Biomarkers of diabetic kidney disease.
Ontology highlight
ABSTRACT:
SUBMITTER: Colhoun HM
PROVIDER: S-EPMC6448994 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundThere are big differences in treatments and prognosis between diabetic kidney disease (DKD) and non-diabetic renal disease (NDRD). However, DKD patients couldn't be diagnosed early due to lack of special biomarkers. Urine is an ideal non-invasive sample for screening DKD biomarkers. This study aims to explore DKD special biomarkers by urinary proteomics.Materials and methodsAccording to the result of renal biopsy, 142 type 2 diabetes mellitus (T2DM) patients were divided into 2 groups: DKD (n = 83) and NDRD (n = 59). Ten patients were selected from each group to define urinary protein profiles by label-free quantitative proteomics. The candidate proteins were further verifyied by parallel reaction monitoring (PRM) methods (n = 40). Proteins which perform the same trend both in PRM and proteomics were verified by enzyme-linked immunosorbent assays (ELISA) with expanding the sample size (n = 82). The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of diagnostic biomarkers.ResultsWe identified 417 peptides in urinary proteins showing significant difference between DKD and NDRD. PRM verification identified C7, SERPINA4, IGHG1, SEMG2, PGLS, GGT1, CDH2, CDH1 was consistent with the proteomic results and p < 0.05. Three potential biomarkers for DKD, C7, SERPINA4, and gGT1, were verified by ELISA. The combinatied SERPINA4/Ucr and gGT1/Ucr (AUC = 0.758, p = 0.001) displayed higher diagnostic efficiency than C7/Ucr (AUC = 0.632, p = 0.048), SERPINA4/Ucr (AUC = 0.661, p = 0.032), and gGT1/Ucr (AUC = 0.661, p = 0.029) respectively.ConclusionsThe combined index SERPINA4/Ucr and gGT1/Ucr can be considered as candidate biomarkers for diabetic nephropathy after adjusting by urine creatinine.
| S-EPMC10873681 | biostudies-literature
Project description:BackgroundDiabetic kidney disease (DKD) is a leading cause of end-stage renal disease throughout the world. In kidney disease, oxidative stress has been linked to both antioxidant depletions and increased reactive oxygen species (ROS) production. Thus, the objective of this study was to identify biomarkers related to oxidative stress in DKD.MethodsThe gene expression profile of the DKD was extracted from the Gene Expression Omnibus (GEO) database. The identification of the differentially expressed genes (DEGs) was performed using the "limma" R package, and weighted gene coexpression network analysis (WGCNA) was used to find the gene modules that were most related to DKD. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using "Org.Hs.eg.db" R package. The protein-protein interaction (PPI) network was constructed using the STRING database. The hub genes were identified by the Molecular Complex Detection (MCODE) plug-in of Cytoscape software. The diagnostic capacity of hub genes was verified using the receiver operating characteristic (ROC) curve. Correlations between diagnostic genes were analyzed using the "corrplot" package. In addition, the miRNA gene transcription factor (TF) network was used to explain the regulatory mechanism of hub genes in DKD.ResultsDEGs analysis and WGCNA-identified 160 key genes were identified in DKD patients. Among them, nine oxidative stress-related genes were identified as candidate hub genes for DKD. Using the PPI network, five hub genes, NR4A2, DUSP1, FOS, JUN, and PTGS2, were subsequently identified. All the hub genes were downregulated in DKD and had a high diagnostic value of DKD. The regulatory mechanism of hub genes was analyzed from the miRNA gene-TF network.ConclusionOur study identified NR4A2, DUSP1, FOS, JUN, and PTGS2 as hub genes of DKD. These genes may serve as potential therapeutic targets for DKD patients.
| S-EPMC9825216 | biostudies-literature
Project description:BackgroundDiabetic kidney disease (DKD) remains one of the leading causes of premature death in diabetes. DKD is classified on albuminuria and reduced kidney function (estimated glomerular filtration rate (eGFR)) but these have modest value for predicting future renal status. There is an unmet need for biomarkers that can be used in clinical settings which also improve prediction of renal decline on top of routinely available data, particularly in the early stages. The iBEAt study of the BEAt-DKD project aims to determine whether renal imaging biomarkers (magnetic resonance imaging (MRI) and ultrasound (US)) provide insight into the pathogenesis and heterogeneity of DKD (primary aim) and whether they have potential as prognostic biomarkers in DKD (secondary aim).MethodsiBEAt is a prospective multi-centre observational cohort study recruiting 500 patients with type 2 diabetes (T2D) and eGFR ≥30 ml/min/1.73m2. At baseline, blood and urine will be collected, clinical examinations will be performed, and medical history will be obtained. These assessments will be repeated annually for 3 years. At baseline each participant will also undergo quantitative renal MRI and US with central processing of MRI images. Biological samples will be stored in a central laboratory for biomarker and validation studies, and data in a central data depository. Data analysis will explore the potential associations between imaging biomarkers and renal function, and whether the imaging biomarkers improve the prediction of DKD progression. Ancillary substudies will: (1) validate imaging biomarkers against renal histopathology; (2) validate MRI based renal blood flow measurements against H2O15 positron-emission tomography (PET); (3) validate methods for (semi-)automated processing of renal MRI; (4) examine longitudinal changes in imaging biomarkers; (5) examine whether glycocalyx and microvascular measures are associated with imaging biomarkers and eGFR decline; (6) explore whether the findings in T2D can be extrapolated to type 1 diabetes.DiscussioniBEAt is the largest DKD imaging study to date and will provide valuable insights into the progression and heterogeneity of DKD. The results may contribute to a more personalised approach to DKD management in patients with T2D.Trial registrationClinicaltrials.gov ( NCT03716401 ).
| S-EPMC7323369 | biostudies-literature
Project description:Biomarkers of diverse pathophysiologic mechanisms may improve risk stratification for incident or progressive diabetic kidney disease (DKD) in persons with type 2 diabetes. To evaluate such biomarkers, we performed a nested case-control study (n=190 cases of incident DKD and 190 matched controls) and a prospective cohort study (n=1156) using banked baseline plasma samples from participants of randomized, controlled trials of early (ACCORD) and advanced (VA NEPHRON-D) DKD. We assessed the association and discrimination obtained with baseline levels of plasma TNF receptor-1 (TNFR-1), TNFR-2, and kidney injury molecule-1 (KIM-1) for the outcomes of incident DKD (ACCORD) and progressive DKD (VA-NEPHRON-D). At baseline, median concentrations of TNFR-1, TNFR-2, and KIM-1 were roughly two-fold higher in the advanced DKD population (NEPHRON-D) than in the early DKD population (ACCORD). In both cohorts, patients who reached the renal outcome had higher baseline levels than those who did not reach the outcome. Associations between doubling in TNFR-1, TNFR-2, and KIM-1 levels and risk of the renal outcomes were significant for both cohorts. Inclusion of these biomarkers in clinical models increased the area under the curve (SEM) for predicting the renal outcome from 0.68 (0.02) to 0.75 (0.02) in NEPHRON-D. Systematic review of the literature illustrated high consistency in the association between these biomarkers of inflammation and renal outcomes in DKD. In conclusion, TNFR-1, TNFR-2, and KIM-1 independently associated with higher risk of eGFR decline in persons with early or advanced DKD. Moreover, addition of these biomarkers to clinical prognostic models significantly improved discrimination for the renal outcome.
| S-EPMC5576932 | biostudies-literature
Project description:BackgroundType 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.MethodsIn 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.ResultsWe 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.ConclusionsOur study demonstrated the potential for urinary proteomics as a noninvasive method for DKD diagnosis and identifying high-risk patients for progression monitoring.
| S-EPMC8903606 | biostudies-literature
Project description:Type 2 diabetes and renal damage are strictly linked. The progressive increase in T2D incidence has stimulated the interest in novel biomarkers to improve the diagnostic performance of the commonly utilized markers such as albuminuria and eGFR. Through microarray method, we analyzed the entire transcriptome expressed in 12 serum samples of diabetic patients, six without DKD and six with DKD; the downregulation of the most dysregulated transcripts was validated in a wider cohort of 69 patients by qPCRs. We identified a total of 33 downregulated transcripts. The downregulation of four mitochondrial messenger RNAs (MT-ATP6, MT-ATP8, MT-COX3, MT-ND1) and other two transcripts (seysnoy, skerdo) was validated in patients with eGFR stage G3 versus G2 and G1. The four messenger RNAs correlated with creatinine and eGFR stages, while seysnoy and skerdo were associated with white blood cell values. All transcripts correlated also with Blood Urea Nitrogen. The four mitochondrial messenger RNAs had a high diagnostic performance in G3 versus G2 discrimination, with AUC values above 0.8. The most performant transcript was MT-ATP6, with an AUC of 0.846; sensitivity = 90%, specificity = 76%, p-value = 7.8 × 10-5. This study led to the identification of a specific molecular signature of DKD, proposing the dosage of RNAs, especially mitochondrial RNAs, as noninvasive biomarkers of diabetes complication.
| S-EPMC9331991 | biostudies-literature
Project description:Diabetic kidney disease (DKD) is the most common diabetic complication and is a leading cause of end-stage kidney disease. Increasing evidence shows that DKD is regulated not only by many classical signaling pathways but also by epigenetic mechanisms involving chromatin histone modifications, DNA methylation, and non-coding RNA (ncRNAs). In this review, we focus on our current understanding of the role and mechanisms of ncRNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in the pathogenesis of DKD. Of them, the regulatory role of TGF-β/Smad3-dependent miRNAs and lncRNAs in DKD is highlighted. Importantly, miRNAs and lncRNAs as biomarkers and therapeutic targets for DKD are also described, and the perspective of ncRNAs as a novel therapeutic approach for combating diabetic nephropathy is also discussed.
| S-EPMC7870688 | biostudies-literature
Project description:The kidney is arguably the most important target of microvascular damage in diabetes. A substantial proportion of individuals with diabetes will develop kidney disease owing to their disease and/or other co-morbidity, including hypertension and ageing-related nephron loss. The presence and severity of chronic kidney disease (CKD) identify individuals who are at increased risk of adverse health outcomes and premature mortality. Consequently, preventing and managing CKD in patients with diabetes is now a key aim of their overall management. Intensive management of patients with diabetes includes controlling blood glucose levels and blood pressure as well as blockade of the renin-angiotensin-aldosterone system; these approaches will reduce the incidence of diabetic kidney disease and slow its progression. Indeed, the major decline in the incidence of diabetic kidney disease (DKD) over the past 30 years and improved patient prognosis are largely attributable to improved diabetes care. However, there remains an unmet need for innovative treatment strategies to prevent, arrest, treat and reverse DKD. In this Primer, we summarize what is now known about the molecular pathogenesis of CKD in patients with diabetes and the key pathways and targets implicated in its progression. In addition, we discuss the current evidence for the prevention and management of DKD as well as the many controversies. Finally, we explore the opportunities to develop new interventions through urgently needed investment in dedicated and focused research. For an illustrated summary of this Primer, visit: http://go.nature.com/NKHDzg.
| S-EPMC7724636 | biostudies-literature
Project description:Diabetic retinopathy (DR) and diabetic kidney disease (DKD) are complications of diabetes and place serious health and economic burdens on society. However, the identification and characterization of early biomarkers for DKD, especially for nonproliferative DR (NPDR) patients with DKD, are still needed. This study aimed to demonstrate the plasma proteomic profiles of NPDR+DKD and NPDR patients and identify potential biomarkers for early diagnosis of DKD. Fifteen plasma samples from the NPDR group and nine from the NPDR+DKD group were analyzed by LC-MS/MS to identify the differentially expressed proteins between the two groups. Functional enrichment, protein-protein interaction and clinical feature correlation analyses revealed the target protein candidates, which were verified using ELISA and receiver operating characteristic (ROC) analysis. In total, 410 proteins were detected in plasma; 15 were significantly upregulated and 7 were downregulated in the NPDR+DKD group. Bioinformatics analysis suggested that DKD is closely related to cell adhesion and immunity pathways. β-2-Microglobulin (B2M) and vimentin (VIM) were upregulated in NPDR+DKD, enriched as hub proteins and strongly correlated with clinical features. ELISA showed that B2M (p<0.001) and VIM (p<0.0001) were significantly upregulated in NPDR+DKD compared with NPDR. In ROC analysis, B2M and VIM could distinguish DKD from NPDR with area under the curve values of 0.9000 (p < 0.0001) and 0.9950. Our proteomic study revealed alterations in the proteomic profile and identified VIM and B2M as early biomarkers of DKD, laying the foundation for the prevention, diagnosis and treatment of DKD.
| S-EPMC9581084 | biostudies-literature
Project description:BackgroundAs one of the most prevalent diagnostic indicators of diabetic kidney disease (DKD), albumin-to-creatinine ratio (ACR) shows considerably limited predictive power in clinical application. We analyzed microarray expression profiling of urine to seek for differentially expressed miRNAs for potential biomarkers of DKD.MethodsUrine samples from type 2 diabetes mellitus (T2DM) patients with (30 mg/g < ACR < 300 mg/g, DKD group) or without DKD (ACR < 30 mg/g, DM group) were collected for miRNA microarray analysis. The differentially expressed miRNAs were screened by bioinformatics analysis and validated by quantitative real-time PCR. Target genes of differentially expressed miRNAs were predicted in miRDB, Targetscan, and microRNA.org databases. We also conducted the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways analysis to explore for potential mechanisms in DKD.ResultsNine miRNAs were down-regulated and seventeen miRNAs were up-regulated in DKD group, compared to DM group. The levels of miR-3137 and miR-4270 in DKD group were 0.670 ± 0.505 and 2.116 ± 1.762 times than those in DM group, respectively, showing great significance. A total of 1076 target genes were simultaneously predicted by miRDB, Targetscan, and microRNA.org databases. According to the GO analysis results, disorders of endomembrane system may be one of the major pathological changes in DKD. In addition, Rap 1 signaling pathway is also altered obviously in DKD, discovered by the KEGG analysis.ConclusionMiR-3137 and miR-4270 show the potential for urinary biomarkers of DKD. The pathological changes of DKD may be related to disorders of endomembrane system and alternation of Rap1 signaling pathway.
| S-EPMC7755759 | biostudies-literature