Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

Peptides as "better biomarkers"? Value, challenges, and potential solutions to facilitate implementation

Peptides carry important functions in normal physiological and pathophysiological processes and can serve as clinically useful biomarkers. Given the ability to diffuse passively across endothelial barriers, endogenous peptides can be examined in several body fluids, including among others urine, blood, and cerebrospinal fluid. This review article provides an update on the recently published literature that reports on investigating native peptides in body fluids using mass spectrometry-based platforms, specifically those studies that focus on the application of peptides as biomarkers to improve clinical management. We emphasize on the critical evaluation of their clinical value, how close they are to implementation, and the associated challenges and potential solutions to facilitate clinical implementation. During the last 5 years, numerous studies have been published, demonstrating the increased interest in mass spectrometry for the assessment of endogenous peptides as potential biomarkers. Importantly, the presence of few successful examples of implementation in patients' management and/or in the context of clinical trials indicates that the peptide biomarker field is evolving. Nevertheless, most studies still report evidence based on small sample size, while validation phases are frequently missing. Therefore, a gap between discovery and implementation still exists.

Exploring Adiposity and Chronic Kidney Disease: Clinical Implications, Management Strategies, Prognostic Considerations

Obesity poses a significant and growing risk factor for chronic kidney disease (CKD), requiring comprehensive evaluation and management strategies. This review explores the intricate relationship between obesity and CKD, emphasizing the diverse phenotypes of obesity, including sarcopenic obesity and metabolically healthy versus unhealthy obesity, and their differential impact on kidney function. We discuss the epidemiological evidence linking elevated body mass index (BMI) with CKD risk while also addressing the paradoxical survival benefits observed in obese CKD patients. Various measures of obesity, such as BMI, waist circumference, and visceral fat assessment, are evaluated in the context of CKD progression and outcomes. Mechanistic insights into how obesity promotes renal dysfunction through lipid metabolism, inflammation, and altered renal hemodynamics are elucidated, underscoring the role of adipokines and the renin-angiotensin-aldosterone system. Furthermore, the review examines current strategies for assessing kidney function in obese individuals, including the strengths and limitations of filtration markers and predictive equations. The management of obesity and associated comorbidities like arterial hypertension, type 2 diabetes mellitus, and non-alcoholic fatty liver disease in CKD patients is discussed. Finally, gaps in the current literature and future research directions aimed at optimizing the management of obesity-related CKD are highlighted, emphasizing the need for personalized therapeutic approaches to mitigate the growing burden of this intertwined epidemic.

Biomarkers of Kidney Disease Progression in ADPKD

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic kidney disorder and the fourth leading cause of kidney failure (KF) in adults. Characterized by a reduction in glomerular filtration rate (GFR) and increased kidney size, ADPKD exhibits significant variability in progression, highlighting the urgent need for reliable and predictive biomarkers to optimize management and treatment approaches. This review explores the roles of diverse biomarkers-including clinical, genetic, molecular, and imaging biomarkers-in evaluating disease progression and customizing treatments for ADPKD. Clinical biomarkers such as biological sex, the predicting renal outcome in polycystic kidney disease (PROPKD) score, and body mass index are shown to correlate with disease severity and progression. Genetic profiling, particularly distinguishing between truncating and non-truncating pathogenic variants in the PKD1 gene, refines risk assessment and prognostic precision. Advancements in imaging significantly enhance our ability to assess disease severity. Height-adjusted total kidney volume (htTKV) and the Mayo imaging classification (MIC) are foundational, whereas newer imaging biomarkers, including texture analysis, total cyst number (TCN), cyst-parenchyma surface area (CPSA), total cyst volume (TCV), and cystic index, focus on detailed cyst characteristics to offer deeper insights. Molecular biomarkers (including serum and urinary markers) shed light on potential therapeutic targets that could predict disease trajectory. Despite these advancements, there is a pressing need for the development of response biomarkers in both the adult and pediatric populations, which can evaluate the biological efficacy of treatments. The holistic evaluation of these biomarkers not only deepens our understanding of kidney disease progression in ADPKD, but it also paves the way for personalized treatment strategies aiming to significantly improve patient outcomes.

Senescence Biomarkers CKAP4 and PTX3 Stratify Severe Kidney Disease Patients

INTRODUCTION

Cellular senescence is the irreversible growth arrest subsequent to oncogenic mutations, DNA damage, or metabolic insult. Senescence is associated with ageing and chronic age associated diseases such as cardiovascular disease and diabetes. The involvement of cellular senescence in acute kidney injury (AKI) and chronic kidney disease (CKD) is not fully understood. However, recent studies suggest that such patients have a higher-than-normal level of cellular senescence and accelerated ageing.

METHODS

This study aimed to discover key biomarkers of senescence in AKI and CKD patients compared to other chronic ageing diseases in controls using OLINK proteomics.

RESULTS

We show that senescence proteins CKAP4 (p-value < 0.0001) and PTX3 (p-value < 0.0001) are upregulated in AKI and CKD patients compared with controls with chronic diseases, suggesting the proteins may play a role in overall kidney disease development.

CONCLUSIONS

CKAP4 was found to be differentially expressed in both AKI and CKD when compared to UHCs; hence, this biomarker could be a prognostic senescence biomarker of both AKI and CKD.

Protein Biomarkers of Ultra-Processed Food Consumption and Risk of Coronary Heart Disease, Chronic Kidney Disease, and All-Cause Mortality

BACKGROUND

There is a need to understand the underlying biological mechanisms through which ultra-processed foods negatively affect health. Proteomics offers a valuable tool with which to examine different aspects of ultra-processed foods and their impact on health.

OBJECTIVES

The aim of this study was to identify protein biomarkers of usual ultra-processed food consumption and assess their relation to the incidence of coronary heart disease (CHD), chronic kidney disease (CKD), and all-cause mortality risk.

METHODS

A total of 9361 participants from the Atherosclerosis Risk in Communities visit 3 (1993-1995) were included. Dietary intake was assessed using a 66-item food-frequency questionnaire and the processing levels were categorized on the basis of the Nova classification. Plasma proteins were detected using an aptamer-based proteomic assay. We used multivariable linear regressions to examine the association between ultra-processed food and proteins, and Cox proportional hazard models to identify associations between ultra-processed food-related proteins and health outcomes. Models extensively controlled for sociodemographic characteristics, health behaviors, and clinical factors.

RESULTS

Eight proteins (6 positive, 2 negative) were identified as significantly associated with ultra-processed food consumption. Over a median follow-up of 22 y, there were 1276, 3084, and 5127 cases of CHD, CKD, and death, respectively. Three, 5, and 3 ultra-processed food-related proteins were associated with each outcome, respectively. One protein (β-glucuronidase) was significantly associated with a higher risk of all 3 outcomes, and 3 proteins (receptor-type tyrosine-protein phosphatase U, C-C motif chemokine 25, and twisted gastrulation protein homolog 1) were associated with a higher risk of 2 outcomes.

CONCLUSIONS

We identified a panel of protein biomarkers that were significantly associated with ultra-processed food consumption. These proteins may be considered potential biomarkers for ultra-processed food intake and may elucidate the biological processes through which ultra-processed foods impact health outcomes.

Fetal biomarkers for lower urinary tract obstruction secondary to posterior urethral valves

Today, prenatal diagnosis of congenital urogenital malformations is mostly dependent on anatomical variations found on imaging. However, these findings can mislead us in telling us when to intervene, and about post-natal prognosis. Since many findings are dependent on multiple assessments, delayed diagnosis can occur, leading to less optimal outcomes compared to early intervention. Analyses of fetal urinary biomarkers have been proposed as a method of finding biological changes that are predictive for diagnosis and prognosis in fetuses at risk of kidney disease. We interviewed a group of researchers that have demonstrated that by combining multiple omics traits extracted from fetal urine, the biological variability found in single omics data can be circumvented. By analyzing multiple fetal urine peptides and metabolites at single time point, the prognostic power of postnatal renal outcome in fetuses with lower urinary tract obstruction is significantly increased. In this interview, we inquired about the technical aspects of the tests, challenges, and limitations the research group have come across, and how they envision the future for multi-omics fetal analysis in the clinic.

Impact of Renal Denervation on Urinary Peptide-Based Biomarkers in Hypertension

BACKGROUND

Catheter-based renal denervation (RDN) reduces blood pressure in hypertension. Urinary peptides are associated with cardiovascular and renal disease and provide prognostic information. We aimed to investigate the effect of RDN on urinary peptide-based classifiers associated with chronic kidney and heart disease and to identify urinary peptides affected by RDN.

METHODS

This single-arm, single-center study included patients undergoing catheter-based RDN. Urine samples were collected before and 24 months after RDN and were analyzed using capillary electrophoresis coupled with mass spectrometry. Predefined urinary peptide-based classifiers for chronic kidney disease (CKD273), coronary artery disease (CAD238), and heart failure (HF1) were applied.

RESULTS

This study included 48 patients (33% female) with uncontrolled hypertension. At 24 months after RDN, systolic blood pressure (165±17 versus 148±20 mm Hg; P<0.0001), diastolic blood pressure (90±17 versus 81±13 mm Hg; P<0.0001), and mean arterial pressure (115±15 versus 103±13 mm Hg; P<0.0001) decreased significantly. A total of 103 urinary peptides from 37 different proteins, mostly collagens, altered following RDN. CAD238, a 238 coronary artery-specific polypeptide-based classifier, significantly improved following RDN (Cohen's d, -0.632; P=0.0001). The classification scores of HF1 (P=0.8295) and CKD273 (P=0.6293) did not change significantly.

CONCLUSIONS

RDN beneficially affected urinary peptides associated with coronary artery disease.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT01888315.

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 biomarkers in kidney disease

BACKGROUND

Chronic kidney disease (CKD) affects many people worldwide and early diagnosis is essential for successful treatment and improved outcome. Unfortunately, current methods are insufficient especially for early disease detection. However, advances in the analytical methods for urinary biomarkers may provide a unique opportunity for diagnosis and management of CKD. This review explores evolving technology and highlights the importance of early marker detection in these patients.

APPROACH

A search strategy was set up using the terms CKD, biomarkers, and urine. The search included 53 studies comprising 37 biomarkers. The value of these biomarkers for CKD are based on their ability to diagnose CKD, monitor progression, assess mortality and nephrotoxicity.

RESULTS

KIM-1 was the best marker for diagnosis as it increased with the development of incident CKD. DKK3 increased in patients with declining eGFR, whereas UMOD decreased in those with declining kidney function. Unfortunately, none fulfilled all criteria to adequately assess mortality and nephrotoxicity.

CONCLUSION

New developments in the field of urinalysis using smart toilets may open several possibilities for urinary biomarkers. This review explored which biomarkers could be used for CKD disease detection and management.

Nontargeted Plasma Proteomic Analysis of Renal Disease and Pulmonary Hypertension in Patients with Sickle Cell Disease

Sickle cell disease (SCD) is characterized by red blood cell sickling, vaso-occlusion, hemolytic anemia, damage to multiple organ systems, and, as a result, shortened life expectancy. Sickle cell disease nephropathy (SCDN) and pulmonary hypertension (pHTN) are common and frequently co-occurring complications of SCD; both are associated with markedly accelerated mortality. To identify candidate circulating biomarkers of SCDN and pHTN, we used mass spectrometry to quantify the relative abundance of >1000 proteins in plasma samples from 189 adults with SCD from the Outcome Modifying Genes in SCD (OMG-SCD) cohort (ProteomeXchange identifier PXD048716). Forty-four proteins were differentially abundant in SCDN, most significantly cystatin-C and collagen α-1(XVIII) chain (COIA1), and 55 proteins were dysregulated in patients with SCDN and pHTN, most significantly insulin-like growth factor-binding protein 6 (IBP6). Network analysis identified a module of 133 coregulated proteins significantly associated with SCDN, that was enriched for extracellular matrix proteins, insulin-like growth factor binding proteins, cell adhesion proteins, EGF-like calcium binding proteins, and several cadherin family members. Collectively, these data provide a comprehensive understanding of plasma protein changes in SCDN and pHTN which validate numerous studies of chronic kidney disease and suggest shared profiles of protein disruption in kidney dysfunction and pHTN among SCD patients.

Multiple urinary peptides display distinct sex-specific distribution

Previous studies have established the association of sex with gene and protein expression. This study investigated the association of sex with the abundance of endogenous urinary peptides, using capillary electrophoresis-coupled to mass spectrometry (CE-MS) datasets from 2008 healthy individuals and patients with type II diabetes, divided in one discovery and two validation cohorts. Statistical analysis using the Mann-Whitney test, adjusted for multiple testing, revealed 143 sex-associated peptides in the discovery cohort. Of these, 90 peptides were associated with sex in at least one of the validation cohorts and showed agreement in their regulation trends across all cohorts. The 90 sex-associated peptides were fragments of 29 parental proteins. Comparison with previously published transcriptomics data demonstrated that the genes encoding 16 of these parental proteins had sex-biased expression. The 143 sex-associated peptides were combined into a support vector machine-based classifier that could discriminate males from females in two independent sets of healthy individuals and patients with type II diabetes, with an AUC of 89% and 81%, respectively. Collectively, the urinary peptidome contains multiple sex-associated differences, which may enable a better understanding of sex-biased molecular mechanisms and the development of more accurate diagnostic, prognostic, or predictive classifiers for each individual sex.

Recent progress in mass spectrometry-based urinary proteomics

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

Association between systemic immune-inflammation index and chronic kidney disease: A population-based study

BACKGROUND

Systemic immune-inflammation index (SII) is a new indicator of inflammation, and chronic kidney disease (CKD) has a connection to inflammation. However, the relationship between SII and CKD is still unsure. The aim of this study was whether there is an association between SII and CKD in the adult US population.

METHODS

Data were from the National Health and Nutrition Examination Survey (NHANES) in 2003-2018, and multivariate logistic regression was used to explore the independent linear association between SII and CKD. Smoothing curves and threshold effect analyses were utilized to describe the nonlinear association between SII and CKD.

RESULTS

The analysis comprised 40,660 adults in total. After adjusting for a number of factors, we found a positive association between SII and CKD [1.06 (1.04, 1.07)]. In subgroup analysis and interaction tests, this positive correlation showed differences in the age, hypertension, and diabetes strata (p for interaction<0.05), but remained constant in the sex, BMI, abdominal obesity, smoking, and alcohol consumption strata. Smoothing curve fitting revealed a non-linear positive correlation between SII and CKD. Threshold analysis revealed a saturation effect of SII at the inflection point of 2100 (1,000 cells/μl). When SII < 2100 (1,000 cells/μl), SII was an independent risk element for CKD.

CONCLUSIONS

In the adult US population, our study found a positive association between SII and CKD (inflection point: 2100). The SII can be considered a positive indicator to identify CKD promptly and guide therapy.

A biomimetic renal fibrosis progression model on-chip evaluates anti-fibrotic effects longitudinally in a dynamic fibrogenic niche

Although renal fibrosis can advance chronic kidney disease and progressively lead to end-stage renal failure, no effective anti-fibrotic drugs have been clinically approved. To aid drug development, we developed a biomimetic renal fibrosis progression model on-chip to evaluate anti-fibrotic effects of natural killer cell-derived extracellular vesicles and pirfenidone (PFD) across different fibrotic stages. First, the dynamic interplay between fibroblasts and kidney-derived extracellular matrix (ECM) resembling the fibrogenic niche on-chip demonstrated that myofibroblasts induced by stiff ECM in 3 days were reversed to fibroblasts by switching to soft ECM, which was within 2, but not 7 days. Second, PFD significantly down-regulated the expression of α-SMA in NRK-49F in medium ECM, as opposed to stiff ECM. Third, a study in rats showed that early administration of PFD significantly inhibited renal fibrosis in terms of the expression levels of α-SMA and YAP. Taken together, both on-chip and animal models indicate the importance of early anti-fibrotic intervention for checking the progression of renal fibrosis. Therefore, this renal fibrosis progression on-chip with a feature of recapitulating dynamic biochemical and biophysical cues can be readily used to assess anti-fibrotic candidates and to explore the tipping point when the fibrotic fate can be rescued for better medical intervention.

A set of urinary peptides can predict early renal damage in primary hypertension

OBJECTIVES

Renal diseases caused by primary hypertension (HTN) are often asymptomatic without sensitive markers for early diagnosis and prediction, easily progressing to severe and irreversible renal damage in patients with clinical manifestations. This study explored whether a set of urinary peptides could serve as a potential biomarker for early prediction of renal damage in HTN.

METHODS

Urinary peptides level of healthy individuals, HTN + normoalbuminuric and HTN + albuminuria patients were compared, and 22 baseline data including sex, age, renal function, hypertensive fundus lesions were collected. Patients diagnosed with HTN, albuminuria, and normal renal function were followed up. According to the follow-up results, the cut-off value of a set of urinary peptides in predicting hypertensive renal injury was calculated and analyzed in the high-risk and low-risk groups of HTN patients for its performance in detecting early hypertensive renal injury.

RESULTS

Among a sum of 319 participants, average urinary peptides level was significantly higher in patients with HTN than in normal individuals. A total of 147 HTN patients with normal albuminuria were followed up for a mean of 3.8 years. Thirty-five patients showed urinary albumin-to-creatinine ratio (uACR) at least 30 mg/g for three consecutive times. The receiver-operating characteristic (ROC) curve showed that the urinary peptides cut-off value for evaluating new-onset proteinuria in patients with HTN was 0.097. Based on this cut-off value, 39 and 108 patients were included in the high-risk and low-risk groups, respectively. Specifically, compared with patients in the low-risk group, those in the high-risk group showed significantly longer duration of HTN, higher proportions of hypertensive fundus lesions and at least 30 mg/g uACR, and higher levels of homocysteine (Hcy), cystatin C (CysC), beta-2 microglobulin (β2-MG), and uACR. 76.9% of high-risk patients had significantly higher new-onset proteinuria than the low-risk group. Correlation analysis demonstrated a positive correlation between urinary peptides and UACR ( r  = 0.494, P  < 0.001). The incidence of new-onset albuminuria was significantly higher in the high-risk group than in the low-risk group, as shown by Cox regression analysis. The areas under the curve of urinary peptides, Hcy, β2-MG and CysC were 0.925, 0.753, 0.796 and 0.769, respectively.

CONCLUSION

A set of urinary peptides is a predictor of new-onset proteinuria in patients with HTN, therefore, it can be used for diagnosing patients with early renal injury in patients with HTN, contributing to early prevention and treatment of hypertensive nephropathy.

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

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

Urine Peptidome Analysis Identifies Common and Stage-Specific Markers in Early Versus Advanced CKD

Given the pathophysiological continuum of chronic kidney disease (CKD), different molecular determinants affecting progression may be associated with distinct disease phases; thus, identification of these players are crucial for guiding therapeutic decisions, ideally in a non-invasive, repeatable setting. Analyzing the urinary peptidome has been proven an efficient method for biomarker determination in CKD, among other diseases. In this work, after applying several selection criteria, urine samples from 317 early (stage 2) and advanced (stage 3b-5) CKD patients were analyzed using capillary electrophoresis coupled to mass spectrometry (CE-MS). The entire two groups were initially compared to highlight the respective pathophysiology between initial and late disease phases. Subsequently, slow and fast progressors were compared within each group in an attempt to distinguish phase-specific disease progression molecules. The early vs. late-stage CKD comparison revealed 929 significantly different peptides, most of which were downregulated and 268 with collagen origins. When comparing slow vs. fast progressors in early stage CKD, 42 peptides were significantly altered, 30 of which were collagen peptide fragments. This association suggests the development of structural changes may be reversible at an early stage. The study confirms previous findings, based on its multivariable-matched progression groups derived from a large initial cohort. However, only four peptide fragments differed between slow vs. fast progressors in late-stage CKD, indicating different pathogenic processes occur in fast and slow progressors in different stages of CKD. The defined peptides associated with CKD progression at early stage might potentially constitute a non-invasive approach to improve patient management by guiding (personalized) intervention.

A classifier based on 273 urinary peptides predicts early renal damage in primary hypertension

OBJECTIVES

Renal diseases caused by primary hypertension (HTN) are often asymptomatic without sensitive markers for early diagnosis and prediction, easily progressing to severe and irreversible renal damage in patients with clinical manifestations. This study explored whether a classifier developed based on 273 urinary peptides (CKD273) could serve as a potential biomarker for early prediction of renal damage in HTN.

METHODS

Urinary CKD273 level of healthy individuals, HTN + normoalbuminuric and HTN + albuminuria patients were compared, and 22 baseline data including sex, age, renal function, and hypertensive fundus lesions were collected. Patients diagnosed with HTN, albuminuria, and normal renal function were followed up. According to the follow-up results, the cut-off value of CKD273 in predicting hypertensive renal injury was calculated and analyzed in the high-risk and low-risk groups of HTN patients for its performance in detecting early hypertensive renal injury.

RESULTS

Among a sum of 319 participants, average urinary CKD273 level was significantly higher in patients with HTN than in normal individuals. A total of 147 HTN patients with normal albuminuria were followed up for a mean of 3.8 years. Thirty-five patients showed urinary albumin-to-creatinine ratio (uACR) at least 30 mg/g for three consecutive times. The receiver-operating characteristic (ROC) curve showed that the urinary CKD273 cut-off value for evaluating new-onset proteinuria in patients with HTN was 0.097. Based on this cut-off value, 39 and 108 patients were included in the high-risk and low-risk groups, respectively. Specifically, compared with patients in the low-risk group, those in the high-risk group showed significantly longer duration of HTN, higher proportions of hypertensive fundus lesions and at least 30 mg/g uACR, and higher levels of homocysteine (Hcy), cystatin C (CysC), beta-2 microglobulin (β2-MG), and uACR. 76.9% of high-risk patients had significantly higher new-onset proteinuria than the low-risk group. Correlation analysis demonstrated a positive correlation between urinary CKD273 and UACR ( r  = 0.494, P  = 0.000). The incidence of new-onset albuminuria was significantly higher in the high-risk group than in the low-risk group, as shown by Cox regression analysis. The areas under the curve of CKD273, Hcy, β2-MG, and CysC were 0.925, 0.753, 0.796, and 0.769, respectively.

CONCLUSION

Urinary CKD273 is a predictor of new-onset proteinuria in patients with HTN, therefore, it can be used for diagnosing patients with early renal injury in patients with HTN, contributing to early prevention and treatment of hypertensive nephropathy.

Urinary collagen peptides: Source of markers for bone metabolic processes in kidney transplant recipients

INTRODUCTION

Kidney transplant recipients (KTRs) are at an increased risk of fractures. Total urinary hydroxyproline excretion served as marker for bone resorption (BR) but was replaced by β-CrossLaps (CTX), a C-terminal collagen α-1(I) chain (COL1A1) telopeptide. We investigated the low-molecular-weight urinary proteome for peptides associated with changes in bone metabolism after kidney transplantation.

METHODS

Clinical and laboratory data including serum levels of CTX in 96 KTR from two nephrology centers were correlated with signal intensities of urinary peptides identified by capillary electrophoresis mass spectrometry.

RESULTS

Eighty-two urinary peptides were significantly correlated with serum CTX levels. COL1A1 was the predominant peptide source. Oral bisphosphonates were administered for decreased bone density in an independent group of 11 KTR and their effect was evaluated on the aforementioned peptides. Study of the peptides cleavage sites revealed a signature of Cathepsin K and MMP9. Seventeen of these peptides were significantly associated with bisphosphonate treatment, all showing a marked reduction in their excretion levels compared to baseline.

DISCUSSION

This study provides strong evidence for the presence of collagen peptides in the urine of KTR that are associated with BR and that are sensitive to bisphosphonate treatment. Their assessment might become a valuable tool to monitor bone status in KTR.

Proteomics: Progress and Promise of High-Throughput Proteomics in Chronic Kidney Disease

Current proteomic tools permit the high-throughput analysis of the blood proteome in large cohorts, including those enriched for chronic kidney disease (CKD) or its risk factors. To date, these studies have identified numerous proteins associated with cross-sectional measures of kidney function, as well as with the longitudinal risk of CKD progression. Representative signals that have emerged from the literature include an association between levels of testican-2 and favorable kidney prognosis and an association between levels of TNFRSF1A and TNFRSF1B and worse kidney prognosis. For these and other associations, however, understanding whether the proteins play a causal role in kidney disease pathogenesis remains a fundamental challenge, especially given the strong impact that kidney function can have on blood protein levels. Prior to investing in dedicated animal models or randomized trials, methods that leverage the availability of genotyping in epidemiologic cohorts-including Mendelian randomization, colocalization analyses, and proteome-wide association studies-can add evidence for causal inference in CKD proteomics research. In addition, integration of large-scale blood proteome analyses with urine and tissue proteomics, as well as improved assessment of posttranslational protein modifications (e.g., carbamylation), represent important future directions. Taken together, these approaches seek to translate progress in large-scale proteomic profiling into the promise of improved diagnostic tools and therapeutic target identification in kidney disease.

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.

SDS Electrophoresis on Gradient Polyacrylamide Gels as a Semiquantitative Tool for the Evaluation of Proteinuria

Proteinuria is an important sign of kidney diseases. Different protein patterns in urine associated with glomerular, tubular and overload proteinuria may be differentiated using the immunochemical detection of indicator proteins or via urinary proteins electrophoresis. Our aim was to characterize sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) using commercially available 4-20% gradient gels as a method to detect and differentiate proteinuria. Our laboratory-based study used excess urine samples collected for routine diagnostic purposes from adult patients of a tertiary-care hospital, including patients with albumin/creatinine < 30 mg/g and patients with dipstick proteinuria. The limit of albumin detection was estimated to be 3 mg/L. In 93 samples with albumin/creatinine < 30 mg/g, an albumin fraction was detected in 87% of samples with a minimum albumin concentration of 2.11 mg/L. The separation of 300 urine samples of patients with proteinuria revealed distinct protein patterns differentiated using the molecular weights of the detected proteins: glomerular (albumin and higher molecular weights) and two types of tubular proteinuria ("upper" ≥20 kDa and "lower" with lower molecular weights). These patterns were associated with different values of the glomerular filtration rate (median 66, 71 and 31 mL/min/1.72 m², respectively, p = 0.004) and different proportions of multiple myeloma and nephrological diagnoses. As confirmed using tandem mass spectrometry and western blot, the SDS-PAGE protein fractions contained indicator proteins including immunoglobulin G, transferrin (glomerular proteinuria), α1-microglobulin, retinol-binding protein, neutrophil gelatinase-associated lipocalin, cystatin C, and β2-microglobulin (tubular), immunoglobulin light chain, myoglobin, and lysozyme (overflow). SDS-PAGE separation of urine proteins on commercially available 4-20% gradient gels is a reliable technique to diagnose proteinuria and differentiate between its main clinically relevant types.

Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.

Discovery of Fibrinogen γ-chain as a potential urinary biomarker for renal interstitial fibrosis in IgA nephropathy

BACKGROUND

IgA nephropathy (IgAN) is a major cause of chronic kidney disease (CKD). Renal interstitial fibrosis is a hallmark of CKD progression. Non-invasive biomarkers are needed to dynamically evaluate renal fibrosis. Data independent acquisition (DIA)-based liquid chromatography-mass spectrometry (DIA-MS) was used to identify candidate urinary biomarkers in IgAN patients with different renal interstitial fibrosis degrees.

METHODS

Eighteen biopsy-proven IgAN patients and six healthy controls were recruited in a discovery cohort. Interstitial fibrosis changes were evaluated according to Oxford MEST-C scores. Urinary samples were analyzed with DIA-MS to identify hub proteins. Hub proteins were then confirmed by enzyme-linked immunosorbent assay (ELISA) in a validation cohort and the associated gene mRNA expression was analyzed using public gene expression omnibus (GEO) datasets.

RESULTS

Complement and coagulation cascades pathway was the main KEGG pathway related to the over-expressed proteins. Fibrinogen γ-Chain (FGG) was selected as the potential urinary marker for further validation. Urinary FGG to creatinine ratio (uFGG/Cr) levels were higher in both disease controls and IgAN group than in healthy controls, but were not significantly different between IgAN and disease groups. uFGG/Cr was confirmed to be increased with the extent of renal fibrosis and presented moderate correlations with T score (r = 0.614, p < 0.01) and eGFR (r = -0.682, p < 0.01), and a mild correlation with UTP (r = 0.497, p < 0.01) in IgAN group. In disease control group, uFGG/Cr was higher in patients with T1 + 2 compared to those with T0. uFGG/Cr had a good discriminatory power to distinguish different fibrosis stages in IgAN: interstitial fibrosis ≤ 5% (minimal fibrosis) vs. interstitial fibrosis (mild fibrosis) > 5%, AUC 0.743; T0 vs. T1 + 2, AUC 0.839; T0 + 1 vs. T2, AUC 0.854. In disease control group, uFGG/Cr showed better performance of AUC than UTP between minimal and mild fibrosis (p = 0.038 for Delong's test). Moreover, GSE104954 dataset showed that FGG mRNA expression was up-regulated (fold change 1.20, p = 0.009) in tubulointerstitium of IgAN patients when compared to healthy living kidney donors.

CONCLUSION

Urinary FGG is associated with renal interstitial fibrosis and could be used as a noninvasive biomarker for renal fibrosis in IgAN.

Glycosylation Analysis of Urinary Peptidome Highlights IGF2 Glycopeptides in Association with CKD

Chronic kidney disease (CKD) is prevalent in 10% of world’s adult population. The role of protein glycosylation in causal mechanisms of CKD progression is largely unknown. The aim of this study was to identify urinary O-linked glycopeptides in association to CKD for better characterization of CKD molecular manifestations. Urine samples from eight CKD and two healthy subjects were analyzed by CE-MS/MS and glycopeptides were identified by a specific software followed by manual inspection of the spectra. Distribution of the identified glycopeptides and their correlation with Age, eGFR and Albuminuria were evaluated in 3810 existing datasets. In total, 17 O-linked glycopeptides from 7 different proteins were identified, derived primarily from Insulin-like growth factor-II (IGF2). Glycosylation occurred at the surface exposed IGF2 Threonine 96 position. Three glycopeptides (DVStPPTVLPDNFPRYPVGKF, DVStPPTVLPDNFPRYPVG and DVStPPTVLPDNFPRYP) exhibited positive correlation with Age. The IGF2 glycopeptide (tPPTVLPDNFPRYP) showed a strong negative association with eGFR. These results suggest that with aging and deteriorating kidney function, alterations in IGF2 proteoforms take place, which may reflect changes in mature IGF2 protein. Further experiments corroborated this hypothesis as IGF2 increased plasma levels were observed in CKD patients. Protease predictions, considering also available transcriptomics data, suggest activation of cathepsin S with CKD, meriting further investigation.

Machine Learning-Based Urine Peptidome Analysis to Predict and Understand Mechanisms of Progression to Kidney Failure

Introduction

The identification of patients with chronic kidney disease (CKD) at risk of progressing to kidney failure (KF) is important for clinical decision-making. In this study we assesed whether urinary peptidome (UP) analysis may help classify patients with CKD and improve KF risk prediction.

Methods

The UP was analyzed using capillary electrophoresis coupled to mass spectrometry in a case-cohort sample of 1000 patients with CKD stage G3 to G5 from the French CKD-Renal Epidemiology and Information Network (REIN) cohort. We used unsupervised and supervised machine learning to classify patients into homogenous UP clusters and to predict 3-year KF risk with UP, respectively. The predictive performance of UP was compared with the KF risk equation (KFRE), and evaluated in an external cohort of 326 patients.

Results

More than 1000 peptides classified patients into 3 clusters with different CKD severities and etiologies at baseline. Peptides with the highest discriminative power for clustering were fragments of proteins involved in inflammation and fibrosis, highlighting those derived from α-1-antitrypsin, a major acute phase protein with anti-inflammatory and antiapoptotic properties, as the most significant. We then identified a set of 90 urinary peptides that predicted KF with a c-index of 0.83 (95% confidence interval [CI]: 0.81-0.85) in the case-cohort and 0.89 (0.83-0.94) in the external cohort, which were close to that estimated with the KFRE (0.85 [0.83-0.87]). Combination of UP with KFRE variables did not further improve prediction.

Conclusion

This study shows the potential of UP analysis to uncover new pathophysiological CKD progression pathways and to predict KF risk with a performance equal to that of the KFRE.

Proteomics profiling of kidney brush border membrane from rats using LC-MS/MS analysis

PURPOSE

Chronic kidney disease (CKD) is defined by a reduced renal function, that is, glomerular filtration rate, and the extent of kidney damage is assessed by determining serum creatinine levels and proteins in urine, diagnosed as proteinuria/albuminuria. Albuminuria increases with age and can result from glomerular and/or proximal tubule (PT) alterations. Brush border membranes (BBMs) on PT cells are important in maintaining the stability of PT functions.

EXPERIMENTAL DESIGN

An LC-MS/MS bottom-up proteomics analysis of BBMs from four groups of rat models was applied to investigate protein abundance alterations associated with CKD progression. Moreover, systems biology analyses were used to identify key proteins that can provide insight into the different regulated molecular pathways and processes associated with CKD.

RESULTS

Our results indicated that 303 proteins showed significantly altered expressions from the severe CKD BBM group when compared to the control. Focusing on renal diseases, several proteins including Ctnnb1, Fah, and Icam1 were annotated to kidney damage and urination disorder. The up-regulation of Ctnnb1 (β-catenin) could contribute to CKD through the regulation of the WNT signaling pathway.

CONCLUSION AND CLINICAL RELEVANCE

Overall, the study of protein abundance changes in BBMs from rat models helps to reveal protein corrections with important pathways and regulator effects involved in CKD. Although this study is focused on rat models, the results provided more information for a deeper insight into possible CKD mechanisms in humans.

Urinary Markers of Tubular Injury and Renal Fibrosis in Patients with Type 2 Diabetes and Different Phenotypes of Chronic Kidney Disease

This study assessed the urinary excretion of markers and mediators of tubular injury and renal fibrosis in patients with type 2 diabetes (T2D) and non-albuminuric and albuminuric patterns of chronic kidney disease (CKD). One hundred and forty patients with long-term T2D and different patterns of CKD and twenty non-diabetic individuals were included. Urinary retinol-binding protein 4 (RBP-4), glutathione-S-transferase α₁ and π (GST-α₁ and GST-π), transforming growth factor β (TGF-β), type I and type IV collagen (Col1 and Col4), bone morphogenic protein 7 (BMP-7), and hepatocyte growth factor (HGF) were assessed by ELISA. Patients with T2D demonstrated increased urinary excretion of RBP-4, GST-π, Col4, BMP-7, and HGF (all p < 0.05 vs. control). The excretion of RBP-4, GST-π, Col1, and Col4 was increased in patients with elevated albumin-to-creatinine ratio (UACR; all p < 0.05 vs. control), while BMP-7 and HGF were increased innormoalbuminuric patients also (p < 0.05). Urinary RBP-4, GST-α₁, Col1, Col4, and HGF correlated positively with UACR; meanwhile, no correlations with glomerular filtration rate were found. The results demonstrate that elevated urinary excretions of the markers of tubular injury (RBP-4, GST-π) and renal fibrosis (Col1, Col4), as well as HGF, an antifibrotic regulator, are associated with the albuminuric pattern of CKD in subjects with T2D.

Molecular profiling of urinary extracellular vesicles in chronic kidney disease and renal fibrosis

Chronic kidney disease (CKD) is a long-term kidney damage caused by gradual loss of essential kidney functions. A global health issue, CKD affects up to 16% of the population worldwide. Symptoms are often not apparent in the early stages, and if left untreated, CKD can progress to end-stage kidney disease (ESKD), also known as kidney failure, when the only possible treatments are dialysis and kidney transplantation. The end point of nearly all forms of CKD is kidney fibrosis, a process of unsuccessful wound-healing of kidney tissue. Detection of kidney fibrosis, therefore, often means detection of CKD. Renal biopsy remains the best test for renal scarring, despite being intrinsically limited by its invasiveness and sampling bias. Urine is a desirable source of fibrosis biomarkers as it can be easily obtained in a non-invasive way and in large volumes. Besides, urine contains biomolecules filtered through the glomeruli, mirroring the pathological state. There is, however, a problem of highly abundant urinary proteins that can mask rare disease biomarkers. Urinary extracellular vesicles (uEVs), which originate from renal cells and carry proteins, nucleic acids, and lipids, are an attractive source of potential rare CKD biomarkers. Their cargo consists of low-abundant proteins but highly concentrated in a nanosize-volume, as well as molecules too large to be filtered from plasma. Combining molecular profiling data (protein and miRNAs) of uEVs, isolated from patients affected by various forms of CKD, this review considers the possible diagnostic and prognostic value of uEVs biomarkers and their potential application in the translation of new experimental antifibrotic therapeutics.

Clinical Risk Factors For Kidney Tubule Biomarker Abnormalities Among Hypertensive Adults With Reduced eGFR in the SPRINT Trial

BACKGROUND

Urine biomarkers of kidney tubule health may distinguish aspects of kidney damage that cannot be captured by current glomerular measures. Associations of clinical risk factors with specific kidney tubule biomarkers have not been evaluated in detail.

METHODS

We performed a cross-sectional study in the Systolic Blood Pressure Intervention Trial among 2,436 participants with a baseline estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2. Associations between demographic and clinical characteristics with urine biomarkers of kidney tubule health were evaluated using simultaneous multivariable linear regression of selected variables.

RESULTS

Each standard deviation higher age (9 years) was associated with 13% higher levels of chitinase-3-like protein-1 (YKL-40), indicating higher levels of tubulointerstitial inflammation and repair. Men had 31% higher levels of alpha-1 microglobulin and 16% higher levels of beta-2 microglobulin, reflecting worse tubule resorptive function. Black race was associated with significantly higher levels of neutrophil gelatinase-associated lipocalin (12%) and lower kidney injury molecule-1 (26%) and uromodulin (22%). Each standard deviation (SD) higher systolic blood pressure (SBP) (16 mmHg) was associated with 10% higher beta-2 microglobulin and 10% higher alpha-1 microglobulin, reflecting lower tubule resorptive function.

CONCLUSIONS

Clinical and demographic characteristics, such as race, sex, and elevated SBP, are associated with unique profiles of tubular damage, which could reflect under-recognized patterns of kidney tubule disease among persons with decreased eGFR.

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

OBJECTIVE

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

RESEARCH DESIGN AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Rethinking Chronic Kidney Disease in the Aging Population

The process of aging population will inevitably increase age-related comorbidities including chronic kidney disease (CKD). In light of this demographic transition, the lack of an age-adjusted CKD classification may enormously increase the number of new diagnoses of CKD in old subjects with an indolent decline in kidney function. Overdiagnosis of CKD will inevitably lead to important clinical consequences and pronounced negative effects on the health-related quality of life of these patients. Based on these data, an appropriate workup for the diagnosis of CKD is critical in reducing the burden of CKD worldwide. Optimal management of CKD should be based on prevention and reduction of risk factors associated with kidney injury. Once the diagnosis of CKD has been made, an appropriate staging of kidney disease and timely prescriptions of promising nephroprotective drugs (e.g., RAAS, SGLT-2 inhibitors, finerenone) appear crucial to slow down the progression toward end-stage kidney disease (ESKD). The management of elderly, comorbid and frail patients also opens new questions on the appropriate renal replacement therapy for this subset of the population. The non-dialytic management of CKD in old subjects with short life expectancy features as a valid option in patient-centered care programs. Considering the multiple implications of CKD for global public health, this review examines the prevalence, diagnosis and principles of treatment of kidney disease in the aging population.

Novel strategies in nephrology: what to expect from the future?

Chronic kidney disease (CKD) will become the fifth global case of death by 2040. Its largest impact is on premature mortality but the number of persons with kidney failure requiring renal replacement therapy (RRT) is also increasing dramatically. Current RRT is suboptimal due to the shortage of kidney donors and dismal outcomes associated with both hemodialysis and peritoneal dialysis. Kidney care needs a revolution. In this review, we provide an update on emerging knowledge and technologies that will allow an earlier diagnosis of CKD, addressing the current so-called blind spot (e.g. imaging and biomarkers), and improve renal replacement therapies (wearable artificial kidneys, xenotransplantation, stem cell-derived therapies, bioengineered and bio-artificial kidneys).

Evaluation of the reliability of MS1-based approach to profile naturally occurring peptides with clinical relevance in urine samples

RATIONALE

The profiling of natural urinary peptides is a valuable indicator of kidney condition. While front-end separation limits the speed of peptidomic profiling, MS1-based results suffer from limited peptide coverage and specificity. Clinical studies on chronic kidney disease require an effective strategy to balance the trade-off between identification depth and throughput.

METHODS

CKD273, a urinary proteome classifier associated with chronic kidney disease, in samples from diabetic nephropathy patients was profiled in parallel using capillary electrophoresis-mass spectrometry (CE-MS), liquid chromatography with mass spectrometry (LC-MS), and matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS). Through cross-comparison of results from MS1 of unfractionated peptides and elution-time-resolved MS1 as well as MS/MS in LC- and CE-MS approaches, we evaluated the contribution of false-positive identification to MS1-based identification and quantitation, and analyzed the benefit of front-end separation in terms of accuracy and efficiency.

RESULTS

In LC- and CE-MS, although MS1 data resulted in higher number of identifications than MS/MS, elution-time-dependent analysis revealed extensive interference by non-CKD273 peptides, which would contribute up to 50% to quantitation if they are not separated from genuine CKD273 peptides. In the absence of separation, MS1 data resulted in lower numbers of identifications and abundance pattern that significantly deviated from those by liquid chromatography with tandem mass spectrometry (LC-MS/MS) or capillary electrophoresis with tandem mass spectrometry (CE-MS/MS). CE showed higher identification efficiency even when less sample was used or achieved faster separation.

CONCLUSIONS

To ensure the reliability of MS1-based urinary peptide profiling, front-end separation should not be omitted, and elution time should be used in addition to intact mass for identification. Including MS/MS in data acquisition does not compromise the speed or identification number, while benefiting data reliability by providing real-time sequence verification.

Urinary matrix Gla protein is associated with mortality risk in Flemish population: A prospective study

Background

Vascular calcification is strongly related to the risk of mortality and cardiovascular (CV) diseases. In vascular calcification, matrix Gla protein (MGP), a small vitamin K-dependent protein, is an important mineralization inhibitor. Recent studies showed that circulating MGP is associated with mortality risk. However, the longitudinal association between urinary excretion of MGP and all-cause mortality was not established.

Materials and methods

Urinary MGP was measured in 776 randomly recruited Flemish population (mean age: 51.2 years; 50.9% women) at baseline (during 2005–2010) using capillary electrophoresis coupled with mass spectrometry. Plasma inactive MGP [desphospho-uncarboxylated MGP (dp-ucMGP)] levels were quantified in 646 individuals by ELISA kits. Mortality status was ascertained through the Belgian Population Registry until 2016. The longitudinal association with mortality was determined by the multivariate-adjusted Cox proportional hazards regression models. The multivariate linear regression models were used to identify determinants of urinary MGP level.

Results

Over the 9.2 years, 47 (6.06%) participants died, including 15 CV deaths. For a doubling of urinary MGP, the hazard ratios (HRs) were 1.31 (95% CI: 1.01–1.69, P = 0.040) for all-cause mortality and 2.05 (95% CI: 1.11–3.79, P = 0.023) for CV mortality with adjustment for covariates, including estimated glomerular filtration rate and urine microalbumin. The addition of urinary MGP to the basic models improved the reclassification as suggested by the increased net reclassification improvement [64.01% (95% CI: 32.64–98.63)] and integrated discrimination improvement [2.33% (95% CI: 0.24–4.71)]. Circulating inactive MGP, total cholesterol, urine microalbumin, and smoking were significantly associated with urinary MGP levels (P ≤ 0.041), independent of sex and age.

Conclusion

Elevated urinary MGP was associated with an increased risk of all-cause mortality and CV mortality and improved the risk reclassification for all-cause mortality. These findings suggested that urinary MGP might be useful in mortality risk assessment in the general population. However, these observations need to be replicated in larger studies with a longer follow-up time.

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Objective

To establish a prediction model for the risk evaluation of chronic kidney disease (CKD) to guide the management and prevention of CKD.

Methods

A total of 1263 patients with CKD and 1948 patients without CKD admitted to the Tongde Hospital of the Zhejiang Province from January 1, 2008, to December 31, 2018, were retrospectively analyzed. Spearman's correlation was used to analyze the relationship between CKD and laboratory parameters. XGBoost, random forest, Naive Bayes, support vector machine, and multivariate logistic regression algorithms were employed to establish prediction models for the risk evaluation of CKD. The accuracy, precision, recall, F1 score, and area under the receiver operating curve (AUC) of each model were compared. The new bidirectional encoder representations from transformers with light gradient boosting machine (MD-BERT-LGBM) model was used to process the unstructured data and transform it into researchable unstructured vectors, and the AUC was compared before and after processing.

Results

Differences in laboratory parameters between CKD and non-CKD patients were observed. The neutrophil ratio and white blood cell count were significantly associated with the occurrence of CKD. The XGBoost model demonstrated the best prediction effect (accuracy = 0.9088, precision = 0.9175, recall = 0.8244, F1 score = 0.8868, AUC = 0.8244), followed by the random forest model (accuracy = 0.9020, precision = 0.9318, recall = 0.7905, F1 score = 0.581, AUC = 0.9519). Comparatively, the predictions of the Naive Bayes and support vector machine models were inferior to those of the logistic regression model. The AUC of all models was improved to some extent after processing using the new MD-BERT-LGBM model.

Conclusion

The new MD-BERT-LGBM model with the inclusion of unstructured data has contributed to the higher accuracy, sensitivity, and specificity of the prediction models. Clinical features such as age, gender, urinary white blood cells, urinary red blood cells, thrombin time, serum creatinine, and total cholesterol were associated with CKD incidence.

One-third of patients with lupus nephritis classified as complete responders continue to accrue progressive renal damage despite resolution of proteinuria

OBJECTIVE

Treatment response in lupus nephritis (LN) is defined based on proteinuria, yet protocol kidney biopsy studies have shown that patients with lupus can have active nephritis in the absence of proteinuria. Using estimated glomerular filtration rate (eGFR) trajectories, we characterised early chronic kidney disease in LN and examined whether certain patients continue to accrue renal damage despite proteinuric response.

METHODS

We conducted a single-centre study of patients diagnosed with their first episode of biopsy-proven class III, IV, and/or V LN (n=37). For each patient, eGFR trajectory was graphed over 5 years following renal biopsy. Participants were divided into those with progressive eGFR loss (eGFR slope <-5 mL/min/1.73 m2/year) versus those with stable eGFR. Participant demographics, renal biopsy features and response status at 1 year (urine protein to creatinine ratio <500 mg/g) were compared between eGFR trajectory groups.

RESULTS

Overall, 30% (n=11) of participants accrued progressive eGFR loss despite standard of care therapy over the first 5 years following renal biopsy. There were no significant differences in baseline renal biopsy features, medication regimens or comorbidities between eGFR trajectory groups. Resolution of proteinuria at 1 year did not differentiate between groups: 6 of 18 (33%) of complete responders continued to accrue renal damage compared with 5 of 17 (29%) of non-responders. Response status could not be assigned for two participants in the stable eGFR group due to missing clinical information at 1 year.

CONCLUSIONS

We identified an understudied category of patients with LN who accrue progressive renal damage despite apparent response to standard of care therapy. Better definitions and biomarkers of response are needed to improve renal outcomes and trial design.

Application of omics in hypertension and resistant hypertension

Hypertension is a major modifiable risk factor that affects the global health burden. Despite the availability of multiple antihypertensive drugs, blood pressure is often not optimally controlled. The prevalence of true resistant hypertension in treated hypertensive patients is ~2-20%, and these patients are at higher risk for adverse events and poor clinical outcomes. Therefore, an in-depth dissection of the pathophysiological mechanisms of hypertension and resistant hypertension is needed to identify more effective targets for regulating blood pressure. Omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and microbiomics, can accurately present the characteristics of organisms at varying molecular levels. Integrative omics can further reveal the network of interactions between molecular levels and provide a complete dynamic view of the organism. In this review, we describe the applications, progress, and challenges of omics technologies in hypertension. Specifically, we discuss the application of omics in resistant hypertension. We believe that omics approaches will produce a better understanding of the pathogenesis of hypertension and resistant hypertension and improve diagnostic and therapeutic strategies, thus increasing rates of blood pressure control and reducing the public health burden of hypertension.

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.

Urine peptidome in combination with transcriptomics analysis highlights MMP7, MMP14 and PCSK5 for further investigation in chronic kidney disease

Chronic kidney disease (CKD) is characterized by the loss of kidney function. The molecular mechanisms underlying the development and progression of CKD are still not fully understood. Among others, the urinary peptidome has been extensively studied, with several urinary peptides effectively detecting disease progression. However, their link to proteolytic events has not been made yet. This study aimed to predict the proteases involved in the generation of CKD-associated urinary excreted peptides in a well-matched (for age, sex, lack of heart disease) case-control study. The urinary peptide profiles from CKD (n = 241) and controls (n = 240) were compared and statistically analyzed. The in-silico analysis of the involved proteases was performed using Proteasix and proteases activity was predicted based on the abundance changes of the associated peptides. Predictions were cross-correlated to transcriptomics datasets by using the Nephroseq database. Information on the respective protease inhibitors was also retrieved from the MEROPS database. Totally, 303 urinary peptides were significantly associated with CKD. Among the most frequently observed were fragments of collagen types I, II and III, uromodulin, albumin and beta-2-microglobulin. Proteasix predicted 16 proteases involved in their generation. Through investigating CKD-associated transcriptomics datasets, several proteases are highlighted including members of matrix metalloproteinases (MMP7, MMP14) and serine proteases (PCSK5); laying the foundation for further studies towards elucidating their role in CKD pathophysiology.

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.

Urine proteomics for prediction of disease progression in patients with IgA nephropathy

BACKGROUND

Risk of kidney function decline in immunoglobulin A (IgA) nephropathy (IgAN) is significant and may not be predicted by available clinical and histological tools. To serve this unmet need, we aimed at developing a urinary biomarker-based algorithm that predicts rapid disease progression in IgAN, thus enabling a personalized risk stratification.

METHODS

In this multicentre study, urine samples were collected in 209 patients with biopsy-proven IgAN. Progression was defined by tertiles of the annual change of estimated glomerular filtration rate (eGFR) during follow-up. Urine samples were analysed using capillary electrophoresis coupled mass spectrometry. The area under the receiver operating characteristic curve (AUC) was used to evaluate the risk prediction models.

RESULTS

Of the 209 patients, 64% were male. Mean age was 42 years, mean eGFR was 63 mL/min/1.73 m2 and median proteinuria was 1.2 g/day. We identified 237 urine peptides showing significant difference in abundance according to the tertile of eGFR change. These included fragments of apolipoprotein C-III, alpha-1 antitrypsin, different collagens, fibrinogen alpha and beta, titin, haemoglobin subunits, sodium/potassium-transporting ATPase subunit gamma, uromodulin, mucin-2, fractalkine, polymeric Ig receptor and insulin. An algorithm based on these protein fragments (IgAN237) showed a significant added value for the prediction of IgAN progression [AUC 0.89; 95% confidence interval (CI) 0.83-0.95], as compared with the clinical parameters (age, gender, proteinuria, eGFR and mean arterial pressure) alone (0.72; 95% CI 0.64-0.81).

CONCLUSIONS

A urinary peptide classifier predicts progressive loss of kidney function in patients with IgAN significantly better than clinical parameters alone.

Urinary Proteomics in Kidney Transplantation

Although kidney transplantation is the best treatment option for end stage kidney disease, it is still associated with long-term graft failure. One of the greater challenges for transplant professionals is the ability to identify grafts with a high risk of failure before initial decline of eGFR with irreversible graft changes. Transplantation medicine is facing an emerging need for novel disease end point-specific biomarkers, with practical application in preventive screening, early diagnostic, and improved prognostic and therapeutic utility. The aim of our review was to evaluate the clinical application of urinary proteomics in kidney transplant recipients at risk for any type of future graft failure.

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

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

Collagen-Derived Peptides in CKD: A Link to Fibrosis

Collagen is a major component of the extracellular matrix (ECM) and has an imminent role in fibrosis, in, among others, chronic kidney disease (CKD). Collagen alpha-1(I) (col1a1) is the most abundant collagen type and has previously been underlined for its contribution to the disease phenotype. Here, we examined 5000 urinary peptidomic datasets randomly selected from healthy participants or patients with CKD to identify urinary col1a1 fragments and study their abundance, position in the main protein, as well as their correlation with renal function. We identified 707 col1a1 peptides that differed in their amino acid sequence and/or post-translational modifications (hydroxyprolines). Well-correlated peptides with the same amino acid sequence, but a different number of hydroxyprolines, were combined into a final list of 503 peptides. These 503 col1a1 peptides covered 69% of the full col1a1 sequence. Sixty-three col1a1 peptides were significantly and highly positively associated (rho > +0.3) with the estimated glomerular filtration rate (eGFR), while only six peptides showed a significant and strong, negative association (rho < −0.3). A similar tendency was observed for col1a1 peptides associated with ageing, where the abundance of most col1a1 peptides decreased with increasing age. Collectively the results show a strong association between collagen peptides and loss of kidney function and suggest that fibrosis, potentially also of other organs, may be the main consequence of an attenuation of collagen degradation, and not increased synthesis.

Urinary peptidomics reveals proteases involved in idiopathic membranous nephropathy

BACKGROUND

Idiopathic membranous nephropathy (IMN) is a cause of nephrotic syndrome that is increasing in incidence but has unclear pathogenesis. Urinary peptidomics is a promising technology for elucidating molecular mechanisms underlying diseases. Dysregulation of the proteolytic system is implicated in various diseases. Here, we aimed to conduct urinary peptidomics to identify IMN-related proteases.

RESULTS

Peptide fingerprints indicated differences in naturally produced urinary peptide components among 20 healthy individuals, 22 patients with IMN, and 15 patients with other kidney diseases. In total, 1,080 peptide-matched proteins were identified, 279 proteins differentially expressed in the urine of IMN patients were screened, and 32 proteases were predicted; 55 of the matched proteins were also differentially expressed in the kidney tissues of IMN patients, and these were mainly involved in the regulation of proteasome-, lysosome-, and actin cytoskeleton-related signaling pathways. The 32 predicted proteases showed abnormal expression in the glomeruli of IMN patients based on Gene Expression Omnibus databases. Western blot revealed abnormal expression of calpain, matrix metalloproteinase 14, and cathepsin S in kidney tissues of patients with IMN.

CONCLUSIONS

This work shown the calpain/matrix metalloproteinase/cathepsin axis might be dysregulated in IMN. Our study is the first to systematically explore the role of proteases in IMN by urinary peptidomics, which are expected to facilitate discovery of better biomarkers for IMN.

Increased Collagen Turnover Is a Feature of Fibromuscular Dysplasia and Associated With Hypertrophic Radial Remodeling: A Pilot, Urine Proteomic Study

Fibromuscular dysplasia (FMD), a nonatherosclerotic, noninflammatory disease of medium-sized arteries, is an underdiagnosed disease. We investigated the urinary proteome and developed a classifier for discrimination of FMD from healthy controls and other diseases. We further hypothesized that urinary proteomics biomarkers may be associated with alterations in medium-sized, but not large artery geometry and mechanics. The study included 33 patients with mostly multifocal, renal FMD who underwent in depth arterial exploration using ultra-high frequency ultrasound. The cohort was separated in a training set of 23 patients with FMD from Belgium and an independent test set of 10 patients with FMD from Italy. For each set, controls matched 2:1 were selected from the Human Urinary Proteome Database. The specificity of the classifier was tested in 700 additional controls from general population studies, patients with chronic kidney disease (n=66) and coronary artery disease (n=31). Three hundred thirty-five urinary peptides, mostly related to collagen turnover, were identified in the training cohort and combined into a classifier. When applying in the test cohort, the area under the receiver operating characteristic curve was 1.00, 100% specificity at 100% sensitivity. The classifier maintained a high specificity in additional controls (98.3%), patients with chronic kidney (90.9%) and coronary artery (96.8%) diseases. Furthermore, in patients with FMD, the proteomic score was positively associated with radial wall thickness and wall cross-sectional area. In conclusion, a proteomic score has the potential to discriminate between patients with FMD and controls. If confirmed in a wider and more diverse cohort, these findings may pave the way for a noninvasive diagnostic test of FMD.

Urinary Protein and Peptide Markers in Chronic Kidney Disease

Chronic kidney disease (CKD) is a non-specific type of kidney disease that causes a gradual decline in kidney function (from months to years). CKD is a significant risk factor for death, cardiovascular disease, and end-stage renal disease. CKDs of different origins may have the same clinical and laboratory manifestations but different progression rates, which requires early diagnosis to determine. This review focuses on protein/peptide biomarkers of the leading causes of CKD: diabetic nephropathy, IgA nephropathy, lupus nephritis, focal segmental glomerulosclerosis, and membranous nephropathy. Mass spectrometry (MS) approaches provided the most information about urinary peptide and protein contents in different nephropathies. New analytical approaches allow urinary proteomic-peptide profiles to be used as early non-invasive diagnostic tools for specific morphological forms of kidney disease and may become a safe alternative to renal biopsy. MS studies of the key pathogenetic mechanisms of renal disease progression may also contribute to developing new approaches for targeted therapy.

Association of the chronic kidney disease urinary proteomic predictor CKD273 with clinical risk factors of graft failure in kidney allograft recipients

BACKGROUND

Kidney transplantation is the best treatment option for end stage kidney disease but is still associated with long term graft failure. In this study, we evaluated the application of urinary proteomics to identify grafts with high failure risk before initial decline of eGFR with irreversible graft changes.

METHODS

Fifty-two living donor kidney transplant recipients (KTR) with 8-years follow up were enrolled. All patients underwent clinical examination and had a routine laboratory screening at 3, 6, 12, 24, 36, 48 and 96 months post-transplantation, including creatinine, urea, albumin and 24h proteinuria. Graft function was estimated according to Nankivell. Urine samples at month 24 were analyzed by CE-MS followed by classification with the chronic kidney disease classifier CKD273.

RESULTS

CKD273 showed significant correlation with serum creatinine at every time point and moderate inverse correlation for the slope in glomerular filtration rates by Nankivell (r = -0.29, P = 0.05). Receiver operating characteristics analysis for graft loss and death within the next six years after proteomic analysis resulted in an area under curve value of 0.89 for CKD273 being superior to 0.67 for Nankivell eGFR. Stratification into CKD273 positive and negative patient groups revealed a hazard ratio of 16.5 for prevalence of graft loss in case of CKD273 positivity.

CONCLUSIONS

Using a representative KTR cohort with 8-years follow-up, we could demonstrate significant value of CKD273 for risk stratification of graft loss. This study provides the conceptual basis for further evaluation of CKD273 as prognostic tool for long-term graft function risk stratification by large prospective clinical trials.