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.

Urinary proteomics combined with home blood pressure telemonitoring for health care reform trial-First progress report.

High blood pressure (BP) and type-2 diabetes (T2DM) are forerunners of chronic kidney disease and left ventricular dysfunction. Home BP telemonitoring (HTM) and urinary peptidomic profiling (UPP) are technologies enabling risk stratification and personalized prevention. UPRIGHT-HTM (NCT04299529) is an investigator-initiated, multicenter, open-label, randomized trial with blinded endpoint evaluation designed to assess the efficacy of HTM plus UPP (experimental group) over HTM alone (control group) in guiding treatment in asymptomatic patients, aged 55-75 years, with ≥5 cardiovascular risk factors. From screening onwards, HTM data can be freely accessed by all patients and their caregivers; UPP results are communicated early during follow-up to patients and caregivers in the intervention group, but at trial closure in the control group. From May 2021 until January 2023, 235 patients were screened, of whom 53 were still progressing through the run-in period and 144 were randomized. Both groups had similar characteristics, including average age (62.0 years) and the proportions of African Blacks (81.9%), White Europeans (16.7%), women 56.2%, home (31.2%), and office (50.0%) hypertension, T2DM (36.4%), micro-albuminuria (29.4%), and ECG (9.7%) and echocardiographic (11.5%) left ventricular hypertrophy. Home and office BP were 128.8/79.2 mm Hg and 137.1/82.7 mm Hg, respectively, resulting in a prevalence of white-coat, masked and sustained hypertension of 40.3%, 11.1%, and 25.7%. HTM persisted after randomization (48 681 readings up to 15 January 2023). In conclusion, results predominantly from low-resource sub-Saharan centers proved the feasibility of this multi-ethnic trial. The COVID-19 pandemic caused delays and differential recruitment rates across centers.

Urinary peptide panel for prognostic assessment of bladder cancer relapse.

Non-invasive tools stratifying bladder cancer (BC) patients according to the risk of relapse are urgently needed to guide clinical intervention. As a follow-up to the previously published study on CE-MS-based urinary biomarkers for BC detection and recurrence monitoring, we expanded the investigation towards BC patients with longitudinal data. Profiling datasets of BC patients with follow-up information regarding the relapse status were investigated. The peptidomics dataset (n = 98) was split into training and test set. Cox regression was utilized for feature selection in the training set. Investigation of the entire training set at the single peptide level revealed 36 peptides being strong independent prognostic markers of disease relapse. Those features were further integrated into a Random Forest-based model evaluating the risk of relapse for BC patients. Performance of the model was assessed in the test cohort, showing high significance in BC relapse prognosis [HR = 5.76, p-value = 0.0001, c-index = 0.64]. Urinary peptide profiles integrated into a prognostic model allow for quantitative risk assessment of BC relapse highlighting the need for its incorporation in prospective studies to establish its value in the clinical management of BC.

The use of urinary proteomics in the assessment of suitability of mouse models for ageing.

Ageing is a complex process characterised by a systemic and progressive deterioration of biological functions. As ageing is associated with an increased prevalence of age-related chronic disorders, understanding its underlying molecular mechanisms can pave the way for therapeutic interventions and managing complications. Animal models such as mice are commonly used in ageing research as they have a shorter lifespan in comparison to humans and are also genetically close to humans. To assess the translatability of mouse ageing to human ageing, the urinary proteome in 89 wild-type (C57BL/6) mice aged between 8-96 weeks was investigated using capillary electrophoresis coupled to mass spectrometry (CE-MS). Using age as a continuous variable, 295 peptides significantly correlated with age in mice were identified. To investigate the relevance of using mouse models in human ageing studies, a comparison was performed with a previous correlation analysis using 1227 healthy subjects. In mice and humans, a decrease in urinary excretion of fibrillar collagens and an increase of uromodulin fragments was observed with advanced age. Of the 295 peptides correlating with age, 49 had a strong homology to the respective human age-related peptides. These ortholog peptides including several collagen (N = 44) and uromodulin (N = 5) fragments were used to generate an ageing classifier that was able to discriminate the age among both wild-type mice and healthy subjects. Additionally, the ageing classifier depicted that telomerase knock-out mice were older than their chronological age. Hence, with a focus on ortholog urinary peptides mouse ageing can be translated to human ageing.

Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides.

Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±-0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD.

Proteome analysis in the assessment of ageing.

Based on demographic trends, the societies in many developed countries are facing an increasing number and proportion of people over the age of 65. The raise in elderly populations along with improved health-care will be concomitant with an increased prevalence of ageing-associated chronic conditions like cardiovascular, renal, and respiratory diseases, arthritis, dementia, and diabetes mellitus. This is expected to pose unprecedented challenges both for individuals and societies and their health care systems. An ultimate goal of ageing research is therefore the understanding of physiological ageing and the achievement of 'healthy' ageing by decreasing age-related pathologies. However, on a molecular level, ageing is a complex multi-mechanistic process whose contributing factors may vary individually, partly overlap with pathological alterations, and are often poorly understood. Proteome analysis potentially allows modelling of these multifactorial processes. This review summarises recent proteomic research on age-related changes identified in animal models and human studies. We combined this information with pathway analysis to identify molecular mechanisms associated with ageing. We identified some molecular pathways that are affected in most or even all organs and others that are organ-specific. However, appropriately powered studies are needed to confirm these findings based in in silico evaluation.

Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = -0.8031; p<0.0001 and ρ = -0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = -0.6557; p = 0.0001 and ρ = -0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.

Improving peptide relative quantification in MALDI-TOF MS for biomarker assessment.

Proteomic profiling by MALDI-TOF MS presents various advantages (speed of analysis, ease of use, relatively low cost, sensitivity, tolerance against detergents and contaminants, and possibility of automation) and is being currently used in many applications (e.g. peptide/protein identification and quantification, biomarker discovery, and imaging MS). Earlier studies by many groups indicated that moderate reproducibility in relative peptide quantification is a major limitation of MALDI-TOF MS. In the present work, we examined and demonstrate a clear effect, in cases apparently random, of sample dilution in complex samples (urine) on the relative quantification of peptides by MALDI-TOF MS. Results indicate that in urine relative abundance of peptides cannot be assessed with confidence based on a single MALDI-TOF MS spectrum. To account for this issue, we developed and propose a novel method of determining the relative abundance of peptides, taking into account that peptides have individual linear quantification ranges in relation to sample dilution. We developed an algorithm that calculates the range of dilutions at which each peptide responds in a linear manner and normalizes the received peptide intensity values accordingly. This concept was successfully applied to a set of urine samples from patients diagnosed with diabetes presenting normoalbuminuria (controls) and macroalbuminuria (cases).

Urinary proteomics in the assessment of chronic kidney disease.

PURPOSE OF REVIEW: Urinary proteomics has emerged as an approach that could deliver relevant clinical information. In this review, we aim at highlighting the recent developments, especially with respect to clinical implementation. We review several of the recent publications reporting on larger cohorts, focusing on those that aim at qualification and/or validation of urinary proteomics biomarkers. RECENT FINDINGS: Several components of the urinary proteome, especially its low molecular weight fraction (sometimes referred to as the 'peptidome'), have been significantly associated with chronic kidney disease (CKD). Independent studies, encompassing sometimes close to 1000 independent samples, indicate that specific peptides from extracellular matrix (ECM) proteins encompass a major component of the urinary proteome. Highly significant changes in the abundance of some of these peptides are associated with CKD indicating that alterations in ECM, reflected via the urinary proteome, may represent an early stage in CKD pathology. These peptides may serve as specific early biomarkers, and interference with pathological ECM accumulation may be a valuable new therapeutic approach in CKD. SUMMARY: Urinary proteomic biomarkers have emerged as clinically relevant variables. First studies involving several hundred individuals indicate a potential added benefit of urinary proteomic biomarkers. First large clinical trials are being initiated to employ urinary proteomics in clinical decision making.

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

BACKGROUND: Previously the angiotensin II receptor blocker Irbesartan has been demonstrated to reduce the risk for progression from microalbuminuria to macroalbuminuria in type 2 diabetic patients. The purpose of this study was to evaluate the effect of treatment with Irbesartan in type 2 diabetic patients with microalbuminuria on the urinary proteome. METHODS: High-resolution capillary-electrophoresis coupled to mass-spectrometry (CE-MS) was used to profile the low-molecular-weight proteome in urine of a subgroup of patients from a two year randomized irbesartan versus placebo therapy trial, which included hypertensive type 2 diabetic patients with microalbuminuria on ongoing antihypertensive medication (IRMA2-substudy). RESULTS: We demonstrate that the therapy with 300 mg Irbesartan daily over a period of two years results in significant changes of the urinary proteome. Both, a classifier developed previously that consists of urinary peptides indicative of chronic kidney disease, as well as several individual peptides changed significantly after treatment. These changes were not observed in the placebo-treated individuals. Most prominent are changes of urinary collagen fragments associated with progression of diabetic nephropathy, indicating normalization in urinary peptides. CONCLUSION: CE-MS analysis of urine enabled identification of peptides as potential surrogate markers for renoprotection in microalbuminuric type 2 diabetic patients, which show persistent improvement after longterm treatment with Irbesartan. The results suggest that a major benefit of treatment by Irbesartan may be improvement of collagen turnover, reduction of fibrosis. They further suggest that urinary proteome analysis could be utilized to assess potential benefit of therapeutic intervention, providing statistically significant results even on a small population.

2nd Combined Working Group and Management Committee Meeting of Urine and Kidney Proteomics COST Action 29-30 March 2009, Nafplio, Greece.

EuroKUP (Urine and Kidney Proteomics; www.eurokup.org) is a COST (European Cooperation in the field of Scientific and Technical research: www.cost.esf.org Action fostering a multi-disciplinary network of investigators from 25 countries and focusing on facilitating translational proteomic research in kidney diseases. Four Working Groups focusing respectively on defining clinically important research questions in kidney diseases, kidney tissue proteomics, urine proteomics and bioinformatics have been generated. The EuroKUP members had their second combined Working Group and Management Committee (MC) meeting in Nafplio, Greece from March 29 to 30, 2009. This report summarizes the main presentations, discussions and agreed action points during this meeting. These refer to the design of collaborative projects and clinical center networks for specific kidney diseases; establishment of guidelines for kidney tissue proteomics analysis by laser-based imaging- and laser capture microdissection-MS; development and characterization of a "standard" urine specimen to be used for assessment of platform capability and data comparability in clinical proteomics applications; definition of statistical requirements in biomarker discovery studies; and development of a specialized kidney and urine ontology. Various training activities are planned involving training schools on laser capture microdissection- and imaging-MS, workshops on ontologies as well as short-term travel grants for junior investigators.

Technical, bioinformatical and statistical aspects of liquid chromatography-mass spectrometry (LC-MS) and capillary electrophoresis-mass spectrometry (CE-MS) based clinical proteomics: a critical assessment.

The search for biomarkers in biological fluids that can be used for disease diagnosis and prognosis using mass spectrometry has emerged to become a state-of-the-art methodology for clinical proteomics. Poor cross platform comparison of the findings, however, makes the need for comparison studies probably as urgent as the need for new ones. It is now increasingly recognized that standardized statistical and bioinformatics approaches during data processing are of utmost importance for such comparisons. This paper reviews two of the currently most promising methods, namely LC-MS and CE-MS techniques, and software tools used to analyze the huge amount of data they generate. We further review the statistical issues of feature selection and sample classification.

CE - a multifunctional application for clinical diagnosis.

CE has been used widely as an analytical tool with high separation power taking advantage of size, charge-to-size ratio, or isoelectric point of various analytes. In combination with detection methods, such as UV absorption, electrochemical detection, fluorescence, or mass spectrometry (MS), it allows the separation and detection of inorganic and organic ions, as well as complex compounds, such as polypeptides, nucleic acids, including PCR amplicons from viruses or bacteria. Recent interest in identification of biomarkers of diseases using body fluids leads to development of CE-MS techniques. These applications allowed identification of new potential biomarkers for clinical diagnosis and monitoring of therapeutic interventions. In this report, we present a technical overview of various CE techniques and discuss their applications in clinical medicine.

Effects of oral vitamin C supplementation in hemodialysis patients: a proteomic assessment.

Evidence indicates that oxidative stress is present in dialysis patients, and is associated with vitamin C deficiency. Limited data are available regarding the effects of vitamin C supplementation on oxidative stress and inflammation markers in these patients. Moreover, there are no data available on plasma polypeptide fingerprints by proteome analysis before and after vitamin C supplementation. Therefore, we analyzed plasma samples from a prospective, randomized, open-labeled trial to assess the effects of oral vitamin C supplementation (250 mg three times per week), to define the plasma polypeptide pattern in hemodialysis patients. Our results reveal that more than 30 polypeptides show significant changes in the dialysis patients in comparison to controls with normal renal function, and that several polypeptides are affected/normalized by oral vitamin C supplementation. These results underline the remarkable potential for proteomics to recognize specific peptide profiles in different pathological situations, which might not be detected by classical methods.

Proteomic analysis for the assessment of diabetic renal damage in humans.

Renal disease in patients with Type II diabetes is the leading cause of terminal renal failure and a major healthcare problem. Hence early identification of patients prone to develop this complication is important. Diabetic renal damage should be reflected by a change in urinary polypeptide excretion at a very early stage. To analyse these changes, we used an online combination of CE/MS (capillary electrophoresis coupled with MS), allowing fast and accurate evaluation of up to 2000 polypeptides in urine. Employing this technology, we have examined urine samples from 39 healthy individuals and from 112 patients with Type II diabetes mellitus and different degrees of albumin excretion rate. We established a 'normal' polypeptide pattern in the urine of healthy subjects. In patients with Type II diabetes and normal albumin excretion rate, the polypeptide pattern in urine differed significantly from normal, indicating a specific 'diabetic' pattern of polypeptide excretion. In patients with higher grade albuminuria, we were able to detect a polypeptide pattern indicative of 'diabetic renal damage'. We also found this pattern in 35% of those patients who had low-grade albuminuria and in 4% of patients with normal albumin excretion. Moreover, we could identify several of the indicative polypeptides using MS/MS sequencing. We conclude that proteomic analysis with CE/MS permits fast and accurate identification and differentiation of polypeptide patterns in urine. Longitudinal studies should explore the potential of this powerful diagnostic tool for early detection of diabetic renal damage.