Accuracy of glomerular filtration rate estimation using creatinine and cystatin C for identifying and monitoring moderate chronic kidney disease: the eGFR-C study

Background

Estimation of glomerular filtration rate using equations based on creatinine is widely used to manage chronic kidney disease. In the UK, the Chronic Kidney Disease Epidemiology Collaboration creatinine equation is recommended. Other published equations using cystatin C, an alternative marker of kidney function, have not gained widespread clinical acceptance. Given higher cost of cystatin C, its clinical utility should be validated before widespread introduction into the NHS.

Objectives

Primary objectives were to: (1) compare accuracy of glomerular filtration rate equations at baseline and longitudinally in people with stage 3 chronic kidney disease, and test whether accuracy is affected by ethnicity, diabetes, albuminuria and other characteristics; (2) establish the reference change value for significant glomerular filtration rate changes; (3) model disease progression; and (4) explore comparative cost-effectiveness of kidney disease monitoring strategies.

Design

A longitudinal, prospective study was designed to: (1) assess accuracy of glomerular filtration rate equations at baseline (n = 1167) and their ability to detect change over 3 years (n = 875); (2) model disease progression predictors in 278 individuals who received additional measurements; (3) quantify glomerular filtration rate variability components (n = 20); and (4) develop a measurement model analysis to compare different monitoring strategy costs (n = 875).

Setting

Primary, secondary and tertiary care.

Participants

Adults (≥ 18 years) with stage 3 chronic kidney disease.

Interventions

Estimated glomerular filtration rate using the Chronic Kidney Disease Epidemiology Collaboration and Modification of Diet in Renal Disease equations.

Main outcome measures

Measured glomerular filtration rate was the reference against which estimating equations were compared with accuracy being expressed as P30 (percentage of values within 30% of reference) and progression (variously defined) studied as sensitivity/specificity. A regression model of disease progression was developed and differences for risk factors estimated. Biological variation components were measured and the reference change value calculated. Comparative costs of monitoring with different estimating equations modelled over 10 years were calculated.

Results

Accuracy (P30) of all equations was ≥ 89.5%: the combined creatinine-cystatin equation (94.9%) was superior (p < 0.001) to other equations. Within each equation, no differences in P30 were seen across categories of age, gender, diabetes, albuminuria, body mass index, kidney function level and ethnicity. All equations showed poor (< 63%) sensitivity for detecting patients showing kidney function decline crossing clinically significant thresholds (e.g. a 25% decline in function). Consequently, the additional cost of monitoring kidney function annually using a cystatin C-based equation could not be justified (incremental cost per patient over 10 years = £43.32). Modelling data showed association between higher albuminuria and faster decline in measured and creatinine-estimated glomerular filtration rate. Reference change values for measured glomerular filtration rate (%, positive/negative) were 21.5/-17.7, with lower reference change values for estimated glomerular filtration rate.

Limitations

Recruitment of people from South Asian and African-Caribbean backgrounds was below the study target.

Future work

Prospective studies of the value of cystatin C as a risk marker in chronic kidney disease should be undertaken.

Conclusions

Inclusion of cystatin C in glomerular filtration rate-estimating equations marginally improved accuracy but not detection of disease progression. Our data do not support cystatin C use for monitoring of glomerular filtration rate in stage 3 chronic kidney disease.

Trial registration

This trial is registered as ISRCTN42955626.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 11/103/01) and is published in full in Health Technology Assessment; Vol. 28, No. 35. See the NIHR Funding and Awards website for further award information.

A new era in the science and care of kidney diseases

Notable progress in basic, translational and clinical nephrology research has been made over the past five decades. Nonetheless, many challenges remain, including obstacles to the early detection of kidney disease, disparities in access to care and variability in responses to existing and emerging therapies. Innovations in drug development, research technologies, tissue engineering and regenerative medicine have the potential to improve patient outcomes. Exciting prospects include the availability of new drugs to slow or halt the progression of chronic kidney disease, the development of bioartificial kidneys that mimic healthy kidney functions, and tissue engineering techniques that could enable transplantable kidneys to be created from the cells of the recipient, removing the risk of rejection. Cell and gene therapies have the potential to be applied for kidney tissue regeneration and repair. In addition, about 30% of kidney disease cases are monogenic and could potentially be treated using these genetic medicine approaches. Systemic diseases that involve the kidney, such as diabetes mellitus and hypertension, might also be amenable to these treatments. Continued investment, communication, collaboration and translation of innovations are crucial to realize their full potential. In addition, increasing sophistication in exploring large datasets, implementation science, and qualitative methodologies will improve the ability to deliver transformational kidney health strategies.

Assessment and Risk Prediction of Chronic Kidney Disease and Kidney Fibrosis Using Non-Invasive Biomarkers

Effective management of chronic kidney disease (CKD), a major health problem worldwide, requires accurate and timely diagnosis, prognosis of progression, assessment of therapeutic efficacy, and, ideally, prediction of drug response. Multiple biomarkers and algorithms for evaluating specific aspects of CKD have been proposed in the literature, many of which are based on a small number of samples. Based on the evidence presented in relevant studies, a comprehensive overview of the different biomarkers applicable for clinical implementation is lacking. This review aims to compile information on the non-invasive diagnostic, prognostic, and predictive biomarkers currently available for the management of CKD and provide guidance on the application of these biomarkers. We specifically focus on biomarkers that have demonstrated added value in prospective studies or those based on prospectively collected samples including at least 100 subjects. Published data demonstrate that several valid non-invasive biomarkers of potential value in the management of CKD are currently available.

Cost-effectiveness of screening for chronic kidney disease in the general adult population: a systematic review

Introduction

Chronic kidney disease (CKD) is a significant public health problem, with rising incidence and prevalence worldwide, and is associated with increased morbidity and mortality. Early identification and treatment of CKD can slow its progression and prevent complications, but it is not clear whether CKD screening is cost-effective. The aim of this study is to conduct a systematic review of the cost-effectiveness of CKD screening strategies in general adult populations worldwide, and to identify factors, settings and drivers of cost-effectiveness in CKD screening.

Methods

Studies examining the cost-effectiveness of CKD screening in the general adult population were identified by systematic literature search on electronic databases (MEDLINE OVID, Embase, Cochrane Library and Web of Science) for peer-reviewed publications, hand-searched reference lists and grey literature of relevant sites, focusing on the following themes: (i) CKD, (ii) screening and (iii) cost-effectiveness. Studies comprising health economic evaluations performed for CKD screening strategies, compared with no CKD screening or usual-care strategy in adult individuals, were included. Study characteristics, model assumptions and CKD screening strategies of selected studies were identified. The primary outcome of interest is the incremental cost-effectiveness ratio (ICER) of CKD screening, in cost per quality-adjusted life year (QALY) and life-year gained (LYG), expressed in 2022 US dollars equivalent.

Results

Twenty-one studies were identified, examining CKD screening in general and targeted populations. The cost-effectiveness of screening for CKD was found to vary widely across different studies, with ICERs ranging from $113 to $430 595, with a median of $26 662 per QALY and from $6516 to $38 372, with a median of $29 112 per LYG. Based on the pre-defined cost-effectiveness threshold of $50 000 per QALY, the majority of the studies found CKD screening to be cost-effective. CKD screening was especially cost-effective in those with diabetes ($113 to $42 359, with a median of $27 471 per QALY) and ethnic groups identified to be higher risk of CKD development or progression ($23 902 per QALY in African American adults and $21 285 per QALY in Canadian indigenous adults), as indicated by a lower ICER. Additionally, the cost-effectiveness of CKD screening improved if it was performed in older adults, populations with higher CKD risk scores, or when setting a higher albuminuria detection threshold or increasing the interval between screening. In contrast, CKD screening was not cost-effective in populations without diabetes and hypertension (ICERs range from $117 769 to $1792 142, with a median of $202 761 per QALY). Treatment effectiveness, prevalence of CKD, cost of CKD treatment and discount rate were identified to be the most common influential drivers of the ICERs.

Conclusions

Screening for CKD is especially cost-effective in patients with diabetes and high-risk ethnic groups, but not in populations without diabetes and hypertension. Increasing the age of screening, screening interval or albuminuria detection threshold, or selection of population based on CKD risk scores, may increase cost-effectiveness of CKD screening, while treatment effectiveness, prevalence of CKD, cost of CKD treatment and discount rate were influential drivers of the cost-effectiveness.

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.

Cardiovascular risk and kidney function profiling using conventional and novel biomarkers in young adults: the African-PREDICT study

BACKGROUND

Low- and middle-income countries experience an increasing burden of chronic kidney disease. Cardiovascular risk factors, including advancing age, may contribute to this phenomenon. We (i) profiled cardiovascular risk factors and different biomarkers of subclinical kidney function and (ii) investigated the relationship between these variables.

METHODS

We cross-sectionally analysed 956 apparently healthy adults between 20 and 30 years of age. Cardiovascular risk factors such as high adiposity, blood pressure, glucose levels, adverse lipid profiles and lifestyle factors were measured. Various biomarkers were used to assess subclinical kidney function, including estimated glomerular filtration rate (eGFR), urinary albumin, uromodulin and the CKD273 urinary proteomics classifier. These biomarkers were used to divide the total population into quartiles to compare extremes (25th percentiles) on the normal kidney function continuum. The lower 25th percentiles of eGFR and uromodulin and the upper 25th percentiles of urinary albumin and the CKD273 classifier represented the more unfavourable kidney function groups.

RESULTS

In the lower 25th percentiles of eGFR and uromodulin and the upper 25th percentile of the CKD273 classifier, more adverse cardiovascular profiles were observed. In multi-variable adjusted regression analyses performed in the total group, eGFR associated negatively with HDL-C (β= -0.44; p < 0.001) and GGT (β= -0.24; p < 0.001), while the CKD273 classifier associated positively with age and these same risk factors (age: β = 0.10; p = 0.021, HDL-C: β = 0.23; p < 0.001, GGT: β = 0.14; p = 0.002).

CONCLUSION

Age, lifestyle and health measures impact kidney health even in the third decade.

Decision-analytic modeling for early health technology assessment of medical devices - a scoping review

Objective

The goal of this review was to identify decision-analytic modeling studies in early health technology assessments (HTA) of high-risk medical devices, published over the last three years, and to provide a systematic overview of model purposes and characteristics. Additionally, the aim was to describe recent developments in modeling techniques.

Methods

For this scoping review, we performed a systematic literature search in PubMed and Embase including studies published in English or German. The search code consisted of terms describing early health technology assessment and terms for decision-analytic models. In abstract and full-text screening, studies were excluded that were not modeling studies for a high-risk medical device or an in-vitro diagnostic test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to report on the search and exclusion of studies. For all included studies, study purpose, framework and model characteristics were extracted and reported in systematic evidence tables and a narrative summary.

Results

Out of 206 identified studies, 19 studies were included in the review. Studies were either conducted for hypothetical devices or for existing devices after they were already available on the market. No study extrapolated technical data from early development stages to estimate potential value of devices in development. All studies except one included cost as an outcome. Two studies were budget impact analyses. Most studies aimed at adoption and reimbursement decisions. The majority of studies were on in-vitro diagnostic tests for personalized and targeted medicine. A timed automata model, to our knowledge a model type new to HTA, was tested by one study. It describes the agents in a clinical pathway in separate models and, by allowing for interaction between the models, can reflect complex individual clinical pathways and dynamic system interactions. Not all sources of uncertainty for in-vitro tests were explicitly modeled. Elicitation of expert knowledge and judgement was used for substitution of missing empirical data. Analysis of uncertainty was the most valuable strength of decision-analytic models in early HTA, but no model applied sensitivity analysis to optimize the test positivity cutoff with regard to the benefit-harm balance or cost-effectiveness. Value-of-information analysis was rarely performed. No information was found on the use of causal inference methods for estimation of effect parameters from observational data.

Conclusion

Our review provides an overview of the purposes and model characteristics of nineteen recent early evaluation studies on medical devices. The review shows the growing importance of personalized interventions and confirms previously published recommendations for careful modeling of uncertainties surrounding diagnostic devices and for increased use of value-of-information analysis. Timed automata may be a model type worth exploring further in HTA. In addition, we recommend to extend the application of sensitivity analysis to optimize positivity criteria for in-vitro tests with regard to benefit-harm or cost-effectiveness. We emphasize the importance of causal inference methods when estimating effect parameters from observational data.

Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts

INTRODUCTION

As novel therapies for chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) become available, their long-term benefits should be evaluated using CKD progression models. Existing models offer different modeling approaches that could be reused, but it may be challenging for modelers to assess commonalities and differences between the many available models. Additionally, the data and underlying population characteristics informing model parameters may not always be evident. Therefore, this study reviewed and summarized existing modeling approaches and data sources for CKD in T2DM, as a reference for future model development.

METHODS

This systematic literature review included computer simulation models of CKD in T2DM populations. Searches were implemented in PubMed (including MEDLINE), Embase, and the Cochrane Library, up to October 2021. Models were classified as cohort state-transition models (cSTM) or individual patient simulation (IPS) models. Information was extracted on modeled kidney disease states, risk equations for CKD, data sources, and baseline characteristics of derivation cohorts in primary data sources.

RESULTS

The review identified 49 models (21 IPS, 28 cSTM). A five-state structure was standard among state-transition models, comprising one kidney disease-free state, three kidney disease states [frequently including albuminuria and end-stage kidney disease (ESKD)], and one death state. Five models captured CKD regression and three included cardiovascular disease (CVD). Risk equations most commonly predicted albuminuria and ESKD incidence, while the most predicted CKD sequelae were mortality and CVD. Most data sources were well-established registries, cohort studies, and clinical trials often initiated decades ago in predominantly White populations in high-income countries. Some recent models were developed from country-specific data, particularly for Asian countries, or from clinical outcomes trials.

CONCLUSION

Modeling CKD in T2DM is an active research area, with a trend towards IPS models developed from non-Western data and single data sources, primarily recent outcomes trials of novel renoprotective treatments.

Quantitative Mass Spectrometry Normalization in Urine Biomarker Analysis in Nephrotic Syndrome

Chronic kidney disease (CKD) affects 30 million adults, costs ~$79 billion dollars (2016) in Medicare expenditures, and is the ninth leading cause of death in the United States. The disease is silent or undiagnosed in almost half of people with severely reduced kidney function. Urine provides an ideal biofluid that is accessible to high-sensitivity mass spectrometry-based proteomic interrogation and is an indicator of renal homeostasis. While the accurate and precise diagnosis and better disease management of CKD can be aided using urine biomarkers, their discovery in excessive protein or nephrotic urine samples can present challenges. In this work we present a mass spectrometry-based method utilizing multiplex tandem mass tag (TMT) quantification and improved protein quantification using reporter ion normalization to urinary creatinine to analyze urinary proteins from patients with a form of nephrotic syndrome (FSGS). A comparative analysis was performed for urine from patients in remission versus active disease flare. Two-dimensional LC-MS/MS TMT quantitative analysis identified over 1058 urine proteins, 580 proteins with 2 peptides or greater and quantifiable. Normalization of TMT abundance values to creatinine per ml of urine concentrated reduced variability in 2D-TMT-LC-MS/MS experiments. Univariate and multivariate analyses showed that 27 proteins were significantly increased in proteinuric disease flare. Hierarchical heatmap clustering showed that SERPINA1 and ORM1 were >1.5 fold increased in active disease versus remission urine samples. ELISA validation of SERPINA1 and ORM1 abundance agreed with our quantitative TMT proteomics analysis. These findings provide support for the utility of this method for identification of novel diagnostic markers of CKD and identify SERPINA1 and ORM1 as promising candidate diagnostic markers for FSGS.

The novel proteomic signature for cardiac allograft vasculopathy

AIMS

Cardiac allograft vasculopathy (CAV) is the major long-term complication after heart transplantation, leading to mortality and re-transplantation. As available non-invasive biomarkers are scarce for CAV screening, we aimed to identify a proteomic signature for CAV.

METHODS AND RESULTS

We measured urinary proteome by capillary electrophoresis coupled with mass spectrometry in 217 heart transplantation recipients (mean age: 55.0 ± 14.4 years; women: 23.5%), including 76 (35.0%) patients with CAV diagnosed by coronary angiography. We randomly and evenly grouped participants into the derivation cohort (n = 108, mean age: 56.4 ± 13.8 years; women: 22.2%; CAV: n = 38) and the validation cohort (n = 109, mean age: 56.4 ± 13.8 years; women: 24.8%, CAV: n = 38), stratified by CAV. Using the decision tree-based machine learning methods (extreme gradient boost), we constructed a proteomic signature for CAV discrimination in the derivation cohort and verified its performance in the validation cohort. The proteomic signature that consisted of 27 peptides yielded areas under the curve of 0.83 [95% confidence interval (CI): 0.75-0.91, P < 0.001] and 0.71 (95% CI: 0.60-0.81, P = 0.001) for CAV discrimination in the derivation and validation cohort, respectively. With the optimized threshold of 0.484, the sensitivity, specificity, and accuracy for CAV differentiation in the validation cohort were 68.4%, 73.2%, and 71.6%, respectively. With adjustment of potential clinical confounders, the signature was significantly associated with CAV [adjusted odds ratio: 1.31 (95% CI: 1.07-1.64) for per 0.1% increment in the predicted probability, P = 0.012]. Diagnostic accuracy significantly improved by adding the signature to the logistic model that already included multiple clinical risk factors, suggested by the integrated discrimination improvement of 9.1% (95% CI: 2.5-15.3, P = 0.005) and net reclassification improvement of 83.3% (95% CI: 46.7-119.5, P < 0.001). Of the 27 peptides, the majority were the fragments of collagen I (44.4%), collagen III (18.5%), collagen II (3.7%), collagen XI (3.7%), mucin-1 (3.7%), xylosyltransferase 1 (3.7%), and protocadherin-12 (3.7%). Pathway analysis performed in Reactome Pathway Database revealed that the multiple pathways involved by the signature were related to the pathogenesis of CAV, such as collagen turnover, platelet aggregation and coagulation, cell adhesion, and motility.

CONCLUSIONS

This pilot study identified and validated a urinary proteomic signature that provided a potential approach for the surveillance of CAV. These proteins might provide insights into CAV pathological processes and call for further investigation into personalized treatment targets.

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.

Serum integrative omics reveals the landscape of human diabetic kidney disease

Objective

Diabetic kidney disease (DKD) is the most common microvascular complication of type 2 diabetes mellitus (2-DM). Currently, urine and kidney biopsy specimens are the major clinical resources for DKD diagnosis. Our study proposes to evaluate the diagnostic value of blood in monitoring the onset of DKD and distinguishing its status in the clinic.

Methods

This study recruited 1,513 participants including healthy adults and patients diagnosed with 2-DM, early-stage DKD (DKD-E), and advanced-stage DKD (DKD-A) from 4 independent medical centers. One discovery and four testing cohorts were established. Sera were collected and subjected to training proteomics and large-scale metabolomics.

Results

Deep profiling of serum proteomes and metabolomes revealed several insights. First, the training proteomics revealed that the combination of α₂-macroglobulin, cathepsin D, and CD324 could serve as a surrogate protein biomarker for monitoring DKD progression. Second, metabolomics demonstrated that galactose metabolism and glycerolipid metabolism are the major disturbed metabolic pathways in DKD, and serum metabolite glycerol-3-galactoside could be used as an independent marker to predict DKD. Third, integrating proteomics and metabolomics increased the diagnostic and predictive stability and accuracy for distinguishing DKD status.

Conclusions

Serum integrative omics provide stable and accurate biomarkers for early warning and diagnosis of DKD. Our study provides a rich and open-access data resource for optimizing DKD management.

•

Serum proteomics and metabolomics are novel, noninvasive approaches to detect DKD.

•

Integrated serum omics enhances the diagnostic stability and accuracy of DKD diagnoses.

•

Galactose/glycerolipid metabolism is the major disturbed metabolic pathway in DKD.

•

Serum metabolite glycerol-3-galactoside is an independent predictive marker of DKD.

A Novel Urinary Proteomics Classifier for Non-Invasive Evaluation of Interstitial Fibrosis and Tubular Atrophy in Chronic Kidney Disease

Non-invasive urinary peptide biomarkers are able to detect and predict chronic kidney disease (CKD). Moreover, specific urinary peptides enable discrimination of different CKD etiologies and offer an interesting alternative to invasive kidney biopsy, which cannot always be performed. The aim of this study was to define a urinary peptide classifier using mass spectrometry technology to predict the degree of renal interstitial fibrosis and tubular atrophy (IFTA) in CKD patients. The urinary peptide profiles of 435 patients enrolled in this study were analyzed using capillary electrophoresis coupled with mass spectrometry (CE-MS). Urine samples were collected on the day of the diagnostic kidney biopsy. The proteomics data were divided into a training (n = 200) and a test (n = 235) cohort. The fibrosis group was defined as IFTA ≥ 15% and no fibrosis as IFTA < 10%. Statistical comparison of the mass spectrometry data enabled identification of 29 urinary peptides with differential occurrence in samples with and without fibrosis. Several collagen fragments and peptide fragments of fetuin-A and others were combined into a peptidomic classifier. The classifier separated fibrosis from non-fibrosis patients in an independent test set (n = 186) with area under the curve (AUC) of 0.84 (95% CI: 0.779 to 0.889). A significant correlation of IFTA and FPP_BH29 scores could be observed Rho = 0.5, p < 0.0001. We identified a peptidomic classifier for renal fibrosis containing 29 peptide fragments corresponding to 13 different proteins. Urinary proteomics analysis can serve as a non-invasive tool to evaluate the degree of renal fibrosis, in contrast to kidney biopsy, which allows repeated measurements during the disease course.

Understanding glomerular diseases through proteomics

INTRODUCTION

Chronic kidney disease is avery common and complex chronic disease. Uncovering the pathological patterns of CKD on the molecular level of bio-fluids and tissue appears to be both vital and promising for a more favorable outcome. We reviewed recently discovered proteomics biomarkers for CKD to provide new insight into disease pathology.

AREAS COVERED

We review the application of proteome analysis in the context of CKD with various etiologies within the last 5 years. Proteins and peptides associated with CKD as derived from multiple sources (urine, blood and tissue) are reported along with their various biological pathways.

EXPERT OPINION

A systematic and theoretical comprehension of the CKD pathology is essential for its successful management. The underlying complexity of the disease further requires specific conditions for reliable and interpretable results. In this context, clinical proteomics has resulted in first encouraging findings in CKD. A more complete understanding of the biological pathways related to the disease, based on the scope of a holistic proteomic approach, could improve substantially the management of CKD, especially when in conjunction with the current trend of personalized medicine.

Value of Urine Peptides in Assessing Kidney and Cardiovascular Disease

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

Opportunities of Antidiabetic Drugs in Cardiovascular Medicine: A Meta-Analysis and Perspectives for Trial Design

To identify potential application of GLP1-RAs (glucagon-like peptide-1 receptor agonists) and SGLT2-Is (sodium-dependent glucose cotrasnsporter-2 inhibitors) in cardiovascular medicine, we performed PubMed search until March 31, 2020 and selected placebo-controlled randomized trials (RCTs) in patients with type 2 diabetes mellitus. Twenty-four hour ambulatory and office blood pressure (BP), major adverse cardiovascular events (MACE), progression of chronic kidney disease (CKD), and changes in glycated hemoglobin and body weight were aggregated across RCTs using random-effect models. In 2238 patients (7 RCTs), SGLT2-Is lowered 24-hour systolic/diastolic BP by 4.4/1.9 mm Hg (95% CI, 3.4-5.5/1.2-2.6 mm Hg), whereas 2 GLP1-RAs RCTs produced contradictory BP results. Over 1.3 to 5.4 years of follow-up of 56 004 patients (7 RCTs), aggregate hazard ratios associated with GLP1-RA treatment were 0.88 (0.84-0.93) for MACE, 0.84 (0.74-0.89) for CKD, and ranged from 0.84 to 0.90 for individual MACE end points (P≤0.01). Across 5 SGLT2-Is RCTs, including 43 467 patients with 1.5 to 4.2 years follow-up, hazard ratios were 0.87 (0.82-0.93) for MACE, 0.68 (0.62-0.75) for HF, 0.82 (0.72-0.93) for cardiovascular death, 0.87 (0.79-0.96) for myocardial infarction, and 0.61 (0.56-0.67) for worsening CKD. The risk of HF and CKD, but not MACE, decreased with more BP lowering. Stricter glycemic control was associated with higher HF risk, but unrelated to MACE or CKD. The aggregate effect sizes on systolic BP, body weight, and glycated hemoglobin were -1.61 mm Hg, -2.40 kg, and -0.69% for GLP1-RAs, and -2.53 mm Hg, -1.15 kg and -0.24%, for SGLT2-Is (P<0.001). In conclusion, GLP1-RAs and SGLT2-Is reduced cardiovascular risk with differential benefit profiles.

Early detection of diabetic kidney disease by urinary proteomics and subsequent intervention with spironolactone to delay progression (PRIORITY): a prospective observational study and embedded randomised placebo-controlled trial

BACKGROUND

Microalbuminuria is an early sign of kidney disease in people with diabetes and indicates increased risk of cardiovascular disease. We tested whether a urinary proteomic risk classifier (CKD273) score was associated with development of microalbuminuria and whether progression to microalbuminuria could be prevented with the mineralocorticoid receptor antagonist spironolactone.

METHODS

In this multicentre, prospective, observational study with embedded randomised controlled trial (PRIORITY), we recruited people with type 2 diabetes, normal urinary albumin excretion, and preserved renal function from 15 specialist centres in ten European countries. All participants (observational cohort) were tested with the CKD273 classifier and classified as high risk (CKD273 classifier score >0·154) or low risk (≤0·154). Participants who were classified as high risk were entered into a randomised controlled trial and randomly assigned (1:1), by use of an interactive web-response system, to receive spironolactone 25 mg once daily or matched placebo (trial cohort). The primary endpoint was development of confirmed microalbuminuria in all individuals with available data (observational cohort). Secondary endpoints included reduction in incidence of microalbuminuria with spironolactone (trial cohort, intention-to-treat population) and association between CKD273 risk score and measures of impaired renal function based on estimated glomerular filtration rate (eGFR; observational cohort). Adverse events (particularly gynaecomastia and hyperkalaemia) and serious adverse events were recorded for the intention-to-treat population (trial cohort). This study is registered with the EU Clinical Trials Register (EudraCT 20120-004523-4) and ClinicalTrials.gov (NCT02040441) and is completed.

FINDINGS

Between March 25, 2014, and Sept 30, 2018, we enrolled and followed-up 1775 participants (observational cohort), 1559 (88%) of 1775 participants had a low-risk urinary proteomic pattern and 216 (12%) had a high-risk pattern, of whom 209 were included in the trial cohort and assigned to spironolactone (n=102) or placebo (n=107). The overall median follow-up time was 2·51 years (IQR 2·0-3·0). Progression to microalbuminuria was seen in 61 (28%) of 216 high-risk participants and 139 (9%) of 1559 low-risk participants (hazard ratio [HR] 2·48, 95% CI 1·80-3·42; p<0·0001, after adjustment for baseline variables of age, sex, HbA_1c, systolic blood pressure, retinopathy, urine albumin-to-creatinine ratio [UACR], and eGFR). Development of impaired renal function (eGFR <60 mL/min per 1·73 m²) was seen in 48 (26%) of 184 high-risk participants and 119 (8%) of 1423 low-risk participants (HR 3·50; 95% CI 2·50-4·90, after adjustment for baseline variables). A 30% decrease in eGFR from baseline (post-hoc endpoint) was seen in 42 (19%) of 216 high-risk participants and 62 (4%) of 1559 low-risk participants (HR 5·15, 95% CI 3·41-7·76; p<0·0001, after adjustment for basline eGFR and UACR). In the intention-to-treat trial cohort, development of microalbuminuria was seen in 35 (33%) of 107 in the placebo group and 26 (25%) of 102 in the spironolactone group (HR 0·81, 95% CI 0·49-1·34; p=0·41). In the safety analysis (intention-to-treat trial cohort), events of plasma potassium concentrations of more than 5·5 mmol/L were seen in 13 (13%) of 102 participants in the spironolactone group and four (4%) of 107 participants in the placebo group, and gynaecomastia was seen in three (3%) participants in the spironolactone group and none in the placebo group. One patient died in the placebo group due to a cardiac event (considered possibly related to study drug) and one patient died in the spironolactone group due to cancer, deemed unrelated to study drug.

INTERPRETATION

In people with type 2 diabetes and normoalbuminuria, a high-risk score from the urinary proteomic classifier CKD273 was associated with an increased risk of progression to microalbuminuria over a median of 2·5 years, independent of clinical characteristics. However, spironolactone did not prevent progression to microalbuminuria in high-risk patients.

FUNDING

European Union Seventh Framework Programme.

A novel urinary biomarker predicts 1-year mortality after discharge from intensive care

Rationale

The urinary proteome reflects molecular drivers of disease.

Objectives

To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality.

Methods

In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses.

Measurements and main results

In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708–0.798) and 0.688 (0.656–0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00–2.91) for ACM128 (+ 1 SD), 1.24 (1.16–1.32) for the Charlson Comorbidity Index (+ 1 point), and ≥ 1.19 (P ≤ 0.022) for other biomarkers (+ 1 SD). ACM128 improved (P ≤ 0.0001) IDI (≥ + 0.50), NRI (≥ + 53.7), and AUC (≥ + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis.

Conclusions

The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.

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.

Lifetime benefits of early detection and treatment of diabetic kidney disease

OBJECTIVES

Diabetic kidney disease (DKD) is a frequent complication of diabetes with potentially devastating consequences that may be prevented or delayed. This study aimed to estimate the health and economic benefit of earlier diagnosis and treatment of DKD.

METHODS

Life expectancy and medical spending for people with diabetes were modeled using The Health Economics Medical Innovation Simulation (THEMIS). THEMIS uses data from the Health and Retirement Study to model cohorts of individuals over age 50 to project population-level lifetime health and economic outcomes. DKD status was imputed based on diagnoses and laboratory values in the National Health and Nutrition Examination Survey. We simulated the implementation of a new biomarker identifying people with diabetes at an elevated risk of DKD and DKD patients at risk of rapid progression.

RESULTS

Compared to baseline, the prevalence of DKD declined 5.1% with a novel prognostic biomarker test, while the prevalence of diabetes with stage 5 chronic kidney disease declined 3.0%. Consequently, people with diabetes gained 0.2 years in life expectancy, while per-capita annual medical spending fell by 0.3%. The estimated cost was $12,796 per life-year gained and $25,842 per quality-adjusted life-year.

CONCLUSIONS

A biomarker test that allows earlier treatment reduces DKD prevalence and slows DKD progression, thereby increasing life expectancy among people with diabetes while raising healthcare spending by less than one percent.

Urinary Peptidomic Biomarker for Personalized Prevention and Treatment of Diastolic Left Ventricular Dysfunction

Diastolic heart failure (DHF) is characterized by slow left ventricular (LV) relaxation, increased LV stiffness, interstitial deposition of collagen, and a modified extracellular matrix proteins. Among Europeans, the frequency of asymptomatic diastolic LV dysfunction (DD) is 25%. This constitutes a large pool of people at high risk of DHF. The goal of this review was to describe the discovery and the initial validation of new multidimensional urinary peptidomic biomarkers (UPB) indicative of DD, mainly consisting of collagen fragments, and to describe a roadmap for their introduction into clinical practice. The availability of new drugs creates a window of opportunity for mounting a randomized clinical trial consolidating the clinical applicability of UPB to screen for DD. If successfully completed, such trial will benefit ≈25% of all people older than 50 years and open a large market for a UPB diagnostic tool and the drug tested. Moreover, sequenced peptides making up UPB will generate novel insights in the pathophysiology of DD and facilitate personalized treatment of patients with DHF for whom prevention came too late. If proven cost-effective, the clinical application of UPB will contribute to the sustainability of health care in aging population in epidemiologic transition.

Characteristics of high- and low-risk individuals in the PRIORITY study: urinary proteomics and mineralocorticoid receptor antagonism for prevention of diabetic nephropathy in Type 2 diabetes

AIM

To compare clinical baseline data in individuals with Type 2 diabetes and normoalbuminuria, who are at high or low risk of diabetic kidney disease based on the urinary proteomics classifier CKD273.

METHODS

We conducted a prospective, randomized, double-blind, placebo-controlled international multicentre clinical trial and observational study in participants with Type 2 diabetes and normoalbuminuria, stratified into high- or low-risk groups based on CKD273 score. Clinical baseline data for the whole cohort and stratified by risk groups are reported. The associations between CKD273 and traditional risk factors for diabetic kidney disease were evaluated using univariate and logistic regression analysis.

RESULTS

A total of 1777 participants from 15 centres were included, with 12.3% of these having a high-risk proteomic pattern. Participants in the high-risk group (n=218), were more likely to be men, were older, had longer diabetes duration, a lower estimated GFR and a higher urinary albumin:creatinine ratio than those in the low-risk group (n=1559, P<0.02). Numerical differences were small and univariate regression analyses showed weak associations (R² < 0.04) of CKD273 with each baseline variable. In a logistic regression model including clinical variables known to be associated with diabetic kidney disease, estimated GFR, gender, log urinary albumin:creatinine ratio and use of renin-angiotensin system-blocking agents remained significant determinants of the CKD273 high-risk group: area under the curve 0.72 (95% CI 0.68-0.75; P<0.01).

CONCLUSIONS

In this population of individuals with Type 2 diabetes and normoalbuminuria, traditional diabetic kidney disease risk factors differed slightly between participants at high risk and those at low risk of diabetic kidney disease, based on CKD273. These data suggest that CKD273 may provide additional prognostic information over and above the variables routinely available in the clinic. Testing the added value will be subject to our ongoing study. (European Union Clinical Trials Register: EudraCT 2012-000452-34 and Clinicaltrials.gov: NCT02040441).

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

Introduction

CKD273 is a urinary biomarker, which in advanced chronic kidney disease predicts further deterioration. We investigated whether CKD273 can also predict a decline of estimated glomerular filtration rate (eGFR) to <60 ml/min per 1.73 m².

Methods

In analyses of 2087 individuals from 6 cohorts (46.4% women; 73.5% with diabetes; mean age, 46.1 years; eGFR ≥ 60 ml/min per 1.73 m², 100%; urinary albumin excretion rate [UAE] ≥20 μg/min, 6.2%), we accounted for cohort, sex, age, mean arterial pressure, diabetes, and eGFR at baseline and expressed associations per 1-SD increment in urinary biomarkers.

Results

Over 5 (median) follow-up visits, eGFR decreased more with higher baseline CKD273 than UAE (1.64 vs. 0.82 ml/min per 1.73 m²; P < 0.0001). Over 4.6 years (median), 390 participants experienced a first renal endpoint (eGFR decrease by ≥10 to <60 ml/min per 1.73 m²), and 172 experienced an endpoint sustained over follow-up. The risk of a first and sustained renal endpoint increased with UAE (hazard ratio ≥ 1.23; P ≤ 0.043) and CKD273 (≥ 1.20; P ≤ 0.031). UAE (≥20 μg/min) and CKD273 (≥0.154) thresholds yielded sensitivities of 30% and 33% and specificities of 82% and 83% (P ≤ 0.0001 for difference between UAE and CKD273 in proportion of correctly classified individuals). As continuous markers, CKD273 (P = 0.039), but not UAE (P = 0.065), increased the integrated discrimination improvement, while both UAE and CKD273 improved the net reclassification index (P ≤ 0.0003), except for UAE per threshold (P = 0.086).

Discussion

In conclusion, while accounting for baseline eGFR, albuminuria, and covariables, CKD273 adds to the prediction of stage 3 chronic kidney disease, at which point intervention remains an achievable therapeutic target.