Performances of acute kidney injury biomarkers vary according to sex

Background

Before implementing individualized strategies to treat acute kidney injury (AKI), identifying clusters of patients with divergent pathophysiological mechanisms, diagnosis criteria or outcomes is of the utmost importance. Here we studied sex-related molecular mechanisms in cardiac bypass (CBP) surgery patients developing AKI.

Methods

We compared the characteristics of 1170 patients referred for CBP surgery using multivariate logistic regression and propensity score-based analysis. Performances of the candidate urinary biomarkers at <4 h post-surgery, urinary neutrophil gelatinase-associated lipocalin (uNGAL), [IGFBP7]·[TIMP-2] product (NephroCheck) and a recently developed AKI signature of 204 urinary peptides (AKI204) to predict AKI were compared in both sexes.

Results

Incidence (∼25%) and severity of AKI were similar in men and women, even after adjustment for the usual risk factors of AKI, including baseline estimated glomerular filtration rate, age, diabetes mellitus, length of CBP and red blood cell transfusion. However, at the molecular level, performances of uNGAL, NephroCheck and AKI204 to predict AKI strongly diverged between men and women. In the full cohort, as well as in subgroups of men and women, the multimarker AKI204 signature outperformed uNGAL and NephroCheck and predicted the development of AKI significantly better in women than in men. Analysis of AKI204 at the single-peptide level suggested divergences of AKI mechanisms between sexes due to increased kidney inflammation in women (increased abundance of urinary fragments of osteopontin and uromodulin).

Conclusions

In patients referred for CBP surgery, significant clinical and biological differences between men and women as well as sexual dimorphism of AKI biomarker performances were identified. The urinary peptide signature points to sex-related molecular mechanisms underlying AKI.

Small peptide CSF fingerprint of amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by abnormal protein aggregation in the motor neurons. Present and earlier proteomic studies to characterize peptides in cerebrospinal fluid (CSF) associated with motoneuron pathology did not target low molecular weight proteins and peptides. We hypothesized that specific changes in CSF peptides or low molecular weight proteins are significantly altered in ALS, and that these changes may support deciphering molecular pathophysiology and even guide approaches towards therapeutic interventions.

METHODS

Cerebrospinal fluid (CSF) from 50 ALS patients and 50 non-ALS controls was collected, centrifuged immediately after collection, aliquoted into polypropylene test tubes, frozen within 30-40 min after the puncture, and stored at -80°C until use. Peptides were sequenced using capillary electrophoresis or liquid chromatography/mass spectrometry (CE-MS/MS or LC-MS/MS).

FINDINGS

In the CSF of 50 patients and 50 non-ALS controls 33 peptides were found, of which 14 could be sequenced using a non-lytic single-pot proteomic detection method, CE/MS. ALS deregulated peptides vs. controls included Integral membrane protein 2B, Neurosecretory protein VGF, Osteopontin, Neuroendocrine protein 7B2 (Secretogranin-V), EGF-containing fibulin-like extracellular matrix protein 1, Xylosyltransferase 1 XT-1, Chromogranin-A, Superoxide dismutase SOD-1, Secretogranin-1 (Chromogranin B), NR2F2 Nuclear Receptor Subfamily 2 Group F Member 2 and Collagen alpha-1(VII) chain.

INTERPRETATION

Most striking deregulations in CSF from ALS patients were found in VGF, Osteopontin, SOD-1 and EFEMP1 peptides. No associations of disease severity, duration and region of onset with sequenced peptides were found.

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 biomarker panels 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 biomarker panels for chronic kidney disease (CKD273), coronary artery disease (CAD238), and heart failure (HF1) were calculated.

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 pattern, 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.

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

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

Multiple urinary peptides are associated with hypertension: a link to molecular pathophysiology

OBJECTIVES

Hypertension is a common condition worldwide; however, its underlying mechanisms remain largely unknown. This study aimed to identify urinary peptides associated with hypertension to further explore the relevant molecular pathophysiology.

METHODS

Peptidome data from 2876 individuals without end-organ damage were retrieved from the Human Urinary Proteome Database: general population (discovery) or type 2 diabetic (validation) cohorts. Participants were divided based on systolic blood pressure (SBP) and diastolic BP (DBP) into hypertensive (SBP ≥140 mmHg and/or DBP ≥90 mmHg) and normotensive (SBP <120 mmHg and DBP <80 mmHg, without antihypertensive treatment) groups. Differences in peptide abundance between the two groups were confirmed using an external cohort (n = 420) of participants without end-organ damage, matched for age, BMI, eGFR, sex, and the presence of diabetes. Furthermore, the association of the peptides with BP as a continuous variable was investigated. The findings were compared with peptide biomarkers of chronic diseases and bioinformatic analyses were conducted to highlight the underlying molecular mechanisms.

RESULTS

Between hypertensive and normotensive individuals, 96 (mostly COL1A1 and COL3A1) peptides were found to be significantly different in both the discovery (adjusted) and validation (nominal significance) cohorts, with consistent regulation. Of these, 83 were consistently regulated in the matched cohort. A weak, yet significant, association between their abundance and standardized BP was also observed.

CONCLUSION

Hypertension is associated with an altered urinary peptide profile with evident differential regulation of collagen-derived peptides. Peptides related to vascular calcification and sodium regulation were also affected. Whether these modifications reflect the pathophysiology of hypertension and/or early subclinical organ damage requires further investigation.

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.

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.

Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease

BACKGROUND AND HYPOTHESIS

Specific urinary peptides hold information on disease pathophysiology, which, in combination with artificial intelligence, could enable non-invasive assessment of chronic kidney disease (CKD) aetiology. Existing approaches are generally specific for the diagnosis of single aetiologies. We present the development of models able to simultaneously distinguish and spatially visualize multiple CKD aetiologies.

METHODS

The urinary peptide data of 1850 healthy control (HC) and CKD [diabetic kidney disease (DKD), immunoglobulin A nephropathy (IgAN) and vasculitis] participants were extracted from the Human Urinary Proteome Database. Uniform manifold approximation and projection (UMAP) coupled to a support vector machine algorithm was used to generate multi-peptide models to perform binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications. This pipeline was compared with the current state-of-the-art single-aetiology CKD urinary peptide models.

RESULTS

In an independent test set, the developed models achieved 90.35% and 70.13% overall predictive accuracies, respectively, for the binary and the multiclass classifications. Omitting the UMAP step led to improved predictive accuracies (96.14% and 85.06%, respectively). As expected, the HC class was distinguished with the highest accuracy. The different classes displayed a tendency to form distinct clusters in the 3D space based on their disease state.

CONCLUSION

Urinary peptide data present an effective basis for CKD aetiology differentiation using machine learning models. Although adding the UMAP step to the models did not improve prediction accuracy, it may provide a unique visualization advantage. Additional studies are warranted to further validate the pipeline's clinical potential as well as to expand it to other CKD aetiologies and also other diseases.

Differentiating primary and secondary FSGS using non-invasive urine biomarkers

Background

Focal segmental glomerulosclerosis (FSGS) is divided into genetic, primary (p), uncertain cause, and secondary (s) forms. The subclasses differ in management and prognosis with differentiation often being challenging. We aimed to identify specific urine proteins/peptides discriminating between clinical and biopsy-proven pFSGS and sFSGS.

Methods

Sixty-three urine samples were collected in two different centers (19 pFSGS and 44 sFSGS) prior to biopsy. Samples were analysed using capillary electrophoresis-coupled mass spectrometry. For biomarker definition, datasets of age-/sex-matched normal controls (NC, n = 98) and patients with other chronic kidney diseases (CKDs, n = 100) were extracted from the urinary proteome database. Independent specificity assessment was performed in additional data of NC (n = 110) and CKD (n = 170).

Results

Proteomics data from patients with pFSGS were first compared to NC (n = 98). This resulted in 1179 biomarker (P < 0.05) candidates. Then, the pFSGS group was compared to sFSGS, and in a third step, pFSGS data were compared to data from different CKD etiologies (n = 100). Finally, 93 biomarkers were identified and combined in a classifier, pFSGS93. Total cross-validation of this classifier resulted in an area under the receiving operating curve of 0.95. The specificity investigated in an independent set of NC and CKD of other etiologies was 99.1% for NC and 94.7% for CKD, respectively. The defined biomarkers are largely fragments of different collagens (49%).

Conclusion

A urine peptide-based classifier that selectively detects pFSGS could be developed. Specificity of 95%-99% could be assessed in independent samples. Sensitivity must be confirmed in independent cohorts before routine clinical application.

Dynamics of urine proteomics biomarker and disease progression in patients with IgA nephropathy

BACKGROUND

Immunoglobulin A nephropathy (IgAN) frequently leads to kidney failure. The urinary proteomics-based classifier IgAN237 may predict disease progression at the time of kidney biopsy. We studied whether IgAN237 also predicts progression later in the course of IgAN.

METHODS

Urine from patients with biopsy-proven IgAN was analyzed using capillary electrophoresis-mass spectrometry at baseline (IgAN237-1, n = 103) and at follow-up (IgAN237-2, n = 89). Patients were categorized as "non-progressors" (IgAN237 ≤0.38) and "progressors" (IgAN237 >0.38). Estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio slopes were calculated.

RESULTS

Median age at biopsy was 44 years, interval between biopsy and IgAN237-1 was 65 months and interval between IgAN237-1 and IgAN237-2 was 258 days (interquartile range 71-531). IgAN237-1 and IgAN237-2 values did not differ significantly and were correlated (rho = 0.44, P < .001). Twenty-eight percent and 26% of patients were progressors based on IgAN237-1 and IgAN237-2, respectively. IgAN237 inversely correlated with chronic eGFR slopes (rho = -0.278, P = .02 for score-1; rho = -0.409, P = .002 for score-2) and with ±180 days eGFR slopes (rho = -0.31, P = .009 and rho = -0.439, P = .001, respectively). The ±180 days eGFR slopes were worse for progressors than for non-progressors (median -5.98 versus -1.22 mL/min/1.73 m2 per year for IgAN237-1, P < .001; -3.02 vs 1.08 mL/min/1.73 m2 per year for IgAN237-2, P = .0047). In multiple regression analysis baseline progressor/non-progressor according to IgAN237 was an independent predictor of eGFR180days-slope (P = .001).

CONCLUSION

The urinary IgAN237 classifier represents a risk stratification tool in IgAN also later in the course of the dynamic disease. It may guide patient management in an individualized manner.

Urinary peptide analysis to predict the response to blood pressure medication

BACKGROUND AND HYPOTHESIS

The risk of Diabetic Kidney Disease (DKD) progression is significant despite renin-angiotensin system (RAS) blocking agents treatment. Current clinical tools cannot predict whether or not patients will respond to the treatment with RAS-inhibitors (RASi). We aimed to investigate if proteome analysis could identify urinary peptides as biomarkers that could predict the response to angiotensin-converting enzyme inhibitor (ACEi) and angiotensin receptor blockers (ARBs) treatment to avoid DKD progression. Furthermore, we investigated the comparability of the estimated glomerular filtration rate (eGFR), calculated using four different GFR-equations, for DKD progression.

METHODS

We evaluated urine samples from a discovery cohort of 199 diabetic patients treated with RASi. DKD progression was defined based on eGFR percentage slope results between visits (∼1 year) and for the entire period (∼3 year) based on the eGFR values of each GFR-equation. Urine samples were analysed using capillary electrophoresis coupled mass spectrometry. Statistical analysis was performed between the uncontrolled (patients who did not respond to RASi treatment) and controlled kidney function groups (patients who responded to the RASi treatment). Peptides were combined in a support vector machine-based model. The area under the receiver operating characteristic curve (AUC) was used to evaluate the risk prediction models in two independent validation cohorts treated with RASi.

RESULTS

The classification of patients into uncontrolled and controlled kidney function varies depending on the GFR-equation used, despite the same sample set. We identified 227 peptides showing nominal significant difference and consistent fold changes between uncontrolled and controlled patients in at least three methods of eGFR calculation. These included fragments of collagens, alpha-1-antitrypsin, antithrombin-III, CD99 antigen, and uromodulin. A model based on 189 of 227 peptides (DKDp189) showed a significant prediction of non-response to the treatment/DKD progression in two independent cohorts.

CONCLUSIONS

The DKDp189 model demonstrates potential as a predictive tool for guiding treatment with RASi in diabetic patients.

Urinary peptides provide information about the risk of mortality across a spectrum of diseases and scenarios

BACKGROUND

There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides.

METHODS

Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated.

RESULTS

In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17-1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47-1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39-1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I).

CONCLUSION

The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death.

Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study

(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient's urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted.

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.

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.

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.

Presence of retinopathy and incident kidney and cardiovascular events in type 2 diabetes with normoalbuminuria - A post-hoc analysis of the PRIORITY randomized clinical trial

AIMS

Baseline diabetic retinopathy (DR) and risk of development of microalbuminuria, kidney function decline, and cardiovascular events (CVEs) in type 2 diabetes.

METHODS

Post-hoc analysis of the PRIORITY study including 1758 persons with type 2 diabetes and normoalbuminuria followed for a median of 2.5 (IQR: 2.0-3.0) years. DR diagnosis included non-proliferative and proliferative abnormalities, macular oedema, or prior laser treatment. Cox models were fitted to investigate baseline DR presence with development of persistent microalbuminuria (urinary albumin-creatinine ratio > 30 mg/g); chronic kidney disease (CKD) G3 (eGFR <60 ml/min/1.73m²); and CVE. Models were adjusted for relevant risk factors.

RESULTS

At baseline, 304 (17.3 %) had DR. Compared to persons without DR, they were older (mean ± SD: 62.7 ± 7.7 vs 61.4 ± 8.3 years, p = 0.019), had longer diabetes duration (17.9 ± 8.4 vs. 10.6 ± 7.0 years, p < 0.001), and higher HbA_1c (62 ± 13 vs. 56 ± 12 mmol/mol, p < 0.001). The adjusted hazard ratios of DR at baseline for development of microalbuminuria (n = 197), CKD (n = 166), and CVE (n = 64) were: 1.50 (95%CI: 1.07, 2.11), 0.87 (95%CI: 0.56, 1.34), and 2.61 (95%CI: 1.44, 4.72), compared to without DR.

CONCLUSIONS

Presence of DR in normoalbuminuric type 2 diabetes was associated with an increased risk of developing microalbuminuria and CVE, but not with kidney function decline.

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.

A universal predictive and mechanistic urinary peptide signature in acute kidney injury

BACKGROUND

The delayed diagnosis of acute kidney injury (AKI) episodes and the lack of specificity of current single AKI biomarkers hamper its management. Urinary peptidome analysis may help to identify early molecular changes in AKI and grasp its complexity to identify potential targetable molecular pathways.

METHODS

In derivation and validation cohorts totalizing 1170 major cardiac bypass surgery patients and in an external cohort of 1569 intensive care unit (ICU) patients, a peptide-based score predictive of AKI (7-day KDIGO classification) was developed, validated, and compared to the reference biomarker urinary NGAL and NephroCheck and clinical scores.

RESULTS

A set of 204 urinary peptides derived from 48 proteins related to hemolysis, inflammation, immune cells trafficking, innate immunity, and cell growth and survival was identified and validated for the early discrimination (&lt; 4 h) of patients according to their risk to develop AKI (OR 6.13 [3.96-9.59], p &lt; 0.001) outperforming reference biomarkers (urinary NGAL and [IGFBP7].[TIMP2] product) and clinical scores. In an external cohort of 1569 ICU patients, performances of the signature were similar (OR 5.92 [4.73-7.45], p &lt; 0.001), and it was also associated with the in-hospital mortality (OR 2.62 [2.05-3.38], p &lt; 0.001).

CONCLUSIONS

An overarching AKI physiopathology-driven urinary peptide signature shows significant promise for identifying, at an early stage, patients who will progress to AKI and thus to develop tailored treatments for this frequent and life-threatening condition. Performance of the urine peptide signature is as high as or higher than that of single biomarkers but adds mechanistic information that may help to discriminate sub-phenotypes of AKI offering new therapeutic avenues.

Predictive performance and clinical application of COV50, a urinary proteomic biomarker in early COVID-19 infection: a prospective multicentre cohort study

Background

The SARS-CoV-2 pandemic is a worldwide challenge. The CRIT-CoV-U pilot study generated a urinary proteomic biomarker consisting of 50 peptides (COV50), which predicted death and disease progression from SARS-CoV-2. After the interim analysis presented for the German Government, here, we aimed to analyse the full dataset to consolidate the findings and propose potential clinical applications of this biomarker.

Methods

CRIT-CoV-U was a prospective multicentre cohort study. In eight European countries (Austria, France, Germany, Greece, North Macedonia, Poland, Spain, and Sweden), 1012 adults with PCR-confirmed COVID-19 were followed up for death and progression along the 8-point WHO scale. Capillary electrophoresis coupled with mass spectrometry was used for urinary proteomic profiling. Statistical methods included logistic regression and receiver operating characteristic curve analysis with a comparison of the area under curve (AUC) between nested models. Hospitalisation costs were derived from the care facility corresponding with the Markov chain probability of reaching WHO scores ranging from 3 to 8 and flat-rate hospitalisation costs adjusted for the gross per capita domestic product of each country.

Findings

From June 30 to Nov 19, 2020, 228 participants were recruited, and from April 30, 2020, to April 14, 2021, 784 participants were recruited, resulting in a total of 1012 participants. The entry WHO scores were 1–3 in 445 (44%) participants, 4–5 in 529 (52%) participants, and 6 in 38 (4%) participants; and of all participants, 119 died and 271 had disease progression. The odds ratio (OR) associated with COV50 in all 1012 participants for death was 2·44 (95% CI 2·05–2·92) unadjusted and 1·67 (1·34–2·07) when adjusted for sex, age, BMI, comorbidities, and baseline WHO score; and for disease progression, the OR was 1·79 (1·60–2·01) when unadjusted and 1·63 (1·41–1·91) when adjusted (p<0·0001 for all). The predictive accuracy of the optimised COV50 thresholds was 74·4% (71·6–77·1%) for mortality (threshold 0·47) and 67·4% (64·4–70·3%) for disease progression (threshold 0·04). When adjusted for covariables and the baseline WHO score, these thresholds improved AUCs from 0·835 to 0·853 (p=0·033) for death and from 0·697 to 0·730 (p=0·0008) for progression. Of 196 participants who received ambulatory care, 194 (99%) did not reach the 0·04 threshold. The cost reductions associated with 1 day less hospitalisation per 1000 participants were million Euro (M€) 0·887 (5–95% percentile interval 0·730–1·039) in participants at a low risk (COV50 <0·04) and M€2·098 (1·839-2·365) in participants at a high risk (COV50 ≥0·04).

Interpretation

The urinary proteomic COV50 marker might be predictive of adverse COVID-19 outcomes. Even in people with mild-to-moderate PCR-confirmed infections (WHO scores 1–4), the 0·04 COV50 threshold justifies earlier drug treatment, thereby potentially reducing the number of days in hospital and associated costs.

Funding

German Federal Ministry of Health.

MO379: Collagen-Derived Peptides in CKD: A Link to Fibrosis

BACKGROUND AND AIMS

Collagen is a major component of the extracellular matrix (ECM) and has an essential role in the onset and progression of fibrosis and chronic kidney disease (CKD). Collagen alpha-1(I) (col1a1) is the most abundant collagen type in humans and the involvement of col1a1in CKD onset and progression is well established. We aimed to assess in detail the association of urinary col1a1 fragments with the progression of CKD and ageing.

METHOD

About 5000 urinary peptidomic datasets from healthy participants and CKD patients were retrieved from the ‘Human Urinary Proteome Database’. Inclusion criteria were age &gt;18 years and eGFR being available. Urinary col1a1 fragments were identified and their abundance and correlation with eGFR and age were investigated.

RESULTS

Of 707 specific col1a1 peptides, 63 were significantly and highly positively associated with eGFR, while only six peptides showed a significant and strong negative association. A similar tendency was observed for ageing, where the abundance of most of the 244 peptides significantly associated with age, decreased with increasing age. The ten most significantly correlated peptides associated with eGFR corrected for age or age in a cohort matched for eGFR are listed in the table.

CONCLUSION

The results show a strong association between the reduced abundance of urinary collagen peptides and loss of kidney function as well as ageing. The data suggest that the col1a1 peptides indicate reduced degradation of collagen in CKD and aging. The results further suggest that fibrosis, potentially also of other organs, may be the consequence of attenuation of collagen degradation, and not increased synthesis.

MO386: Visualising and Differentiating Kidney Disorders by Urinary Peptidomics Using a Machine Learning Approach

BACKGROUND AND AIMS

Currently &gt;20 000 native peptides in urine are known that are highly dynamic and able to display the status of different organs, especially the kidney. The characterization of urinary peptide profiles (UPP) enables the depiction of kidney disease severity, progression, fibrosis, and informs about the disease etiology. Advanced machine learning algorithms enable combining the changes in the very complex UPP associated with specific disease etiologies and reducing the dataspace to only few dimensions. Here, we show the application of a supervised machine learning pipeline for the visualization of different CKD etiologies based on high-dimensional peptidomics data, toward non-invasive disease classification.

METHOD

The Uniform Manifold Approximation and Projection (UMAP) algorithm was used as a novel nonlinear dimensionality-reduction technique to visualize and differentiate the UPP of patients with CKD of different etiologies. UPP of individual CKD patients (with diabetic kidney disease DKD, (n = 386), IgA nephropathy (n = 743) and vasculitis (n = 150)) and 369 healthy controls were extracted from the Human Urinary Proteome Database which contains &gt;85 000 proteomics datasets analyzed using capillary electrophoresis coupled mass spectrometry. About 80% of the extracted datasets were used as a training and 20% as validation set.

RESULTS

When applying supervised-UMAP to the DKD patient and control datasets, excellent separation with an F1 score of 99.5% ± 0.9% in the training set, and 93.1% ± 3.3% in the independent test set could be observed. Subsequently, this approach was applied to differentiate controls and three kidney diseases (DKD, IgA nephropathy and vasculitis) simultaneously. In the training set an accuracy of up to 98% in DN and controls, and an overall F1 score of 93.7% ± 2.3% (Figure) was achieved. In the independent test set, accuracy decreased as expected to around 90% for controls, 83.8% for IgA nephropathy, 79.2% for DKD and 66.7% for vasculitis. The overall F1 score in the test set is 81.9% ± 2.2%. Of note, controls (n = 369) were consistently classified with the highest accuracy across all groups, the disease with smallest sample size (vasculitis, n = 150) always showed the lowest accuracy. A substantial proportion of vasculitis was classified as IgA nephropathy, which has the largest sample size of n = 743.

For the validation of the pipeline the permutation test was used. Permutation test was repeated 100 times using all the samples of CKD -free controls and three kidney diseases. The resulted scores were normally distributed, with a mean of 32.5% and standard deviation of 1.2%. Compared with the true F1 score, which was calculated as 81.9% from above, the probability of obtaining such a high score by chance is very low (P &lt; 0.01).

CONCLUSION

We show that UMAP combined with supervised machine learning applied to high dimensional peptidomics data, enables distinguishing multiple kidney diseases with good accuracy and with very small standard deviation between multiple train-test splits. To our knowledge, our study is the first of its kind to reduce the complexity of the urinary peptidome to a single point in space, and categorize disease etiology based on the spatial information. The approach presented has the potential to enable non-invasive differential diagnosis of kidney disease etiologies. To improve accuracy of this non-invasive method, inclusion of additional clinical parameters will be tested.

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.

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.

Molecular Mapping of Urinary Complement Peptides in Kidney Diseases

Defective complement activation has been associated with various types of kidney disease. This led to the hypothesis that specific urine complement fragments may be associated with kidney disease etiologies, and disease progression may be reflected by changes in these complement fragments. We investigated the occurrence of complement fragments in urine, their association with kidney function and disease etiology in 16,027 subjects, using mass spectrometry based peptidomics data from the Human Urinary Proteome/Peptidome Database. Twenty-three different urinary peptides originating from complement proteins C3, C4 and factor B (CFB) could be identified. Most C3-derived peptides showed inverse association with estimated glomerular filtration rate (eGFR), while the majority of peptides derived from CFB demonstrated positive association with eGFR. Several peptides derived from the complement proteins C3, C4 and CFB were found significantly associated with specific kidney disease etiologies. These peptides may depict disease-specific complement activation and could serve as non-invasive biomarkers to support development of complement interventions through assessing complement activity for patients’ stratification and monitoring of drug impact. Further investigation of these complement peptides may provide additional insight into disease pathophysiology and could possibly guide therapeutic decisions, especially when targeting complement factors.

Urinary peptidomic profiles to address age-related disabilities: a prospective population study

Background

The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 called for innovation in addressing age-related disabilities. Our study aimed to identify and validate a urinary peptidomic profile (UPP) differentiating healthy from unhealthy ageing in the general population, to test the UPP predictor in independent patient cohorts, and to search for targetable molecular pathways underlying age-related chronic diseases.

Methods

In this prospective population study, we used data from participants in the Flemish Study on Environment, Genes and Health Outcomes (FLEMENGHO), done in northern Belgium from 1985 to 2019, and invited participants to a follow-up examination in 2005-10. Participants were eligible if their address was within 15 km of the examination centre and if they had not withdrawn consent in any of the previous examination cycles (1985-2004). All participants (2005-10) were also invited to an additional follow-up examination in 2009-13. Participants who took part in both the 2005-10 follow-up examination and in the additional 2009-13 follow-up visit constituted the derivation dataset, which included their 2005-10 data, and the time-shifted internal validation dataset, which included their 2009-13 data. The remaining participants who only had 2005-10 data constituted the synchronous internal validation dataset. Participants were excluded from analyses if they were incapacitated, had not undergone UPP, or had either missing or outlying (three SDs greater than the mean of all consenting participants) values of body-mass index, plasma glucose, or serum creatinine. The UPP was assessed by capillary electrophoresis coupled with mass spectrometry. The multidimensional UPP signature reflecting ageing was generated from the derivation dataset and validated in the time-shifted internal validation dataset and the synchronous validation dataset. It was further validated in patients with diabetes, COVID-19, or chronic kidney disease (CKD). In FLEMENGHO, the mortality endpoints were all-cause, cardiovascular, and non-cardiovascular mortality; other endpoints were fatal or non-fatal cancer and musculoskeletal disorders. Molecular pathway exploration was done using the Reactome and Kyoto Encyclopedia of Genes and Genomes databases.

Findings

778 individuals (395 [51%] women and 383 [49%] men; aged 16·2-82·1 years; mean age 50·9 years [SD 15·8]) from the FLEMENGHO cohort had a follow-up examination between 2005 and 2010, of whom 559 participants had a further follow-up from Oct 28, 2009, to March 19, 2013, and made up the derivation (2005-10) and time-shifted internal validation (2009-13) datasets. 219 were examined once and constituted the synchronous internal validation dataset (2005-10). With correction for multiple testing and multivariable adjustment, chronological age was associated with 210 sequenced peptides mainly showing downregulation of collagen fragments. The trained model relating chronological age to UPP, derived by elastic net regression, included 54 peptides from 17 proteins. The UPP-age prediction model explained 76·3% (r=0·87) of chronological age in the derivation dataset, 54·4% (r=0·74) in the time-shifted validation dataset, and 65·3% (r=0·81) in the synchronous internal validation dataset. Compared with chronological age, the predicted UPP-age was greater in patients with diabetes (chronological age 50·8 years [SE 0·37] vs UPP-age 56·9 years [0·30]), COVID‑19 (53·2 years [1·80] vs 58·5 years [1·67]), or CKD (54·6 years [0·97] vs 62·3 years [0·85]; all p<0·0001). In the FLEMENGHO cohort, independent of chronological age, UPP-age was significantly associated with various risk markers related to cardiovascular, metabolic, and renal disease, inflammation, and medication use. Over a median of 12·4 years (IQR 10·8-13·2), total mortality, cardiovascular mortality, and osteoporosis in the population was associated with UPP-age independent of chronological age, with hazard ratios per 10 year increase in UPP-age of 1·54 (95% CI 1·22-1·95) for total mortality, 1·72 (1·20-2·47) for cardiovascular mortality, and 1·40 (1·06-1·85) for osteoporosis and fractures. The most relevant molecular pathways informed by the proteins involved deregulation of collagen biology and extracellular matrix maintenance.

Interpretation

The UPP signature indicative of ageing reflects fibrosis and extracellular matrix remodelling and was associated with risk factors and adverse health outcomes in the population and with accelerated ageing in patients. Innovation in addressing disability should shift focus from the ontology of diseases to shared disease mechanisms, in particular ageing-related fibrotic degeneration.

Funding

European Research Council, Ministry of the Flemish Community, OMRON Healthcare.

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.

SGLT2-Inhibition reverts urinary peptide changes associated with severe COVID-19: An in-silico proof-of-principle of proteomics-based drug repurposing

Severe COVID‐19 is reflected by significant changes in urine peptides. Based on this observation, a clinical test predicting COVID‐19 severity, CoV50, was developed and registered as in vitro diagnostic in Germany. We have hypothesized that molecular changes displayed by CoV50, likely reflective of endothelial damage, may be reversed by specific drugs. Such an impact by a drug could indicate potential benefits in the context of COVID‐19. To test this hypothesis, urinary peptide data from patients without COVID‐19 prior to and after drug treatment were collected from the human urinary proteome database. The drugs chosen were selected based on availability of sufficient number of participants in the dataset (n > 20) and potential value of drug therapies in the treatment of COVID‐19 based on reports in the literature. In these participants without COVID‐19, spironolactone did not demonstrate a significant impact on CoV50 scoring. Empagliflozin treatment resulted in a significant change in CoV50 scoring, indicative of a potential therapeutic benefit. The study serves as a proof‐of‐principle for a drug repurposing approach based on human urinary peptide signatures. The results support the initiation of a randomized control trial testing a potential positive effect of empagliflozin for severe COVID‐19, possibly via endothelial protective mechanisms.

CD99 and polymeric immunoglobulin receptor peptides deregulation in critical COVID-19: A potential link to molecular pathophysiology?

Identification of significant changes in urinary peptides may enable improved understanding of molecular disease mechanisms. We aimed towards identifying urinary peptides associated with critical course of COVID-19 to yield hypotheses on molecular pathophysiological mechanisms in disease development. In this multicentre prospective study urine samples of PCR-confirmed COVID-19 patients were collected in different centres across Europe. The urinary peptidome of 53 patients at WHO stages 6-8 and 66 at WHO stages 1-3 COVID-19 disease was analysed using capillary electrophoresis coupled to mass spectrometry. 593 peptides were identified significantly affected by disease severity. These peptides were compared with changes associated with kidney disease or heart failure. Similarities with kidney disease were observed, indicating comparable molecular mechanisms. In contrast, convincing similarity to heart failure could not be detected. The data for the first time showed deregulation of CD99 and polymeric immunoglobulin receptor peptides and of known peptides associated with kidney disease, including collagen and alpha-1-antitrypsin. Peptidomic findings were in line with the pathophysiology of COVID-19. The clinical corollary is that COVID-19 induces specific inflammation of numerous tissues including endothelial lining. Restoring these changes, especially in CD99, PIGR and alpha-1-antitripsin, may represent a valid and effective therapeutic approach in COVID-19, targeting improvement of endothelial integrity. This article is protected by copyright. All rights reserved.

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

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

A urinary peptidomic profile predicts outcome in SARS-CoV-2-infected patients

BACKGROUND

COVID-19 prediction models based on clinical characteristics, routine biochemistry and imaging, have been developed, but little is known on proteomic markers reflecting the molecular pathophysiology of disease progression.

METHODS

The multicentre (six European study sites) Prospective Validation of a Proteomic Urine Test for Early and Accurate Prognosis of Critical Course Complications in Patients with SARS-CoV-2 Infection Study (Crit-COV-U) is recruiting consecutive patients (≥ 18 years) with PCR-confirmed SARS-CoV-2 infection. A urinary proteomic biomarker (COV50) developed by capillary-electrophoresis-mass spectrometry (CE-MS) technology, comprising 50 sequenced peptides and identifying the parental proteins, was evaluated in 228 patients (derivation cohort) with replication in 99 patients (validation cohort). Death and progression along the World Health Organization (WHO) Clinical Progression Scale were assessed up to 21 days after the initial PCR test. Statistical methods included logistic regression, receiver operating curve (ROC) analysis and comparison of the area under the curve (AUC).

FINDINGS

In the derivation cohort, 23 patients died, and 48 developed worse WHO scores. The odds ratios (OR) for death per 1 standard deviation (SD) increment in COV50 were 3·52 (95% CI, 2·02-6·13, p <0·0001) unadjusted and 2·73 (1·25-5·95, p = 0·012) adjusted for sex, age, baseline WHO score, body mass index (BMI) and comorbidities. For WHO scale progression, the corresponding OR were 2·63 (1·80-3·85, p<0·0001) and 3·38 (1·85-6·17, p<0·0001), respectively. The area under the curve (AUC) for COV50 as a continuously distributed variable was 0·80 (0·72-0·88) for mortality and 0·74 (0·66-0·81) for worsening WHO score. The optimised COV50 thresholds for mortality and worsening WHO score were 0·47 and 0·04 with sensitivity/specificity of 87·0 (74·6%) and 77·1 (63·9%), respectively. On top of covariates, COV50 improved the AUC, albeit borderline for death, from 0·78 to 0·82 (p = 0·11) and 0·84 (p = 0·052) for mortality and from 0·68 to 0·78 (p = 0·0097) and 0·75 (p = 0·021) for worsening WHO score. The validation cohort findings were confirmatory.

INTERPRETATION

This first CRIT-COV-U report proves the concept that urinary proteomic profiling generates biomarkers indicating adverse COVID-19 outcomes, even at an early disease stage, including WHO stages 1-3. These findings need to be consolidated in an upcoming final dataset.

FUNDING

The German Federal Ministry of Health funded the study.

Urinary Peptides as Potential Non-Invasive Biomarkers for Lupus Nephritis: Results of the Peptidu-LUP Study

Background: Lupus nephritis (LN) is a severe manifestation of Systemic Lupus Erythematosus (SLE). The therapeutic strategy relies on kidney biopsy (KB) results. We tested whether urinary peptidome analysis could non-invasively differentiate active from non-active LN. Design: Urinary samples were collected from 93 patients (55 with active LN and 38 with non-active LN), forming a discovery (n = 42) and an independent validation (n = 51) cohort. Clinical characteristics were collected at inclusion and prospectively for 24 months. The urinary peptidome was analyzed by capillary-electrophoresis coupled to mass-spectrometry, comparing active LN to non-active LN, and assessing chronic lesions and response to therapy. The value of previously validated prognostic (CKD273) and differential diagnostic (LN172) signatures was evaluated. Results: Urinary peptides could not discriminate between active and non-active LN or predict early response to therapy. Tubulo-interstitial fibrosis was correlated to the CKD273. The LN172 score identified 92.5% of samples as LN. Few patients developed new-onset CKD. Conclusions: We validated the CKD273 and LN172 classifiers but did not identify a robust signature that could predict active LN and replace KB. The value of urinary peptidome to predict long-term CKD, or renal flares in SLE, remains to be evaluated.

Understanding glomerular diseases through proteomics

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Value of Urine Peptides in Assessing Kidney and Cardiovascular Disease

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

MO041URINE PROTEOMICS FOR PREDICTION OF DISEASE PROGRESSION IN PATIENTS WITH IGA NEPHROPATHY

Background and Aims

Although IgA nephropathy (IgAN) is the most common primary glomerulonephritis in many parts of the world, risk of progressive kidney function decline is significant. Furthermore, the effect of immunosuppressive treatment in IgAN currently remains uncertain. There is no validated tool to predict disease progression or response to treatment. To serve this unmet need, we aimed at developing a urinary biomarker-based algorithm that predicts fast disease progression in patients with IgAN, thus enabling a personalized risk stratification and potentially improving patient management.

Method

In this multicenter study in 7 centers in Europe and in Canada urine samples were collected as part of clinical routine at time of biopsy in n= 300 patients (63% male, age 42±14 years) with biopsy proven IgAN. The follow-up data were collected for at least one year. Progressive disease was defined as an annual loss of kidney function (estimated glomerular filtration rate, eGFR) of more than -5ml/min/1.73m2 per year or when end-stage kidney disease (ESKD) was reached. The institutional review boards of each of the participating centers approved this study.

Urine samples were analyzed using capillary electrophoresis coupled mass spectrometry (CE-MS). Whole proteome/peptidome profile was obtained for each sample.

This study received funding from the European Union´s ERA PerMed program.

Results

Urine proteome/peptidome profiles were obtained from n=294 patients. On average, 2383 peptides were detected per sample. The data were subsequently divided into a discovery (n=154) and validation cohort (n=140). The comparison of the progressors (n=35) and non-progressors (n=119) in the discovery cohort resulted in the definition of more than 100 significant peptides. These included mainly fragments from collagen, mostly type 1 (decreased in progressors) and from different blood derived proteins like alpha-1-antitrypsin, alpha-2-HS-glycoprotein and apolipoproteins (increased in progressors). The distribution of the 100 most significant peptides in the progressor and non-progressor group is shown in the figure. The peptides were combined into a classifier using support vector machine. After optimizing the classifier employing a take-one-out procedure combined with n-1 cross-validation, the urine-peptide based algorithm enabled separation of progressors versus no progressors with an accuracy of 90% in take-one-out cross-validation. This classifier was subsequently applied to the validation cohort and resulted in highly significant separation of progressive from non-progressive IgAN patients. Furthermore, this classifier will be further applied blinded in an independent well characterized multicenter cohort of 267 IgAN patients.

Conclusion

We identified a urinary proteome profile which was associated with progressive loss of GFR in patients with IgAN. Further validation of this profile in an independent cohort is ongoing. The data indicate that CE-MS-based urinary proteomics enables identifying IgAN patients at high risk of disease progression. These patients may benefit from aggressive immunosuppressive treatment. Upon validation of the classifier in an independent cohort, its value in predicting response to immunosuppression will be assessed, aiming at establishing an innovative strategy that could improve patient management and personalize treatment of IgAN patients.

P0106URINE COMPLEMENT FRAGMENTS ARE ASSOCIATED WITH KIDNEY FUNCTION AND DISEASE ETIOLOGY

Background and Aims

Changes in complement factors have been associated with chronic kidney disease, most prominently in the context of C3 glomerulopathy. Based on these reports, the hypothesis was generated that specific urine complement fragments are associated with kidney disease etiologies, and progression of the disease may be reflected by changes in these proteins or peptides. In this study, we aimed at investigating the occurrence of complement fragments in urine, and their association with kidney function in general and disease etiology in particular.

Methods

Urine samples from patients with kidney disease of different etiologies and control subjects with normal kidney function were investigated using tandem mass spectrometry coupled with capillary electrophoresis and liquid chromatography to identify complement derived peptides based on their amino acid sequence. Subsequently, distribution of these peptides in the different kidney disease etiologies and normal kidney function was investigated via analysis of datasets recorded in the human proteome/peptidome database. All datasets, where eGFR was available and at least one of the complement peptides detected, were included.

Results

Twenty one different urinary peptides derived from complement could be identified. These peptide fragments originated from the complement factors C3, C4A and C4B, and CFB. Further investigation of 4557 datasets recorded in the urine proteome/peptidome database meeting the aforementioned inclusion requirements (eg available eGFR values and with at least one of the complement peptides detected), revealed, for most C3-derived peptides, an inverse, while for the majority of peptides derived from CFB, a positive association with eGFR.

The table lists the detected complement derived peptides, the parental protein (Complement X), rho and p-value of the association with eGFR, and the number of samples (n) where the peptide was detected.

When investigating the urine complement fragments, as a result of disease etiology, disease-specific distribution was detected, indicating a distinct association of complement factors and associated proteases, depending on disease etiology. As an example, the peptide distribution in AKI, IgA-Nephropathy (IgA-N) and vasculitis is shown in the Figure.

Conclusion

Multiple complement-derived peptides are significantly associated with kidney function, and with certain kidney disease etiologies, such as AKI, IgA-N or vasculitis. Proteomic screening of these complement factors may provide a basis, not only for non-invasively assessing kidney disease, but also for an opportunity to develop novel drugs; the latter, via targeting the associated proteases responsible for the release of these complement-derived peptides, in a specific kidney disease manner, in urine.

P0724ASSOCIATIONS OF URINARY POLYMERIC IMMUNOGLOBULIN RECEPTOR PEPTIDES IN THE CONTEXT OF CARDIO-RENAL SYNDROM

Background and Aims

The polymeric immunoglobulin receptor (pIgR) which transports immunoglobulins from the basolateral to the apical surface of epithelial cells was recently shown to be associated with kidney dysfunction. The immune defense is initiated at the apical surface where the N-terminal domain of pIgR, termed secretory component (SC), is proteolytically cleaved and released either unbound (free SC) or bound to immunoglobulins. The aim of our study was to show the association of pIgR peptides with the cardio-renal syndrome in a large cohort and to get information on how the SC will be released.

Method

We investigated urinary peptides of 2707 individuals available in the Human Urine Proteome Database using capillary electrophoresis coupled to mass spectrometry.

Results

The mean abundance of 23 different pIgR peptides correlates negatively with the estimated glomerular filtration rate (eGFR, r=-0.314, p&lt;0.0001). Furthermore, pIgR peptides are significantly increased in coronary artery disease after adjustment for eGFR. We further predicted the proteases involved in urinary peptide generation using the Proteasix tool. Peptide cleavage site analysis suggests that several, and not one, proteases are involved in the generation of the SC.

Conclusion

In this large cohort, we could demonstrate that pIgR is associated with the cardio-renal syndrome and provide more detailed insights on how pIgR can be potentially cleaved to release the SC.

P0652THYMOSIN BETA 4 AND KIDNEY FUNCTION: AN INVERSE RELATIONSHIP?

Background and Aims

Thymosin β4 (Tβ4) is an abundant peptide in urine and plasma, involved in multiple processes including cell morphology, wound healing, and inflammation. A major, yet not well defined role of Tβ4 was described in the context of kidney and cardiovascular disease. Knock out of Tβ4 resulted, among others, in increased albuminuria in a nephrotoxic nephritis model. In animal models it has been shown that Tβ4 can modify renal and cardiovascular disease, fibrosis and inflammation. However, most of the data are derived from animal models and the relevance of Tβ4 in human is less clear. As a first step towards investigating the potential relevance of Tβ4 in chronic kidney disease (CKD) in human, we investigated the abundance of Tβ4 in human urine.

Method

We investigated human urine in 2240 subjects with a wide spread of kidney function for the presence of Tβ4 using capillary-electrophoresis coupled mass spectrometry (CE-MS) and tandem mass spectrometry. In addition, we evaluated the presence of Tβ4 abundance in hemodialysis fluid of 10 subjects.

Results

The full length acetylated Tβ4 peptide could be identified in human urine in high abundance (molecular mass 4960.485 Da). In addition, an oxidized isoform could also be observed at high abundance. When investigating the distribution of both isoforms in control subjects and patients at different stages of CKD, a highly significant inverse association of the abundance of Tβ4 with eGFR could be observed for both, with rho -0.388 for the non-oxidized and -0.387 for the oxidized isoform, p&lt;0.0001 in each case. In hemodialysate, Tβ4 is found as the most abundant peptide in 2 of the 10 analyzed subjects, on average the second most abundant peptide, with β2 microglobuline being the most abundant one.

Conclusion

Tβ4 is highly significantly inversely associated with kidney function, suggesting a role of Tβ4 in kidney disease and/or its complications. Based on the experimental data published from animal models, the increase in Tβ4 may reflect a potential physiological mechanism to counteract CKD and its complications. Investigation of association of Tβ4 levels with progression in CKD, cardiovascular complications and death may inform on a potentially beneficial effect of Tβ4, is currently ongoing in longitudinal datasets and will be presented.

P0651PROTEOMIC CHARACTERIZATION OF OBESITY-RELATED NEPHROPATHY

Background and Aims

During the last years, with the increase in secondary diseases following obesity the focus on the context of abnormally high body weight and chronic kidney disease (CKD) has changed. While in individual care, CKD along with obesity and improving renal function along with weight loss can be seen frequently and trials on obesity reduction yielded promising renal results, systematic insight into the pathogenesis and epidemiology of nephropathy related to obesity without diabetes (Obesity-related nephropathy, ORN) is rather limited. This work reaches out to investigate impact of BMI on urine peptides and to characterize yet non-specified peptide patterns in urine that may be indicative of ORN and may indicate possible molecular mechanisms of ORN pathophysiology. We used urine samples from earlier projects which could be characterized for presence of diabetes, BMI, eGFR, and age. By defining such urinary peptide patterns, pathophysiology-enlightening hypotheses should be stimulated and pathways suggested to enable specific future characterization of ORN.

Method

In this study we investigated the association of urinary peptides with body mass index (BMI), and renal function in proteome datasets from 4015 individuals. For this study urinary proteome data stored in the Human Urine Proteome Database obtained by capillary electrophoresis coupled to mass spectrometry (CE-MS) were assessed.

Results

365 urinary peptides significantly associated with BMI could be identified. The majority of these peptides were collagen fragments. In addition, most of the peptides also demonstrated a significant concordant association with eGFR in the investigated cohort with diabetes exhibiting no significant association. A classifier based on 150 urine peptides enabled distinguishing non-obese subjects with preserved kidney function from obese, non-diabetic subjects with eGFR&gt;45 mL/min/1.73m in an independent cohort with an area under curve (AUC) of 0.93 (Fig.). The vast majority of significantly associated peptides are specific collagen fragments, most of them from collagen type I, and most of them (in fact all of the 10 most significant ones) are reduced with increasing BMI.

Conclusion

These analyses in a huge cohort of urine samples from patients with known eGFR, BMI and diabetes status showed a complex multi-level association between peptide fragments with renal risk in obesity. Of particular note, these relationships could be retrieved although the overall cohort inherited an almost negative eGFR – BMI gross correlation, presumably due to the origin of the dataset from a non-obesity-directed approach. Most peptides associated with GFR and BMI belonged to the collagen superfamily and were inversely associated with BMI but directly with eGFR.

This concordant observation indicates a reduction of collagen degradation with increasing BMI and/or reduced kidney function. It is tempting to speculate that collagen homeostasis along with body mass on one hand, and kidney function on the other, are substantially interconnected. The very strict association of BMI with collagen fragments was surprising, but also indicates specificity.

P0360URINARY PEPTIDOMIC ANALYSIS IN PROLIFERATIVE VERSUS NON-PROLIFERATIVE LUPUS NEPHRITIS : RESULTS OF THE PEPTIDU-LUP STUDY

Background and Aims

Lupus nephritis (LN) is a frequent manifestation of Systemic Lupus Erythematosus (SLE). The therapeutic strategy relies on the result of kidney biopsy, which differentiates proliferative LN (PLN) from non-proliferative LN (NPLN). The analysis of the urinary peptidome has led to the identification of prognostic biomarkers in chronic kidney disease (CKD, namely the CKD273 classifier), or, as a liquid biopsy, for the diagnosis of glomerulonephritides. We verified whether urinary peptidomics could predict the severity of renal pathological injury and damage in LN.

Method

Urine samples, collected before kidney biopsy, were analyzed by capillary-electrophoresis coupled to mass-spectrometry (CE-MS). Urinary peptide profiles were compared between PLN and NPLN. Predictive values for chronic pathological lesions (glomerulosclerosis and interstitial fibrosis/tubular atrophy (IF/TA)), response to therapy and development of CKD were also assessed. Clinical characteristics, routine laboratory parameters and immunological SLE markers were collected at inclusion and prospectively for at least 24 months.

Results

We collected 100 urinary samples from patients with LN, forming a discovery (n=67) and an independent validation (n=33) cohort. Overall there were 69 patients with PLN (class III or IV +/-V with active lesions) and 31 patients with NPLN (class II or V or chronic lesions). In the discovery cohort, the abundance of 36 urinary peptides differed between PLN and NPLN (Mann-Whitney test). However, these peptides did not resist multiple testing correction. Among them, 17 could be sequenced (fragments of collagen-I, -II, -III, apolipoprotein A-I, Fibrinogen alpha chain and Histone H2B). Of the 17 sequenced peptides, 7 were also part of the CKD273 classifier. A mathematical model combining the 36 peptides classified PLN and NPLN patients from the validation cohort with an AUC of 0.75 and sensitivity and specificity of 0.81 and 0.53, respectively. Positivity of anti-dsDNA antibodies had the best sensitivity (0.96) and sterile pyuria the best specificity (0.68) to differentiate PLN from NPLN, and including the CKD273 score did not improve the accuracy of the predictive models. No urinary peptidomics profile was identified to predict early response to therapy in patients with LN.

Glomerulosclerosis was observed in 63/100 biopsies. In the discovery cohort, the abundance of 38 urinary peptides (including 22 sequenced) differed between patients with vs without glomerulosclerosis (Mann-Whitney test). Again, these peptides did not resist multiple testing correction. Of the 22 sequenced peptides, 7 (different from the peptides associated with PLN) belonged to the CKD273 classifier. A mathematical model combining the 38 peptides classified patients from the validation cohort with an AUC of 0.75 and sensitivity and specificity of 0.82 and 0.54, respectively, for the presence of glomerulosclerosis. Although the quantification of IF/TA was correlated to the pre-existing CKD273 classifier, including the CKD273 score did not improve the prediction of IF/TA when tested in combination with simple clinical parameters. Only 3 patients developed CKD after a mean follow-up of 4 years, therefore contribution of urinary peptidomics for the prediction of CKD in LN could not be evaluated.

Conclusion

Different urinary peptidomics signatures were identified among patients with LN, according to the presence of active proliferative lesions or chronic lesions. However, these panels did not resist multiple testing correction, and did not improve the diagnostic accuracy when combined with clinical or immunological markers. The contribution of urinary peptidomics to predict CKD in patients with LN, or as an early predictor of renal flares, could not be evaluated. Kidney biopsy remains central for the care of patients with LN.

Associations of urinary polymeric immunoglobulin receptor peptides in the context of cardio-renal syndrome

The polymeric immunoglobulin receptor (pIgR) transports immunoglobulins from the basolateral to the apical surface of epithelial cells. PIgR was recently shown to be associated with kidney dysfunction. The immune defense is initiated at the apical surface of epithelial cells where the N-terminal domain of pIgR, termed secretory component (SC), is proteolytically cleaved and released either unbound (free SC) or bound to immunoglobulins. The aim of our study was to evaluate the association of pIgR peptides with the cardio-renal syndrome in a large cohort and to obtain information on how the SC is released. We investigated urinary peptides of 2964 individuals available in the Human Urine Proteome Database generated using capillary electrophoresis coupled to mass spectrometry. The mean amplitude of 23 different pIgR peptides correlated negatively with the estimated glomerular filtration rate (eGFR, rho = −0.309, p < 0.0001). Furthermore, pIgR peptides were significantly increased in cardiovascular disease (coronary artery disease and heart failure) after adjustment for eGFR. We further predicted potential proteases involved in urinary peptide generation using the Proteasix algorithm. Peptide cleavage site analysis suggested that several, and not one, proteases are involved in the generation of the SC. In this large cohort, we could demonstrate that pIgR is associated with the cardio-renal syndrome and provided a more detailed insight on how pIgR can be potentially cleaved to release the SC.

Proteomic characterization of obesity-related nephropathy

Background

Nephropathy related to obesity lacks a pathophysiological understanding and definite diagnostic pathways by biomarkers.

Methods

In this study we investigated the association between urinary peptides and body mass index (BMI) and renal function in proteome data sets from 4015 individuals.

Results

A total of 365 urinary peptides were identified to be significantly associated with BMI. The majority of these peptides were collagen fragments. In addition, most of the peptides also demonstrated a significant concordant association with estimated glomerular filtration rate (eGFR) in the investigated cohort, with the presence of diabetes exhibiting no significant association. A new classifier was developed, based on 150 urinary peptides, that enabled the distinction of non-obese subjects with preserved kidney function from obese, non-diabetic subjects with eGFR >45 mL/min/1.73 m² in an independent cohort, with an area under the curve of 0.93.

Conclusions

On a molecular level, the data strongly suggest a link between obesity and fibrosis, which may be a major cause of obesity-related nephropathy.

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

BACKGROUND

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

METHODS

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

FINDINGS

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

INTERPRETATION

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

FUNDING

European Union Seventh Framework Programme.

A single-center study to evaluate the efficacy of a fetal urine peptide signature predicting postnatal renal outcome in fetuses with posterior urethral valves

BACKGROUND

Posterior urethral valves (PUVs) account for 17% of pediatric renal failure. The management of pregnancies involving fetuses with PUV is hampered by the fact that current clinical parameters obtained from fetal ultrasound and/or fetal urine biochemistry are insufficient to predict postnatal renal function. We previously have developed a fetal urine peptide signature (12PUV) that predicted with high precision postnatal renal failure at 2 years of age in fetuses with PUV. Here, we evaluated the accuracy of this signature to predict postnatal renal outcome in fetuses with PUV in an independent single-center study.

METHODS

Thirty-three women carrying fetuses with suspected PUV were included. Twenty-five fetuses received vesicoamniotic shunts during pregnancy. PUV was confirmed postnatally in 23 patients. Of those 23 fetuses, 2 were lost in follow-up. Four and 3 patients died in the pre- and perinatal periods, respectively. Follow-up renal function at 6 months of age was obtained for the remaining 14 patients. The primary outcome was early renal failure, defined by an eGFR < 60 mL/min/1.73 m² before 6 months of age or pre- or perinatal death.

RESULTS

The peptide signature predicted postnatal renal outcome in postnatally confirmed PUV fetuses with an AUC of 0.94 (95%CI 0.74-1.0) and an accuracy of 90% (95%CI 78-100). The signature predicted postnatal renal outcome for the suspected PUV cases with an AUC of 0.89 (95%CI 0.72-0.97) and an accuracy of 84% (95%CI 71-97).

CONCLUSIONS

This single-center study confirms the predictive power of the previously identified 12PUV fetal urinary peptide signature.

The urinary proteomics classifier chronic kidney disease 273 predicts cardiovascular outcome in patients with chronic kidney disease

Background

The urinary proteomic classifier chronic kidney disease 273 (CKD273) is predictive for the development and progression of chronic kidney disease (CKD) and/or albuminuria in type 2 diabetes. This study evaluates its role in the prediction of cardiovascular (CV) events in patients with CKD Stages G1–G5.

Methods

We applied the CKD273 classifier in a cohort of 451 patients with CKD Stages G1–G5 followed prospectively for a median of 5.5 years. Primary endpoints were all-cause mortality, CV mortality and the composite of non-fatal and fatal CV events (CVEs).

Results

In multivariate Cox regression models adjusting for age, sex, prevalent diabetes and CV history, the CKD273 classifier at baseline was significantly associated with total mortality and time to fatal or non-fatal CVE, but not CV mortality. Because of a significant interaction between CKD273 and CV history (P = 0.018) and CKD stages (P = 0.002), a stratified analysis was performed. In the fully adjusted models, CKD273 classifier was a strong and independent predictor of fatal or non-fatal CVE only in the subgroup of patients with CKD Stages G1–G3b and without a history of CV disease. In those patients, the highest tertile of CKD273 was associated with a &gt;10-fold increased risk as compared with the lowest tertile.

Conclusions

The urinary CKD273 classifier provides additional independent information regarding the CV risk in patients with early CKD stage and a blank CV history. Determination of CKD273 scores on a random urine sample may improve the efficacy of intensified surveillance and preventive strategies by selecting patients who potentially will benefit most from early risk management.