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.

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.

Non-Invasive Biomarkers for Diagnosis, Risk Prediction, and Therapy Guidance of Glomerular Kidney Diseases: A Comprehensive Review.

Effective management of glomerular kidney disease, one of the main categories of chronic kidney disease (CKD), requires accurate diagnosis, prognosis of progression, assessment of therapeutic efficacy, and, ideally, prediction of drug response. Multiple biomarkers and algorithms for the assessment of specific aspects of glomerular diseases have been reported in the literature. Though, the vast majority of these have not been implemented in clinical practice or are not available on a global scale due to limited access, missing medical infrastructure, or economical as well as political reasons. The aim of this review is to compile all currently available information on the diagnostic, prognostic, and predictive biomarkers currently available for the management of glomerular diseases, and provide guidance on the application of these biomarkers. As a result of the compiled evidence for the different biomarkers available, we present a decision tree for a non-invasive, biomarker-guided diagnostic path. The data currently available demonstrate that for the large majority of patients with glomerular diseases, valid biomarkers are available. However, despite the obvious disadvantages of kidney biopsy, being invasive and not applicable for monitoring, especially in the context of rare CKD etiologies, kidney biopsy still cannot be replaced by non-invasive strategies.

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.

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.

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.

Antifibrotic Soluble Thy-1 Correlates with Renal Dysfunction in Chronic Kidney Disease.

Kidney fibrosis is a major culprit in the development and progression of chronic kidney disease (CKD), ultimately leading to the irreversible loss of organ function. Thymocyte differentiation antigen-1 (Thy-1) controls many core functions of fibroblasts relevant to fibrogenesis but is also found in a soluble form (sThy-1) in serum and urine. We investigated the association of sThy-1 with clinical parameters in patients with CKD receiving hemodialysis treatment compared to individuals with a preserved renal function. Furthermore, Thy-1 tissue expression was detected in a mouse model of diabetic CKD (eNOS-/-; db/db) and non-diabetic control mice (eNOS-/-). Serum and urinary sThy-1 concentrations significantly increased with deteriorating renal function, independent of the presence of diabetes. Serum creatinine is the major, independent, and inverse predictor of serum sThy-1 levels. Moreover, sThy-1 is not only predicted by markers of renal function but is also itself an independent and strong predictor of markers of renal function, i.e., serum creatinine. Mice with severe diabetic CKD show increased Thy-1 mRNA and protein expression in the kidney compared to control animals, as well as elevated urinary sThy-1 levels. Pro-fibrotic mediators, such as interleukin (IL)-4, IL-13, IL-6 and transforming growth factor ß, increase Thy-1 gene expression and release of sThy-1 from fibroblasts. Our data underline the role of Thy-1 in the control of kidney fibrosis in CKD and raise the opportunity that Thy-1 may function as a renal antifibrotic factor.

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.

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.

Weight-reduction and changes in renal function in CKD patients participating in a conservative multimodal obesity program.

INTRODUCTION: Due to the global epidemic of obesity, there is increasing interest in a distinct entity, called obesity-related nephropathy (ORN). Data on sustainable effects of weight reductions, with conservative, non-surgical treatment programs, on renal function in CKD patients are scarce. MATERIALS AND METHODS: We retrospectively investigated patients with CKD (eGFR ≤ 60 mL/min/1.73m2) from a non-surgical multimodality obesity treatment program over 12 months. RESULTS: We identified 17 obese patients with CKD (estimated glomerular filtration rate (eGFR) ≤ 60 mL/min at baseline). 76% were female, 41% had type II diabetes mellitus, and the mean age was 59.6 ± 8.4 years (mean ± SD). Mean serum creatinine and eGFR at baseline were 106.4 ± 17.6 µmol/L and 53.4 ± 5.8 mL/min, respectively. Mean weight and body mass index (BMI) were 134.9 ± 26.4 and 50.1 ± 10.5 kg/m2, respectively. All subjects lost weight, with average weight loss of -32.2 ± 15.1 kg (p < 0.001) by the end of 12 months (BMI at 12 months 38.1 ± 7.8 kg/m2 (-12.0 ± 6.0 kg/m2, p < 0.001). Average 12-month creatinine was 92.2 ± 23.3 µmol/L, representing a drop of 14.2 ± 15.6 µmol/L (p = 0.004). Average eGFR increased by 14.8 ± 18.0 mL/min to a 12-month value of 68.2 ± 19.3 mL/min (p = 0.002). There were no significant differences when comparing patients with and without diabetes mellitus. CONCLUSION: These results demonstrate the potential renal impact of a non-surgical multimodal obesity program on renal function in very obese patients with CKD. Weight loss intervention should be highly encouraged especially in obese CKD patients.

Urinary Proteomics Associates with COVID-19 Severity: Pilot Proof-of-Principle Data and Design of a Multicentric Diagnostic Study.

SARS-CoV-2 infection results in a mild-to-moderate disease course in most patients, allowing outpatient self-care and quarantine. However, in approx. 10% of cases a two- or three-phasic critical disease course with starting from day 7 to 10 is observed. To facilitate and plan outpatient care, biomarkers prognosing such worsening at an early stage appear of outmost importance. In this accelerated article, we report on the identification of urinary peptides significantly associated with SARS-CoV-2 infection, and the development of a multi-marker urinary peptide based test, COVID20, that may enable prognosis of critical and fatal outcomes in COVID-19 patients. COVID20 is composed of 20 endogenous peptides mainly derived from various collagen chains that enable differentiating moderate or severe disease from critical state or death with 83% sensitivity at 100% specificity. Based on the performance in this pilot study, testing in a prospective study on 1000 patients has been initiated. This article is protected by copyright. All rights reserved.

Reproducibility of Heart Rate Variability Revealed by Repeated Measurements during and after Hemodialysis.

BACKGROUND/AIMS: Trajectory of heart rate variability (HRV) represents a noninvasive real-time measure of autonomous nervous system (ANS) and carries the capability of providing new insights into the hemodynamic compensation reserve during hemodialysis (HD). However, studies on HRV reproducibility during HD are scarce and did not refer to different reading periods. In this observational study, we aimed to establish the best suited and most reliable and reproducible HRV index in routine HD treatments including different reading rates. METHODS: HRV was characterized by standardized mathematical variation expressions of R/R' intervals: SD of all R/R' intervals (ms), square root of the root mean square of the sum of all differences between adjacent R/R' intervals (ms), percentage of consecutive R/R' intervals that differ by >50 ms (%), low-frequency spectral analysis HRV (LF, expressing sympathetic activity), and high-frequency HRV (HF, expressing parasympathetic activity). To compare robustness of these HRV indices during HD procedures, we compared HRV indices means between different HD sessions and controlled for association with clinical parameters. RESULTS: In 72 HD treatments of 34 patients, we detected the highest reproducibility (89%) of HRV measures when analyzing the low-frequency to high-frequency (LF/HF) ratio in long-term (3 h) readings. Long-term LF/HF was able to discriminate -between patients with and without heart failure NYHA classes ≥3 (p = 0.009) and type 2 diabetes (p = 0.023). We were unable to study relationships between ANS and intradialytic complications because they did not appear in our cohort. Short-term readings of HRV indices did not show any significance of pattern change during HD. CONCLUSION: In summary, our data provide evidence for high robustness of long-term LF/HF in analyzing HRV in HD patients using future automated monitoring systems. For short-term analysis, mathematical real-time analysis must evolve.

Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis.

BACKGROUND: In spite of its invasive nature and risks, kidney biopsy is currently required for precise diagnosis of many chronic kidney diseases (CKDs). Here, we explored the hypothesis that analysis of the urinary proteome can discriminate different types of CKD irrespective of the underlying mechanism of disease. METHODS: We used data from the proteome analyses of 1180 urine samples from patients with different types of CKD, generated by capillary electrophoresis coupled to mass spectrometry. A set of 706 samples served as the discovery cohort, and 474 samples were used for independent validation. For each CKD type, peptide biomarkers were defined using statistical analysis adjusted for multiple testing. Potential biomarkers of statistical significance were combined in support vector machine (SVM)-based classifiers. RESULTS: For seven different types of CKD, several potential urinary biomarker peptides (ranging from 116 to 619 peptides) were defined and combined into SVM-based classifiers specific for each CKD. These classifiers were validated in an independent cohort and showed good to excellent accuracy for discrimination of one CKD type from the others (area under the receiver operating characteristic curve ranged from 0.77 to 0.95). Sequence analysis of the biomarkers provided further information that may clarify the underlying pathophysiology. CONCLUSIONS: Our data indicate that urinary proteome analysis has the potential to identify various types of CKD defined by pathological assessment of renal biopsies and current clinical practice in general. Moreover, these approaches may provide information to model molecular changes per CKD.

FSTL3 is increased in renal dysfunction.

BACKGROUND: Follistatin-like 3 (FSTL3) is a novel cytokine that regulates insulin sensitivity and counteracts activin/myostatin signalling. In the present study, regulation of FSTL3 in renal dysfunction was investigated in both human chronic kidney disease (CKD) and acute kidney dysfunction (AKD). Furthermore, mFSTL3 expression was analysed in insulin-sensitive tissues in a mouse model of CKD. METHODS: Circulating FSTL3 was quantified by enzyme-linked immunosorbent assay in 581 patients with CKD covering the whole spectrum of estimated glomerular filtration rate (eGFR) categories from G1 to G5. Furthermore, FSTL3 was measured in 61 patients before and within 30 h after elective unilateral nephrectomy, an established model of AKD. Moreover, mFSTL3 mRNA expression was investigated in an animal CKD model, that is, eNOS-/-db/db mice, and compared with littermate controls. RESULTS: Median circulating FSTL3 levels significantly and continuously increased with deteriorating renal function (eGFR category G1: 6.1; G2: 8.2; G3: 12.7; G4: 18.5; G5: 32.1 µg/L; P < 0.001). In both human CKD and AKD, renal dysfunction remained the strongest independent predictor of FSTL3 serum concentrations in multivariate analyses. FSTL3 was independently associated with an adverse cardiometabolic profile. In CKD mice, hepatic mFSTL3 mRNA expression was increased more than 6-fold as compared with controls. CONCLUSIONS: Circulating FSTL3 is significantly and independently associated with renal function in both patients with CKD and AKD. Hepatic mFSTL3 mRNA upregulation might contribute to increased FSTL3 levels in CKD. Our results are in agreement with the hypothesis that FSTL3 is eliminated by the kidneys and might counteract adverse activin/myostatin signalling observed in renal dysfunction.

Endemic influences of political regimes, healthcare systems, and preferences on the frequencies and incidences of nephropathies in eastern Saxony, Germany
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INTRODUCTION: The possible confounding influence of investigator-related preferences, available histological techniques, and healthcare systems on the frequencies and incidences of primary and secondary nephropathies was evaluated in this long-term observation. MATERIALS AND METHODS: The observation time from 1983 to 2010 was divided in regard to the political regimes: a) prior to and after German reunification: German Democratic Republic (GDR, period 1 from 1983 to 1990)/Federal Republic of Germany (FRG, period 2 from 1990 to 2010); and the two heads of the division of nephrology, b) conductor 1 (1983 - 2006) and conductor 2 (2006 - 2010). 467 kidney biopsies at the University Hospital of Leipzig were included in our analysis. RESULTS: In period 1, due to the unavailability of immunofluorescence methods, mesangioproliferative glomerulonephritis (MesP) was the most dominating nephropathy. In period 2, IgA nephropathy (IgAN) was the most common nephropathy (17%). IgAN was followed by crescentic glomerulonephritis (13%), hypertensive nephropathy (10%), minimal-change disease, and membranous glomerulonephritis (each 9%). From period 1 to period 2, MesP/IgAN (62% to 16%), membranoproliferative glomerulonephritis and postinfectious glomerulonephritis decreased significantly (p < 0.05). IgAN decreased significantly (p < 0.05) from conductor 1 to conductor 2 (21% to 6%), while diabetic nephropathy significantly increased. Focal-segmental glomerulosclerosis (FSGS) had the highest incidence rate with 1.0, followed by IgAN with 0.8 (per 100,000 per year). CONCLUSION: In a nearly ethnically identical cohort, we have demonstrated that confounding factors, e.g., healthcare systems and preferences of conductors, have a strong influence - more than 10-fold variance - on frequency and incidence on the spectrum of nephropathies.
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CC Chemokine Ligand 18 in ANCA-Associated Crescentic GN.

ANCA-associated vasculitis is the most frequent cause of crescentic GN. To define new molecular and/or cellular biomarkers of this disease in the kidney, we performed microarray analyses of renal biopsy samples from patients with ANCA-associated crescentic GN. Expression profiles were correlated with clinical data in a prospective study of patients with renal ANCA disease. CC chemokine ligand 18 (CCL18), acting through CC chemokine receptor 8 (CCR8) on mononuclear cells, was identified as the most upregulated chemotactic cytokine in patients with newly diagnosed ANCA-associated crescentic GN. Macrophages and myeloid dendritic cells in the kidney were detected as CCL18-producing cells. The density of CCL18(+) cells correlated with crescent formation, interstitial inflammation, and impairment of renal function. CCL18 protein levels were higher in sera of patients with renal ANCA disease compared with those in sera of patients with other forms of crescentic GN. CCL18 serum levels were higher in patients who suffered from ANCA-associated renal relapses compared with those in patients who remained in remission. Using a murine model of crescentic GN, we explored the effects of the CCL18 murine functional analog CCL8 and its receptor CCR8 on kidney function and morphology. Compared with wild-type mice, Ccr8(-/-) mice had significantly less infiltration of pathogenic mononuclear phagocytes. Furthermore, Ccr8(-/-) mice maintained renal function better and had reduced renal tissue injury. In summary, our data indicate that CCL18 drives renal inflammation through CCR8-expressing cells and could serve as a biomarker for disease activity and renal relapse in ANCA-associated crescentic GN.

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

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