Utility of the CKD273 peptide classifier in predicting chronic kidney disease progression

Nicotinamide N-Methyl Transferase as a Predictive Marker of Tubular Fibrosis in CKD

Purpose

Chronic kidney disease (CKD) progression is complex. There are not standardized methods for predicting the prognosis of CKD. Nicotinamide N-methyltransferase (NNMT) has been shown to be associated with renal fibrosis. This study aimed to validate NNMT as a prognostic biomarker of progressive CKD.

Patients and Methods

We explored the relationship between NNMT expression and CKD-related outcome variables using the NephroseqV5 and GEO databases. Additionally, a validation set of 37 CKD patients was enrolled to measure the correlation between NNMT expression levels and CKD outcomes. Furthermore, single-cell RNA sequencing data and the Human Protein Atlas were reanalyzed to investigate the expression specificity of NNMT in the kidney. Finally, to detect the status of NNMT expression with tubular fibrosis in vivo, we constructed a unilateral ureteral obstruction (UUO) mouse treated with an NNMT inhibitor.

Results

Analyzing the datasets showed that NNMT was expressed mainly in proximal tubule compartments. And patients with high NNMT expression levels had a significantly lower overall survival rate compared to those with low NNMT expression levels (P = 0.013). NNMT was independent of prognosis factors in the multivariate Cox regression model, and the AUCs for CKD progression at 1, 3, and 5 years were 0.849, 0.775, and 0.877, respectively. Pathway enrichment analysis indicated that NNMT regulates the biological processes of tubulointerstitial fibrosis (TIF). In the validation group, NNMT levels were significantly higher in the CKD group combined with interstitial fibrosis. In vivo, NNMT was a high expression in the UUO group, peaking at postoperative day 21. Treatment with an NNMT inhibitor improved renal tubular interstitial fibrosis, and expression levels of FN, α-SMA, VIM, and TGF-β1 were decreased compared with UUO (P < 0.05).

Conclusion

NNMT was expressed mainly in tubular renal compartments, and associated with CKD prognosis. It holds potential as a diagnostic biomarker for tubular fibrosis in CKD.

Circulating Plasma Biomarkers in Biopsy-Confirmed Kidney Disease

BACKGROUND AND OBJECTIVES

Biomarkers for noninvasive assessment of histopathology and prognosis are needed in patients with kidney disease.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Using a proteomics assay, we measured a multimarker panel of 225 circulating plasma proteins in a prospective cohort study of 549 individuals with biopsy-confirmed kidney diseases and semiquantitative assessment of histopathology. We tested the associations of each biomarker with histopathologic lesions and the risks of kidney disease progression (defined as ≥40% decline in eGFR or initiation of KRT) and death.

RESULTS

After multivariable adjustment and correction for multiple testing, 46 different proteins were associated with histopathologic lesions. The top-performing markers positively associated with acute tubular injury and interstitial fibrosis/tubular atrophy were kidney injury molecule-1 (KIM-1) and V-set and Ig domain-containing protein 2 (VSIG2), respectively. Thirty proteins were significantly associated with kidney disease progression, and 35 were significantly associated with death. The top-performing markers for kidney disease progression were placental growth factor (hazard ratio per doubling, 5.4; 95% confidence interval, 3.4 to 8.7) and BMP and activin membrane-bound inhibitor (hazard ratio, 3.0; 95% confidence interval, 2.1 to 4.2); the top-performing markers for death were TNF-related apoptosis-inducing ligand receptor-2 (hazard ratio, 2.9; 95% confidence interval, 2.0 to 4.0) and CUB domain-containing protein-1 (hazard ratio, 2.4; 95% confidence interval, 1.8 to 3.3).

CONCLUSION

We identified several plasma protein biomarkers associated with kidney disease histopathology and adverse clinical outcomes in individuals with a diverse set of kidney diseases.

PODCAST

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_12_28_CJN09380721.mp3.

Precision Nephrology Is a Non-Negligible State of Mind in Clinical Research: Remember the Past to Face the Future

CKD is a major public health problem. It is characterized by a multitude of risk factors that, when aggregated, can strongly modify outcome. While major risk factors, namely, albuminuria and low estimated glomerular filtration rate (eGFR) have been well analyzed, a large variability in disease progression still remains. This happens because (1) the weight of each risk factor varies between populations (general population or CKD cohort), countries, and single individuals and (2) response to nephroprotective drugs is so heterogeneous that a non-negligible part of patients maintains a high cardiorenal risk despite optimal treatment. Precision nephrology aims at individualizing cardiorenal prognosis and therapy. The purpose of this review is to focus on the risk stratification in different areas, such as clinical practice, population research, and interventional trials, and to describe the strategies used in observational or experimental studies to afford individual-level evidence. The future of precision nephrology is also addressed. Observational studies can in fact provide more adequate findings by collecting more information on risk factors and building risk prediction models that can be applied to each individual in a reliable fashion. Similarly, new clinical trial designs can reduce the individual variability in response to treatment and improve individual outcomes.

Multiplexed MRM-based protein quantification of putative prognostic biomarkers for chronic kidney disease progression in plasma

Current diagnostic measures for Chronic Kidney Disease (CKD) include detection of reduced estimated glomerular filtration rate (eGFR) and albuminuria, which have suboptimal accuracies in predicting disease progression. The disease complexity and heterogeneity underscore the need for multiplex quantification of different markers. The goal of this study was to determine the association of six previously reported CKD-associated plasma proteins [B2M (Beta-2-microglobulin), SERPINF1 (Pigment epithelium-derived factor), AMBP (Protein AMBP), LYZ (Lysozyme C), HBB (Hemoglobin subunit beta) and IGHA1 (Immunoglobulin heavy constant alpha 1)], as measured in a multiplex format, with kidney function, and outcome. Antibody-free, multiple reaction monitoring mass spectrometry (MRM) assays were developed, characterized for their analytical performance, and used for the analysis of 72 plasma samples from a patient cohort with longitudinal follow-up. The MRM significantly correlated (Rho = 0.5–0.9) with results from respective ELISA. Five proteins [AMBP, B2M, LYZ, HBB and SERPINF1] were significantly associated with eGFR, with the three former also associated with unfavorable outcome. The combination of these markers provided stronger associations with outcome (p < 0.0001) compared to individual markers. Collectively, our study describes a multiplex assay for absolute quantification and verification analysis of previously described putative CKD prognostic markers, laying the groundwork for further use in prospective validation studies.

Proteomic Analysis of Human Serum from Patients with Chronic Kidney Disease

Chronic kidney disease (CKD) is an important public health problem in the world. The aim of our research was to identify novel potential serum biomarkers of renal injury. ELISA assay showed that cytokines and chemokines IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12 (p70), IL-13, IL-15, IL-17, Eotaxin, FGFb, G-CSF, GM-CSF, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-1bb, RANTES, TNF-α and VEGF were significantly higher (R > 0.6, p value < 0.05) in the serum of patients with CKD compared to healthy subjects, and they were positively correlated with well-established markers (urea and creatinine). The multiple reaction monitoring (MRM) quantification method revealed that levels of HSP90B2, AAT, IGSF22, CUL5, PKCE, APOA4, APOE, APOA1, CCDC171, CCDC43, VIL1, Antigen KI-67, NKRF, APPBP2, CAPRI and most complement system proteins were increased in serum of CKD patients compared to the healthy group. Among complement system proteins, the C8G subunit was significantly decreased three-fold in patients with CKD. However, only AAT and HSP90B2 were positively correlated with well-established markers and, therefore, could be proposed as potential biomarkers for CKD.

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.

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.

Identification of serum metabolites associating with chronic kidney disease progression and anti-fibrotic effect of 5-methoxytryptophan

Early detection and accurate monitoring of chronic kidney disease (CKD) could improve care and retard progression to end-stage renal disease. Here, using untargeted metabolomics in 2155 participants including patients with stage 1–5 CKD and healthy controls, we identify five metabolites, including 5-methoxytryptophan (5-MTP), whose levels strongly correlate with clinical markers of kidney disease. 5-MTP levels decrease with progression of CKD, and in mouse kidneys after unilateral ureteral obstruction (UUO). Treatment with 5-MTP ameliorates renal interstitial fibrosis, inhibits IκB/NF-κB signaling, and enhances Keap1/Nrf2 signaling in mice with UUO or ischemia/reperfusion injury, as well as in cultured human kidney cells. Overexpression of tryptophan hydroxylase-1 (TPH-1), an enzyme involved in 5-MTP synthesis, reduces renal injury by attenuating renal inflammation and fibrosis, whereas TPH-1 deficiency exacerbates renal injury and fibrosis by activating NF-κB and inhibiting Nrf2 pathways. Together, our results suggest that TPH-1 may serve as a target in the treatment of CKD.

Accurate monitoring of chronic kidney disease (CKD) progression is essential for efficient disease management. Here Chen et al. identify five serum metabolites in patients with stage 1–5 CKD whose levels associate with disease progression, and find that 5-methoxytryptophan and its regulatory enzyme TPH-1 exert anti-fibrotic effects in mouse models of kidney injury.

Kidney disease risk factors associate with urine biomarkers concentrations in HIV-positive persons; a cross-sectional study

Background

HIV-positive persons bear an excess burden of chronic kidney disease (CKD); however, conventional methods to assess kidney health are insensitive and non-specific for detecting early kidney injury. Urinary biomarkers can detect early kidney injury, and may help mitigate the risk of overt CKD.

Methods

Cross-sectional study of HIV-positive persons in the Multicenter AIDS Cohort Study and the Women’s Interagency HIV Study. We measured levels of 14 biomarkers, capturing multiple dimensions of kidney injury. We then evaluated associations of known CKD risk factors with urine biomarkers using separate multivariable adjusted models for each biomarker.

Results

Of the 198 participants, one third were on HAART and virally suppressed. The vast majority (95%) had preserved kidney function as assessed by serum creatinine, with a median eGFR of 103 ml/min/1.73 m² (interquartile range (IQR): 88, 116). In our multivariable analyses, the associations of each CKD risk factor with urinary biomarker levels varied in magnitude. For example, HIV viral load was predominantly associated with elevations in interleukin(IL)-18, and albuminuria, while higher CD4 levels were associated with lower monocyte chemoattractant protein-1 (MCP-1) and β2-microglobulin. In contrast, older age was significantly associated with elevations in α1-microglobulin, kidney injury marker-1, clusterin, MCP-1, and chitinase-3-like protein-1 levels, as well as lower epidermal growth factor, and uromodulin levels.

Conclusions

Among HIV-positive persons, CKD risk factors are associated with unique and heterogeneous patterns of changes in urine biomarkers levels. Additional work is needed to develop parsimonious algorithms that integrate multiple biomarkers and clinical data to discern the risk of overt CKD and its progression.

Electronic supplementary material

The online version of this article (10.1186/s12882-018-1192-y) contains supplementary material, which is available to authorized users.

Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes

AIMS/HYPOTHESIS

As part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts.

METHODS

We used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case-control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30-75 ml min^-1 [1.73 m]^-2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function.

RESULTS

Twelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β₂-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical covariates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the curve in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multiplatform panels previously identified, consistently improved prediction further across all validation sets.

CONCLUSIONS/INTERPRETATION

Serum KIM-1 and B2M independently improve prediction of renal decline from an eGFR of 30-75 ml min^-1 [1.73 m]^-2 in type 2 diabetes beyond clinical factors and prior eGFR and are robust to varying sample storage conditions. Larger panels of biomarkers did not improve prediction beyond these two biomarkers.

Proteomic approaches in kidney disease biomarker discovery

Biomarkers have the potential to greatly facilitate diagnosis and treatment of patients with various forms of kidney disease. State-of-the-art mass spectrometry-based methods possess the capability, on a proteome scale and in an unbiased manner, to detect alterations in protein abundances and/or posttranslational modifications in plasma, urine, or tissue. Such approaches can provide a large, unbiased database to facilitate identification of potential biomarkers. In the diagnosis of kidney diseases, urine is usually a more favorable specimen than plasma and kidney tissue due to its noninvasive collection and simplicity of processing. However, whether analysis of proteins in urine faithfully reflects their changes in the kidney tissue remains unclear. The use of proteomics to analyze kidney tissue samples collected during late-stage kidney diseases has also recently gathered pace. The goal of this minireview is to provide an overview of the proteomic technologies currently applied to studies of kidney and their limitations, present existing kidney and urine proteome databases, and highlight a few applications of such approaches in kidney disease biomarker discovery.

Circulating proteins as predictors of cardiovascular mortality in end-stage renal disease

Introduction

Proteomic profiling of end-stage renal disease (ESRD) patients could lead to improved risk prediction and novel insights into cardiovascular disease mechanisms. Plasma levels of 92 cardiovascular disease-associated proteins were assessed by proximity extension assay (Proseek Multiplex CVD-1, Olink Bioscience, Uppsala, Sweden) in a discovery cohort of dialysis patients, the Mapping of Inflammatory Markers in Chronic Kidney disease cohort [MIMICK; n = 183, 55% women, mean age 63 years, 46 cardiovascular deaths during follow-up (mean 43 months)]. Significant results were replicated in the incident and prevalent hemodialysis arm of the Salford Kidney Study [SKS dialysis study, n = 186, 73% women, mean age 62 years, 45 cardiovascular deaths during follow-up (mean 12 months)], and in the CKD5-LD-RTxcohort with assessments of coronary artery calcium (CAC)-score by cardiac computed tomography (n = 89, 37% women, mean age 46 years).

Results

In age and sex-adjusted Cox regression in MIMICK, 11 plasma proteins were nominally associated with cardiovascular mortality (in order of significance: Kidney injury molecule-1 (KIM-1), Matrix metalloproteinase-7, Tumour necrosis factor receptor 2, Interleukin-6, Matrix metalloproteinase-1, Brain-natriuretic peptide, ST2 protein, Hepatocyte growth factor, TNF-related apoptosis inducing ligand receptor-2, Spondin-1, and Fibroblast growth factor 25). Only plasma KIM-1 was associated with cardiovascular mortality after correction for multiple testing, but also after adjustment for dialysis vintage, cardiovascular risk factors and inflammation (hazard ratio) per standard deviation (SD) increase 1.84, 95% CI 1.26–2.69, p = 0.002. Addition of KIM-1, or nine of the most informative proteins to an established risk-score (modified AROii CVM-score) improved discrimination of cardiovascular mortality risk from C = 0.777 to C = 0.799 and C = 0.823, respectively. In the SKS dialysis study, KIM-1 predicted cardiovascular mortality in age and sex adjusted models (hazard ratio per SD increase 1.45, 95% CI 1.03–2.05, p = 0.034) and higher KIM-1 was associated with higher CACscores in the CKD5-LD-RTx-cohort.

Conclusions

Our proteomics approach identified plasma KIM-1 as a risk marker for cardiovascular mortality and coronary artery calcification in three independent ESRD-cohorts. The improved risk prediction for cardiovascular mortality by plasma proteomics merit further studies.

Electronic supplementary material

The online version of this article (10.1007/s40620-018-0556-5) 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.

Association of kidney fibrosis with urinary peptides: a path towards non-invasive liquid biopsies?

Chronic kidney disease (CKD) is a prevalent cause of morbidity and mortality worldwide. A hallmark of CKD progression is renal fibrosis characterized by excessive accumulation of extracellular matrix (ECM) proteins. In this study, we aimed to investigate the correlation of the urinary proteome classifier CKD273 and individual urinary peptides with the degree of fibrosis. In total, 42 kidney biopsies and urine samples were examined. The percentage of fibrosis per total tissue area was assessed in Masson trichrome stained kidney tissues. The urinary proteome was analysed by capillary electrophoresis coupled to mass spectrometry. CKD273 displayed a significant and positive correlation with the degree of fibrosis (Rho = 0.430, P = 0.0044), while the routinely used parameters (glomerular filtration rate, urine albumin-to-creatinine ratio and urine protein-to-creatinine ratio) did not (Rho = -0.222; -0.137; -0.070 and P = 0.16; 0.39; 0.66, respectively). We identified seven fibrosis-associated peptides displaying a significant and negative correlation with the degree of fibrosis. All peptides were collagen fragments, suggesting that these may be causally related to the observed accumulation of ECM in the kidneys. CKD273 and specific peptides are significantly associated with kidney fibrosis; such an association could not be detected by other biomarkers for CKD. These non-invasive fibrosis-related biomarkers can potentially be implemented in future trials.

Establishing the presence or absence of chronic kidney disease: Uses and limitations of formulas estimating the glomerular filtration rate

The development of formulas estimating glomerular filtration rate (eGFR) from serum creatinine and cystatin C and accounting for certain variables affecting the production rate of these biomarkers, including ethnicity, gender and age, has led to the current scheme of diagnosing and staging chronic kidney disease (CKD), which is based on eGFR values and albuminuria. This scheme has been applied extensively in various populations and has led to the current estimates of prevalence of CKD. In addition, this scheme is applied in clinical studies evaluating the risks of CKD and the efficacy of various interventions directed towards improving its course. Disagreements between creatinine-based and cystatin-based eGFR values and between eGFR values and measured GFR have been reported in various cohorts. These disagreements are the consequence of variations in the rate of production and in factors, other than GFR, affecting the rate of removal of creatinine and cystatin C. The disagreements create limitations for all eGFR formulas developed so far. The main limitations are low sensitivity in detecting early CKD in several subjects, e.g., those with hyperfiltration, and poor prediction of the course of CKD. Research efforts in CKD are currently directed towards identification of biomarkers that are better indices of GFR than the current biomarkers and, particularly, biomarkers of early renal tissue injury.

A Urinary Fragment of Mucin-1 Subunit α Is a Novel Biomarker Associated With Renal Dysfunction in the General Population

INTRODUCTION

Sequencing peptides included in the urinary proteome identifies the parent proteins and may reveal mechanisms underlying the pathophysiology of chronic kidney disease.

METHODS

In 805 randomly recruited Flemish individuals (50.8% women; mean age, 51.1 years), we determined the estimated glomerular filtration rate (eGFR) from serum creatinine using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. We categorized eGFR according to the National Kidney Foundation Kidney Disease Outcomes Quality Initiative guideline. We analyzed 74 sequenced urinary peptides with a detectable signal in more than 95% of participants. Follow-up measurements of eGFR were available in 597 participants.

RESULTS

In multivariable analyses, baseline eGFR decreased (P ≤ 0.022) with urinary fragments of mucin-1 (standardized association size expressed in ml/min/1.73 m², -4.48), collagen III (-2.84), and fibrinogen (-1.70) and was bi-directionally associated (P ≤ 0.0006) with 2 urinary collagen I fragments (+2.28 and -3.20). The eGFR changes over 5 years (follow-up minus baseline) resulted in consistent estimates (P ≤ 0.025) for mucin-1 (-1.85), collagen (-1.37 to 1.43) and fibrinogen (-1.45) fragments. Relative risk of having or progressing to eGFR <60 ml/min/1.73 m² was associated with mucin-1. Partial least-squares analysis confirmed mucin-1 as the strongest urinary marker associated with decreased eGFR, with a score of 2.47 compared with 1.80 for a collagen I fragment as the next contender. Mucin-1 predicted eGFR decline to <60 ml/min/1.73 m² over and above microalbuminuria (P = 0.011) and retained borderline significance (P = 0.05) when baseline eGFR was accounted for.

DISCUSSION

In the general population, mucin-1 subunit α, an extracellular protein that is shed from renal tubular epithelium, is a novel biomarker associated with renal dysfunction.

Use of Proteomics To Investigate Kidney Function Decline over 5 Years

BACKGROUND AND OBJECTIVES

Using a discovery/replication approach, we investigated associations between a multiplex panel of 80 circulating proteins associated with cardiovascular pathology or inflammation, and eGFR decline per year and CKD incidence.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We used two cohorts, the Prospective Investigation of the Vasculature in Uppsala Seniors Study (PIVUS; n=687, mean age of 70 years, 51% women) and the Uppsala Longitudinal Study of Adult Men (ULSAM; n=360 men, mean age of 78 years), with 5-year follow-up data on eGFR. There were 231 and 206 incident cases of CKD during follow-up in the PIVUS and ULSAM studies, respectively. Proteomic profiling of 80 proteins was assessed by a multiplex assay (proximity extension assay). The assay uses two antibodies for each protein and a PCR step to achieve a high-specific binding and the possibility to measure multiple proteins in parallel, but gives no absolute concentrations.

RESULTS

In the discovery cohort from the PIVUS Study, 28 plasma proteins were significantly associated with eGFR decline per year, taking into account the multiple testing. Twenty of these proteins were significantly associated with eGFR decline per year in the replication cohort from the ULSAM Study after adjustment for age, sex, cardiovascular risk factors, medications, and urinary albumin-to-creatinine ratio (in order of significance: TNF-related apoptosis-inducing ligand receptor 2*, CD40L receptor, TNF receptor 1*, placenta growth factor*, thrombomodulin*, urokinase plasminogen activator surface receptor*, growth/differentiation factor 15*, macrophage colony-stimulating factor 1, fatty acid-binding protein*, cathepsin D, resistin, kallikrein 11*, C-C motif chemokine 3, proteinase-activated receptor 1*, cathepsin L, chitinase 3-like protein 1, TNF receptor 2*, fibroblast growth factor 23*, monocyte chemotactic protein 1, and kallikrein 6). Moreover, 11 of the proteins predicted CKD incidence (marked with * above). No protein consistently predicted eGFR decline per year independently of baseline eGFR in both cohorts.

CONCLUSIONS

Several circulating proteins involved in phosphate homeostasis, inflammation, apoptosis, extracellular matrix remodeling, angiogenesis, and endothelial dysfunction were associated with worsening kidney function. Multiplex proteomics appears to be a promising way of discovering novel aspects of kidney disease pathology.

Clinical proteomics in kidney disease as an exponential technology: heading towards the disruptive phase

Exponential technologies double in power or processing speed every year, whereas their cost halves. Deception and disruption are two key stages in the development of exponential technologies. Deception occurs when, after initial introduction, technologies are dismissed as irrelevant, while they continue to progress, perhaps not as fast or with so many immediate practical applications as initially thought. Twenty years after the first publications, clinical proteomics is still not available in most hospitals and some clinicians have felt deception at unfulfilled promises. However, there are indications that clinical proteomics may be entering the disruptive phase, where, once refined, technologies disrupt established industries or procedures. In this regard, recent manuscripts in CKJ illustrate how proteomics is entering the clinical realm, with applications ranging from the identification of amyloid proteins in the pathology lab, to a new generation of urinary biomarkers for chronic kidney disease (CKD) assessment and outcome prediction. Indeed, one such panel of urinary peptidomics biomarkers, CKD273, recently received a Food and Drug Administration letter of support, the first ever in the CKD field. In addition, a must-read resource providing information on kidney disease-related proteomics and systems biology databases and how to access and use them in clinical decision-making was also recently published in CKJ.

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.

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.

Metabolic biomarkers for chronic kidney disease

Chronic kidney disease (CKD) is an increasingly recognized burden for patients and health care systems with high (and growing) global incidence and prevalence, significant mortality, and disproportionately high treatment costs. Yet, the available diagnostic tools are either impractical in clinical routine or have serious shortcomings impeding a well-informed disease management although optimized treatment strategies with proven benefits for the patients have become available. Advances in bioanalytical technologies have facilitated studies that identified genomic, proteomic, and metabolic biomarker candidates, and confirmed some of them in independent cohorts. This review summarizes the CKD-related markers discovered so far, and focuses on compounds and pathways, for which there is quantitative data, substantiating evidence from translational research, and a mechanistic understanding of the processes involved. Also, multiparametric marker panels have been suggested that showed promising diagnostic and prognostic performance in initial analyses although the data basis from prospective trials is very limited. Large-scale studies, however, are underway and will provide the information for validating a set of parameters and discarding others. Finally, the path from clinical research to a routine application is discussed, focusing on potential obstacles such as the use of mass spectrometry, and the feasibility of obtaining regulatory approval for targeted metabolomics assays.