Urinary Proteomics for the Early Diagnosis of Diabetic Nephropathy in Taiwanese Patients

A candidate panel of eight urinary proteins shows potential of early diagnosis and risk assessment for diabetic kidney disease in type 1 diabetes

Diabetic kidney disease (DKD) poses a significant health challenge for individuals with diabetes. At its initial stages, DKD often presents asymptomatically, and the standard for non-invasive diagnosis, the albumin-creatinine ratio (ACR), employs discrete categorizations (normal, microalbuminuria, macroalbuminuria) with limitations in sensitivity and specificity across diverse population cohorts. Single biomarker reliance further restricts the predictive value in clinical settings. Given the escalating prevalence of diabetes, our study uses proteomic technologies to identify novel urinary proteins as supplementary DKD biomarkers. A total of 158 T1D subjects provided urine samples, with 28 (15 DKD; 13 non-DKD) used in the discovery stage and 131 (45 DKD; 40 pDKD; 46 non-DKD) used in the confirmation. We identified eight proteins (A1BG, AMBP, AZGP1, BTD, RBP4, ORM2, GM2A, and PGCP), all of which demonstrated excellent area-under-the-curve (AUC) values (0.959 to 0.995) in distinguishing DKD from non-DKD. Furthermore, this multi-marker panel successfully segregated the most ambiguous group (microalbuminuria) into three distinct clusters, with 80% of subjects aligning either as DKD or non-DKD. The remaining 20% exhibited continued uncertainty. Overall, the use of these candidate urinary proteins allowed for the better classification of DKD and offered potential for significant improvements in the early identification of DKD in T1D populations.

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.

Integration of metabolomics and peptidomics reveals distinct molecular landscape of human diabetic kidney disease

Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes, and there is an urgent need to discover reliable biomarkers for early diagnosis. Here, we established an effective urine multi-omics platform and integrated metabolomics and peptidomics to investigate the biological changes during DKD pathogenesis. Methods: Totally 766 volunteers (221 HC, 198 T2DM, 175 early DKD, 125 overt DKD, and 47 grey-zone T2DM patients with abnormal urinary mALB concentration) were included in this study. Non-targeted metabolic fingerprints of urine samples were acquired on matrix-free LDI-MS platform by the tip-contact extraction method using fluorinated ethylene propylene coated silicon nanowires chips (FEP@SiNWs), while peptide profiles hidden in urine samples were uncovered by MALDI-TOF MS after capturing urine peptides by porous silicon microparticles. Results: After multivariate analysis, ten metabolites and six peptides were verified to be stepwise regulated in different DKD stages. The altered metabolic pathways and biological processes associated with the DKD pathogenesis were concentrated in amino acid metabolism and cellular protein metabolic process, which were supported by renal transcriptomics. Interestingly, multi-omics significantly increased the diagnostic accuracy for both early DKD diagnosis and DKD status discrimination. Combined with machine learning, a stepwise prediction model was constructed and 89.9% of HC, 75.5% of T2DM, 69.6% of early DKD and 75.7% of overt DKD subjects in the external validation cohort were correctly classified. In addition, 87.5% of grey-zone patients were successfully distinguished from T2DM patients. Conclusion: This multi-omics platform displayed a satisfactory ability to explore molecular information and provided a new insight for establishing effective DKD management.

Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies

Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG).

Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics

Diabetic kidney disease (DKD) affects an estimated 20-40% of type 2 diabetes patients and is among the most prevalent microvascular complications in this patient population, contributing to high morbidity and mortality rates. Currently, changes in albuminuria status are thought to be a primary indicator of the onset or progression of DKD, yet progressive nephropathy and renal impairment can occur in certain diabetic individuals who exhibit normal urinary albumin levels, emphasizing the lack of sensitivity and specificity associated with the use of albuminuria as a biomarker for detecting diabetic kidney disease and predicting DKD risk. According to the study, a non-invasive method for early detection or prediction of DKD may involve combining proteomic analytical techniques such second generation sequencing, mass spectrometry, two-dimensional gel electrophoresis, and other advanced system biology algorithms. Another category of proteins of relevance may now be provided by renal tissue biomarkers. The establishment of reliable proteomic biomarkers of DKD represents a novel approach to improving the diagnosis, prognostic evaluation, and treatment of affected patients. In the present review, a series of protein biomarkers that have been characterized to date are discussed, offering a theoretical foundation for future efforts to aid patients suffering from this debilitating microvascular complication.

Urinary proteome of dogs with renal disease secondary to leishmaniosis

Canine leishmaniosis is frequently associated with the development of renal disease. Its pathogenesis is complex and not fully understood. For this reason, this study aimed to describe the urinary proteome, and identify possible new biomarkers in dogs with kidney disease secondary to leishmaniosis. Urine samples were collected from 20 dogs, 5 from healthy dogs, and 15 from stages Leishvet III and IV. Urine samples were analyzed by UHPLC-MS/MS. The data are available via ProteomeXchange with identifier PXD029165. A total of 951 proteins were obtained. After bioinformatic analysis, 93 urinary proteins were altered in the study group. Enrichment analysis performed on these proteins showed an overrepresentation of the complement activation pathway, among others. Finally, 12 discriminant variables were found in dogs with renal disease secondary to leishmaniosis, highlighting C4a anaphylatoxin, apolipoprotein A-I, haptoglobin, leucine-rich alpha-2-glycoprotein 1, and beta-2-microglobulin. This study is the first to describe the urinary proteomics of dogs with renal disease caused by leishmaniosis, and it provides new possible biomarkers for the diagnosis and monitoring of this disease.

Urine proteomics identifies biomarkers for diabetic kidney disease at different stages

BACKGROUND

Type 2 diabetic kidney disease is the most common cause of chronic kidney diseases (CKD) and end-stage renal diseases (ESRD). Although kidney biopsy is considered as the 'gold standard' for diabetic kidney disease (DKD) diagnosis, it is an invasive procedure, and the diagnosis can be influenced by sampling bias and personal judgement. It is desirable to establish a non-invasive procedure that can complement kidney biopsy in diagnosis and tracking the DKD progress.

METHODS

In this cross-sectional study, we collected 252 urine samples, including 134 uncomplicated diabetes, 65 DKD, 40 CKD without diabetes and 13 follow-up diabetic samples, and analyzed the urine proteomes with liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We built logistic regression models to distinguish uncomplicated diabetes, DKD and other CKDs.

RESULTS

We quantified 559 ± 202 gene products (GPs) (Mean ± SD) on a single sample and 2946 GPs in total. Based on logistic regression models, DKD patients could be differentiated from the uncomplicated diabetic patients with 2 urinary proteins (AUC = 0.928), and the stage 3 (DKD3) and stage 4 (DKD4) DKD patients with 3 urinary proteins (AUC = 0.949). These results were validated in an independent dataset. Finally, a 4-protein classifier identified putative pre-DKD3 patients, who showed DKD3 proteomic features but were not diagnosed by clinical standards. Follow-up studies on 11 patients indicated that 2 putative pre-DKD patients have progressed to DKD3.

CONCLUSIONS

Our study demonstrated the potential for urinary proteomics as a noninvasive method for DKD diagnosis and identifying high-risk patients for progression monitoring.

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.

Diabetic nephropathy: A twisted thread to unravel

Diabetic nephropathy (DN), a persistent microvascular problem of diabetes mellitus is described as an elevated level of albumin excretion in urine and impaired renal activity. The morbidity and mortality of type-1 diabetics and type-2 diabetics due to end stage renal disease is also a result of the increased prevalence of DN. DN typically occurs as a consequence of an association among metabolic and hemodynamic variables, activating specific pathways leading to renal injury. According to current interventions, intensive glucose regulation decreases the threat of DN incidence and growth, and also suppressing the renin-angiotensin system (RAS) is a significant goal for hemodynamic and metabolism-related deformities in DN. However, the pathogenesis of DN is multifactorial so novel approaches other than glucose and blood pressure control are required for treatment. This review briefly summarizes the reported pathogenesis of DN, current interventions for its treatment, and possible novel interventions to unweave the thread of DN.

Development of a sensitive and reliable ELISA kit of urinary haptoglobin to predict progress of diabetic kidney disease

AIMS

Urinary haptoglobin (UHp) is a potential biomarker for predicting progress of diabetic kidney disease (DKD) to remedy the defects of currently used urinary albumin. The clinical application of UHp is however limited, owing to the extremely low level in urine. This study aims to establish an enzyme-linked immunosorbent assay (ELISA) kit specifically for detecting UHp in urine samples of patients with diabetes and DKD.

MATERIALS AND METHODS

Supersensitive human haptoglobin antibodies were generated for ELISA kit development, and the sensitivity, specificity and reproducibility of the kit was evaluated. This kit was used to detect UHp in 246 healthy individuals and 83 patients with type 2 diabetes (T2D). The interference of blood haptoglobin genotypes on UHp measurement was analysed.

RESULTS

The UHp ELISA kit had a standard curve ranging from 5 to 200 ng/ml. The low detection limit was 0.11 ng/ml. The coefficients of variation of intra- and interassay were 5.5% and 8.3%, respectively. The kit showed high accuracy with 100.9% mean recovery rate, and linearity R²  = 0.999. The reference range of UHp was 0-42.3 ng/g creatinine (0-Q95) in the healthy individuals. UHp level was significantly higher in T2D patients with microalbuminuria and macroalbuminuria than that in T2D without microalbuminuria (p < 0.01). The UHp concentration measured by this kit was not affected by haptoglobin genotypes.

CONCLUSIONS

We have generated an ELISA kit to accurately detect UHp levels, which is potentially a reliable biomarker of DKD.

Alleviative effects of 20(R)-Rg3 on HFD/STZ-induced diabetic nephropathy via MAPK/NF-κB signaling pathways in C57BL/6 mice

ETHNOPHARMACOLOGICAL RELEVANCE

Diabetic nephropathy (DN) is a major complication of diabetes. The kidney disease develops in nearly 20%-40% of type 2 diabetes (T2D) patients. Ginseng is the root of Panax ginseng C. A. Meyer and has been used in prevention and treatment of diseases for more than 2000 years as a traditional oriental medicine. The 20(R)-ginsenoside Rg3, an active saponin isolated from ginseng, can prevent and treat many diseases. The object of this research was to explore the alleviative effects of 20(R)-Rg3 on DN in mice.

MATERIALS AND METHODS

The T2D animal model was induced by continuous access to a high fat diet (HFD) combined with a single injection of 100 mg/kg streptozotocin (STZ) in C57BL/6 mice. The mice were treated by oral gavage of the 20(R)-Rg3 (10, 20 mg/kg) for 8 weeks. Functional and histopathological analyses of the kidneys were then performed. Protein expression levels of MAPKs and NF-κB signal pathways in the kidney were evaluated by western blotting. The expressions of HO-1 and NF-κB in the kidney were measured by fluorescent labeling staining. Other assessments including fasting blood glucose (FBG) levels, blood lipids, oxidative indicators, and inflammatory factors were all performed.

RESULTS

Abnormally elevated FBG levels were observed in HFD/STZ mice, contributing significantly to the occurrence of DN. Simultaneously, HFD/STZ mice showed the rise of serum total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) levels, and the decrease in high density lipoprotein cholesterol (HDL-C). DN was evidenced by the overproduction of malondialdehyde (MDA), decreased levels of superoxide dismutase (SOD) and catalase (CAT) enzymatic activities, high levels of serum blood urea nitrogen (BUN) and creatinine (Cr). Simultaneously, the results of the immunofluorescence assay showed an increased expression level in NF-κB p65 while a decrease in antioxidant enzyme HO-1 was observed. Herein, 20(R)-Rg3 treatment for 8 weeks not only attenuated FBG levels and advanced glycation end products (AGEs) levels but also improved insulin (INS) level, blood lipids, oxidative stress, and renal function by regulating MAPKs and NF-κB signal pathways in DN mice.

CONCLUSION

Taken together, the findings from the present study explicitly confirmed that 20(R)-Rg3 exerted ameliorative effects on DN mice via improving anti-oxidative activity and reducing renal inflammation.

An updated pharmacological insight of resveratrol in the treatment of diabetic nephropathy

As one of the most common complications of diabetes, nephropathy develops in approximately 40% of diabetic individuals. Although end stage kidney disease is known as one of the most consequences of diabetic nephropathy, the majority of diabetic individuals might die from cardiovascular diseases and infections before renal replacement treatment. Moreover, the routine medical treatments for diabetes hold undesirable side effects. The explosive prevalence of diabetes urges clinicians and scientists to investigate the complementary or alternative therapies. Phytochemicals are emerging as alternatives with a wide range of therapeutic effects on various pathologies, including diabetic kidney disease. Of those phytochemicals, resveratrol, a natural polyphenolic stilbene, has been found to exert a broad spectrum of health benefits via various signaling molecules. In particular, resveratrol has gained a great deal of attention because of its anti-oxidative, anti-inflammatory, anti-diabetic, anti-obesity, cardiovascular-protective, and anti-tumor properties. In the renal system, emerging evidence shows that resveratrol has already been used to ameliorate chronic or acute kidney injury. This review critically summarizes the current findings and molecular mechanisms of resveratrol in diabetic renal damage. In addition, we will discuss the adverse and inconsistent effects of resveratrol in diabetic nephropathy. Although there is increasing evidence that resveratrol affords great potential in diabetic nephropathy therapy, these results should be treated with caution before its clinical translation. In addition, the unfavorable pharmacokinetics and/or pharmacodynamics profiles, such as poor bioavailability, may limit its extensive clinical applications. It is clear that further research is needed to unravel these limitations and improve its efficacy against diabetic nephropathy. Increasing investigation of resveratrol in diabetic kidney disease will not only help us better understand its pharmacological actions, but also provide novel potential targets for therapeutic intervention.

Urinary Proteomics Identifying Novel Biomarkers for the Diagnosis of Adult-Onset Still's Disease

Adult-onset Still’s disease (AOSD) is a systemic, multigenic autoinflammatory disease, and the diagnosis of AOSD must rule out neoplasms, infections, and other autoimmune diseases. Development of a rapid and efficient but non-invasive diagnosis method is urgently needed for improving AOSD therapy. In this study, we first performed a urinary proteomic study using isobaric tags for relative and absolute quantification (iTRAQ) labeling combined with liquid chromatography–tandem mass spectrometry analysis in patients with AOSD and healthy control (HC) subjects. The urinary proteins were enriched in pathways of the innate immune system and neutrophil degranulation, and we identified that the α-1-acid glycoprotein 1 (LRG1), orosomucoid 1 (ORM1), and ORM2 proteins were highly expressed in patients with AOSD. The elevated urine levels of LRG1, ORM1, and ORM2 were further validated by enzyme-linked immunosorbent assay in active patients with AOSD, disease controls, and HC subjects. Receiver operating characteristic curves showed that the areas under the curve of LRG1, ORM1, and ORM2 were 0.700, 0.837, and 0.736, respectively (all p < 0.05). Furthermore, we found that the urine levels of LRG1, ORM1, and ORM2 were positively correlated with the systemic score and erythrocyte sedimentation rate and that the urine levels of LRG1 were positively correlated with interleukin 1β (IL-1β), IL-6, and IL-18 levels, whereas the urine levels of ORM1 were positively correlated with the IL-1β level. Together, our study identified novel urinary markers for non-invasive and simple screening of AOSD.

Multiplexed MRM-based protein quantification of putative prognostic biomarkers for chronic kidney disease progression in plasma

Current diagnostic measures for Chronic Kidney Disease (CKD) include detection of reduced estimated glomerular filtration rate (eGFR) and albuminuria, which have suboptimal accuracies in predicting disease progression. The disease complexity and heterogeneity underscore the need for multiplex quantification of different markers. The goal of this study was to determine the association of six previously reported CKD-associated plasma proteins [B2M (Beta-2-microglobulin), SERPINF1 (Pigment epithelium-derived factor), AMBP (Protein AMBP), LYZ (Lysozyme C), HBB (Hemoglobin subunit beta) and IGHA1 (Immunoglobulin heavy constant alpha 1)], as measured in a multiplex format, with kidney function, and outcome. Antibody-free, multiple reaction monitoring mass spectrometry (MRM) assays were developed, characterized for their analytical performance, and used for the analysis of 72 plasma samples from a patient cohort with longitudinal follow-up. The MRM significantly correlated (Rho = 0.5–0.9) with results from respective ELISA. Five proteins [AMBP, B2M, LYZ, HBB and SERPINF1] were significantly associated with eGFR, with the three former also associated with unfavorable outcome. The combination of these markers provided stronger associations with outcome (p < 0.0001) compared to individual markers. Collectively, our study describes a multiplex assay for absolute quantification and verification analysis of previously described putative CKD prognostic markers, laying the groundwork for further use in prospective validation studies.

Diabetic kidney diseases revisited: A new perspective for a new era

BACKGROUND

Globally, diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. As the most common microvascular complication of diabetes, DKD is a thorny, clinical problem in terms of its diagnosis and management. Intensive glucose control in DKD could slow down but not significantly halt disease progression. Revisiting the tremendous advances that have occurred in the field would enhance recognition of DKD pathogenesis as well as improve our understanding of translational science in DKD in this new era.

SCOPE OF REVIEW

In this review, we summarize advances in the understanding of the local microenvironmental changes in diabetic kidneys and discuss the involvement of genetic and epigenetic factors in the pathogenesis of DKD. We also review DKD prevalence changes and analyze the challenges in optimizing the diagnostic approaches and management strategies for DKD in the clinic. As we enter the era of 'big data', we also explore the possibility of linking systems biology with translational medicine in DKD in the current healthcare system.

MAJOR CONCLUSION

Newer understanding of the structural changes of diabetic kidneys and mechanisms of DKD pathogenesis, as well as emergent research technologies will shed light on new methods of dealing with the existing clinical challenges of DKD.

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

Artificial intelligence (AI) is expected to support clinical judgement in medicine. We constructed a new predictive model for diabetic kidney diseases (DKD) using AI, processing natural language and longitudinal data with big data machine learning, based on the electronic medical records (EMR) of 64,059 diabetes patients. AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis. AI could predict DKD aggravation with 71% accuracy. Furthermore, the group with DKD aggravation had a significantly higher incidence of hemodialysis than the non-aggravation group, over 10 years (N = 2,900). The new predictive model by AI could detect progression of DKD and may contribute to more effective and accurate intervention to reduce hemodialysis.

iTRAQ plasma proteomics analysis for candidate biomarkers of type 2 incipient diabetic nephropathy

Background

Diabetic nephropathy is the most frequent cause of end-stage renal disease worldwide. Identification of biomarkers for diabetic nephropathy for early diagnosis may be the key to avoiding damage from this condition.

Methods

Proteomic iTRAQ technology was first used to identify differentially expressed plasma proteins in type 2 incipient diabetic nephropathy (IDN) using a Q-Exactive mass spectrometer.

Results

Compared with controls, 57 proteins (32 upregulated and 25 downregulated proteins) were identified. Furthermore, the gelsolin, collectin-11, PTPRJ, and AKAP-7 proteins were confirmed by Western blots as candidate biomarkers for type 2 IDN through ROC analysis.

Conclusions

These findings offer a theoretical basis for the early treatment of diabetic nephropathy.