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Catanese, L.; Siwy, J.; Mischak, H.; Wendt, R.; Beige, J.; Rupprecht, H. Urinary Peptide and Proteomic Biomarkers in CKD. Encyclopedia. Available online: (accessed on 11 December 2023).
Catanese L, Siwy J, Mischak H, Wendt R, Beige J, Rupprecht H. Urinary Peptide and Proteomic Biomarkers in CKD. Encyclopedia. Available at: Accessed December 11, 2023.
Catanese, Lorenzo, Justyna Siwy, Harald Mischak, Ralph Wendt, Joachim Beige, Harald Rupprecht. "Urinary Peptide and Proteomic Biomarkers in CKD" Encyclopedia, (accessed December 11, 2023).
Catanese, L., Siwy, J., Mischak, H., Wendt, R., Beige, J., & Rupprecht, H.(2023, June 05). Urinary Peptide and Proteomic Biomarkers in CKD. In Encyclopedia.
Catanese, Lorenzo, et al. "Urinary Peptide and Proteomic Biomarkers in CKD." Encyclopedia. Web. 05 June, 2023.
Urinary Peptide and Proteomic Biomarkers in CKD

Biomarker development, improvement, and clinical implementation in the context of kidney disease have been a central focus of biomedical research. 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.

biomarkers chronic kidney disease peptide

1. Introduction

Chronic kidney disease (CKD) is one of the most challenging global health burdens in present time and has a severe impact on the morbidity and mortality of western societies [1]. The guidelines from the global organization Kidney Disease Improving Global Outcomes (KDIGO) regarding the evaluation and management of CKD are currently being updated after the comprehensive guidelines published in 2012 [2]. The diagnosis and management of CKD have been linked to a handful of well-established and routinely assessed biomarkers, including serum creatinine and more specifically the creatinine-derived and -calculated estimated glomerular filtration rate (eGFR), as well as urinary albumin excretion or the urine albumin creatinine ratio (UAE and UACR). After decades of scientific evidence and clinical experience using these biomarkers, they have become a valuable tool for physicians and scientists. However, these biomarkers have well known limitations and shortcomings. Creatinine levels have interindividual variances, depend on other factors such as muscle mass, and often only rise when significant kidney function has already been lost [3]. To minimize the effect of these variabilities in order to optimize the estimation of the eGFR, over 70 equations accounting for sex, ethnicities, and disease entities have been proposed over the last decade, and yet eGFR has not been able to reach the accuracy of measured GFR, which never has become a routine biomarker due to its limited applicability [4]. Albuminuria finds a broad usage in the monitoring and guiding of therapeutical decisions in the context of diabetes and diabetic nephropathy [5], but it is far from an ideal biomarker for CKD due to its high variability, even in measurements within the same individual, and its low specificity when a diagnosis of kidney disease has not yet been established [6][7][8]. Therefore, finding the correct diagnosis of CKD, predicting the disease’s progression, and the guidance of therapeutic decisions still may require the performance of a kidney biopsy and histopathological analysis. Using histopathological biomarkers, such as the extent of renal interstitial fibrosis, can significantly improve the prediction of the disease progression and many therapeutical decisions, especially in the context of glomerular disease, which strongly relies on kidney biopsies. However, in day to day clinical business, biopsies are often not performed or are unavailable due to the invasive nature of obtaining specimens, the associated risks, and contraindications [9]. Hence, the need for better biomarkers has risen and has been the subject of scientific research in the CKD area over the last decades. A comprehensive review regarding single peptide non-invasive biomarkers has been published in the past [10]. Taking into account the complex pathogenesis of kidney disorders, multi-peptide approaches have become a promising approach in biomarker discovery and proteomic research has now been fairly well established in the nephrological community [11]. Serum and urine provide optimal sources for mass-spectrometry-coupled proteomics due to their broad availability in clinical routine. The collection of urine is entirely non-invasive and urinary proteomics might allow for more precise insights, due to the obvious direct link to kidney conditions.

2. Uprising Single-Protein Urine Biomarkers of Chronic Kidney Disease

2.1. CD80

Nephrotic syndrome is the second most common cause of CKD in the first three decades of life. Its underlying pathologies move on a spectrum of diseases and a correct diagnosis is pivotal for an estimation of its prognosis and therapy, as some are steroid-sensitive and others are not. There has been ongoing discussion about the differentiation of minimal change disease (MCD) and focal segmental glomerular sclerosis (FSGS), which may be manifestations of the same pathomechanism at different stages [12]. Discrimination through kidney biopsy findings is, to some extent, possible, but often remains inconclusive [12]. Thus, hope for a biomarker-based differentiation has risen. Gonzalez Guerrico et al. [13] studied a large cohort of 411 patients with different causes of nephrotic syndrome. An ELISA-based measurement of CD80 was performed on urine samples from the patients. They found the CD80 levels to be significantly higher in the MCD patients than those in any other groups and also a significant increase in CD80 in active FSGS and MCD. They concluded CD80 to be a discriminator of MCD from other forms of nephrotic syndrome, especially secondary FSGS. In a pediatric study, 64 patients with nephrotic syndrome were evaluated for their urinary CD80 levels. Here, patients with high urinary CD80 had a good response to immunosuppressive therapy and a significantly lower risk of progressing to CKD, possibly underlining the differentiation between MCD and FSGS [14]. CD80 has previously been suggested for the differentiation of MCD and FSGS [15][16].

2.2. Dickkopf-Related Protein 3

Dickkopf-related protein 3 (DKK3) is a secreted glycoprotein derived from tubular epithelia cells. Its involvement in the canonical WNT-β-Catenin signalling pathway has been shown to be a potential driver of kidney fibrosis, a hallmark of progressing CKD [17][18]. DKK3 may have potential as a urinary biomarker for kidney disease because it is secreted into the urine under tubular stress. In a prospective cohort of 351 patients with CKD stages two and three, Sánchez-Álamo et al. showed that the urinary DKK3 to creatinine ratio was significantly higher in patients that reached the primary composite outcome: a 50% increase in serum creatinine, end-stage kidney disease (ESKD), or death [19]. The uDKK3 levels correlated with the baseline proteinuria and subsequently rose in subjects with higher proteinuria. Treatment with RAS-blockers did not affect the uDKK3 levels. In another prospective cohort of 575 patients with CKD stages two–four, with various underlying CKD etiologies and 481 healthy controls, the baseline urinary DKK3 to creatinine ratio was shown to be significantly higher in the CKD group than that in the healthy population. In the CKD cohort, a urinary DKK3 to creatinine ratio of >4000 pg/mg was associated with an annual eGFR decline of 7.6%, and its predictive properties were superior to eGFR and albuminuria alone. Furthermore, uDKK3 levels were correlated with the degree of tubulointerstitial fibrosis [20]. Another study examined the preoperative uDKK3 levels in patients undergoing cardiac surgery. In 471 patients, the DKK3 to creatinine ratios of >471 pg/mg were predictive of short-term acute kidney injury (AKI), persistent kidney impairment, and dialysis dependency [21]. These three studies showed evidence for DKK3 as a urinary biomarker independent of the underlying CKD etiology.

2.3. Epidermal Growth Factor

Epidermal growth factor (EGF) is a tubule-specific kidney polypeptide which confers biological functions such as cellular metabolism and glomerular hemodynamics, cell growth, and injury repair [22]. While EGF is absent in plasma samples, urinary EGF excretion is a physiological phenomenon in healthy individuals. Several recent studies have found decreasing levels of urinary EGF to be associated with several kidney diseases and progressive kidney damage. In a study on 1032 patients with type 2 diabetes and normal kidney function, several single peptide biomarkers were assessed for their predictive value for early kidney function decline in a 5–12-year follow-up period. Urinary EGF and the EGF to MCP-1 ratio were significantly associated with the risk of early kidney function decline and a combination of all these markers resulted in a significant improvement in the predictive performance regarding early kidney function decline [23] In a cross-sectional study, 1811 patients with early-stage diabetic kidney disease (DKD) and type 2 diabetes patients without DKD and 208 patients with advanced-stage DKD were included. The urinary EGF to creatinine ratio (uEGF/Cr) was positively correlated with eGFR and negatively correlated with the occurrence of DKD (OR 0.65; p < 0.001). In the longitudinal observation of the advanced DKD cases, the uEGF/Cr was associated with a percentage change in the eGFR slope, a composite endpoint of ESKD, and a 30% reduction in eGFR [24]. Menez et al. measured the uEGF levels in 865 patients after cardiac surgery. In addition to the urinary biomarker study, a tissue transcriptomic analysis was performed. The authors studied patients with and without clinically apparent CKD and found that higher levels of uEGF were protective with regard to a complex composite outcome of the incidence and progression of CKD [25]. In a Norwegian and Dutch cooperation, patients from the RENIS and PREVEND cohorts were recruited and investigated for their urinary EGF levels. The study populations included individuals without diabetes or CKD and kidney function was assessed using iohexol-measured GFR in the RENIS cohort and CKD-EPI-based eGFR in the PREVEND cohort. After adjustments for GFR, the ACR in urine, and CKD risk factors, lower uEGF levels were associated with a rapid GFR loss in both cohorts and a lower uEGF was associated with incident CKD in a combined analysis [26]. In a smaller study on 83 patients with DKD, the authors investigated, among others uEGF/Cr. The primary outcome was defined as an eGFR loss of more than 25% per year. During a follow-up time of 23 months, patients with a rapid eGFR decline showed significantly lower levels of uEGFR/Cr. Other biomarkers were also tested for their predictive value in eGFR decline and none were superior to the classic marker UACR [27]. In a pediatric study on 117 patients with Alport syndrome and 146 healthy children, uEGF/Cr was inversely correlated with eGFR. Moreover, it was found that uEGF/Cr was inversely associated with aging and a more rapid eGFR decline was observed in children with Alport syndrome. A longitudinal follow-up was available for 38 children. In these patients, there was a significant correlation between uEGF/Cr and the eGFR slope (r = 0.58, p < 0.001), and the predictive value of uEGF/Cr was superior to eGFR or proteinuria, with an AUC of 0.88 vs 0.77 and 0.81, respectively. These findings show promise for uEGF as a progression marker for CKD and especially DKD [28].

2.4. Kidney Injury Molecule 1

Kidney injury molecule 1 (KIM-1) is a membrane protein expressed in the liver, spleen, and kidney. It has been shown to play a role in kidney disease and kidney injury through a number of different molecular targets and serve as a biomarker for AKI and CKD [29]. In a study on 602 patients with type 2 diabetes, their serum and urinary KIM-1 levels were assessed and found to be correlated with UACR. However, only serum KIM-1 was associated with eGFR [30]. In the early stages of DKD, urinary KIM-1 showed an association with higher incidences of albuminuria and also progressions of albuminuria in a longitudinal observation [31]. Brunner et al. included an evaluation of 10 different urinary biomarkers in the context of lupus nephritis (LN). The biomarkers most closely and consistently associated with the histological scores of LN were adiponectin and osteopontin, though KIM-1 showed an association with eGFR decline and the histology of LN [32]. Another study on 257 patients with type 2 diabetes evaluating five different urine biomarkers showed a higher risk for rapid eGFR loss and progression to ESKD for the highest quartile of urinary KIM-1 among the population (hazard ratio (HR) 2.77, 95% CI, 1.27–6.05) [33]. In the previous mentioned study by Nowak et al., urinary KIM-1 was also found to be associated with an early decline in kidney function in type 2 diabetes patients [23].

2.5. Monocyte Chemoattractant Protein-1

Monocyte chemoattractant protein-1 (MCP-1) or CC-chemokine ligand 2 (CCL2) is a chemotactic cytokine that confers innate immunity and tissue inflammation through its role in monocyte/macrophage recruitment and migration. Several kidney cells, including mesangial cells and podocytes, have been shown to release MCP-1 after a variety of inflammatory stimuli and induce numerous inflammatory cascades [34]. In a large prospective multicenter study on 1538 hospitalized patients, several urinary proteins were assessed in the context of CKD progression. MCP-1 levels were correlated with a rapid loss of kidney function and associated with a higher incidence of the composite outcome encompassing CKD incidence, CKD progression, ESKD, and death [35]. The eGFR loss in the highest MCP-1 quartile was 17.8% (95% CI, 16.7–18.8), annually compared to 8.0% (95% CI, 7.1–9.0) in the lowest quartile. The HR for the association of the composite kidney outcome with MCP-1 levels was 1.32. In the earlier mentioned study by Wu et al., with evidence for uEGF/Cr as a potential biomarker for DKD, uMCP-1/Cr was also assessed, but no significant difference in uMCP-1/Cr was observed between diabetic patients with or without DKD. However, a significant correlation between uMCP-1/Cr and the extent of albuminuria was found and both the uMCP-1/Cr and uEGF/MCP-1 ratios were independently associated with the composite kidney endpoint [24]. In another study on DKD with 83 patients, rapid progressors had higher levels of uMCP-1 and lower uEGF and uEGF/uMCP-1 ratios. A prediction of the composite outcome showed an area under the receiver operating characteristic curve (ROC-AUC) of 0.73 and 0.74 for uMCP-1 and uEGF/uMCP-1, respectively. In contrast to uEGF alone, uMCP-1 and uEGF/uMCP-1 were independently associated with rapid eGFR decline in a multivariate analysis [27]. In the above mentioned study on cardiac surgery patients, uMCP-1 levels were also independently positively associated with the composite CKD outcome, with an HR of 1.10 [25]. Three studies focused on MCP-1 in patients with systemic lupus erythematodes (SLE) and LN, highlighting the potential role of MCP-1 as a disease-specific biomarker for kidney involvement in SLE. In a study on 197 Caucasian SLE patients, a panel of six urinary biomarkers, including MCP-1, was assessed and compared to healthy controls (n = 48). The prediction of kidney involvement, as well as the treatment response to Rituximab, were tested. The uMCP-1 levels were higher in the SLE patients compared to those in the healthy controls, and MCP-1, among four other markers, was higher in patients with active LN compared to non-active LN. An ROC analysis using a combined biomarker, including MCP-1, showed an AUC of 0.898 for predicting LN. A different biomarker combination encompassing MCP-1 was predictive of the treatment response to Rituximab [36]. In a second study on 120 SLE patients, several urinary biomarkers were investigated for their correlations with the histological signs of kidney disease activity and chronic kidney damage in the biopsy specimen. A histopathological analysis was performed on 55 patients. uMCP-1 was higher in patients with chronic kidney involvement, but also patients with a crescent formation and higher levels of kidney fibrosis [32]. In a third study on 89 patients with childhood-onset SLE, uMCP-1 was analyzed among nine other urinary biomarkers and its correlation with the histological features of LN, as well as its correlation with a rapid loss of eGFR after 12 months, was investigated.

2.6. Matrix Metalloproteinase 7

Matrix Metalloproteinase 7 is a zinc-dependent endopeptidase upregulated in the kidney in acute or chronic kidney damage, transcriptionally activated by the WNT/β-Catenin pathway. Two independent studies showed the potential role of MMP-7 in AKI and CKD. In a cohort of 102 CKD patients, the urinary levels of MMP-7 were elevated in the CKD group compared to the healthy controls. MMP-7 was correlated with the degree of kidney fibrosis and inversely correlated with kidney function in patients with moderate CKD [37]. In a prospective multicenter cohort study on 721 patients (adults and children) undergoing cardiac surgery, urinary MMP-7 predicted moderate to severe AKI and was associated with a composite outcome for severe AKI, dialysis, and death, outperforming other biomarkers, including proteinuria or neutrophil gelatinase-associated lipocalin (NGAL), with an ROC-AUC of 0.81 in children and 0.76 in adults [38].

2.7. Neutrophil Gelatinase-Associated Lipocalin

NGAL is a protein that was initially discovered in activated neutrophils, which then was shown to be produced in a variety of other cells, including kidney tubule cells, as a response to injury. NGAL has been shown to have predictive properties in AKI and, subsequently, evidence has been found for its importance in CKD, specifically in polycystic kidney disease and glomerulonephritis [39]. The first study on 80 patients with type 2 diabetes, with a median eGFR of 92.4 mL/min/1.73 m² and median UACR of 4.69 mg/g, showed the urinary NGAL to creatinine ratio, among other markers, to be associated with albuminuria. NGAL was also correlated with diabetes duration. In a subgroup analysis and retrospective analysis, urinary NGAL, among others, was associated with the eGFR slope and changes in UACR [31]. In a cross-sectional study on 209 normoalbuminuric type 2 diabetes patients, the subgroup with eGFR < 60 mL/min per 1.73 m² had higher levels of urinary NGAL and NGAL was negatively correlated with eGFR. A multiple linear regression showed NGAL (β = −0 287, p =0008) to be independently correlated with eGFR [40]. In a retrospective study on 100 patients with type 2 diabetes and CKD, their urinary NGAL to creatinine ratios were assessed. Kidney biopsy results were available for the patients and allowed for them to be grouped into DKD and non-DKD. The urinary NGAL was significantly higher in the patients with DKD and urinary NGAL was an independent risk factor for DKD in the CKD patients with type 2 diabetes. Urinary NGAL showed, among others, correlations with proteinuria, eGFR, histological markers of inflammation, and CKD. In an adjusted model, urinary NGAL was associated with a higher probability of nephrotic-range proteinuria and lower event-free survival rates [41]. A prospective cohort study on type 2 diabetes patients with advanced nephropathy showed that patients in the highest quartile of urinary NGAL had a higher risk of reaching a composite outcome, including a rapid eGFR decline and ESKD, during a follow-up of 3 years [33]. In the above mentioned study by Brunner et al., in a prospective cohort of pediatric patients, urinary NGAL was also assessed and proved to be a moderate predictor of the histological features of kidney damage and a rapid eGFR decline, with a similar level of association as KIM-1, inferior to osteopontin and adiponectin [32].

2.8. Uromodulin

Uromodulin (also known as Tamm–Horsfall protein) is produced in the kidney and physiologically excreted into the urine. It has been associated with immunological mechanisms and electrolyte balance and shown to have protective functions against urinary tract infections and kidney stone formation in animal knockout models. Several studies have investigated uromodulin as a serum marker for AKI and CKD [42][43][44][45][46][47]. It has been identified as a risk factor for CKD through GWAS and interest in its performance as a urinary biomarker has risen [48]. In a study on 364 patients who underwent kidney biopsies, urinary uromodulin levels were negatively associated with serum creatinine and patients with higher uromodulin levels had lower degrees of kidney fibrosis and glomerulosclerosis [49]. In the previously mentioned study by Puthumana et al., higher uromodulin levels were associated with a smaller eGFR decline and decreased risk of the composite kidney outcome. Combining urinary uromodulin with other biomarkers has improved its predictive performance [35]. In contrast to NGAL, urinary uromodulin could not be associated with eGFR, eGFR changes, or albuminuria, and its only significant association was with markers of diabetes control [31]. In a study on 101 patients who received cardiopulmonary bypass, preoperative levels of urinary uromodulin were inversely correlated with the incidence of AKI and urinary uromodulin strongly predicted postoperative AKI with an ROC-AUC of 0.90 [50].

3. Peptidomic/Proteomic-Based Biomarker Panels

3.1. CKD273

CKD273 is a urinary proteomic classifier containing 273 peptides that was originally discovered in 2010 [51]. It was derived from a human urinary database that contained, at that time, the urinary peptide data of 3600 patients analyzed using capillary electrophoresis coupled with mass spectrometry (CE-MS), a high-resolution, reproducible method for peptidome analyses. The diagnostic and prognostic properties of CKD273 in the various stages of CKD and numerous CKD etiologies, especially DKD, have been shown in a number of studies [52][53][54][55][56][57][58][59].
Recent studies on CKD273 have almost exclusively been focused on its predictive performance in patients with early-stage CKD, where the shortcomings of classical biomarkers such as eGFR and albuminuria limit their potential.
In a study by Pontillo et al., the question if CKD273 is superior to UACR in predicting CKD progression up to stage three (eGFR < 60 mL/min/1.73 m2) was raised. A total of 2087 individuals with an eGFR of >60 mL/min/1.73 m2 and minimal to normal albuminuria were included. Over a median follow-up of 4.6 years, CKD273 was superior to UACR in predicting a first and sustained renal endpoint [60]. This was also shown in a retrospective cohort of 1014 individuals with a baseline eGFR of ≥70 mL/min/1.73 m2 and urinary albumin excretions of <20 μg/min, showing the ability of CKD273 to identify the progression to eGFRs of <60 mL/min/1.73 m2 [61]. The risk stratification of eGFR loss in early-stage CKD was later improved by the generation of CKD273 sub-classifiers derived from the different eGFR strata within the entire patient cohort. Especially in patients without CKD or in early-stage CKD, these sub-classifiers outperformed albuminuria, the clinical Kidney Failure Risk Equation [62], and CKD273 [63]. In a smaller cohort of 155 type 2 diabetes patients with preserved kidney function and microalbuminuria, CKD273 showed correlations with eGFR and albuminuria. In a longitudinal follow-up, however, it failed to predict rapid eGFR loss and albuminuria. However, after multiple adjustments, CKD273 was a predictor for death but not for cardiovascular events in this cohort [64]. These findings raised the question of if screening patients with type 2 diabetes without known kidney impairments using CKD273 could be beneficial from an epidemiological and economic standpoint. Critselis et al. developed a decision analytic model evaluating individual costs and health outcomes, while hypothetically applying an annual CKD273 screening instead of albuminuria screening for these patients. The incremental costs exceeded the albuminuria screening, but the health benefits, including quality-adjusted life years, were predicted to outweigh the cost factors when focusing on high-risk patients [65]. Another study emphasizing the importance of CKD273 in the early CKD stages was published by Verbeke et al., including 451 patients with all the CKD stages over a median follow-up time of 5.5 years. They showed, after a multiple adjustment, that CKD273 was strongly predictive of fatal and non-fatal cardiovascular events in patients with early CKD stages and without an apparent history of cardiovascular events [66].
The performance of CKD273 in early-stage CKD led to a multicentre, prospective, observational study with an embedded randomized controlled trial (PRIORITY). The recruited patients had type 2 diabetes, a normal urinary albumin excretion, and preserved kidney function. The cohort was divided into a high- and a low-risk group according to their CKD273 scoring. The high-risk patients underwent placebo-controlled treatment, with 25 mg daily of spironolactone. The primary endpoint was the development of microalbuminuria. CKD273 proved to be predictive for the development of albuminuria; however, treatment with spironolactone did not significantly alter the progression course [67]. Similarly, CKD273 was able to predict microalbuminuria, independently of numerous other factors, including treatment with candesartan vs. a placebo in a large cohort of normoalbuminuric type 2 diabetes patients (Diabetic Retinopathy Candesartan Trials (DIRECT-Protect 2 study)) [68]. While no benefit of spironolactone treatment could be detected for early-stage DKD, the treatment response to spironolactone (reduction in UACR) had previously been demonstrated at more advanced-stage CKD. In this cohort of 101 patients with type 2 diabetes, the treatment response was predictable based on CKD273 [69].

3.2. Other Biomarkers of DKD

In addition to CKD273, other proteomic-based biomarkers have been suggested for the assessment of DKD within the last 5 years. In a Taiwanese cohort of early-stage DKD patients, a proteomic approach was used to identify the candidate biomarkers, which were subsequently verified by an enzyme-linked immunosorbent assay. An analysis of a total of 114 patients led to the identification of candidate biomarkers, eight of which could be validated. This ultimately led to the identification of haptoglobin as a urine biomarker for early DKD detection and the prediction of early decline in kidney function [70]. In another study based on liquid chromatography–mass spectrometry (LC-MS), the authors sought to identify a peptide panel for a differentiation of the severity of DKD in 60 patients with different levels of albuminuria. The generated panel included collagen fragments and alpha-1 antitrypsin among the differentially occurring urinary peptides, similar to the observations with the classifier CKD273 [51][71]. Using LC-MS to identify the potential biomarkers of DKD in a retrospective study, 54 patients were grouped according to kidney outcomes and a urinary analysis was performed, which led to the identification of 66 peptides with differential abundances between the two groups. A combination of 5 of the 66 peptides was superior to albuminuria or eGFR in predicting kidney outcome [72].

3.3. Biomarkers of Kidney Fibrosis

Kidney fibrosis is a hallmark of progressive disease in virtually all entities of CKD [73]. As of now, the degrees of interstitial fibrosis and tubular atrophy (IFTA) can only be assessed by invasive kidney biopsies, which come along with a number of problems and limitations, as outlined above. Several single peptide biomarkers have been proposed for a non-invasive estimation of kidney fibrosis [17][37][49][74][75]. In recent years, novel proteomic biomarkers reflecting the level of kidney fibrosis have been generated. CKD273 was correlated with the biopsy-proven degrees of kidney fibrosis in a cohort of 42 patients, whereas UACR and eGFR showed no association with fibrosis. This led to the identification of seven differentially abundant, fibrosis-associated peptides. All of the peptides were collagen fragments and displayed significant and negative correlations with the degree of kidney fibrosis, highlighting the role of collagen in the accumulation of the extracellular matrix, a hallmark of fibrosis [76]. In a subsequent study using CE-MS, with a larger cohort of 435 patients with various etiologies of CKD, a proteomic classifier containing 29 differentially excreted fibrosis-associated peptides (FFP_BH29) could be identified. The classifier was able to distinguish between patients with and without kidney fibrosis, with an ROC-AUC of 0.840 (95% CI: 0.779 to 0.889, p < 0.0001), and was significantly correlated with the degree of IFTA [77]. A large study focusing on collagen alpha 1(I) (col1a1) identified 501 different col1a1 fragments in the urine of 5000 patients with and without CKD. The vast majority of the differentially expressed fragments were positively correlated with eGFR and negatively correlated with ageing. The authors suggested that kidney fibrosis may be a consequence of decreased collagen degradation, rather than increased synthesis [78].

3.4. Biomarkers in Different CKD Entities

IgA nephropathy (IgAN) is the most common primary glomerulonephritis and is characterized by a wide range of progression rates [79]. Therefore, risk stratification is highly relevant to identify individuals more likely to rapidly progress towards ESKD, in order to offer tailored therapies. Using an analysis of 209 patients’ urine samples via CE-MS, 237 peptides were identified that showed significantly different abundances in fast-progressing IgAN vs. slowly progressing IgAN. These peptides included, among others, fragments of apolipoprotein C-III, alpha-1 antitrypsin, different collagens, and uromodulin. A classifier based on these 237 peptides showed a significantly added value to clinical parameters for a prediction of IgAN progression [80]. In the context of SLE and LN, Pejchinovski et al. developed a panel of 65 urine peptides, including uromodulin and fibrinogen alpha, which was able to discriminate between SLE patients and healthy controls. The classifier was shown to identify patients with LN in a validation cohort with an ROC-AUC of 0.80 (p < 0.0001, 95%-CI 0.65–0.90) [81]. Autosomal dominant polycystic kidney disease (ADPKD) is a genetic disease characterized by bilateral kidney cyst formation and progression into ESKD. A proteomic biomarker panel containing 20 urinary peptides was able to predict rapid eGFR decline and an in silico analysis of cleavage sites revealed the potentially involved proteolytic pathways, including matrix-metalloproteinases and cathepsins, suggesting that altered proteolytic pathways are a part of disease progression [82]. In Fabry’s disease, a rare multisystemic disease with kidney involvement, a proteomic analysis was used to identify the different urinary biomarkers associated with asymptomatic, pre-symptomatic, and symptomatic disease, as well as kidney involvement [83]. In another small cohort for a different rare disease, Bardet-Biedl syndrome, proteomic profiling displayed 42 differentially occurring urinary peptides mostly involved in fibrosis and extracellular matrix organization [84]. A differential diagnosis of minimal change disease and focal segmental sclerosis was addressed in a proteomic approach with an ELISA validation, revealing a panel of biomarkers enabling discrimination between these hardly distinguishable CKD etiologies [85]. In a proteomic analysis of 120 patients with LN, uEGF was significantly associated with disease activity, histopathological findings, and CKD progression [86].


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