Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Minimally Invasive Versus Invasive Proteomics: Urine and Blood Biomarkers in Coronary Artery Disease

Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide. This underlines the urgent need for effective biomarkers for early diagnosis, risk stratification, and therapeutic counseling. Proteomic signatures from plasma and urine have emerged as promising tools for these efforts, each offering unique advantages and challenges. This review provides a detailed comparison of urine and blood proteomic analyzes in the context of CAD and explores their respective advantages and limitations. Urine proteomics offers a minimally invasive, easily repeatable, and temporally stable sampling method, but faces challenges such as lower protein concentrations and potential contamination. Despite its invasive nature, blood proteomics captures high protein concentration and directly reflects systemic physiological changes, making it valuable for acute assessments. Advances in artificial intelligence (AI) have significantly improved the analysis and interpretation of proteomic data, enabling greater accuracy in diagnosis and predictive modeling. AI algorithms, particularly in pattern recognition and data integration, are helping to uncover subtle relationships between biomarkers and disease progression and supporting the discovery of plasma- and urine-based CAD biomarkers. This review demonstrates the potential of combining urine and blood proteomic data using AI to advance personalized approaches in CAD diagnosis and treatment. Future research should focus on standardization of collection protocols, validation of biomarkers in different populations, and the complexity of integrating data from different sources to maximize the potential of proteomics in the treatment of CAD.

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.

The Potential Use of Targeted Proteomics and Metabolomics for the Identification and Monitoring of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus and is associated with a significantly worse prognosis compared to diabetic patients without kidney involvement, other microvascular complications, or non-diabetic chronic kidney disease, due to its higher risk of cardiovascular events, faster progression to end-stage kidney disease, and increased mortality. In clinical practice, diagnosis is based on estimated glomerular filtration rate (eGFR) and albuminuria. However, given the limitations of these diagnostic markers, novel biomarkers must be identified. Omics is a new field of study involving the comprehensive analysis of various types of biological data at the molecular level. In different fields, they have shown promising results in (early) detection of diseases, personalized medicine, therapeutic monitoring, and understanding pathogenesis. DKD is primarily utilized in scientific research and has not yet been implemented in routine clinical practice. The aim of this review is to provide an overview of currently available data on targeted omics. After an extensive literature search, 25 different (panels of) omics were withheld and analyzed. Both serum/plasma and urine proteomics and metabolomics have been described with varying degrees of evidence. For all omics, there is still a relative paucity of data from large, prospective, longitudinal cohorts, presumably because of the heterogeneity of DKD and the lack of patient selection in studies, the complexity of omics technologies, and various practical and ethical considerations (e.g., limited accessibility, cost, and privacy concerns).

Proteomic Analysis Identifies Dysregulated Proteins in Albuminuria: A South African Pilot Study

Albuminuria may precede decreases in the glomerular filtration rate (GFR) and both tests are insensitive predictors of early stages of kidney disease. Our aim was to characterise the urinary proteome in black African individuals with albuminuria and well-preserved GFR from South Africa. This case-controlled study compared the urinary proteomes of 52 normoalbuminuric (urine albumin: creatinine ratio (uACR) < 3 mg/mmol) and 56 albuminuric (uACR ≥ 3 mg/mmol) adults of black African ethnicity. Urine proteins were precipitated, reduced, alkylated, digested, and analysed using an Evosep One LC (Evosep Biosystems, Odense, Denmark) coupled to a Sciex 5600 Triple-TOF (Sciex, Framingham, MA, USA) in data-independent acquisition mode. The data were searched on SpectronautTM 15. Differentially abundant proteins (DAPs) were filtered to include those with a ≥2.25-fold change and a false discovery rate ≤ 1%. Receiver-operating characteristic curves were used to assess the discriminating abilities of proteins of interest. Pathway analysis was performed using Enrichr software. As expected, the albuminuric group had higher uACR (7.9 vs. 0.55 mg/mmol, p < 0.001). The median eGFR (mL/min/1.73 m2) showed no difference between the groups (111 vs. 114, p = 0.707). We identified 80 DAPs in the albuminuria group compared to the normoalbuminuria group, of which 59 proteins were increased while 21 proteins were decreased in abundance. We found 12 urinary proteins with an AUC > 0.8 and a p < 0.001 in the multivariate analysis. Furthermore, an 80-protein model was developed that showed a high AUC ˃ 0.907 and a predictive accuracy of 91.3% between the two groups. Pathway analysis found that the DAPs were involved in insulin growth factor (IGF) functions, innate immunity, platelet degranulation, and extracellular matrix organization. In albuminuric individuals with a well-preserved eGFR, pathways involved in preventing the release and uptake of IGF by insulin growth factor binding protein were significantly enriched. These proteins are indicative of a homeostatic imbalance in a variety of cellular processes underlying renal dysfunction and are implicated in chronic kidney disease.

Serum high mobility group box 1 as a potential biomarker for the progression of kidney disease in patients with type 2 diabetes

Background

As a damage-associated molecular pattern protein, high mobility group box 1 (HMGB1) is associated with kidney and systemic inflammation. The predictive and therapeutic value of HMGB1 as a biomarker has been confirmed in various diseases. However, its value in diabetic kidney disease (DKD) remains unclear. Therefore, this study aimed to investigate the correlation between serum and urine HMGB1 levels and DKD progression.

Methods

We recruited 196 patients with type 2 diabetes mellitus (T2DM), including 109 with DKD and 87 T2DM patients without DKD. Additionally, 60 healthy participants without T2DM were also recruited as controls. Serum and urine samples were collected for HMGB1 analysis. Simultaneously, tumor necrosis factor receptor superfamily member 1A (TNFR-1) in serum and kidney injury molecule (KIM-1) in urine samples were evaluated for comparison.

Results

Serum and urine HMGB1 levels were significantly higher in patients with DKD than in patients with T2DM and healthy controls. Additionally, serum HMGB1 levels significantly and positively correlated with serum TNFR-1 (R 2 = 0.567, p<0.001) and urine KIM-1 levels (R 2 = 0.440, p<0.001), and urine HMGB1 has a similar correlation. In the population with T2DM, the risk of DKD progression increased with an increase in serum HMGB1 levels. Multivariate logistic regression analysis showed that elevated serum HMGB1 level was an independent risk factor for renal function progression in patients with DKD, and regression analysis did not change in the model corrected for multiple variables. The restricted cubic spline depicted a nonlinear relationship between serum HMGB1 and renal function progression in patients with DKD (p-nonlinear=0.007, p<0.001), and this positive effect remained consistent across subgroups.

Conclusion

Serum HMGB1 was significantly correlated with DKD and disease severity. When the HMGB1 level was ≥27 ng/ml, the risk of renal progression increased sharply, indicating that serum HMGB1 can be used as a potential biomarker for the diagnosis of DKD progression.

Recent progress in mass spectrometry-based urinary proteomics

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

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Postprandial glucagon-like peptide 1 secretion is associated with urinary albumin excretion in newly diagnosed type 2 diabetes patients

BACKGROUND

Microalbuminuria is an early and informative marker of diabetic nephropathy. Our study found that microalbuminuria developed in patients with newly diagnosed type 2 diabetes mellitus (T2DM).

AIM

To investigate the association between glucagon-like peptide 1 (GLP-1) and microalbuminuria in newly diagnosed T2DM patients.

METHODS

In total, 760 patients were recruited for this cross-sectional study. The GLP-1 levels during a standard meal test and urinary albumin-creatinine ratio (UACR) were determined.

RESULTS

Patients with microalbuminuria exhibited lower GLP-1 levels at 30 min and 120 min during a standard meal test than patients with normal albuminuria (30 min GLP-1, 16.7 ± 13.3 pmol vs 19.9 ± 15.6 pmol, P = 0.007; 120 min GLP-1, 16.0 ± 14.1 pmol vs 18.4 ± 13.8 pmol, P = 0.037). The corresponding area under the curve for active GLP-1 (AUCGLP-1) was also lower in microalbuminuria patients (2257, 1585 to 3506 vs 2896, 1763 to 4726, pmol × min, P = 0.003). Postprandial GLP-1 levels at 30 min and 120 min and AUCGLP-1 were negatively correlated with the UACR (r = 0.159, r = 0.132, r = 0.206, respectively, P < 0.001). The prevalence of microalbuminuria in patients with newly diagnosed T2DM was 21.7%, which decreased with increasing quartiles of AUCGLP-1 levels (27.4%, 25.3%, 18.9% and 15.8%). After logistic regression analysis adjusted for sex, age, hemoglobin A1c, body mass index, systolic blood pressure, estimated glomerular filtration rate, homeostasis model assessment of insulin resistance, AUC_glucose and AUC_glucagon, patients in quartile 4 of the AUCGLP-1 presented a lower risk of microalbuminuria compared with the patients in quartile 1 (odds ratio = 0.547, 95% confidence interval: 0.325-0.920, P = 0.01). A consistent association was also found between 30 min GLP-1 or 120 min GLP-1 and microalbuminuria.

CONCLUSION

Postprandial GLP-1 levels were independently associated with microalbuminuria in newly diagnosed Chinese T2DM patients.

Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS

BACKGROUND

Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis.

METHODS

We performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles.

RESULTS

The comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis.

CONCLUSION

In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.

Novel biomarkers for diabetic kidney disease

Although diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in patients with diabetes mellitus; its prevalence has failed to decline over the past 30 years. To identify those at high risk of developing DKD and disease progression at an early stage, extensive research has been ongoing in the search for prognostic and surrogate endpoint biomarkers for DKD. Although biomarkers are not used routinely in clinical practice or prospective clinical trials, many biomarkers have been developed to improve the early identification and prognostication of patients with DKD. Novel biomarkers that capture one specific mechanism of the DKD disease process have been developed, and studies have evaluated the prognostic value of assay-based biomarkers either in small sets or in combinations involving multiple biomarkers. More recently, several studies have assessed the prognostic value of omics- based biomarkers that include proteomics, metabolomics, and transcriptomics. This review will first describe the biomarkers used in current practice and their limitations, and then summarize the current status of novel biomarkers for DKD with respect to assay- based protein biomarkers, proteomics, metabolomics, and transcriptomics.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Urinary complement proteins and risk of end-stage renal disease: quantitative urinary proteomics in patients with type 2 diabetes and biopsy-proven diabetic nephropathy

PURPOSE

To investigate the association between urinary complement proteins and renal outcome in biopsy-proven diabetic nephropathy (DN).

METHODS

Untargeted proteomic and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses and targeted proteomic analysis using parallel reaction-monitoring (PRM)-mass spectrometry was performed to determine the abundance of urinary complement proteins in healthy controls, type 2 diabetes mellitus (T2DM) patients, and patients with T2DM and biopsy-proven DN. The abundance of each urinary complement protein was individually included in Cox proportional hazards models for predicting progression to end-stage renal disease (ESRD).

RESULTS

Untargeted proteomic and functional analysis using the KEGG showed that differentially expressed urinary proteins were primarily associated with the complement and coagulation cascades. Subsequent urinary complement proteins quantification using PRM showed that urinary abundances of C3, C9, and complement factor H (CFAH) correlated negatively with annual estimated glomerular filtration rate (eGFR) decline, while urinary abundances of C5, decay-accelerating factor (DAF), and CD59 correlated positively with annual rate of eGFR decline. Furthermore, higher urinary abundance of CFAH and lower urinary abundance of DAF were independently associated with greater risk of progression to ESRD. Urinary abundance of CFAH and DAF had a larger area under the curve (AUC) than that of eGFR, proteinuria, or any pathological parameter. Moreover, the model that included CFAH or DAF had a larger AUC than that with only clinical or pathological parameters.

CONCLUSION

Urinary abundance of complement proteins was significantly associated with ESRD in patients with T2DM and biopsy-proven DN, indicating that therapeutically targeting the complement pathway may alleviate progression of DN.

Urinary Protein and Peptide Markers in Chronic Kidney Disease

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Current applications of capillary electrophoresis-mass spectrometry for the analysis of biologically important analytes in urine (2017 to mid-2021): A review

Capillary electrophoresis coupled online with mass detection is a modern tool for analyzing wide ranges of compounds in complex samples, including urine. Capillary electrophoresis with mass spectrometry allows the separation and identification of various analytes spanning from small ions to high molecular weight protein complexes. Similarly to the much more common liquid chromatography‐mass spectrometry techniques, the capillary electrophoresis separation reduces the complexity of the mixture of analytes entering the mass spectrometer resulting in reduced ion suppression and a more straightforward interpretation of the mass spectrometry data. This review summarizes capillary electrophoresis with mass spectrometry studies published between the years 2017 and 2021, aiming at the determination of various compounds excreted in urine. The properties of the urine, including its diagnostical and analytical features and chemical composition, are also discussed including general protocols for the urine sample preparation. The mechanism of the electrophoretic separation and the instrumentation for capillary electrophoresis with mass spectrometry coupling is also included. This review shows the potential of the capillary electrophoresis with mass spectrometry technique for the analyses of different kinds of analytes in a complex biological matrix. The discussed applications are divided into two main groups (capillary electrophoresis with mass spectrometry for the determination of drugs and drugs of abuse in urine and capillary electrophoresis with mass spectrometry for the studies of urinary metabolome).

Proteomic Biomarkers in the Cardiorenal Syndrome: Toward Deciphering Molecular Pathophysiology

Cardiorenal syndrome (CRS) is defined by coexisting heart and renal dysfunctions. Malfunction of 1 organ may cause dysfunction of the other with variable causative disease that defines the type of CRS (1-5). Numerous studies showed that the prevalence of cardiovascular disease is increased in patients with chronic kidney disease (CKD). Similarly, CKD affects a large proportion of patients with heart failure. This overlap between primary heart or primary kidney disease blurs cause-effect inferences of the initiator/target organ. The classical subdivision of CRS in 5 categories does not provide pathophysiological suggestions for targeted intervention. It seems timely to revisit the value of CRS biomarkers in a pathophysiology-centered approach. We systematically reviewed the literature in CRS, which revealed 53 clinical studies describing the use of 44 biomarkers and 4 proteomic panels. All biomarkers are involved in at least one of the CRS comorbidities. Among the pathways affected, inflammation, aberrant glucose metabolism, neurohormonal activation, and oxidative stress are well described. There is growing evidence that fibrosis may be the "cornerstone" that unifies most of the pathways leading to CRS. Formation of excess fibrous connective tissue antedates CRS in many cases. This review highlights that biomarkers reflecting fibrosis may be of substantial clinical value in the early detection, prognostication, and guiding treatment of CRS. Biomarkers detecting changes in collagen turnover in the extracellular matrix of heart and kidney appear able to depict subclinical changes in the fibrotic remodeling of tissues and constitute a promising approach toward personalized intervention in CRS.

Multisample Mass Spectrometry-Based Approach for Discovering Injury Markers in Chronic Kidney Disease

Urinary proteomics studies have primarily focused on identifying markers of chronic kidney disease (CKD) progression. Here, we aimed to determine urinary markers of CKD renal parenchymal injury through proteomics analysis in animal kidney tissues and cells and in the urine of patients with CKD. Label-free quantitative proteomics analysis based on liquid chromatography-tandem mass spectrometry was performed on urine samples obtained from 6 normal controls and 9, 11, and 10 patients with CKD stages 1, 3, and 5, respectively, and on kidney tissue samples from a rat CKD model by 5/6 nephrectomy. Tandem mass tag-based quantitative proteomics analysis was performed for glomerular endothelial cells (GECs) and proximal tubular epithelial cells (PTECs) before and after inducing 24-h hypoxia injury. Upon hierarchical clustering, out of 858 differentially expressed proteins (DEPs) in the urine of CKD patients, the levels of 416 decreased and 403 increased sequentially according to the disease stage, respectively. Among 2965 DEPs across 5/6 nephrectomized and sham-operated rat kidney tissues, 86 DEPs showed same expression patterns in the urine and kidney tissue. After cross-validation with two external animal proteome data sets, 38 DEPs were organized; only ten DEPs, including serotransferrin, gelsolin, poly ADP-ribose polymerase 1, neuroblast differentiation-associated protein AHNAK, microtubule-associated protein 4, galectin-1, protein S, thymosin beta-4, myristoylated alanine-rich C-kinase substrate, and vimentin, were finalized by screening human GECs and PTECs data. Among these ten potential candidates for universal CKD marker, validation analyses for protein S and galectin-1 were conducted. Galectin-1 was observed to have a significant inverse correlation with renal function as well as higher expression in glomerulus with chronic injury than protein S. This constitutes the first multisample proteomics study for identifying key renal-expressed proteins associated with CKD progression. The discovered proteins represent potential markers of chronic renal cell and tissue damage and candidate contributors to CKD pathophysiology.

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.

Current updates on protein as biomarkers for diabetic kidney disease: a systematic review

BACKGROUND

In the past decade, researchers have been focused on discovering protein biomarkers for diabetic kidney disease. This paper aims to search for, analyze, and synthesize current updates regarding the development of these efforts.

METHODS

We systematically searched the ScienceDirect, SpringerLink, and PubMed databases for observational studies of protein biomarkers in patients with diabetes mellitus. We included studies published between January 2018 and April 2020, that were based on a population of patients with type-1 or type-2 diabetes mellitus aged ⩾18 years, with an observational design such as cross-sectional, case-control, or cohort studies. The dependent variable of the research results was in the form of protein biomarkers from urine, plasma, or serum.

RESULTS

Following the screening process, 20 research articles with available full text met the inclusion criteria. These could be categorized as glomerular biomarkers (ANGPTL4, beta-2 microglobulin, Smad1, and glypican-5); inflammatory biomarkers (MCP-1 and adiponectin); and tubular biomarkers (NGAL, VDBP, megalin, sKlotho, and KIM-1). The development of a panel of biomarkers showed more promising results than those for a single biomarker in diagnosing diabetic kidney disease.

CONCLUSION

All the biomarkers discussed in this review showed promising results for predicting diabetic kidney disease because they correlate with albuminuria, eGFR, or both. However, of the 11 protein biomarkers, none have prognostic value beyond albuminuria and eGFR.

Acarbose Reduces Low-Grade Albuminuria Compared to Metformin in Chinese Patients with Newly Diagnosed Type 2 Diabetes

PURPOSE

To assess the effect of acarbose in lowering low-grade albuminuria compared to metformin in newly diagnosed Chinese type 2 diabetes (T2DM) patients.

PATIENTS AND METHODS

The Metformin and AcaRbose Clinical Trial was a randomized, open-label trial in newly diagnosed T2DM patients. Participants received 48 weeks of monotherapy with acarbose (100 mg three times a day) or metformin (1500 mg once a day). As the hypoglycemic effect of acarbose and metformin has been evaluated in previous reports. This analysis studied the effect of the two antidiabetic drugs on reducing urinary albumin. The percent change in the urinary albumin/creatinine ratio (uACR) from baseline to week 48 was analyzed, and ANCOVA was employed to establish whether the effect in decreasing uACR was mediated by metabolic improvement.

RESULTS

Acarbose reduced the adjusted mean percent uACR by -31.5% (95% confidence interval [CI] -48.4 to -7.5) compared with metformin. When adjusting for changes in glycated hemoglobin, body weight, systolic blood pressure and triglycerides or changes in area under the curve of glucagon-like peptide 1 (AUCGLP-1) in the standard meal test, the uACR-lowering effect was not attenuated. If stratified by eGFR, blood glucose level, sex or uACR level, the effect of acarbose versus metformin was consistent across subgroups. The proportion of patients with a reduction in uACR of at least 70% was 48.6% in the acarbose group and 34.1% in the metformin group.

CONCLUSION

Acarbose lowered the uACR compared to metformin in newly diagnosed T2DM patients independent of improvements in hyperglycemia, blood pressure, body weight and triglycerides.

Multistate Models to Predict Development of Late Complications of Type 2 Diabetes in an Open Cohort Study

BACKGROUND

Increase in the prevalence of type 2 diabetic mellitus (T2DM) as a complex disease, its complications, and spread has become a dominant global health threat in recent decades.

OBJECTIVE

The aim of the current study was to investigate the impact of risk factors and transition probability on the development and progression of the late complications of T2DM.

METHODS

This study was an open cohort one which was conducted at Isfahan Endocrine and Metabolism Research Center (IEMRC). The data were collected from 1993 to 2018. The sample size consisted of 2519 adults diagnosed with type 2 diabetes. We applied the homogeneous multistate models including no complication, retinopathy alone, coronary artery disease (CAD), microalbuminuria, retinopathy and CAD, and the final absorbing mortality states.

RESULTS

Based on our results, time-varying hypertension strongly intensified the hazard of transition to mortality in CAD, no complication, CAD and retinopathy, and retinopathy patients by 4.99, 4.09, 3.42, and 2.65 times, respectively. Hypertension seemed to be a potential factor for the transition of microalbuminuria to no complication in diabetic patients. One-unit increase in LDL increased the hazard ratio of transition from CAD, and retinopathy and CAD to mortality by 1.8% and 2.4%, respectively. Moreover, one level increase in time-varying HbA1c increased the hazard ratio of transition to retinopathy and mortality among no complication diabetic patients by 30% and 67%, respectively. One level increase in time-varying HbA1c also intensified the hazard ratio of transition from retinopathy to mortality by 45%. The same level of increase in time-varying HbA1c also intensified the hazard ratio of transition from CAD alone to CAD and retinopathy, and microalbuminuria to retinopathy by 26% and 50%, respectively.

CONCLUSION

In addition to glycemic control, our study indicates that controlling hypertension and hyperlipidemia is more effective in reducing mortality and the diabetic macro- and microvascular complications.

Urinary Peptidomic Biomarkers in Kidney Diseases

In order to effectively develop personalized medicine for kidney diseases we urgently need to develop highly accurate biomarkers for use in the clinic, since current biomarkers of kidney damage (changes in serum creatinine and/or urine albumin excretion) apply to a later stage of disease, lack accuracy, and are not connected with molecular pathophysiology. Analysis of urine peptide content (urinary peptidomics) has emerged as one of the most attractive areas in disease biomarker discovery. Urinary peptidome analysis allows the detection of short and long-term physiological or pathological changes occurring within the kidney. Urinary peptidomics has been applied extensively for several years now in renal patients, and may greatly improve kidney disease management by supporting earlier and more accurate detection, prognostic assessment, and prediction of response to treatment. It also promises better understanding of kidney disease pathophysiology, and has been proposed as a "liquid biopsy" to discriminate various types of renal disorders. Furthermore, proteins being the major drug targets, peptidome analysis may allow one to evaluate the effects of therapies at the protein signaling pathway level. We here review the most recent findings on urinary peptidomics in the setting of the most common kidney diseases.

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.

Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease

Chronic kidney disease (CKD) is an aging-related disorder that represents a major global public health burden. Current biochemical biomarkers, such as serum creatinine and urinary albumin, have important limitations when used to identify the earliest indication of CKD or in tracking the progression to more advanced CKD. These issues underline the importance of finding and testing new molecular biomarkers that are capable of successfully meeting this clinical need. The measurement of changes in nature and/or levels of proteins and metabolites in biological samples from patients provide insights into pathophysiological processes. Proteomic and metabolomic techniques provide opportunities to record dynamic chemical signatures in patients over time. This review article presents an overview of the recent developments in the fields of metabolomics and proteomics in relation to CKD. Among the many different proteomic biomarkers proposed, there is particular interest in the CKD273 classifier, a urinary proteome biomarker reported to predict CKD progression and with implementation potential. Other individual non-invasive peptidomic biomarkers that are potentially relevant for CKD detection include type 1 collagen, uromodulin and mucin-1. Despite the limited sample sizes and variability of the metabolomics studies, some metabolites such as trimethylamine N-oxide, kynurenine and citrulline stand out as potential biomarkers in CKD.

Application of urinary proteomics as possible risk predictor of renal and cardiovascular complications in patients with type 2-diabetes and microalbuminuria

BACKGROUND

Analyses of the urinary proteome have been proposed as a novel approach for early assessment of increased risk of renal- or cardiovascular disease. Here we investigate the potentials of various classifiers derived from urinary proteomics for prediction of renal and cardiovascular comorbidities in patients with type 2-diabetes.

METHODS

The study was a post hoc analysis of the randomized controlled Steno-2 trial comparing intensified multifactorial intervention to conventional treatment of type 2-diabetes and microalbuminuria. 151 diabetic patients with persistent microalbuminuria were included in year 1995 and followed for up to 19 years. For renal outcomes, two classifiers (CKD273 and a novel, GFR-based classifier) and for cardiovascular outcomes, three classifiers (CAD238, ACSP and ACSP75) were applied. Renal endpoints were progression to macroalbuminuria, impaired renal function (GFR < 45 ml/min/1.73 m²) or progression to end stage renal disease (ESRD) or death. Cardiovascular endpoints were coronary artery disease and a composite endpoint of incident death of cardiovascular disease, myocardial infarction or revascularization, stroke, amputation or peripheral revascularization.

RESULTS

CKD273 was not consistently associated with renal outcomes. The GFR-based classifier was associated with impaired renal function, but lost significance in extensively adjusted models. Both the ACSP75 and ACSP-scores, but not the CAD238-score were inversely associated (opposing the hypothesis) with cardiovascular endpoints. None of the classifiers improved prediction of any outcome on top of standard risk factors.

CONCLUSIONS

Risk-scores based upon urinary proteomics did not improve prediction of renal and cardiovascular endpoints on top of standard risk factors such as age and GFR during long-term (19 years) follow up in patients with type 2-diabetes and microalbuminuria.

Recent advances in capillary electrophoresis-mass spectrometry: Instrumentation, methodology and applications

Capillary electrophoresis (CE) offers fast and high-resolution separation of charged analytes from small injection volumes. Coupled to mass spectrometry (MS), it represents a powerful analytical technique providing (exact) mass information and enables molecular characterization based on fragmentation. Although hyphenation of CE and MS is not straightforward, much emphasis has been placed on enabling efficient ionization and user-friendly coupling. Though several interfaces are now commercially available, research on more efficient and robust interfacing with nano-electrospray ionization (ESI), matrix-assisted laser desorption/ionization (MALDI) and inductively coupled plasma mass spectrometry (ICP) continues with considerable results. At the same time, CE-MS has been used in many fields, predominantly for the analysis of proteins, peptides and metabolites. This review belongs to a series of regularly published articles, summarizing 248 articles covering the time between June 2016 and May 2018. Latest developments on hyphenation of CE with MS as well as instrumental developments such as two-dimensional separation systems with MS detection are mentioned. Furthermore, applications of various CE-modes including capillary zone electrophoresis (CZE), nonaqueous capillary electrophoresis (NACE), capillary gel electrophoresis (CGE) and capillary isoelectric focusing (CIEF) coupled to MS in biological, pharmaceutical and environmental research are summarized.