Early Prediction Of Diabetic Nephropathy Research Articles

Dysregulated miRNA-375, IL-17, TGF-β, and Microminerals Are Associated with Calpain-10 SNP 19 in Diabetic Patients: Correlation with Diabetic Nephropathy Stages.

Zinc (Zn) and copper (Cu) have been shown to have the potential to improve glucose metabolism through interactions with cytokines and signaling events with multiple genes. miRNA-375 and the Calpin-10 gene are potential genetic biomarkers for the early prediction of diabetic nephropathy (DN). 128 healthy controls and 129 type 2 diabetic (T2DM) participants were matched for age and sex. Three subgroups were identified from the T2DM group: 39 patients had microalbuminuria, 41 had macroalbuminuria, and 49 patients had renal problems. Circulating miR-375 expression levels were measured via qPCR. Calpain-10 SNP 19 (rs3842570) genotyping was assessed with allele-specific PCR in all the included participants. Spectrophotometry was used to measure the concentrations of serum copper, zinc, and magnesium, while ELISA was used to measure the levels of TGF-β and IL-17. There was significant up-regulation in the expression of miR-375 and serum levels of TGF-β, IL-17, Cu, and the Cu/Zn ratio, whereas, in contrast to the control group, the Zn and Mg levels were lower in the T2DM group. The DN groups had significantly lower miR-375, TGF-β, IL-17, Mg, and Zn levels compared with the T2DM without nephropathy group. Furthermore, between TGF-β, IL-17, and miRNA-375, there were notable correlations. Calpain-10 SNP 19 genotype 22 and allele 2 were linked to a higher incidence of T2DM and DN. Significant TGF-β, Cu, Cu/Zn ratio, HbAc1, and creatinine levels, but insignificant miRNA-375 levels, were associated with genotype 22 of Calpain-10 SNP 19. interactions between the Calpain-10 SNP 19 genotype 22 and IL-17, TGF-β, mineral levels, and miRNA-375 might contribute to the aetiology of DN and T2DM and may have clinical implications for diagnosis and management.

Serum fatty acid-binding protein 4 as a biomarker for early detection of diabetic nephropathy in type 2 diabetes

BackgroundDiabetic nephropathy (DN) is one of the major causes of ESKD, and its complications are characterized by proteinuria, decreased glomerular filtration, and renal fibrosis resulting in the deterioration of renal functions, so early detection of nephropathy is essential to slow down and prevent the progression of the disease. Fatty acid-binding protein 4 (FABP4) is expressed in renal proximal tubule cells and released in response to hypoxia caused by decreased peritubular capillary blood flow, so serum FABP 4 is one of the promising biomarkers for early prediction of diabetic nephropathy in patients with type 2 diabetes.MethodsThis was a case–control study that included 120 patients with type-2 diabetes mellitus selected from Kasr Alainy Hospital, Cairo University Hospital, who were divided into 2 groups: the first group comprised 60 diabetic patients divided into 3 sub-groups according to their urinary albumin/creatinine ratio (normo-, micro-, and macroalbuminuria). The second group included 60 apparently healthy individuals. All patients were subjected to history, clinical examination, laboratory investigations, and serum FABP4 by ELISA.ResultsThere was a significant increase in serum FABP4 in the macroalbuminuria group, followed by the microalbuminuria group, then the normoalbuminuria group, in comparison to the normal control group. There was a significant positive correlation between serum FABP 4 level and the duration of diabetes and HBA1c. There was a significantly negative correlation between serum FABP4 and serum albumin in the macroalbuminuria group. Receiver operating characteristic curve analysis found that serum FABP 4 discriminate micro- and macroalbuminuric patients with diabetes from controls with 96.6% and 98.3% diagnostic specificity and 100% diagnostic sensitivity respectively.ConclusionSerum FABP 4 can be used as a biomarker for the early detection of diabetic nephropathy.

Development of an intelligent clinical decision support system for the early prediction of diabetic nephropathy

BackgroundDiabetic nephropathy (DN) is the most common microvascular complication of diabetes mellitus (DM) and is identified as a leading cause of the end-stage renal disease (ESRD). Considering the importance of early prediction of individuals at risk of this complication, the use of intelligent models through machine learning (ML) algorithms can be helpful. Therefore, this study aimed to identify the influential variables in predicting DN and fed them as inputs to develop an ML-based decision support system (DSS) for DN diagnosis. MethodsThe data of 327 patients with diabetes (types 1 and 2) were retrospectively analyzed. After data preparation, the genetic algorithm (GA) feature selection method was used to identify the predictor variables affecting DN. Then, several ML algorithms, including the support vector machine (SVM), decision tree (DT), K-nearest neighbors (KNN), and artificial neural networks (ANN) were used to train predictive models based on the selected features. Afterward, the performance of the developed models was evaluated using sensitivity, specificity, and accuracy criteria in 10 independent runs. Finally, the DSS was developed based on the best fit model in the C# programming language. ResultsOur findings illustrated that age, hemoglobin A1c (HbA1c) test, diastolic arterial pressure (DAP), systolic arterial pressure (SAP), fasting glycemia rate (FGR), and DM involvement time were the most important factors in predicting DN. Moreover, to predict the DN, GA combined with the DT algorithm obtained the highest performance in terms of accuracy, sensitivity, specificity, and area under the curve (AUC), equal to 98.9, 98.6, 99.2, and 98.9%, respectively. ConclusionsThe results revealed that GA combined with the DT classifier predicted DN with significant accuracy. Thus, the DSS developed based on DT can be considered a reliable tool to help physicians make decisions. Future studies are warranted to further validate the applicability of our model in clinical settings.

Osteopontin As A Marker of Diabetic Nephropathy in Patients With Type 2 Diabetes Mellitus

Background: Diabetic nephropathy is the most common cause of end stage renal disease (ESRD) that is associated with high rates of morbidity and mortality. osteopontin (OPN) is a secreted phosphorylated glycoprotein that mediates diverse biological functions. Objective: The study aimed to determine the relation between serum OPN and the progression of diabetic nephropathy in patients with type 2 DM. That may open a door for early prediction of diabetic nephropathy. Patients and Methods: This case-control study was conducted in Internal Medicine, Endocrinology Unit and Biochemistry Departments, Zagazig University Hospital. This study was carried out on 96 patients devided into: Group 1: 20 age and sex matched healthy volunteers (control group). Group 2: 38 patients with type 2 DM without nephropathy (DWN group). Group3: 38 patients with type 2 DM with nephropathy (DN group). Serum osteopontin concentrations was measured for all cases. Results: HOMA-IR increased significantly in DN group (8.59 ± 1.34) compared to DWN group (4. 73 ± 0.59) and control group (0.90 ± 0.18). Serum osteopontin level (ng/ml) increased significantly in the DN group (258.52 ± 46.93 ng/ml) compared to DWN group (159.12 ± 19.56 ng/ml) and control group (70.90 (± 20.47 ng/ml)(p value =0.000). There was a high significant correlation between S. OPN and HOMA-IR. Conclusion: Type 2 diabetic patients with or without nephropathy showed increased osteopontin levels than control group. Serum osteopontin may be considered as an early prognostic marker for the risk of nephropathy in patients with type 2 diabetes mellitus.

Plausible diagnostic value of urinary isomeric dimethylarginine ratio for diabetic nephropathy

Altered circulatory asymmetric and symmetric dimethylarginines have been independently reported in patients with end-stage renal failure suggesting their potential role as mediators and early biomarkers of nephropathy. These alterations can also be reflected in urine. Herein, we aimed to evaluate urinary asymmetric to symmetric dimethylarginine ratio (ASR) for early prediction of diabetic nephropathy (DN). In this cross-sectional study, individuals with impaired glucose tolerance (IGT), newly diagnosed diabetes (NDD), diabetic microalbuminuria (MIC), macroalbuminuria (MAC), and normal glucose tolerance (NGT) were recruited from Dr. Mohans’ Diabetes Specialties centre, India. Urinary ASR was measured using a validated high-throughput MALDI-MS/MS method. Significantly lower ASR was observed in MIC (0.909) and MAC (0.741) in comparison to the NGT and NDD groups. On regression models, ASR was associated with MIC [OR: 0.256; 95% CI: 0.158–0.491] and MAC [OR 0.146; 95% CI: 0.071–0.292] controlled for all the available confounding factors. ROC analysis revealed ASR cut-point of 0.95 had C-statistic of 0.691 (95% CI: 0.627-0.755) to discriminate MIC from NDD with 72% sensitivity. Whereas, an ASR cut-point of 0.82 had C-statistic of 0.846 (95% CI: 0.800 - 0.893) had 91% sensitivity for identifying MAC. Our results suggest ASR as a potential early diagnostic biomarker for DN among the Asian Indians.