Purpose
To assess diabetic retinopathy prevalence and its risk factors in a society with type 2 diabetes epidemic using the Saudi National Diabetes Registry (SNDR).
Method
This is a cross‐sectional ...study using patient's clinical data found in SNDR data base. A cohort of 50 464 Saudi patients with type 2 diabetes aged ≥25 years were selected to assess for the prevalence and risk factors for diabetic retinopathy.
Results
The overall prevalence of diabetic retinopathy is 19.7%, where 9.1% have non‐proliferative diabetic retinopathy (NPDR), 10.6% have proliferative diabetic retinopathy (PDR) and 5.7% have macular oedema (ME). Duration of diabetes and age are the most significant risk factors for diabetic retinopathy with odds ratio (OR) and 95% confidence interval (95% CI) 8.88 (8.30–9.50) and 5.76 (5.10–6.55), respectively. Nephropathy, neuropathy, insulin use, poor glycemic control, hypertension and male gender significantly increased the risk for diabetic retinopathy. Smoking, hyperlipidemia and obesity significantly reduced the risk for diabetic retinopathy among type 2 Saudi diabetic cohort.
Conclusion
vThe low prevalence of diabetic retinopathy in our registry may be a result of the shortage or absence of well‐structured screening programmes. Therefore, many patients with NPDR might have been missed. A prevention programme is needed to reduce the effect of diabetic retinopathy risk factors in this society.
Diabetic Retinopathy (DR) is a consequence of long-term diabetes which affects the eyes. It causes affliction to the veins in the eyes causing raptures on the retina which can impact vision. If the ...condition is failed to be detected at an early stage, it can lead to complete vision loss. The conventional diagnosing cycle of DR using fundus images requires very skilled practitioners due to the minute nature of features of the anomalies which can even lead to misdiagnosis and is time consuming. Therefore, devising an automated method for the diagnosis of DR can assist individuals with diabetes to identify symptoms of DR at a very early stage. This research also encompasses the classification of the detected images into its corresponding 3 stages namely No Diabetic Retinopathy (No DR), Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR) that can greatly aid in monitoring the dynamics of the key features including lesions, hemorrhages, and density of blood vessels. Deep learning algorithms has gotten to become a well-known method that can accomplish a wide variety of classification tasks. However, most of these methods are only efficient in classifying the various stages of DR with low accuracies notably, for the early stages. The devised algorithm in this research uses enriched images processing techniques, automatic hyperparameter tuning and neural network training strategies to provide more emphasis on the minute features for better prediction. The algorithm was tested and compared with modified Resnet50, VGG16, Mobilenetv2, Inceptionv3 and InceptionResnetv2 which gave a classification accuracy of 94.7%, 86.1%, 85.8%, 85.3% and 87% respectively with corresponding detection accuracies of 99.8%, 94%, 94.2%, 94.9% and 98.2% respectively on a test set of 508 images. Using the proposed algorithm, the results indicate Resnet50 based network gave superior performance for both detection and classification tasks.
Clinical evidence suggests an association between phosphate concentrations in aqueous humor and the risk of intraocular lens (IOL) calcification. To test this hypothesis the influence of different ...phosphate concentrations on IOL calcification was evaluated in an in vitro electrophoresis model.BACKGROUNDClinical evidence suggests an association between phosphate concentrations in aqueous humor and the risk of intraocular lens (IOL) calcification. To test this hypothesis the influence of different phosphate concentrations on IOL calcification was evaluated in an in vitro electrophoresis model.20 IOLs of two hydrophilic IOL models (CT Spheris 204, Zeiss; Lentis L-313, Oculentis) and one hydrophobic control IOL model (Clareon CNA0T0, Alcon) were exposed to physiologic and elevated phosphate concentrations, similar to diabetic aqueous humor. IOL calcification was analyzed by alizarin red staining, von Kossa staining, scanning electron microscopy, energy dispersive x-ray spectroscopy and transmission electron microscopy with electron diffraction.METHODS20 IOLs of two hydrophilic IOL models (CT Spheris 204, Zeiss; Lentis L-313, Oculentis) and one hydrophobic control IOL model (Clareon CNA0T0, Alcon) were exposed to physiologic and elevated phosphate concentrations, similar to diabetic aqueous humor. IOL calcification was analyzed by alizarin red staining, von Kossa staining, scanning electron microscopy, energy dispersive x-ray spectroscopy and transmission electron microscopy with electron diffraction.Higher phosphate concentrations were associated with IOL calcification. Analyses of IOL surfaces and cross-sections documented calcification in no CT Spheris and 4 Lentis IOLs following exposure to 10 mM Na2HPO4, compared with 7 and 11 positive analyses following exposure to 14 mM Na2HPO4, respectively. Furthermore, a clear association between IOL calcification and the duration of electrophoresis was demonstrated, confirming increased phosphate concentrations and duration of exposure as risk factors of IOL calcification.RESULTSHigher phosphate concentrations were associated with IOL calcification. Analyses of IOL surfaces and cross-sections documented calcification in no CT Spheris and 4 Lentis IOLs following exposure to 10 mM Na2HPO4, compared with 7 and 11 positive analyses following exposure to 14 mM Na2HPO4, respectively. Furthermore, a clear association between IOL calcification and the duration of electrophoresis was demonstrated, confirming increased phosphate concentrations and duration of exposure as risk factors of IOL calcification.Findings suggest that higher phosphate concentrations in aqueous humor, as seen in diabetic patients, contribute to an increased IOL calcification risk, potentially explaining clinical observations showing an increased risk of IOL calcification in patients with diabetes.CONCLUSIONSFindings suggest that higher phosphate concentrations in aqueous humor, as seen in diabetic patients, contribute to an increased IOL calcification risk, potentially explaining clinical observations showing an increased risk of IOL calcification in patients with diabetes.
Purpose:
To reveal molecular mechanisms of diabetic retinopathy (DR) in Asians and facilitate the identification of new therapeutic targets through untargeted metabolomics. To determine the ...differences in serum metabolites and metabolic pathways between different stages of diabetic retinopathy in patients with type 2 diabetic mellitus (T2DM) and proliferative DR (PDR) and non-proliferative DR (NPDR) and identify differential metabolites between T2DM and DR (NPDR and PDR) patients.
Methods:
This prospective observational registration study described the differential metabolites between 45 T2DM patients and 15 control cases with no significant differences in clinical characteristics. Their biospecimens and clinical information were collected and recorded in their medical reports. DR phenotypes of the subjects were verified by retina specialists. Serum metabolites were analyzed using high-resolution mass spectrometry with liquid chromatography. Untargeted metabolomics was performed on serum samples from 15 T2DM patients, 15 non-proliferative diabetic retinopathy patients, 15 proliferative diabetic retinopathy patients, and 15 diabetic controls. Discriminatory metabolic features were identified through partial least squares discriminant analysis (PLS-DA), hierarchical clustering analysis (HCA), and generalized linear regression models.
Result:
Through untargeted metabolomics, 931 features (523 in positive and 408 in negative modes) with 102 common metabolites highly relevant to the presence of DR were detected. In the adjusted analysis, 67 metabolic features differed significantly between T2DM and NPDR patients. Pathway analysis revealed alterations in metabolisms of amino acids and fatty acids. Glutamate, phosphatidylcholine, and 13-hydroperoxyoctadeca-9,11-dienoic acid (13-PHODE) were key contributors to these pathway differences. A total of 171 features distinguished PDR patients from T2DM patients, and pathway analysis revealed alterations in amino acid metabolism, fatty acid metabolism, nitrogen metabolism, and tricarboxylic acid cycle. Aspartate, glutamate, glutamine, ornithine, N-acetyl-
l
-glutamate, N-acetyl-
l
-aspartate, citrate, succinate, N-(L-arginino)succinate, 2-oxoglutarate, 13-hydroperoxyoctadeca-9,11-dienoic acid, methionine, lysine, threonine, phenylalanine, N(pi)-methyl-
l
-histidine, phosphatidylcholine, and linoleate were major contributors to the pathway differences. Between NPDR patients and PDR patients, there were 79 significant differential metabolites. Enrichment pathway analysis showed changes in amino acid metabolism, fatty acid metabolism, pantothenate, and CoA biosynthesis. Aspartate, glutamine, N-acetyl-
l
-glutamate, N-acetyl-
l
-aspartate, pantothenate, dihomo-gamma-linolenate, docosahexaenoic acid, and icosapentaenoic acid were key factors for the differences of these pathways.
Conclusion:
This study demonstrated that the pathways of arginine biosynthesis metabolism, linoleic acid metabolism, alanine, aspartate, and glutamate metabolism, as well as
d
-glutamine and
d
-glutamate metabolism, were dysregulated in DR patients of the Asian population. Increased levels of glutamate, aspartate, glutamine, N-acetyl-
l
-glutamate, and N-acetyl-
l
-aspartate and decreased levels of dihomo-gamma-linolenate, docosahexaenoic, and icosapentaenoic were considered as the metabolic profile that could distinguish PDR from NPDR in Asians. Phosphatidylcholine and 13-PHODE were identified as two major novel metabolite markers in advanced stages of DR in our study.
Purpose
The study aims to classify the eyes with proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR) based on the optical coherence tomography angiography ...(OCTA) vascular density maps using a supervised machine learning algorithm.
Methods
OCTA vascular density maps (at superficial capillary plexus (SCP), deep capillary plexus (DCP), and total retina (R) levels) of 148 eyes from 78 patients with diabetic retinopathy (45 PDR and 103 NPDR) was used to classify the images to NPDR and PDR groups based on a supervised machine learning algorithm known as the support vector machine (SVM) classifier optimized by a genetic evolutionary algorithm.
Results
The implemented algorithm in three different models reached up to 85% accuracy in classifying PDR and NPDR in all three levels of vascular density maps. The deep retinal layer vascular density map demonstrated the best performance with a 90% accuracy in discriminating between PDR and NPDR.
Conclusions
The current study on a limited number of patients with diabetic retinopathy demonstrated that a supervised machine learning–based method known as SVM can be used to differentiate PDR and NPDR patients using OCTA vascular density maps.
PurposeInflammation, angiogenesis and fibrosis are pathological hallmarks of proliferative diabetic retinopathy (PDR). The CD146/sCD146 pathway displays proinflammatory and proangiogenic properties. ...We investigated the role of this pathway in the pathophysiology of PDR. MethodsVitreous samples from 41 PDR and 27 nondiabetic patients, epiretinal fibrovascular membranes from 18 PDR patients, rat retinas, human retinal microvascular endothelial cells (HRMECs) and human retinal Müller glial cells were studied by ELISA, Western blot analysis, immunohistochemistry and immunofluorescence microscopy analysis. Blood-retinal barrier breakdown was assessed with fluorescein isothiocyanate-conjugated dextran. ResultssCD146 and VEGF levels were significantly higher in vitreous samples from PDR patients than in nondiabetic patients. In epiretinal membranes, immunohistochemical analysis revealed CD146 expression in leukocytes, vascular endothelial cells and myofibroblasts. Significant positive correlations were detected between numbers of blood vessels expressing CD31, reflecting angiogenic activity of PDR, and numbers of blood vessels and stromal cells expressing CD146. Western blot analysis showed significant increase of CD146 in diabetic rat retinas. sCD146 induced upregulation of phospho-ERK1/2, NF-κB , VEGF and MMP-9 in Müller cells. The hypoxia mimetic agent cobalt chloride, VEGF and TNF-α induced upregulation of sCD146 in HRMECs. The MMP inhibitor ONO-4817 attenuated TNF-α-induced upregulation of sCD146 in HRMECs. Intravitreal administration of sCD146 in normal rats significantly increased retinal vascular permeability and induced significant upregulation of phospho-ERK1/2, intercellular adhesion molecule-1 and VEGF in the retina. sCD146 induced migration of HRMECs. ConclusionsThese results suggest that the CD146/sCD146 pathway is involved in the initiation and progression of PDR.
Guo H, Li W, Wang K, et al. Diabetes Metab Syndr Obes. 2023;16:2865-2874. The authors have advised affiliation 1 on page 2865 is incorrect. The text "Tianjin University Eye Hospital" should read ..."Tianjin Medical University Eye Hospital". The authors apologize for this error.
This article presents a database containing 757 color fundus images acquired at the Department of Ophthalmology of the Hospital de Clínicas, Facultad de Ciencias Médicas (FCM), Universidad Nacional ...de Asunción (UNA), Paraguay. Firstly, the retinal images were acquired with a clinical procedure presented in this paper. The acquisition of the retinographies was made through the Visucam 500 camera of the Zeiss brand. Next, two expert ophthalmologists have classified the dataset. These data can help physicians and researchers in the detection of cases of Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), in their different stages. The dataset generated will be useful for ophthalmologists and researchers to work on automatic detection algorithms for Diabetic Retinopathy (DR).
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