Dataset Information

Development of Diagnostic Biomarkers for Detecting Diabetic Retinopathy at Early Stages Using Quantitative Proteomics.

ABSTRACT: Diabetic retinopathy (DR) is a common microvascular complication caused by diabetes mellitus (DM) and is a leading cause of vision impairment and loss among adults. Here, we performed a comprehensive proteomic analysis to discover biomarkers for DR. First, to identify biomarker candidates that are specifically expressed in human vitreous, we performed data-mining on both previously published DR-related studies and our experimental data; 96 proteins were then selected. To confirm and validate the selected biomarker candidates, candidates were selected, confirmed, and validated using plasma from diabetic patients without DR (No DR) and diabetics with mild or moderate nonproliferative diabetic retinopathy (Mi or Mo NPDR) using semiquantitative multiple reaction monitoring (SQ-MRM) and stable-isotope dilution multiple reaction monitoring (SID-MRM). Additionally, we performed a multiplex assay using 15 biomarker candidates identified in the SID-MRM analysis, which resulted in merged AUC values of 0.99 (No DR versus Mo NPDR) and 0.93 (No DR versus Mi and Mo NPDR). Although further validation with a larger sample size is needed, the 4-protein marker panel (APO4, C7, CLU, and ITIH2) could represent a useful multibiomarker model for detecting the early stages of DR.

SUBMITTER: Jin J

PROVIDER: S-EPMC4657408 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Development of Diagnostic Biomarkers for Detecting Diabetic Retinopathy at Early Stages Using Quantitative Proteomics.

Jin Jonghwa J Min Hophil H Kim Sang Jin SJ Oh Sohee S Kim Kyunggon K Yu Hyeong Gon HG Park Taesung T Kim Youngsoo Y

Journal of diabetes research 20151109

Diabetic retinopathy (DR) is a common microvascular complication caused by diabetes mellitus (DM) and is a leading cause of vision impairment and loss among adults. Here, we performed a comprehensive proteomic analysis to discover biomarkers for DR. First, to identify biomarker candidates that are specifically expressed in human vitreous, we performed data-mining on both previously published DR-related studies and our experimental data; 96 proteins were then selected. To confirm and validate the ...[more]

PMID: 26665153

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Project description:ObjectiveDiabetic retinopathy (DR) is a common diabetic microvascular complication and a major cause of acquired vision loss. Finding effective biomarkers for the early identification and diagnosis of DR is crucial. This study aimed to comprehensively evaluate the accuracy of microRNAs (miRNAs) in the diagnosis of DR via a meta-analysis of previously published diagnostic studies. This study has been registered on the PROSPERO website, with the number CRD42022323238.MethodsWe searched PubMed, Cochrane Library, Embase, Web of Science, China Wanfang database, and China Knowledge Network database to identify relevant articles published from the time of database creation to April 10, 2022. Stata 14.0 software was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic ratio (DOR), and area under the summary receiver operating characteristic (ROC) curve to assess the accuracy of miRNAs in the diagnosis of DR. Heterogeneity between studies was assessed using Cochran-Q test and I2 statistic for quantitative analysis. The random-effect model was selected due to significant heterogeneity. Subgroup analysis and regression analysis were also performed to determine the potential sources of heterogeneity.ResultsWe included 25 articles detailing 52 studies with 1987 patients with DR and 1771 non-DR controls. The findings demonstrated overall sensitivity (0.82, 95% CI: 0.78 ~ 0.85), specificity (0.84, 95% CI: 0.81 ~ 0.86), PLR (5.0, 95% CI: 4.2 ~ 5.9), NLR (0.22, 95% CI: 0.18 ~ 0.26), and the area under the summary ROC curve (0.90, 95% CI: 0.87 ~ 0.92). Furthermore, we performed subgroup analysis and found that panels of multiple miRNAs could enhance the pooled sensitivity (sensitivity, specificity, and AUC values were 0.89, 0.87, and 0.94, respectively).ConclusionThe meta-analysis showed that miRNAs can be used as potential diagnostic markers for DR, with high accuracy of diagnoses observed with the detection of miRNAs in plasma and serum.

Project description:INTRODUCTION:Diabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus and the leading cause of impaired vision in adults worldwide. Early detection and treatment for DR could improve patient outcomes. Traditional methods of detecting DR include the gold standard Early Treatment Diabetic Retinopathy Study seven standard fields fundus photography, ophthalmoscopy and slit-lamp biomicroscopy. These modalities can be expensive, difficult to access and require involvement of specialised healthcare professionals. With the development of mobile phone technology, there is a growing interest in their use for DR identification as this approach is potentially more affordable, accessible and easier to use. Smartphones can be employed in a variety of ways for ophthalmoscopy including the use of smartphone camera, various attachments and artificial intelligence for obtaining and grading of retinal images. The aim of this scoping review is to determine the diagnostic test accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients. METHODS AND ANALYSIS:We will perform an electronic search of MEDLINE, Embase and Cochrane Library for literature published from 2000 onwards. Two reviewers will independently analyse studies for eligibility and assess study quality using the QUADAS-2 tool. Data for a 2?2 contingency table will be extracted. If possible, we will pool sensitivity and specificity data using the random-effects model and construct a summary receiver operating characteristic curve. In case of high heterogeneity, we will present the findings narratively. Subgroup analysis and sensitivity analysis will be performed where appropriate. ETHICS AND DISSEMINATION:This scoping review aims to provide an overview of smartphone ophthalmoscopy in DR identification. It will present findings on the accuracy of smartphone ophthalmoscopy in detecting DR, identify gaps in the literature and provide recommendations for future research. This review does not require ethical approval as we will not collect primary data.

Dataset Information

Development of Diagnostic Biomarkers for Detecting Diabetic Retinopathy at Early Stages Using Quantitative Proteomics.

Publications

Development of Diagnostic Biomarkers for Detecting Diabetic Retinopathy at Early Stages Using Quantitative Proteomics.

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