Survival analysis for diabetic retinopathy in diabetic patients using Extended Cox Model

Background: Diabetic Retinopathy (DR), one of the major microvascular complications commonly occurring in diabetic patients, can be classified into Proliferative Diabetic Retinopathy (PDR) and Non-Proliferative Diabetic Retinopathy (NPDR). Currently available therapies are only targeted for later stages of the disease in which some pathologic changes may be irreversible. Thus, there is a need to develop new treatment options for earlier stages of DR through revealing pathological mechanisms of PDR and NPDR. Objective: The purpose of this study was to characterize proteomes of diabetic through quantitative analysis of PDR and NPDR. Methods: Vitreous body was collected from three groups: control (non-diabetes mellitus), NPDR, and PDR. Vitreous proteins were digested to peptide mixtures and analyzed using LC-MS/MS. MaxQuant was used to search against the database and statistical analyses were performed using Perseus. Gene ontology analysis, related-disease identification, and protein-protein interaction were performed using the differential expressed proteins. Results: Twenty proteins were identified as critical in PDR and NPDR. The NPDR group showed different expressions of kininogen-1, serotransferrin, ribonuclease pancreatic, osteopontin, keratin type II cytoskeletal 2 epidermal, and transthyretin. Also, prothrombin, signal transducer and activator of transcription 4, hemoglobin subunit alpha, beta, and delta were particularly up-regulated proteins for PDR group. The up-regulated proteins related to complement and coagulation cascades. Statherin was down-regulated in PDR and NPDR compared with the control group. Transthyretin was the unique protein that increased its abundance in NPDR compared with the PDR and control group. Conclusion: This study confirmed the different expressions of some proteins in PDR and NPDR. Additionally, we revealed uniquely expressed proteins of PDR and NPDR, which would be differential biomarkers: prothrombin, alpha-2-HS-glycoprotein, hemoglobin subunit alpha, beta, and transthyretin.

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Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190219102427 ◽

2020 ◽

Vol 16 (4) ◽

pp. 397-426

Author(s):

Muhammad Nadeem Ashraf ◽

Muhammad Hussain ◽

Zulfiqar Habib

Keyword(s):

Diabetic Retinopathy ◽

Blood Vessels ◽

Early Stage ◽

Spherical Shape ◽

Cost Effective ◽

Lesion Detection ◽

Diabetic Patients ◽

Cad System ◽

Non Invasive ◽

Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

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Renal Injury Following Intravitreal Anti-VEGF Administration in Diabetic Patients with Proliferative Diabetic Retinopathy and Chronic Kidney Disease - A Possible Side Effect?

Current Drug Safety ◽

10.2174/1574886309666140211113635 ◽

2014 ◽

Vol 9 (2) ◽

pp. 156-158 ◽

Cited By ~ 20

Author(s):

Ilias Georgalas ◽

Dimitris Papaconstantinou ◽

Kostas Papadopoulos ◽

Dionisis Pagoulatos ◽

Dimitris Karagiannis ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Diabetic Retinopathy ◽

Kidney Disease ◽

Proliferative Diabetic Retinopathy ◽

Renal Injury ◽

Side Effect ◽

Diabetic Patients ◽

Anti Vegf

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Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

Scientific Reports ◽

10.1038/s41598-020-80058-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Reza Mirshahi ◽

Pasha Anvari ◽

Hamid Riazi-Esfahani ◽

Mahsa Sardarinia ◽

Masood Naseripour ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Healthy Subjects ◽

Optical Coherence Tomography Angiography ◽

Validation Dataset ◽

Diabetic Patients ◽

Dice Similarity Coefficient ◽

Optical Coherence ◽

Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

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The effect of insulin pump therapy in retinal vasculature in type 1 diabetic patients

European Journal of Ophthalmology ◽

10.1177/1120672121990576 ◽

2021 ◽

pp. 112067212199057

Author(s):

Tomás de Oliveira Loureiro ◽

João Nobre Cardoso ◽

Carlos Diogo Pinheiro Lima Lopes ◽

Ana Rita Carreira ◽

Sandra Rodrigues-Barros ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Metabolic Control ◽

Insulin Pump Therapy ◽

Vascular Density ◽

Diabetic Patients ◽

Retinal Vasculature ◽

Increased Risk ◽

The Mean ◽

Type 1 Diabetic Patients

Background/objectives: Continuous subcutaneous insulin infusion (CSII) is a treatment for type 1 diabetes that improves metabolic control and reduces micro and macrovascular complications. The aim of this study was to compare the effect of CSII versus traditional multiple daily injections (MDI) therapy on retinal vasculature. Methods: We performed a prospective study with type 1 diabetic patients with no prior history of ocular pathology other than mild diabetic retinopathy. The patients were divided into two groups according to their therapeutic modality (CSII vs MDI). The retinal nerve fiber layers thickness and vascular densities were compared between groups in both macula and optic disc. The correlations between vascular density and clinical features were also determined. Statistical significance was defined as p < 0.05. Results: The study included 52 eyes, 28 in the insulin CSII group. The mean age was 36.66 ± 12.97 years, with no difference between groups ( p = 0.49). The mean glycated hemoglobin (HbA1c) was found to be lower in the CSII group (7.1% ± 0.7 vs 7.5% ± 0.7 p < 0.01). The parafoveal vascular density was found to be higher in the CSII group (42.5% ± 0.4 vs 37.7% ± 0.6, p < 0.01). We found an inverse correlation between HbA1c value and parafoveal vascular densities ( p < 0.01, r = −0.50). Conclusion: We found that CSII provided better metabolic control than MDI and this seemed to result in higher parafoveal vascular density. As lower vascular density is associated with an increased risk of diabetic retinopathy, these results suggest that CSII could be the safest therapeutic option to prevent retinopathy.

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Plasma microRNA signature associated with retinopathy in patients with type 2 diabetes

Scientific Reports ◽

10.1038/s41598-021-83047-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Donato Santovito ◽

Lisa Toto ◽

Velia De Nardis ◽

Pamela Marcantonio ◽

Rossella D’Aloisio ◽

...

Keyword(s):

Type 2 Diabetes ◽

Diabetic Retinopathy ◽

Vision Loss ◽

External Validation ◽

Diabetic Patients ◽

Disease Biomarkers ◽

Response To Stress ◽

Severity Of The Disease ◽

Plasma Microrna

AbstractDiabetic retinopathy (DR) is a leading cause of vision loss and disability. Effective management of DR depends on prompt treatment and would benefit from biomarkers for screening and pre-symptomatic detection of retinopathy in diabetic patients. MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression which are released in the bloodstream and may serve as biomarkers. Little is known on circulating miRNAs in patients with type 2 diabetes (T2DM) and DR. Here we show that DR is associated with higher circulating miR-25-3p (P = 0.004) and miR-320b (P = 0.011) and lower levels of miR-495-3p (P < 0.001) in a cohort of patients with T2DM with DR (n = 20), compared with diabetic subjects without DR (n = 10) and healthy individuals (n = 10). These associations persisted significant after adjustment for age, gender, and HbA1c. The circulating levels of these miRNAs correlated with severity of the disease and their concomitant evaluation showed high accuracy for identifying DR (AUROC = 0.93; P < 0.001). Gene ontology analysis of validated targets revealed enrichment in pathways such as regulation of metabolic process (P = 1.5 × 10–20), of cell response to stress (P = 1.9 × 10–14), and development of blood vessels (P = 2.7 × 10–14). Pending external validation, we anticipate that these miRNAs may serve as putative disease biomarkers and highlight novel molecular targets for improving care of patients with diabetic retinopathy.

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