Retinopathy Grading Research Articles

Prevalence of Retinopathy in Prediabetic Populations: A Systematic Review and Meta-Analysis.

Among the leading causes of vision impairment and blindness globally, diabetic retinopathy (DR) is one of the most important causes. There is increasing evidence of DR prevalence in the prediabetic population. This systematic review presents collective data on retinopathy in the prediabetic population. This review article aimed to estimate the reported prevalence of retinopathy in prediabetes, impaired glucose tolerance test (GTT) without diabetes mellitus, and the risk factors involved and to summarize it. Literature searches were done using the Web of Science, CINAHL, Google Scholar, Cochrane, EMBASE, and PubMed databases from inception to April 2023. Our search included the words prediabetes, DR, and risk factors. All searches were looked at for methodological quality and evidence. Thirty-one studies were included after the screening. Population-based data were used in 23 studies (82.1%). The prediabetic population screened was 10,539. The prevalence of retinopathy ranged between 0.3% and 20.9%, showing a median of 8.1% with an interquartile range (IQR) of 4.2-11%, showing great variance in estimates due to the use of different screening methods, methods used for retinopathy grading, and study populations. Several studies compared the population with normal GTT with impaired glucose tolerance (IGT) and inferred that there was a lower prevalence of retinopathy in the normal GTT population (3.0%, IQR 0.3-7.4%) than prediabetes (6.7%, IQR 1.9-10.1%). According to this data, a greater retinopathy prevalence was found in prediabetic populations.

TSNet: Task-specific network for joint diabetic retinopathy grading and lesion segmentation of ultra-wide optical coherence tomography angiography images

TSNet: Task-specific network for joint diabetic retinopathy grading and lesion segmentation of ultra-wide optical coherence tomography angiography images

Innovative Automated Diabetic Retinopathy Severity Grading for Advanced Clinical Insights

Diabetic retinopathy (DR) is a hazardous eye disorder that affects the retina and may lead to vision loss and blindness, especially in diabetics. Early identification is critical for a good outcome, however diabetic retinopathy can only be diagnosed through time-consuming and labor-intensive colour fundus pictures. In order to overcome this challenge, this study proposes a Deep Learning-based strategy that useIntelligent Diabetic Retinopathy Grading through Enhanced Neural Networks (IDR-ENN)to classify retinal pictures into distinct stages of diabetic retinopathy. The proposed approach was trained on a dataset that included 2200 photos from the testing set and 11000 coloured retinal images from the training set. The simulation results suggest that the IDR-ENNbased algorithm can achieve excellent levels of accuracy, sensitivity, and specificity.In this study, we propose a method to significantly reduce the computational time for diabetic retinopathy detection. A novelIDR-ENN approach achieves a remarkable 85% reduction in training computational time for diabetic retinopathy detection. The paper's overall conclusion underlines the potential of deep learning to improve the diagnosis and grading of diabetic retinopathy, which might have a significant impact on the prevention of blindness caused by this disease.

Abstract 17639: Association of Retinal Microvascular Changes With Incidence and Progression of Coronary Artery Calcium Score in Individuals With vs Without Diabetes Mellitus; Data From the Multi-Ethnic Study of Atherosclerosis

Introduction: Retinopathy (RP) is a microvascular complication of diabetes mellitus (DM); however, it is also increasingly recognized in persons without DM. Microvascular disease may play a prominent role in coronary heart disease (CHD) development. We performed this study to evaluate the association of grades of RP with incidence of CAC (Coronary Artery Calcium) among those with zero CAC at baseline. Hypothesis: We performed this study to evaluate the association of grades of RP with incidence of CAC among those with zero CAC at baseline and to Identify the association of grades of MA and RP with progression of CAC among those with positive CAC at baseline. Methods: We included 5709 subjects with and without DM from the Multi-Ethnic Study of Atherosclerosis (MESA), who had retinal photos and CAC score available. We analyzed the association of grades of RP with incidence and progression of CAC among subjects with and without DM with zero CAC and positive CAC at baseline from robust regression models. The grades of RP were defined as: no retinopathy, minimal non-proliferative R (NPR) (level 14 -20), early to moderate NPR (level 31-41) and severe NPR or proliferative RP (level 51-80). Results: Among those with zero CAC, in the diabetic group, early to moderate (95% CI [1.2, 2]) and severe (95% CI [1.3, 3]) NPR had significantly higher incident CAC risk than the non-retinopathy group. Among those with positive CAC at baseline, in the diabetic group, the early to moderate NPR group had higher CAC progression than the non-retinopathy group. (95% CI [-146, 228]). In the non-diabetic group, the minimal NPR group had significantly higher CAC progression than the non-retinopathy group (95% CI [1.8, 52.7]). Conclusions: In conclusion, after adjustment for major CHD risk factors, RP with any severity was associated with incident CAC in DM individuals, however only early to moderate NPDR was associated with CAC progression in them.

Blood Vessel Segmentation and Classification for Diabetic Retinopathy Grading Using Dandelion Optimization Algorithm with Deep Learning Model

Blood Vessel Segmentation and Classification for Diabetic Retinopathy Grading Using Dandelion Optimization Algorithm with Deep Learning Model

Clinical indicators for diabetic eyecare delivered by optometrists in Australia: a Delphi study

ABSTRACT Clinical relevance Valid and updated clinical indicators can serve as important tools in assessing and improving eyecare delivery. Background Indicators for diabetic eyecare in Australia were previously developed from guidelines published before 2013 and then used to assess the appropriateness of care delivery through a nationwide patient record card audit (the iCareTrack study). To reflect emerging evidence and contemporary practice, this study aimed to update clinical indicators for optometric care for people with type 2 diabetes in Australia. Methods Forty-five candidate indicators, including existing iCareTrack and new indicators derived from nine high-quality evidence-based guidelines, were generated. A two-round modified Delphi process where expert panel members rated the impact, acceptability, and feasibility of the indicators on a 9-point scale and voted for inclusion or exclusion of the candidate indicators was used. Consensus on inclusion was reached when the median scores for impact, acceptability, and feasibility were ≥7 and >75% of experts voted for inclusion. Results Thirty-two clinical indicators with high acceptability, impact and feasibility ratings (all median scores: 9) were developed. The final indicators were related to history taking (n = 12), physical examination (n = 8), recall period (n = 5), referral (n = 5), and patient education/communication (n = 2). Most (14 of 15) iCareTrack indicators were retained either in the original format or with modifications. New indicators included documenting the type of diabetes, serum lipid level, pregnancy, systemic medications, nephropathy, Indigenous status, general practitioner details, pupil examination, intraocular pressure, optical coherence tomography, diabetic retinopathy grading, recall period for high-risk diabetic patients without retinopathy, referral of high-risk proliferative retinopathy, communication with the general practitioner, and patient education. Conclusion A set of 32 updated diabetic eyecare clinical indicators was developed based on contemporary evidence and expert consensus. These updated indicators inform the development of programs to assess and enhance the eyecare delivery for people with diabetes in Australia.

Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral

Democratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets. To determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets to design high-performing diabetic retinopathy classification models. This diagnostic quality improvement study was conducted from January 1, 2021, to December 31, 2021. A self-training method without coding was used on 2 public data sets with retinal images from patients in France (Messidor-2 [n = 1748]) and the UK and US (EyePACS [n = 58 689]) and externally validated on 1 data set with retinal images from patients of a private Egyptian medical retina clinic (Egypt [n = 210]). An AI model was trained to classify referable diabetic retinopathy as an exemplar use case. Messidor-2 images were assigned adjudicated labels available on Kaggle; 4 images were deemed ungradable and excluded, leaving 1744 images. A total of 300 images randomly selected from the EyePACS data set were independently relabeled by 3 blinded retina specialists using the International Classification of Diabetic Retinopathy protocol for diabetic retinopathy grade and diabetic macular edema presence; 19 images were deemed ungradable, leaving 281 images. Data analysis was performed from February 1 to February 28, 2021. Using public data sets, a teacher model was trained with labeled images using supervised learning. Next, the resulting predictions, termed pseudolabels, were used on an unlabeled public data set. Finally, a student model was trained with the existing labeled images and the additional pseudolabeled images. The analyzed metrics for the models included the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. The Fisher exact test was performed, and 2-tailed P values were calculated for failure case analysis. For the internal validation data sets, AUROC values for performance ranged from 0.886 to 0.939 for the teacher model and from 0.916 to 0.951 for the student model. For external validation of automated ML model performance, AUROC values and accuracy were 0.964 and 93.3% for the teacher model, 0.950 and 96.7% for the student model, and 0.890 and 94.3% for the manually coded bespoke model, respectively. These findings suggest that self-training using automated ML is an effective method to increase both model performance and generalizability while decreasing the need for costly expert labeling. This approach advances the democratization of AI by enabling clinicians without coding expertise or access to large, well-labeled private data sets to develop their own AI models.

Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading

Diabetic Retinopathy (DR) is a major source of sightlessness and permanent visual damage. Manual Analysis of DR is a labor-intensive and costly task that requires skilled ophthalmologists to observe and evaluate DR utilizing digital fundus images. The images can be employed for analysis and disease screening. This laborious task can gain a great advantage in automated detection by exploiting Artificial Intelligence (AI) techniques. Content-Based Image Retrieval (CBIR) approaches are utilized to retrieve related images in massive databases and are helpful in many application regions and most healthcare systems. With this motivation, this article develops the new Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification (MRFODL-FIRC) approach for the grading of DR. The suggested MRFODL-FIRC model investigates the retinal fundus imaging effectively to retrieve the relevant images and identify class labels. To achieve this, the MRFODL-FIRC technique uses Median Filtering (MF) as a pre-processing step. The Capsule Network (CapsNet) model is used to produce feature vectors with the MRFO algorithm as a hyperparameter optimizer. For the image retrieval process, the Manhattan distance metric is used. Finally, the Variational Autoencoder (VAE) model is used for recognizing and classifying DR. The investigational assessment of the MRFODL-FIRC technique is accomplished on medical DR and the outputs highlighted the improved performance of the MRFODL-FIRC algorithm over the current approaches.

Comparative study between fundus fluorescein angiography and optical coherence tomography angiography in evaluation of diabetic maculopathy

Background Diabetic maculopathy is a serious consequence of diabetic retinopathy (DR) that greatly affects the visual acuity. Diabetic macular edema (DME) and diabetic macular ischemia (DMI) are major causes of vision affection in diabetic patients. Purpose The aim of this study was to compare fluorescein angiography (FA) and optical coherence tomography angiography (OCTA) in evaluation of diabetic maculopathy. Patients and methods This comparative cross-sectional study was performed on 87 eyes of 54 patients with type I or type II diabetes, older than 18 years old with any diabetic retinal changes, and 108 eyes of 54 control subjects. An ocular examination was performed as well as color fundus photography, FA, and OCTA. Investigations were done on the same day. FA and OCTA findings were summarized and compared. Results The mean age of the patients was 59.4±8.5 years. OCTA was statistically significantly superior to FA in diagnosing DMI. The foveal avascular zone (FAZ) was ischemic (enlarged) in 74 (85.06%) eyes by OCTA compared with only 46 (52.87%) eyes by FA, with a statistically significant difference (P = 0.001). In addition, OCTA detected microaneurysms significantly more than FA (P = 0.001). The mean FAZ area of the control eyes by OCTA was significantly less than in the ischemic eyes (U = 56.602, P < 0.001), while the superficial capillary plexus (SCP) vascular density (VD) and the deep capillary plexus (DCP) VD in the control eyes was statistically significantly higher than in the ischemic eyes (U = 36.984, P < 0.001, and U = 35.955, P < 0.001, respectively). FA showed diffuse leakage (edema) at the macula as the most common pattern of DME that was detected in 37.9% of the eyes followed by focal macular leakage in 18.4% of the eyes and cystoid leakage in 8% of the eyes while 35.5% of the eyes showed no macular edema. Conclusion OCTA is a cornerstone in DMI diagnosis both qualitatively and quantitatively. In addition, it is essential in evaluating the deep retinal vasculature, while FA is essential in diabetic retinopathy grading and in the diagnosis of DME in a qualitative manner. Both investigations are complementary to each other in diabetic maculopathy evaluation.

The Prevalence of Diabetic Retinopathy and Associated Risk Factors among Diabetic Patients in a Tertiary Care Hospital

Background: One of the most serious complications of diabetes that places an enormous strain on the patient, the healthcare system, and the world economy is diabetic retinopathy (DR). It includes long-term exposure to the metabolic changes linked to diabetes, which cause damage to the retina's microvasculature. Objectives: The aim of the study was to assess the prevalence of diabetic retinopathy and associated risk factors among diabetic patients in a tertiary care hospital. Methods: This cross-section observational study was carried out in the Department of Ophthalmology, North Bengal medical College. Convenience sampling technique was followed. Face to face interview was done to collect data with a semi-structured questionnaire. After collection, the data were checked and cleaned, followed by editing, compiling, coding and categorizing according to the objectives and variable to detect errors and to maintain consistency, relevancy and quality control. Statistical evaluation of the results used to be obtained via the use of a window-based computer software program devised with Statistical Packages for Social Sciences (SPSS-24). The duration of the period from July 2018 to July 2022. A total of 120 patients were participate in the study. Results: About 58.33% respondents were male and 41.67% were female. Majority (33.33%) of the respondents were within the age group of 50-59. 33.33% were service holder, 41.67% were businessman, 12.5 were day labor, 6.67% were farmer. 75% were from urban area and 25% from rural area. The prevalence of DR in this study was 41.1%. About 90% had DM of <5 years, 55% had 6-10 years, 40% had 15-20 Years, 30% had 16-20 years and only 7% had >21 years. About 50% had normal grade of diabetic retinopathy in the worse eye, 15% had Mild NPDR, 8.33% had Moderate NPDR, 6.67% had Mild to moderate NPDR with non-CSME, 2.5% Moderate NPDR with CSME, Severe NPDR with non-CSME Severe NPDR with CSME and Advanced PDR respectively. Mean FBS (mg/dl) was 160.86±70.6, Total cholesterol (mg/dl) was 183.2 ± 2.1, Triglycerides (mg/dl) was 161.2 ± 2.6, Systolic BP was 130.56±15.7 and Diastolic BP was 80.86±13.4. Patients with baseline age of <60 years were three times (AOR = 3.2:95% CI: 1.19–8.63) more likely to develop DR. The odds of DR is about 3 times (AOR = 2.91:95% CI: 1.01–8.35) higher for patients with disease duration of ≥6 years as compared to disease duration of <6 years. Systolic blood pressure also had statistically significant association with patients having blood pressure of <140 mmHg being about 3.6 times (AOR = 0.28:95% CI: 0.09–0.82) less likely to have DR as compared to hypertensive patients with systolic blood pressure of ≥140 mmHg. The other cofactors, history of hypertension, and total cholesterol level were not independent significant factors for the development of DR in our study. Conclusion: Systolic hypertension, being on insulin alone or in combination with OHA, and having diabetes for a longer period of time were all independently linked to the occurrence of DR. The significant DR prevalence our study suggests that routine patient care, including treatment facilities, has to be improved. Healthcare practitioners must make a consistent effort to educate diabetic patients about the importance of blood sugar control and hypertension management in lowering the risk of the start and progression of DR. It is advised that diabetic patients get health education regarding the importance of routine eye exams for the early detection and treatment of diabetes-related eye problems.

The relationship of central macular thickness with clinical grades of diabetic retinopathy

To assess central macular thickness (CMT) in diabetics with or without diabetic retinopathy (DR) and compare it with various clinical grades of DR. All patients attending the ophthalmic OPD fulfilling the inclusion and met no exclusion criteria were enrolled in this study. All eyes underwent comprehensive and standardized ophthalmic examination. The CMT was measured using spectral domain optical coherence tomography using Cirrus HD OCT Model 500. The mean CMT in cases is significantly greater than controls. It is also significantly greater in diabetics with DR as compared to diabetics without DR and it significantly varies with various grades of DR. The mean CMT is also increased in patients with diabetic macular edema (DME) and panretinal photocoagulation (PRP) untreated DR as compared to diabetics without DME and PRP treated DR respectively. Central macular thickness can be used as an indicator to monitor individuals with diabetes as it increased in diabetics and the presence of macular edema decreased in PRP treated eyes. It also varies with various grades of DR.

Joint ordinal regression and multiclass classification for diabetic retinopathy grading with transformers and CNNs fusion network

Joint ordinal regression and multiclass classification for diabetic retinopathy grading with transformers and CNNs fusion network

Diabetic Retinopathy Image Classification Using Shift Window Transformer

Diabetic retinopathy is one of the most dangerous complications for diabetic patients, leading to blindness if not diagnosed early. However, early diagnosis can control and prevent the disease from progressing to blindness. Transformers are considered state-of-the-art models in natural language processing that do not use convolutional layers. In transformers, means of multi-head attention mechanisms capture long-range contextual relations between pixels. For grading diabetic retinopathy, CNNs currently dominate deep learning solutions. However, the benefits of transformers, have led us to propose an appropriate transformer-based method to recognize diabetic retinopathy grades. A major objective of this research is to demonstrate that the pure attention mechanism can be used to determine diabetic retinopathy and that transformers can replace standard CNNs in identifying the degrees of diabetic retinopathy. In this study, a Swin Transformer-based technique for diagnosing diabetic retinopathy is presented by dividing fundus images into nonoverlapping batches, flattening them, and maintaining positional information using a linear and positional embedding procedure. Several multi-headed attention layers are fed into the resulting sequence to construct the final representation. In the classification step, the initial token sequence is passed into the SoftMax classification layer, which produces the recognition output. This work introduced the Swin transformer performance on the APTOS 2019 Kaggle for training and testing using fundus images of different resolutions and patches. The test accuracy, test loss, and test top 2 accuracies were 69.44%, 1.13, and 78.33%, respectively for 160*160 image size, patch size=2, and embedding dimension C=64. While the test accuracy was 68.85%, test loss: 1.12, and test top 2 accuracy: 79.96% when the patch size=4, and embedding dimension C=96. And when the size image is 224*224, patch size=2, and embedding dimension C=64, the test accuracy: 72.5%, test loss: 1.07, and test top 2 accuracy: 83.7%. When the patch size =4, embedding dimension C=96, the test accuracy was 74.51%, test loss: 1.02, and the test top 2 accuracy was 85.3%. The results showed that the Swin Transformer can achieve flexible memory savings. The proposed method highlights that an attention mechanism based on the Swin Transformer model is promising for the diabetic retinopathy grade recognition task.

Automated detecting and severity grading of diabetic retinopathy using transfer learning and attention mechanism

Automated detecting and severity grading of diabetic retinopathy using transfer learning and attention mechanism

A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification

Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enable adapting foundation models in downstream tasks efficiently using only a few training samples, e.g., in-context learning. Yet, the application of such learning paradigms in medical image analysis remains scarce due to the shortage of publicly accessible data and benchmarks. In this paper, we aim at approaches adapting the foundation models for medical image classification and present a novel dataset and benchmark for the evaluation, i.e., examining the overall performance of accommodating the large-scale foundation models downstream on a set of diverse real-world clinical tasks. We collect five sets of medical imaging data from multiple institutes targeting a variety of real-world clinical tasks (22,349 images in total), i.e., thoracic diseases screening in X-rays, pathological lesion tissue screening, lesion detection in endoscopy images, neonatal jaundice evaluation, and diabetic retinopathy grading. Results of multiple baseline methods are demonstrated using the proposed dataset from both accuracy and cost-effective perspectives.

A Datasheet for the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Screening Dataset.

Diabetic retinopathy(DR) is the most common microvascular complication associated with diabetes mellitus (DM), affecting approximately 40% of this patient population. Early detection of DR is vital to ensure monitoring of disease progression and prompt sight saving treatments as required. This article describes the data contained within the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Dataset. Dataset descriptor for routinely collected eye screening data. All diabetic patients aged 12 years and older, attending annual digital retinal photography-based screening within the Birmingham, Solihull, and Black Country Eye Screening Programme. The INSIGHT Health Data Research Hub for Eye Health is a National Health Service (NHS)-led ophthalmic bioresource that provides researchers with safe access to anonymized, routinely collected data from contributing NHS hospitals to advance research for patient benefit. This report describes the INSIGHT Birmingham, Solihull, and Black Country DR Screening Dataset, a dataset of anonymized images and linked screening data derived from the United Kingdom's largest regional DR screening program. This dataset consists of routinely collected data from the eye screening program. The data primarily include retinal photographs with the associated DR grading data. Additional data such as corresponding demographic details, information regarding patients' diabetic status, and visual acuity data are also available. Further details regarding available data points are available in the supplementary information, in addition to the INSIGHT webpage included below. At the time point of this analysis (December 31, 2019), the dataset comprised 6 202 161 images from 246 180 patients, with a dataset inception date of January 1, 2007. The dataset includes 1 360 547 grading episodes between R0M0 and R3M1. This dataset descriptor article summarizes the content of the dataset, how it has been curated, and what its potential uses are. Data are available through a structured application process for research studies that support discovery, clinical evidence analyses, and innovation in artificial intelligence technologies for patient benefit. Further information regarding the data repository and contact details can be found at https://www.insight.hdrhub.org/. Proprietary or commercial disclosure may be found after the references.

Terminology for Retinal Findings in Sickle Cell Disease Research: A Scoping Review

To review the current sickle cell disease (SCD) literature to assess how "retinopathy" has been defined and to identify ocular outcomes that have been measured and described. A systematic scoping review of SCD literature was completed regarding ocular manifestations of SCD and vision outcomes across all medical specialties. Participants with SCD and control patients were included in our data extraction. We reviewed English-language literature from 2000 to 2021 for eligible studies by searching PubMed, Google Scholar, Embase, and the Cochrane library using terms to encompass SCD and ocular findings. Data collection included study information, patient characteristics, vision-related findings (inclusion criteria and/or study outcomes), and retinopathy characteristics (definition, when, how and by whom diagnosed). We identified 4006 unique citations and 111 were included in the analysis. Ophthalmologists were senior authors of about half (59/111; 53.2%) of the articles; most articles were published between 2016 and 2021 (71/111; 70.0%). The studies had been conducted primarily in North America (54/111; 48.6%) or Europe (23/111; 20.7%); designs were cross-sectional (51/111; 45.9%), prospective cohort (28/111; 25.2%), retrospective cohort (27/111; 24.3%), and case-control (4/111; 3.6%). Among studies reporting any retinopathy, it was commonly defined as a combination of nonproliferative sickle cell retinopathy and proliferative sickle cell retinopathy (PSR;52/87; 59.8%), infrequently as PSR only (6/87; 6.9%), or not defined at all (23/87; 26.4%). The Goldberg classification was used to grade retinopathy in almost half of the studies (41/87; 47.1%). Investigators reporting diagnostic methods used clinical fundus examination (56/111; 50.4%), OCT (24/111; 21.6%), fluorescein angiography (20/111; 18.0%), ultrawidefield fundus photographs (15/111; 13.5%), and OCT angiography (10/111; 9.0%), or did not report methods (28/111; 25.2%). There are inconsistencies in documentation of methods and outcomes in studies of SCD ophthalmic findings. Particularly concerning is the lack of documentation of ophthalmic examination methods, qualifications of examiners, and clarity and specificity of sickle cell retinopathy definitions. With the increase in SCD treatment research and novel systemic therapies available, it is important to adopt clear and consistent descriptions and rigorous data collection and reporting of ophthalmic outcomes in SCD studies. The authors have no proprietary or commercial interest in any materials discussed in this article.

Association of retinopathy with risk of all-cause and specific-cause mortality in the National Health and Nutrition Examination Survey, 2005 to 2008.

This study aimed to elucidate the relationship between retinopathy status or severity and the all-cause and specific-cause mortality risk based on the updated National Health and Nutrition Examination Survey (NHANES) database and 2019 Public Access Link mortality file. In this prospective cohort study, a total of 6,797 participants aged over 40 years based on NHANES 2005-2008 were analyzed. The severity of retinopathy was classified into 4 grades-no retinopathy, mild non-proliferative retinopathy (NPR), moderate to severe NPR, and proliferative retinopathy (PR). Multiple covariate-adjusted Cox proportional hazards regression models and Fine and Gray competing risk regression models were used to assess the all-cause and cause-specific mortality risks, respectively. The propensity score matching (PSM) approach was also applied additionally to adequately balance between-group covariates to validate our findings. A final total of 4,808 participants representing 18,282,772 United States (US) non-hospitalized participants were included for analysis, 50.27% were male (n = 2,417), 55.32% were non-hispanic white (n = 2,660), and mean [SE] age, 56.10 [0.40] years. After a median follow-up of 12.24 years (interquartile range, 11.16-13.49 years), 1,164 participants died of all-cause mortality, of which 941 (80.84%) died without retinopathy and 223 (19.16%) died with retinopathy at baseline. The presence of retinopathy was associated with increased all-cause mortality, cardiovascular disease (CVD), and diabetes mellitus (DM)-specific mortality, and the results remain consistent after PSM. Severity analysis showed that only mild NPR was associated with an increased all-cause mortality risk (hazard ratio (HR) = 2.01; 95% confidence interval (CI), 1.00-4.03), while increased CVD and DM-specific mortality risk were associated with all grades of retinopathy and were exponentially greater with increasing retinopathy severity, and the trend test was also significant (P for trend 0.004 and 0.04, respectively). Our findings suggest that the diagnosis of retinopathy is an independent risk factor for all-cause mortality in people over 40 years old. Retinopathy grading is significantly associated with the survival risk of patients with CVD or DM, it can be a valuable predictor in the stratified management and risk warning of CVD or DM patients, as well as in the monitoring of systemic vasculopathy status.

Plasma Levels of Pentraxin 3 and Diabetic Retinopathy in Patients with Type 2 Diabetes Mellitus

Abstract Background Diabetes mellitus is a heterogeneous metabolic disorder characterized by the presence of hyperglycemia which is associated with long-term micro-vascular complications affecting the eyes resulting in diabetic retinopathy (a leading cause of blindness). Pentraxin 3 (PTX3) is a recently discovered mediator of acute inflammation which has been shown to be a sensitive biomarker of localized inflammatory reactions with a possibility of being used as a biomarker of peripheral vascular damage. Objective To evaluate the relation between levels of plasma Pentraxin 3 (PTX3) and diabetic retinopathy (DR) in patients with type 2 diabetes mellitus. Methods This case control study was conducted on70 patients with type 2 DM and 20 healthy subjects as control and their ages ranged from 40-70 years, selected from outpatient diabetes and endocrinology clinic at El Demerdash Hospital/Ain-Shams University and outpatient diabetes and endocrinology clinic at National Institute of Diabetes and Endocrinology. The included subjects were allocated into three groups: Group I (type 2 DM complicated with DR) and this group was further subdivided to subgroup Ia (diabetic patients with proliferative diabetic retinopathy) and subgroup Ib (diabetic patients with nonproliferative diabetic retinopathy); group II (type 2 DM without retinopathy) and group III (apparently healthy subjects representing control group). All subjects were subjected to full medical history taking, physical examination including BMI and blood pressure measurement as well as fundus examination. Fasting and 2 hr post-prandial plasma glucose, HbA1C, serum creatinine and urea, ALT, AST, serum albumin, microalbuminuria as well as plasma PTX3 wereassessed. Results Plasma PTX3 level was significantly higher in diabetic patients with diabetic retinopathy than that in both diabetic patients without diabetic retinopathy and control healthy subjects (3.20 ± 0.035 ng/dl versus 2.88 ± 0.32 ng/dl and 2.88 ± 0.167 ng/dl respectively; P-value =0.000).The Correlation between pentraxin 3 and other measured parameters in all studied subjects showed a statistical positive correlation between log PTX3 and age, duration of diabetes, DBP, FBG, 2HPP, HBA1c, albumin, creatinine, urea, microalbuminuria. Conclusion Serum pentraxin 3 is strongly correlated to diabetic retinopathy and the higher the grade of retinopathy, the higher the pentraxin 3 level. So, pentraxin 3 can be considered a reliable predictor for diabetic retinopathy. Serum pentraxin 3 and diabetic retinopathy are strongly correlated to level of glycaemia and duration of diabetes as well as to creatinine, urea and micro-albuminuria. This draws the attention to the role of tight glycemic control in the prevention or slowing diabetic retinopathy progression and to the relation between diabetic retinopathy and nephropathy.

Automatic grading of non-proliferative diabetic retinopathy

Automatic grading of non-proliferative diabetic retinopathy