM1), factor H, and myoglobin in the urine of macroalbuminuria patients. ECM1 and factor H are known to influence mesangial cell proliferation, and CD59 causes microvascular damage by influencing membrane attack complex deposition, suggestive their biological relevance to DN. Thus, we have developed a proteome database where various proteins exclusively present in the patients may be further investigated for their role as stage-specific markers and possible therapeutic targets.

Differential urinary glycoproteome analysis of type 2 diabetic nephropathy using 2D-LC-MS/MS and iTRAQ quantification

Background

Diabetic nephropathy (DN) is the leading cause of chronic kidney failure and end-stage kidney disease. More accurate and non-invasive test for the diagnosis and monitoring the progression of DN is urgently needed for the better care of such patients.

Methods

In this study we utilized urinary glycoproteome to discover the differential proteins during the course of type 2 DN. The urinary glycoproteins from normal controls, normalbuminuira, microalbuminura, and macroalbuminuria patients were enriched by concanavalin A (ConA) and analyzed by 2DLC/MS/MS and isobaric tags for relative and absolute quantitation quantification.

Results

A total of 478 proteins were identified and 408 were annotated as N-linked glycoproteins. A total of 72, 107 and 123 differential proteins were identified in normalbuminuria, microalbuminuria and macroalbuminuria, respectively. By bioinformatics analysis, in normalbuminruia state, cell proliferation and cell movement were activated, which might reflect the compensatory phase during the disease development. In micro- and macro-albuminuria, cell death and apoptosis was activated, which might reflect the de-compensatory phase. Pathway analysis showed acute phase proteins, the member of high density lipoprotein and low density lipoprotein proteins were changed, indicating the role of the inflammatory response and lipid metabolism abnormality in the pathogenesis of DN. Six selected differential proteins were validated by Western Blot. Alpha-1-antitrypsin (SERPINA1) and Ceruloplasmin are the two markers with excellent area under curve values (0.929 and 1.000 respectively) to distinguish the microalbuminuria and normalbuminuria. For the first time, we found pro-epidermal growth factor and prolactin-inducible protein were decreased in macroalbuminuria stage, which might reflect the inhibition of cell viability and the activation of cell death in kidney.

Conclusions

Above data indicated that urinary glycoproteome could be useful to distinguish the differences in protein profiles in different stages in DN, which will help better individualized care of patients in DN.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0712-9) contains supplementary material, which is available to authorized users.

Proteomic urinary biomarker approach in renal disease: from discovery to implementation

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.

Proteomic biomarkers in kidney disease: issues in development and implementation

Proteomic biomarkers offer the hope of improving the management of patients with kidney diseases by enabling more accurate and earlier detection of renal pathology than is possible with currently available biomarkers, serum creatinine and urinary albumin. In addition, proteomic biomarkers could also be useful to define the most suitable therapeutic targets in a given patient or disease setting. This Review describes the current status of proteomic and protein biomarkers in the context of kidney diseases. The valuable lessons learned from early clinical studies of potential proteomic biomarkers in kidney disease are presented to give context to the newly identified biomarkers, which have potential for actual clinical implementation. This article also includes an overview of protein-based biomarker candidates that are undergoing development for use in nephrology, focusing on those with the greatest potential for clinical implementation. Relevant issues and problems associated with the discovery, validation and clinical application of proteomic biomarkers are discussed, along with suggestions for solutions that might help to guide the design of future proteomic studies. These improvements might remove some of the current obstacles to the utilization of proteomic biomarkers, with potentially beneficial results.

Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development

Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic-based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research.