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

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

This retrospective study included 299 patients with type 2 diabetes mellitus and biopsy-confirmed diabetic nephropathy (DN) to investigate the prognostic value of alkaline phosphatase (ALP) for ren...

Association between serum alkaline phosphatase and renal outcome in patients with type 2 diabetes mellitus

Purpose: To investigate the expression of various angiogenesis and inflammation mediators in the vitreous fluid of eyes with proliferative diabetic retinopathy (PDR). Methods: A total of 38 eyes with PDR and 37 control eyes were included. Vitreous fluid was collected during vitrectomy. Vitreous levels of colony stimulating factor-1 receptor (CSF-1R), syndecan-1, placental growth factor (PIGF), and angiopoietin-like protein 4 (ANGPTL-4) were measured by multiplex immunoassay. Vitreous levels of vascular endothelial growth factor (VEGF), interleukin-6 (IL-6), interleukin-8 (IL-8), monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor-α (TNF-α), and intercellular adhesion molecule-1 (ICAM-1) were measured by cytometric beads array. Levels of these mediators were compared between the PDR and control eyes. Correlations between levels of different mediators and between these mediators and kidney function metrics in the PDR group were also analyzed. Results: Vitreous levels of syndecan-1, PIGF, ANGPTL-4, VEGF, and IL-8 were significantly higher in the PDR group compared to the control group (all p < 0.05). Levels of VEGF were significantly correlated with levels of syndecan-1, PIGF, and ANGPTL-4 (r = 0.370 to 0.497, all p < 0.05). Significant positive correlations were detected between levels of any two of the following mediators including syndecan-1, PIGF, ANGPTL-4, and IL-8 (r = 0.370 to 0.906, all p < 0.05). Apart from VEGF, levels of these mediators were positively correlated with serum creatinine and blood urea nitrogen (r = 0.328 to 0.638, all p < 0.05), and negatively correlated with fasting blood glucose and estimated glomerular filtration rate (r = -0.325 to -0.603, all p < 0.05). Conclusions: Correlations between different angiogenesis and inflammation mediators were observed in eyes with PDR, suggesting cross-talks of different angiogenesis and inflammation pathways in the pathogenesis of PDR. The levels of angiogenesis and inflammation in PDR are correlated with kidney damage, indicating possible common pathways in diabetic retinopathy and nephropathy.

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Correlations Between Different Angiogenic and Inflammatory Factors in Vitreous Fluid of Eyes With Proliferative Diabetic Retinopathy.

Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution.

/pdf/untangling-computer-aided-diagnostic-system-for-screening-1bw8f7m0.pdf

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Background The prevalence of diabetic microvascular diseases has increased significantly worldwide, the most common of which are diabetic nephropathy (DN) and diabetic retinopathy (DR). Microvascular endothelial cells are thought to be major targets of hyperglycemic damage, while the underlying mechanism of diffuse endothelial dysfunction in multiple organs needs to be further investigated. Aim The aim of this study is to explore the endothelial dysfunction mechanisms of serum exosomes (SExos) extracted from DR and DN (DRDN) patients. Methods In this study, human glomerular endothelial cells (HGECs) were used as the cell model. Metabolomics ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and proteomics tandem mass tag (TMT)-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) together with bioinformatics, the correlation analysis, and the joint pathway analysis were employed to discover the underlying mechanisms of endothelial dysfunction caused by patient’s SExos. Results It can be assumed that serum exosomes extracted by DRDN patients might cause endothelial dysfunction mainly by upregulating alpha subunit of the coagulation factor fibrinogen (FIBA) and downregulating 1-methylhistidine (1-MH). Bioinformatics analysis pointed to an important role in reducing excess cysteine and methionine metabolism. Conclusion FIBA overexpression and 1-MH loss may be linked to the pathogenicity of diabetic endothelial dysfunction in DR/DN, implying that a cohort study is needed to further investigate the role of FIBA and 1-MH in the development of DN and DR, as well as the related pathways between the two proteins.

https://www.frontiersin.org/articles/10.3389/fendo.2022.830466/pdf

Integration of Metabolomics and Proteomics in Exploring the Endothelial Dysfunction Mechanism Induced by Serum Exosomes From Diabetic Retinopathy and Diabetic Nephropathy Patients

Dan Meng

Papers

Diabetic retinopathy, classified using the lesion-aware deep learning system, predicts diabetic end-stage renal disease in chinese patients.