Clinical Diabetic Retinopathy Disease Severity Research Articles

Performance of a Support Vector Machine Learning Tool for Diagnosing Diabetic Retinopathy in Clinical Practice.

To examine the real-world performance of a support vector machine learning software (RetinaLyze) in order to identify the possible presence of diabetic retinopathy (DR) in patients with diabetes via software implementation in clinical practice. 1001 eyes from 1001 patients-one eye per patient-participating in the Danish National Screening Programme were included. Three independent ophthalmologists graded all eyes according to the International Clinical Diabetic Retinopathy Disease Severity Scale with the exact level of disease being determined by majority decision. The software detected DR and no DR and was compared to the ophthalmologists' gradings. At a clinical chosen threshold, the software showed a sensitivity, specificity, positive predictive value and negative predictive value of 84.9% (95% CI: 81.8-87.9), 89.9% (95% CI: 86.8-92.7), 92.1% (95% CI: 89.7-94.4), and 81.0% (95% CI: 77.2-84.7), respectively, when compared to human grading. The results from the routine screening were 87.0% (95% CI: 84.2-89.7), 85.3% (95% CI: 81.8-88.6), 89.2% (95% CI: 86.3-91.7), and 82.5% (95% CI: 78.5-86.0), respectively. AUC was 93.4%. The reference graders Conger's Exact Kappa was 0.827. The software performed similarly to routine grading with overlapping confidence intervals, indicating comparable performance between the two groups. The intergrader agreement was satisfactory. However, evaluating the updated software alongside updated clinical procedures is crucial. It is therefore recommended that further clinical testing before implementation of the software as a decision support tool is conducted.

Association of serum lipids and random plasma glucose levels with severity of diabetic retinopathy

Diabetic retinopathy is the most common vision-threatening chronic microvascular, microangiopathy complication of diabetes militias.We have designed this cross sectional observational study to understand the role of serum lipids and random plasma glucose level with severity of diabetic retinopathy (DR). This study was accomplished with 210 patients that came to us in the diabetic clinic. There were two groups: Group A - All DM patients with no apparent sign of diabetic retinopathy (Grade I) and Group B - All DM Patients with any signs of diabetic retinopathy (Grade II - V). Diabetic retinopathy (DR) was graded according to The International Clinical Diabetic Retinopathy Disease Severity Scale on dilated fundus examination and fundus phtotograpgy. Serum lipids (Total, LDL, and HDL cholesterol and triglycerides) were then assessed according to NCEP ATP III guidlines. The correlation of the severity DR was then assessed with serum lipids and random plasm glucose.We found that total cholesterol and triglycerides played a detrimental role in the severity of diabetic retinopathy and the likelihood of developing a more severe form of diabetic retinopathy was dependent on their levels (P<0.05). The Association of HDL and LDL with different grades of diabetic retinopathy was also found significant (P<0.05). However, the association of VLDL (P>0.05) was found insignificant. Similarly, the values of total cholesterol, triglycerides and HDL showed a significant rise with severity of diabetic macular edema (DME)In this present study, the significant association of total cholesterol, triglycerides and HDL with DR thus concludes that along with strict glycemic monitoring and control, the of control of hyperlipidemia is also noteworthy to prevent the progression of DR and DME.

Inter-observer agreement in grading severity of diabetic retinopathy in wide-field fundus photographs.

To examine the inter-observer agreement between two retina specialists in grading diabetic retinopathy (DR) severity in ultra-wide-field fundus photographs. Two hundred and seventy patients with diabetes, who visited the vitreoretinal specialty at a tertiary eye care hospital, with or without DR underwent comprehensive ophthalmic examination, dilated retinal exam and Optos ultra-wide-field (UWF) retinal photography. Optos images were graded for DR severity based on the International Clinical Diabetic Retinopathy Disease Severity Scale by two retina specialists with same number of years of experience, masked to the clinical details of the participants. The two graders showed agreement in 229/270 images (84.8%) and disagreement in 41/270 images (15.2%). The unweighted kappa for agreement between graders was k = 0.715, SE = 0.037 and the weighted kappa was k = 0.838, SE = 0.022. No DR was identified in 170/270 (62.9%) patients, mild NPDR in 15/270 (5.6%) patients, moderate NPDR in 35/270 (12.9%) patients, severe NPDR in 4/270 (1.48%) patient and PDR in 5/270 (1.85%) patients by both graders. Disagreement was neither related to the learning curve of graders nor with the patient's age (p = 0.574), gender (p = 0.169), duration of diabetes (0.660) or the lens being phakic or pseudophakic (p = 0.171) on logistic regression. The impact of disagreement noted between observers in grading DR on UWF fundus photographs should be considered when utilizing UWF system in clinical studies.

Cross-Camera External Validation for Artificial Intelligence Software in Diagnosis of Diabetic Retinopathy.

Aims To investigate the applicability of deep learning image assessment software VeriSee DR to different color fundus cameras for the screening of diabetic retinopathy (DR). Methods Color fundus images of diabetes patients taken with three different nonmydriatic fundus cameras, including 477 Topcon TRC-NW400, 459 Topcon TRC-NW8 series, and 471 Kowa nonmyd 8 series that were judged as “gradable” by one ophthalmologist were enrolled for validation. VeriSee DR was then used for the diagnosis of referable DR according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Gradability, sensitivity, and specificity were calculated for each camera model. Results All images (100%) from the three camera models were gradable for VeriSee DR. The sensitivity for diagnosing referable DR in the TRC-NW400, TRC-NW8, and non-myd 8 series was 89.3%, 94.6%, and 95.7%, respectively, while the specificity was 94.2%, 90.4%, and 89.3%, respectively. Neither the sensitivity nor the specificity differed significantly between these camera models and the original camera model used for VeriSee DR development (p = 0.40, p = 0.065, respectively). Conclusions VeriSee DR was applicable to a variety of color fundus cameras with 100% agreement with ophthalmologists in terms of gradability and good sensitivity and specificity for the diagnosis of referable DR.

Early Onset Age Increased the Risk of Diabetic Retinopathy in Type 2 Diabetes Patients with Duration of 10-20 Years and HbA1C ≥7%: A Hospital-Based Case-Control Study.

Background The relationship between onset age of diabetes and diabetic retinopathy (DR) is controversy and not concluded. Therefore, this hospital-based case-control study aimed to investigate the influence of diabetes onset age on the development of DR in patients with type 2 diabetes (T2D), independent of diabetic duration and HbA1c levels. Methods A sample of 780 T2D patients with diabetic duration of 10–20 years and glycated hemoglobin (HbA1c) ≥7% were enrolled in the study. 338 T2D patients with onset age ≤45 years were further selected as cases (early onset) and 79 with onset age ≥ 55 years were chosen as controls (elderly onset). International Clinical Diabetic Retinopathy Disease Severity Scale was applied to estimate the severity of DR. Results The prevalence of DR and proliferative diabetic retinopathy (PDR) was notably increased in the early onset group. When stratified by duration of diabetes, the impact of younger age on the risk of DR turned to be greatest in patients with diabetic duration ≥15 years (OR = 5.202, 95% CI 2.625–10.310). In groups stratified by HbA1c, the risk of DR was highest in patients with younger onset age and HbA1c ≥ 9% (OR = 3.889, 95% CI 1.852–8.167). Compared with the elderly onset group, the risk of DR (OR = 1.776, 95% CI = 1.326–2.380, p < 0.001) and PDR (OR = 1.605, 95% CI = 1.106–2.329, p = 0.013) in younger diagnosed patients was increased after multivariable adjustment. Conclusions Age of onset was an independent risk factor for developing DR and PDR. This suggests that it is urgent to closely monitor and treat the metabolic disorders in younger T2D patients to delay the occurrence and progression of DR.

Detection of Diabetic Retinopathy from Ultra-Widefield Scanning Laser Ophthalmoscope Images: A Multicenter Deep Learning Analysis

To develop a deep learning (DL) system that can detect referable diabetic retinopathy (RDR) and vision-threatening diabetic retinopathy (VTDR) from images obtained on ultra-widefield scanning laser ophthalmoscope (UWF-SLO). Observational, cross-sectional study. A total of 9392 UWF-SLO images of 1903 eyes from 1022 subjects with diabetes from Hong Kong, the United Kingdom, India, and Argentina. All images were labeled according to the presence or absence of RDR and the presence or absence of VTDR. Labeling was performed by retina specialists from fundus examination, according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Three convolutional neural networks (ResNet50) were trained with a transfer-learning procedure for assessing gradability and identifying VTDR and RDR. External validation was performed on 4 datasets spanning different geographical regions. Area under the receiver operating characteristic curve (AUROC); area under the precision-recall curve (AUPRC); sensitivity, specificity, and accuracy of the DL system in gradability assessment; and detection of RDR and VTDR. For gradability assessment, the system achieved an AUROC of 0.923 (95% confidence interval [CI], 0.892-0.947), sensitivity of 86.5% (95% CI, 77.6-92.8), and specificity of 82.1% (95% CI, 77.3-86.2) for the primary validation dataset, and >0.82 AUROCs, >79.6% sensitivity, and >70.4% specificity for the geographical external validation datasets. For detecting RDR and VTDR, the AUROCs were 0.981 (95% CI, 0.977-0.984) and 0.966 (95% CI, 0.961-0.971), with sensitivities of 94.9% (95% CI, 92.3-97.9) and 87.2% (95% CI, 81.5-91.6), specificities of 95.1% (95% CI, 90.6-97.9) and 95.8% (95% CI, 93.3-97.6), and positive predictive values (PPVs) of 98.0% (95% CI, 96.1-99.0) and 91.1% (95% CI, 86.3-94.3) for the primary validation dataset, respectively. The AUROCs and accuracies for detecting both RDR and VTDR were >0.9% and >80%, respectively, for the geographical external validation datasets. The AUPRCs were >0.9, and sensitivities, specificities, and PPVs were >80% for the geographical external validation datasets for RDR and VTDR detection. The excellent performance achieved with this DL system for image quality assessment and detection of RDR and VTDR in UWF-SLO images highlights its potential as an efficient and effective diabetic retinopathy screening tool.

Prevalence of Diabetic Retinopathy and its Associated Factors among Diabetic Patients in Primary Care Clinics, Kuantan, Pahang

Introduction: Diabetic retinopathy (DR) is one of the commonest complications of diabetes mellitus. This study was to determine the prevalence of DR and its association with chronic kidney disease (CKD), high HbA1c and dyslipidemia among diabetic patients in government primary care clinics. Materials and Methods: A cross sectional study was carried out. The respondents were selected from diabetic registry at two government primary care clinics in Kuantan, Pahang via stratified random sampling method during the study period from May 2010 to April 2011. The respondents were interviewed and assessed clinically using a structured questionnaire. Retinal examination was performed by accredited staff using non-mydratic retinal imaging and DR was classified according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Results: Out of 400 respondents, 58.8% were diagnosed with diabetes less than 5 years and 51.0% had uncontrolled blood pressure (&gt;130/80 mmHg). The prevalence of DR and maculopathy were 33.5% and of 17.8% respectively. Most of these patients (22.3%) had mild non-proliferative DR. DR patients had higher percentages CKD (17.9% vs. 6.8%; p&lt;0.001) and a higher mean of HbA1C (8.69 vs. 8.11; p=0.015) compared to non-DR patients. The study revealed that DR was independently associated with CKD {OR: 3.46, 95% CI (1.76, 6.80)} and high HbA1c {OR: 1.12, 95% CI (1.02, 1.23)}. Those with dyslipidemia however, has 39% less risk of DR {OR: 0.61, 95% CI (0.39, 0.94)}. Conclusion: This study showed that diabetic patients with CKD and high HbA1c have greater risks to develop DR but has protective risk with dyslipidemia.

Fluorophotometric Measurement of Corneal Autofluorescence in Diabetic and Normal Eyes.

Corneal autofluorescence (AF) has been recommended as an indicator of diabetic retinopathy. This study was conducted to evaluate corneal autofluorescence in non-insulin dependent diabetes subjects (with and without diabetic retinopathy) and compare the changes with healthy subjects. In this cross-sectional study, 145 eyes of diabetes mellitus subjects with and without diabetic retinopathy and 34 eyes of normal healthy subjects were included. Diabetic patients were subdivided by the severity of retinopathy based on international clinical diabetic retinopathy disease severity scale. The investigations included fluorophotometric determination of corneal autofluorescence, fasting glucose level (FBS) and glycosylated hemoglobin (HbA1c). The corneal autofluorescence values were significantly higher in moderate NPDR, severe NPDR and PDR when compared with healthy subjects (p<0.001). FBS values and HbA1c were significantly higher in all diabetic groups than the healthy subjects (p<0.001). In linear regression test, corneal autofluorescence seem to be related to duration of diabetes and FBS, but not with age and HbA1c. We conclude that the level of corneal autofluorescence is dependent on the severity of diabetic retinopathy. Our study suggests that the formation of autofluorescent products in cornea depend upon the glucose concentration in the aqueous humor.

Gender Specific Distribution Of different patterns of Diabetic Retinopathy In Patients With Type 2 Diabetes Mellitus. A Cross-Sectional Study From a tertiary care specialized center in Lahore, Pakistan

BACKGROUND:The patients of diabetic retinopathy are not aware of early retinal vascular dysfunction, which is a feature of diabetic retinopathy.&#x0D; AIM:This study aimed to detect the different patterns of diabetic retinopathy and distribution in different genders, at a tertiary care specialized center.&#x0D; MATERIALS AND METHODS:Consecutive patients attending the Diabetes Endocrine and Metabolic Center (DEMC) were consented and examined for a period of six months. Direct and indirect ophthalmoscopes were used by an ophthalmologist for fundus examination .Grading of the retinopathy patterns was done by International Clinical Diabetic Retinopathy Disease Severity Scale (ICDRDSS). The data was analyzed using SPSS version 23.&#x0D; RESULTS: A total number of 250 patients were examined during the six months period of study. The frequency of diabetic retinopathy was more in females as compared to males. The incidence of non-proliferative diabetic retinopathy was more in males as compared to females, and the prevalence of proliferative diabetic retinopathy was same in both genders. Greater number of females had clinically significant macular edema and advance diabetic retinopathy was observed in only males.&#x0D; CONCLUSION: Diabetic retinopathy (DR) is common in our setup, with different genders having different prevalence of eye problems. A screening strategy system is needed to develop for the early detection and treatment of preventable cause of blindness in patients with type2 diabetes mellitus.

Evidence-based Canadian guidelines for tele-retina screening for diabetic retinopathy: recommendations from the Canadian Retina Research Network (CR2N) Tele-Retina Steering Committee

ObjectiveThe purpose of this report is to develop a consensus for Canadian national guidelines specific to a tele-medicine approach to screening for diabetic retinopathy (DR) using evidence-based and clinical data. MethodsCanadian Tele-Screening Grading Scales for DR and diabetic macular edema (DME) were created primarily based on severity grading scales outlined by the International Clinical Diabetic Retinopathy Disease Severity Scale (ICDR) and the Scottish DR Grading Scheme 2007. Other grading scales used in international screening programs and the clinical expertise of the Canadian Retina Research Network members and retina specialists nationwide were also used in the creation of the guidelines. ResultsNational Tele-Screening Guidelines for DR and DME with and without optical coherence tomography (OCT) images are proposed. These outline a diagnosis and management algorithm for patients presenting with different stages of DR and/or DME. General guidelines detailing the requirements for imaged retina fields, image quality, quality control, and follow-up care and the role of visual acuity, pupil dilation, OCT, ultra-wide-field imaging, and artificial intelligence are discussed. ConclusionsTele-retina screening can help to address the need for timely and effective screening for DR, whose prevalence continues to rise. A standardized and evidence-based national approach to DR tele-screening has been proposed, based on DR/DME grading using two 45° image fields or a single widefield or ultra-wide-field image, preferable use of OCT imaging, and a focus on local quality control measures.

Retinopathy among Chinese subjects with type 2 diabetes mellitus in Shanghai: A community-based follow-up study.

To determine the incidence, progression, and regression rates of diabetic retinopathy (DR), as well as their associated factors, in Chinese type 2 diabetic patients. Diabetic patients who participated in a previous survey were recruited for a 1-year follow-up study. Nonmydriatic fundus photographs were acquired to assess the severity of DR as per the International Clinical Diabetic Retinopathy Disease Severity Scale (2002). Factors that potentially influence DR outcomes, including its incidence, progression, and regression, were identified via statistical analyses. We initially recruited 2453 subjects, among whom 2331 were followed and included in the final analysis. The incidences of new and progressed (ie, ≥2 scale steps) DR were 10.6% and 6.1%, respectively. Moreover, 7.3% of patients with established DR at baseline experienced complete regression. Multivariate logistic regression analysis revealed that high glycosylated haemoglobin (HbA1c) (odds ratio [OR] = 1.50, P = .021) and hyperlipidaemia (OR = 1.46, P = .025) were independent predictors of DR development, high HbA1c (OR = 4.16, P = .027) and macroalbuminuria (OR = 5.60, P = .010) predicted DR progression, and low HbA1c (OR = 0.20, P = .001) and low triglyceride levels (OR = 0.34, P = .026) were associated with DR regression. Albumin and HbA1c levels should be closely monitored as signs of progressive retinal damage in diabetic subjects. Optimized control of glucose and triglyceride levels is vital for reducing the incidence of DR or promoting its regression in afflicted patients.

Prevalencia y factores de riesgo asociados a la retinopatía diabética en Santander. Norte de España

ObjetiveTo assess the prevalence and risk factors associated with diabetic retinopathy (DR) in Cantabria. Designross-sectional population based study. LocationHealth center of Cantabria. ParticiantsA random sample of 442 patients with type 2 diabetes. Main measurementsNon-mydiatric retinography, classifying them according to the International Clinical Diabetic Retinopathy Disease Severity Scale. The analyzed risk factors were: age, gender, age at diabetes onset, duration of diabetes, glycated haemoglobin levels (A1C), treatment of diabetes, blood pressure (systolic and diastolic), serum lipids concentration, body mass index, smoking status, hematocrit, pregnancy, serum vitamin D (25 OH D) levels, nephropathy and cardiovascular events. ResultsThe prevalence of DR was 8.56% (CI: 5.81-11.32): Mild non-proliferative DR: 5.07% (CI: 2.89-7.25); Moderate non-proliferative DR: 1.38% (CI: 0.17-2.60); Severe non-proliferative DR: 0.27% (CI: 0.006-1.28); proliferative DR: 1.84% (CI: 0.46-3.22). Diabetic macular oedema: 2.30% (CI: 0.77-3.83). Mean age: 70 years, mean diagnostic age: 58.97 years, mean body mass index 29.86, 78.40% patients with hypertension, 67.30% dyslipidemia and median A1C: 6.7%. A deficit of 25 (OH) D was identified in 77% of patients. In the multivariate analysis, treatment of type 2 diabetes, body mass index, duration of diabetes and metabolic control of glycaemia were identified as independent risk factors. ConclusionsThe prevalence of DR, compared with former studies, has decreased to 8.56%; this decrease is associated with the improvement in the control of modifiable risk factors. The associated independent risk factors were: treatment, body mass index, duration and control of diabetes. The variables antihypertensive treatment, cardiovascular events and nephropathy showed predictive value for DR.

Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy

To understand the impact of deep learning diabetic retinopathy (DR) algorithms on physician readers in computer-assisted settings. Evaluation of diagnostic technology. One thousand seven hundred ninety-six retinal fundus images from 1612 diabetic patients. Ten ophthalmologists (5 general ophthalmologists, 4 retina specialists, 1 retina fellow) read images for DR severity based on the International Clinical Diabetic Retinopathy disease severity scale in each of 3 conditions: unassisted, grades only, or grades plus heatmap. Grades-only assistance comprised a histogram of DR predictions (grades) from a trained deep-learning model. For grades plus heatmap, we additionally showed explanatory heatmaps. For each experiment arm, we computed sensitivity and specificity of each reader and the algorithm for different levels of DR severity against an adjudicated reference standard. We also measured accuracy (exact 5-class level agreement and Cohen's quadratically weighted κ), reader-reported confidence (5-point Likert scale), and grading time. Readers graded more accurately with model assistance than without for the grades-only condition (P < 0.001). Grades plus heatmaps improved accuracy for patients with DR (P < 0.001), but reduced accuracy for patients without DR (P= 0.006). Both forms of assistance increased readers' sensitivity moderate-or-worse DR: unassisted: mean, 79.4% [95% confidence interval (CI), 72.3%-86.5%]; grades only: mean, 87.5% [95% CI, 85.1%-89.9%]; grades plus heatmap: mean, 88.7% [95% CI, 84.9%-92.5%] without a corresponding drop in specificity (unassisted: mean, 96.6% [95% CI, 95.9%-97.4%]; grades only: mean, 96.1% [95% CI, 95.5%-96.7%]; grades plus heatmap: mean, 95.5% [95% CI, 94.8%-96.1%]). Algorithmic assistance increased the accuracy of retina specialists above that of the unassisted reader or model alone; and increased grading confidence and grading time across all readers. For most cases, grades plus heatmap was only as effective as grades only. Over the course of the experiment, grading time decreased across all conditions, although most sharply for grades plus heatmap. Deep learning algorithms can improve the accuracy of, and confidence in, DR diagnosis in an assisted read setting. They also may increase grading time, although these effects may be ameliorated with experience.

Role of a mydriasis-free, full-field flicker ERG device in the detection of diabetic retinopathy.

To determine if the RETeval system can be used for the screening of diabetic retinopathy (DR) to provide early diagnosis. The subjects were 42 diabetic patients selectively recruited by examination of their medical records to have varying severities of DR. The severity of DR was classified into four groups according to the International Clinical Diabetic Retinopathy Disease Severity Scale. Full-field electroretinograms (ERG) without mydriasis were obtained by the DR assessment protocol of the RETeval system. Macular retinal nerve fiber layer (RNFL) thickness was measured by optical coherence tomography. We compared the DR assessment protocol results and the macular RNFL thickness among four groups. Moreover, an analysis was conducted on whether there was any correlation among the DR assessment protocol results, duration of diabetes mellitus, and RNFL thickness individually for each group of patients. The mean ages and mean duration of diabetes mellitus of the four groups were similar. The DR assessment protocol results in the moderate-severe nonproliferative DR, and proliferative DR groups were significantly higher than those in the other groups (p < 0.001). The mean macular RNFL thickness was similar in all groups. No significant correlation was found between the DR assessment protocol results and duration of DM and the RNFL thickness. Our results suggest that the RETeval full-field ERG system can be used as an adjunctive tool for the mass screening of DR, while macular RNFL thickness would not be useful.

Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy.

Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33,000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33,000 photographs, 30,000 images were randomly selected as the training set, and the remaining 3,000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2–4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (all p values <0.001). The entropy image quantifies the amount of information in the fundus photograph and efficiently accelerates the generating of feature maps in the CNN. The research results draw the conclusion that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system.

Study of anemia in diabetes and its association with diabetic retinopathy

Background: Anemia is an increasingly recognized entity in patients with diabetes mellitus. Reduced hemoglobin levels identify diabetic patients with an increased risk of microvascular complications.Methods: A hospital based observational prospective study was conducted in Department of medicine, J.A. Group of Hospitals, Gwalior from June 2014 to October 2015. Adults with diabetes mellitus both type 1 and type 2 were selected as subjects are included and anemia due to blood loss and anemia due to chronic kidney diseases were excluded from the study. Estimation of hemoglobin was done by using capillary method by calorimetric hemoglobinometer. Anemia was defined as hemoglobin &lt;13 g/dl in men and &lt;12 g/ dl in women. All the patients were examined for diabetic retinopathy and graded as none, mild, moderate, severe and proliferative retinopathy as per International clinical diabetic retinopathy disease severity scale.Results: 100 patients were enrolled as subjects. Most of the patients 26 (26%) were in the age group 46-55 years. There were 53 males and 47 females. 42%, 45% and 13% had diabetes of &lt;5 years, 5-10 years and &gt; 10 years duration respectively. HbA1c levels were &lt;7.5 in 74 (74%), 7.5-10 in 23 (23%) patients and &gt; 10 in 3 (3%) patients. Overall 67 (67%) diabetics had anemia. Out of 53 males 30 (56.6%) had anemia and amongst females, out of 47 cases 37 (78.72%) had anemia P value 0.009. Anemia was more common in patients less than 50 years 36 (70.59%) compared to31 (63.2%) with anemia in patients more than 50 years. 65 (65%) patients had diabetic retinopathy (DR). 30 (46.1%) males and 35 (53.8%) females had diabetic retinopathy. All patients with diabetic retinopathy had anemia. Among 35 (35%) patients without DR only 2 (5.71%) had anemia. P value &lt;0.001.Conclusions: Anemia is a common accompaniment to diabetes. Anemia was more common in females and in those less than 50 years. Anemia was frequently associated with diabetic retinopathy. The high prevalence of anaemia supports regular screening for anemia, alongside that for other diabetes-related complications. This might help to delay the progression of vascular complications in these patients.

Retinopathy among patients with diabetes in Ramallah Governorate: a clinic-based study

BackgroundIn 2010, diabetes mellitus was the fourth cause of death in West Bank, causing 9% of deaths. Few reliable data on diabetes complications exist. We aimed to estimate prevalence of diabetic retinopathy and determinants in a clinic-based sample in Ramallah Governorate, West Bank. MethodsThe study was done in 11 clinics (four United Nations Relief and Works Agency [UNRWA], five Ministry of Health, two joint Ministry of Health and non-governmental organizations). It was approved by ethics committee at Institute of Community and Public Health (Birzeit University), Ministry of Health, and UNRWA and oral witnessed consent was obtained from patients. Patients with type 2 diabetes older than 25 years were identified in clinics or from clinic databases and were invited to participate in a slit lamp eye exam by a licensed ophthalmologist. Questionnaire, physical examination, and lab tests (glycated haemoglobin [HbA1c], lipid profile) were done. We classified retinopathy using International Clinical Diabetic Retinopathy Disease Severity Scale. Data were collected between Feb 28, 2012, and June 7, 2012, and analysed using the statistical package for social sciences (SPSS) version 22. FindingsMost eligible patients (517) consented to participate and were referred for eye exam; 376 (73%) underwent eye exam (127 men and 249 women). Mean age was 57·8 years (SD 9·0) and mean duration of diabetes was 8·9 years (SD 7·0). Only 21% (77) of patients had controlled diabetes (HbA1c<7%) and 37% (138) had never had an eye exam. The prevalence of retinopathy was 37% (138 patients, 52 [41%] men and 86 [35%] women). 26% (98) had mild-to-moderate non-proliferative retinopathy, 3% (11) had severe non-proliferative retinopathy, and 8% (29) had proliferative retinopathy. Multivariate regression showed that HbA1c, diabetes duration, and systolic blood pressure were associated with retinopathy (p<0·001). Patients with uncontrolled diabetes (HbA1c >7%) were four times more likely to have retinopathy compared with patients with controlled disease (odds ratio [OR] 4·16, 95% CI 1·76–9·85). Patients with diabetes duration more than 7 years were eight times more likely to have retinopathy than were patients whose diabetes duration was 7 years or less (OR 7·7, 95% CI 4·30–13·79). There was a 2% increase in risk of retinopathy per 1 mm Hg increase in systolic blood pressure (OR 1·02, 95% CI 1·01–1·04). InterpretationA third of patients with diabetes had retinopathy. Early regular screening might help early diagnosis and control of diabetic retinopathy, especially among high-risk patients (ie, those with uncontrolled diabetes, high systolic blood pressure, and long diabetes duration). FundingSwedish International Development Agency (SIDA); Umeå Centre for Global Health Research, Umeå University, Sweden.

Influence of serum lipids on the incidence and progression of diabetic retinopathy and macular oedema: Sankara Nethralaya Diabetic Retinopathy Epidemiology And Molecular genetics Study-II.

The importance of lipids on incidence and progression of diabetic retinopathy has not been studied in the Indian population. To elucidate the influence of serum lipid control on the incidence and progression of diabetic retinopathy and diabetic macular oedema in subjects with type 2 diabetes. Population-based longitudinal observational study in a hospital setting. Eight hundred ninety subjects were examined at baseline and follow-up. Diabetic retinopathy was graded per Modified Early Treatment Diabetic Retinopathy Study scales; 45°, 4-field dilated stereoscopic digital photography was performed with an additional 30°, 7-field for those who had retinopathy. Macular oedema was evaluated per Proposed International Clinical Diabetic Retinopathy and Diabetic Macular Oedema Disease Severity Scales. Association of serum lipids and incidence and progression of diabetic retinopathy. Poor control of total cholesterol was associated with the incidence of sight-threatening retinopathy (odds ratio = 7.2 [95% confidence interval: 1.5-34.3], P = 0.012) and macular oedema (odds ratio = 5.5 [95% confidence interval: 1.4-27.4], P = 0.037) after adjusting for potential confounders. Poor control of triglycerides was associated with progression to proliferative diabetic retinopathy (odds ratio = 3.2 [95% confidence interval: 1.1-10.5], P = 0.048). Risk for incident macular oedema (P = 0.041) and progression to proliferative diabetic retinopathy (P = 0.028) was greater when all lipid types were abnormal. Poor control of lipids is a risk factor for incidence of and progression to late stages of retinopathy. Abnormal levels of all lipid types are associated with risk of incident macular oedema and progression to proliferative diabetic retinopathy.

Age-related macular degeneration in a South Indian population, with and without diabetes.

PurposeTo elucidate the prevalence and risk factors of age-related macular degeneration (AMD) in people with diabetes.MethodsOf the 5495 subjects ≥60 years of age recruited in the population-based study in south India, 4791 subjects with gradable images on 30° three-field retinal photographs were analyzed. AMD and diabetic retinopathy (DR) were graded based on the International ARM Epidemiological Study Group classification and International Clinical Diabetic Retinopathy Disease Severity Scale, respectively. All subjects underwent a detailed history, physical examination, and a comprehensive ocular examination.ResultsOf the 4791 subjects, 1256 had diabetes. In those with diabetes, 166 (13.2%) had DR: of which, 9.6% had AMD. Of those with diabetes but no DR, 15.6% had AMD. Presence of DR (OR=0.57, 95% CI: 0.33-0.99, P=0.046) was a protective factor for AMD in diabetes. When adjusted for potential confounding factors, those with AMD and diabetes were from urban areas (OR=1.65, 95% CI: 1.09-2.49, P=0.018), had raised systolic blood pressure (OR=1.02, 95% CI: 1.00-1.03, P=0.01), higher BMI (OR=1.06, 95% CI: 1.02-1.10, P=0.005), and higher serum triglycerides (OR=1.00, 95% CI: 1.00-1.01, P=0.011). A higher level of high-density lipoprotein (HDL) (OR=0.98, 95% CI: 0.96-0.99, P=0.038) was a protective factor for AMD in subjects with diabetes.ConclusionsThe presence of DR and higher serum HDL are protective factors whereas obesity and higher systolic blood pressure are risk factors for AMD in subjects with diabetes.

Optimal Glycemic and Hemoglobin A1c Thresholds for Diagnosing Diabetes Based on Prevalence of Retinopathy in an Iranian Population.

BackgroundThe use of glycemic thresholds for diabetes diagnosis is controversial. However, no information is available regarding glycemic and glycated hemoglobin (HbA1c) thresholds for detecting diabetic retinopathy (DR) in the Iranian population.ObjectivesThe main purpose of the current investigation was to examine the association of fasting plasma glucose (FPG) and HbA1c levels with diabetic retinopathy (DR), and to determine the relevant cut-off levels in an Iranian population.Patients and MethodsThis cross-sectional, population-based study was performed during 2012-2013 in Urmia, the capital of West Azerbaijan province, Iran. The subjects were 3,010 Iranians aged 40-81 years. The FPG levels were determined using the glucose oxidase method whereas, the HbA1c values were measured using a standardized assay by high performance liquid chromatography. DR was evaluated by an examination of the fundus photograph of each eye. The photographs were graded according to the international clinical diabetic retinopathy disease severity scale by photograph graders who were masked to the clinical information.ResultsOf the subjects, 59 had DR. The prevalence of DR increased steeply between the ninth and the tenth deciles for both variables. The ROC curve analysis showed overall glycemic thresholds for DR of 6.5 mmol/L (117 mg/dL) for FPG and 6.2% (44 mmol/mol) for HbA1c. The sensitivities and specificities were 78.0% and 87.1% for FPG and 89.8% and 89.5% for HbA1c, respectively. The areas under the ROC curves indicated that HbA1c was a stronger discriminator of retinopathy: the area under curve was 0.880 for FPG and 0.946 for HbA1c P < 0.001). However, the thresholds for detecting DR for the two measures showed no significant differences after excluding individuals receiving anti-hyperglycemic medication.ConclusionsThese findings suggest that the HbA1c and FPG thresholds for detecting diabetes in the Iranian population are lower than the current diagnostic criteria.