Severe Non-proliferative Diabetic Retinopathy Research Articles

The effects of SOD Melon Gliadin as an Antioxidant on VEGF and TGF β blood serum and vitreoretina in hyperglycemia rat model a literature review

Diabetes Mellitus is a chronic metabolic disorder characterized by hyperglycemia resulting from impaired insulin secretion, action, or both. Persistent hyperglycemia can lead to organ damage, including ocular complications such as diabetic retinopathy. Diabetic retinopathy is a primary cause of preventable blindness and can become vision-threatening if complications are persistent. Over one-third of individuals with diabetes exhibit signs of diabetic retinopathy, and among them, approximately one-third experience vision-threatening diabetic retinopathy (VTDR), defined as severe non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), or the presence of diabetic macular edema (DME). Diabetic retinopathy triggers the production of reactive oxygen species (ROS). Excessive accumulation of ROS can damage tissues surrounding retinal blood vessels, leading to oxidative stress that compromises the blood-retina barrier (BRB). Vascular endothelial growth factor (VEGF) and transforming growth factor-beta (TGF-β) are two markers that indicate retinal dysfunction. Cells possess an intrinsic defense system against ROS, comprising antioxidants such as superoxide dismutase (SOD), glutathione reductase, glutathione peroxidase, and catalase. SOD melon gliadin (Glisodin®) is a potent antioxidant that elevates endogenous SOD levels, reduces ROS, and protects retinal tissues from damage and cell death. Research on SOD melon gliadin (Glisodin®) shows its potential as an antioxidant for preventing diabetic complications in the retina, specifically diabetic retinopathy.

The Role of Urban Residence, Race and Ethnicity, and Glycemic Control in Receiving Standards of Care and Progression to Vision-Threatening Diabetic Retinopathy.

Among patients with diabetes living in the U.S. with newly detected mild or moderate nonproliferative diabetic retinopathy (NPDR) without diabetic macular edema (DME), we aimed to characterize determinants for receiving standards of care and progression to vision-threatening diabetic retinopathy (VTDR) (severe NPDR, proliferative diabetic retinopathy, DME). Electronic health records of patients newly detected with NPDR without DME between 2015 and 2023 were analyzed with use of the Epic Cosmos research platform. We characterized the adjusted associations of urban versus rural residence, race and ethnicity (Hispanic, non-Hispanic [NH] White, NH Black, other), and glycemic control (HbA1c <7.0%, 7.0%-8.9%, ≥9%, unavailable) separately with guideline-recommended care (two of three: ophthalmology visit, primary care visit, and measurement of HbA1c, blood pressure, and LDL cholesterol) in the 2 years after diagnosis and with progression to VTDR. Average (SD) age for the analytic sample (n = 102,919) was 63 (13.5) years, and 51% were female, 59% NH White, and 7% rural residents. Only 40% received guideline-recommended care, and 14% progressed to VTDR (median follow-up 35 months [interquartile range 18-63]). Urban residence was associated with receiving standards of care in both years (risk ratio 1.08 [95% CI 1.05-1.12]) and progression to VTDR (hazard ratio 1.07 [95% CI 0.99-1.15]). Racial and ethnic minority individulas were more likely to progress to VTDR. Individuals with poor or unknown glycemic control were less likely to receive standards of care and more likely to progress to VTDR. Understanding the management and progression of newly detected NPDR will require disentangling the independent and interdependent contributions of geography, race and ethnicity, and glycemia.

Is longer axial length protective of vision-threatening diabetic retinopathy across different ages? A multicenter cohort of 736 patients

PurposeVision-threatening diabetic retinopathy (VTDR) included severe non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR) and clinically significant diabetic macular edema (DME). To compare the axial length (AL) and assess its influence on VTDR across different ages.MethodsA retrospective cohort study. Medical chart review was performed in 736 consecutive patients with VTDR. The patients were divided into young (≤ 45 years) and elderly group (> 45 years) based on their age at the diagnosis of VTDR. After at least one year of standardized treatments, all eligible patients were followed up. The main outcome measures included the presence of tractional retinal detachment (TRD) involving foveal, final best-corrected visual acuity (BCVA), the development of neovascular glaucoma (NVG), and recurrent vitreous hemorrhage (VH) post-vitrectomy. ALs were compared between two age groups. The impact of AL on clinical outcomes was determined by logistic analyses after controlling for systemic parameters.ResultsThe study included 144 patients ≤ 45 years and 592 patients > 45 years. Young patients had significantly longer AL than elderly participants (23.9 mm vs 23.0 mm, p < 0.001). Over a median follow-up of 25.9 months, a larger proportion of young patients developed TRD (34.7% vs 16.2%, p < 0.001) and recurrent VH (18.6% vs 10.3%, p = 0.040) than elderly patients. In elderly group, longer AL is an independent protective factor in preventing TRD (odds ratio [OR], 0.5; 95% confidence interval [CI], 0.4–0.7; P < 0.001). However, this beneficial effect was not observed in young patients.ConclusionsYoung patients with VTDR exhibited significantly longer AL but more aggressive clinical signs with compromised prognosis. In elderly group, a longer AL independently reduced the risk of TRD, while this protective effect did not exist for young patients.

Prevalence and Assessment of Diabetic Retinopathy in a Densely Populated Suburban Area of Tianjin, China – The Beichen Eye Study

PurposeThe aim of this study is to explore the prevalence, associated risk factors, and the influence of urbanization on the epidemiology of diabetic retinopathy (DR) and diabetic macular edema (DME). MethodsThis study utilized a cross-sectional design to survey residents aged 50 and older in the Beichen community, Tianjin. Participants underwent thorough examinations, including questionnaire surveys, laboratory blood tests, eye assessments, fundus photography, and optical coherence tomography (OCT) scans. DR was diagnosed using the retinopathy severity scale from fundus photographs based on the Early Treatment Diabetic Retinopathy Study (ETDRS) classification system. DME was identified through medical history records and OCT evaluations. In this study, we analyzed and assessed the prevalence and risk factors associated with DR and DME. ResultsThe study included 5,648 participants, and 1,182 of these were diagnosed with diabetes. Among the diabetic individuals, the prevalence of DR was 28.8%. Among the eyes of those diagnosed with DR, 37.12% had mild non-proliferative DR (NPDR), 54.81% had moderate NPDR, 6.35% (33 eyes) showed severe NPDR, and 1.73% had proliferative DR (PDR). The prevalence of DME among diabetic patients was 14.13%. Age was negatively correlated with DR (OR, 0.924), while blood glucose levels (OR, 1.123) and the duration of diabetes (OR, 1.090) were positively correlated. Additionally, blood glucose levels (OR, 1.121) and the duration of diabetes (OR, 1.070) were positively associated with DME. ConclusionIn this study, the prevalence of DR and DME was high in the Beichen District of Tianjin than in other parts of China. Maintaining effective glycemic control is the most important modifiable factor in reducing the risk of DR and DME progression and minimizing the risk of vision loss.

Association of Various Optical Coherence Tomographic Patterns of Diabetic Macular Edema With Central Subfield Thickness and Visual Acuity: A Cross-Sectional Observational Study.

Introduction Diabetic macular edema (DME) is the most common and vision-threatening complication in diabetic patients with diabetic retinopathy (DR), especially in those with Type 2 diabetes mellitus. Optical coherence tomography (OCT) is a reliable tool most commonly used for assessing macular morphology and provides quantitative information on the macula. OCT also examines the outer retinal layers, which can predict visual outcomes. Thus, our study aims to identify the association of various OCT-detected DME morphological patterns with central subfield thickness (CST) and visual acuity. Materials and methods This is a cross-sectional observational study of 50 patients with DME detected on OCT who visited the Ophthalmology Department of Saveetha Medical College and Hospitals for a period of six months, from November 2023 to April 2024. A complete ocular examination, including best corrected visual acuity, scored with the logMAR scale, anterior segment examination, and fundus biomicroscopy using 90D and 78D lenses, was performed. Early Treatment of Diabetic Retinopathy Study (ETDRS) grading of DR into mild to very severe non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) was noted. Spectral-domain OCT was used to diagnose DME. The CST was measured, and DME was classified into four patterns: sponge-like retinal swelling (SLRS), cystoid macular edema (CME), subretinal fluid (SRF), and posterior hyaloid traction (PHT). Results In the present study, males represented 60%, and females represented 40%. The mean age of the patients was 58.07 ± 6.80 years, with a mean duration of diabetes of 11.91 ± 5.14 years. Of the 50 patients with 100 eyes, only 60 eyes showed DME on OCT. CME was the most common morphological pattern (37%), while the least common pattern was PHT (10%). No significant association was found between a specific morphological pattern and control of diabetes. The most common pattern observed was SLRS in moderate NPDR, CME in severe NPDR, SRF in very severe NPDR, and PHT in PDR. Very severe NPDR patients showed all patterns of DME, and the PHT pattern was observed only in very severe NPDR and PDR. The highest mean CST was observed in the very severe NPDR stage, and the least was in the moderate NPDR stage. The mean CST was highest in SRF patterns and lowest in SLRS patterns. The best mean visual acuity was observed in the SLRS pattern, while the worst mean visual acuity was observed in the SRF pattern, followed by the PHT pattern. Conclusion Our study highlights the importance of OCT in patients with diabetes, as OCT patterns of DME are critical for predicting visual outcomes in DR. Severe grades of DR are usually associated with SRF and PHT patterns. Since patients with SRF and PHT patterns have the worst visual outcomes, these patients, upon identification, need to be counseled about their poor visual prognosis. Those with less severe DR should be closely monitored and advised on effective diabetes control to prevent progression and protect their vision.

Evaluation of Foveal Vasculature by Optical Coherence Tomography Angiography after Pan-Retinal Photocoagulation versus Intravitreal Anti-VEGF Injections.

This study aimed to compare macular vascular changes one and three months after treatment with either panretinal photocoagulation (PRP) or intravitreal bevacizumab (IVB). A total of 62 eyes with very severe non-proliferative diabetic retinopathy or early proliferative diabetic retinopathy without center-involved diabetic macular edema, were included in this retrospective study. Thirty-nine eyes were allocated to the PRP group, while 23 eyes were treated with IVB. Optical coherence tomography angiography (OCTA) was performed to measure foveal avascular zone (FAZ) characteristics as well as the densities of superficial and deep capillary plexuses (SCP and DCP). In the IVB group, the FAZ area and perimeter expanded at month one but returned to baseline level after three months. In the PRP group, however, the FAZ area and perimeter were rather steady. Changes in the FAZ area were significantly different between the treatment groups at month one (P = 0.02), but not at month three (P = 0.31). There was no significant difference in the change in FAZ circularity index between the two groups at each time point (P = 0.55 and P = 0.31). Similarly, changes in SCP density were not statistically significant between the two groups at both time points (all Ps 0.05). A comparison of the two treatment arms based on the mean change in DCP density revealed a significant difference at month one, but not at month three (P = 0.01 and P = 0.49, respectively). Although bevacizumab and PRP have different short-term macular vascular responses, both therapies have the ability to normalize or stabilize vascular measures over time.

Choroidal thickness in type 2 diabetes mellitus patients using spectral domain optical coherence tomography

To measure the choroidal thickness in patients with type 2 diabetes mellitus and its comparison to healthy age-matched individuals.This prospective comparative study conducted at departments of Ophthalmology, Shri B.M. Patil Medical College, Hospital and Research Centre, Vijayapura. Study included 192 patients, 96 with diabetes and 96 age-matched controls. Data acquired comprised demographics, a thorough eye examination, fundus photography and spectral domain optical coherence tomography measurements of choroidal thickness using enhanced depth imaging.Mean age was 60.17±7.727 years with male predominance in both cases and controls. The mean duration of diabetes was 5.67±3.39 years. Positive correlation was noted between duration of diabetes and severity of retinopathy (p = 0.001). 21 patients did not have any evidence of diabetic retinopathy, while 75 patents had features of non-proliferative diabetic retinopathy, of which (31) had mild, (21) moderate, (12) severe and (11) with proliferative diabetic retinopathy. Mean choroidal thickness of controls was 327.308±18.945 microns, as compared to thinner choroids 295±15.082 microns in cases without retinopathy and 271 ± 36.122, 242 ±30.048, 193±15.748 microns in mild, moderate, severe non-proliferative diabetic retinopathy respectively and 191.492± 23.834 microns in proliferative diabetic retinopathy. Statistically significant differences were noted on comparing choroidal thickness between controls and cases (p = 0.001).Choroidal thickness was maximum in control group with a p value of &amp;#60;0.001, while cases showed overall thinner choroids with increasing severity of retinopathy. Thinnest choroids were observed in patient with proliferative diabetic retinopathy. Choroidal thickness can be used as a non-invasive prognostic bio marker for diabetic retinopathy.

Effects of retinopathy on visual function in type 2 diabetes mellitus

Objective: To study the influence of the severity of diabetic retinopathy (DR) on the visual function of patients with type 2 diabetes, to provide scientific basis for the early prevention and control of DR. Methods: This study was designed as a cross-sectional study, recruiting already-diagnosed type 2 diabetes patients in four community health service centers in Guizhou Province between February and September 2022. Employing the Chinese version of the Visual Function Index-14 (VF-14), assess the participants' near vision, visual adaptation, subjective visual perception, and stereo vision, with higher scores indicating poorer visual function. Categorize the severity of each eye's damage into no diabetic retinopathy (DR), mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, and proliferative diabetic retinopathy (PDR), and use a 5-level DR grading system to evaluate the overall severity of diabetic retinopathy in both eyes. Employing linear regression analysis to investigate the linear relationship between DR and visual function index. Local weighted regression evaluates the nonlinear relationship between the DR composite score and the scores of visual function, with a steeper slope indicating poorer visual function for that level. Results: A total of 542 patients with type 2 diabetes were investigated, including 244 (45.02%) males, 298 (54.98%) females, and 162 (29.89%) patients with DR. After adjusting for confounders, compared with those without DR, patients with binocular DR Had overall scores (β=0.136, P=0.003), near vision (β=0.163, P<0.001), visual adaptation (β=0.092, P=0.042), subjective vision (β=0.120, P=0.009) and stereo vision (β=0.094, P=0.044) were higher than those without DR. There were no differences in visual functions between DR And monocular DR. The local weighted regression curve showed that near vision (slope: 23.78) and overall score (slope: 58.37) increased sharply from mild to moderate NPDR in both eyes. Visual adaptation (slope: 5.37, 7.72), subjective vision (slope: 6.53, 7.93), stereovision (slope: 0.74, 0.91) increased slowly in mild to moderate NPDR in both eyes and in moderate to severe NPDR/PDR in both eyes. Conclusion: Binocular DR is associated with impaired visual function, but there is no difference between monocular DR And non-DR visual function. The early damage of DR To visual function is mainly manifested in near vision. In the prevention and control of DR, more attention should be paid to visual function, especially the change of near vision, and retinal damage should not be assessed solely by visual status.

The Role of Widefield Optical Coherence Tomography Angiography in Assessing the Severity of Diabetic Retinopathy.

Physicians need an accurate understanding of diabetic retinopathy (DR) severity to optimally manage patients. The aim of this prospective study is to correlate the severity of macular and peripheral retinal vascular abnormalities seen on widefield (WF) optical coherence tomography angiography (OCTA) with DR grading based on WF fundus photography. The study included 150 eyes from 82 patients with treatment-naïve DR. All patients were imaged with WF fundus photography and swept-source WF OCTA. Quantitative and qualitative analyses of the foveal avascular zone (FAZ) size and shape, and measurement of capillary nonperfusion (CNP) areas, were performed from the OCTA images. The mixed-effects model was used to compare the DR grading from WF photography with the vascular changes seen on WF-OCTA, and Bonferroni correction was applied to the gradings. The mean [± standard deviation (SD)] age of patients was 55.5 (± 9.4) years. The WF-OCTA showed that an increasing size of the FAZ (from 0.442 (± 0.059) µm to 0.933 (± 0.086) µm) correlated with increasing severity of the DR (as determined with WF photography). The deep capillary plexus, FAZ size, and CNP areas in eyes with proliferative diabetic retinopathy (PDR) differed from those with mild nonproliferative diabetic retinopathy (NPDR) (p < 0.001). Most eyes with severe nonproliferative DR were found to have CNP in four quadrants [superficial capillary plexus (SCP) 60%, deep capillary plexus (DCP) 50%]. The WF-OCTA detected subtle neovascularization of the disc (NVD) in 7 eyes (10%) and neovascularization elsewhere (NVE) in 13 eyes (18%) that had been diagnosed with only moderate NPDR on WF photography. FAZ and CNP areas as measured by WF-OCTA correlate with DR severity. WF-OCTA can also detect subtle NVE and NVD that cannot be seen with fundus photography.

Quantitative analysis of peripapillary capillary volume using dense B-scan OCT angiography in normal and diabetic retina

BackgroundThe value of quantitatively analyzing peripapillary capillary volume (PPCV) distribution was explored in normal and diabetic retinopathy (DR) eyes using dense B-scan optical coherence tomography angiography (DB OCTA).MethodsThis was a cross-sectional observational study followed by prospective follow-up for those with DR, which enrolled 101 healthy subjects and 140 DR patients. Dense, automatic, real-time (DART) volume scans of DB OCTA were performed using a Spectralis HRA + OCT2. ImageJ and MATLAB were used to process and calculate PPCV distribution detected by DB OCTA.ResultsIn normal subjects, PPCV distribution were significantly correlated with the age and quadrant location (all P < 0.001). The PPCV distribution in each quadrant was significantly lower in severe nonproliferative DR patients than in normal subjects in all age groups (all P < 0.05, t-test). Compared to normal subjects, the PPCV distribution improved significantly in the pan-retinal photocoagulation treatment and surgery groups (all P < 0.001). No significant variation was observed in the anti-VEGF treatment group and normal subjects (P > 0.05). The PPCV distribution is significantly correlated with post-treatment best-corrected visual acuity in both the pan-retinal photocoagulation treatment and surgery groups (all P < 0.003) but not in the anti-VEGF treatment group (P = 0.940).ConclusionsQuantitative assessment of PPCV distribution using DB OCTA is valuable in prognosis evaluation of DR with pan-retinal photocoagulation and surgery.

Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population

BackgroundIn healthcare systems in general, access to diabetic retinopathy (DR) screening is limited. Artificial intelligence has the potential to increase care delivery. Therefore, we trained and evaluated the diagnostic accuracy of a machine learning algorithm for automated detection of DR.MethodsWe included color fundus photographs from individuals from 4 databases (primary and specialized care settings), excluding uninterpretable images. The datasets consist of images from Brazilian patients, which differs from previous work. This modification allows for a more tailored application of the model to Brazilian patients, ensuring that the nuances and characteristics of this specific population are adequately captured. The sample was fractionated in training (70%) and testing (30%) samples. A convolutional neural network was trained for image classification. The reference test was the combined decision from three ophthalmologists. The sensitivity, specificity, and area under the ROC curve of the algorithm for detecting referable DR (moderate non-proliferative DR; severe non-proliferative DR; proliferative DR and/or clinically significant macular edema) were estimated.ResultsA total of 15,816 images (4590 patients) were included. The overall prevalence of any degree of DR was 26.5%. Compared with human evaluators (manual method of diagnosing DR performed by an ophthalmologist), the deep learning algorithm achieved an area under the ROC curve of 0.98 (95% CI 0.97–0.98), with a specificity of 94.6% (95% CI 93.8–95.3) and a sensitivity of 93.5% (95% CI 92.2–94.9) at the point of greatest efficiency to detect referable DR.ConclusionsA large database showed that this deep learning algorithm was accurate in detecting referable DR. This finding aids to universal healthcare systems like Brazil, optimizing screening processes and can serve as a tool for improving DR screening, making it more agile and expanding care access.

Association of Anemia and Diabetic Retinopathy Among Patients With Type 2 Diabetes Mellitus: Retrospective Cross-Sectional Study.

Introduction Type 2 diabetes mellitus (T2DM) is a common metabolic disorder characterized by the combination of defective insulin secretion and the inability of insulin-sensitive tissues to respond appropriately to insulin. Diabetic retinopathy (DR) is a common microvascular complication that can result in a preventable cause of blindness. Research to determine the prevalence of anemia among diabetic patients is necessary to assess whether treatment practices should be changed. Anemia is a common complication in patients with T2DM and has been associated with the progression of DR. In this study, our aim is to determine the prevalence of DR and its association with hemoglobin levels in patients diagnosed with T2DM in Dubai, UAE. Methods In this retrospective cross-sectional study, we extracted the data using electronic medical records. The study was performed over a span of three years in Dubai from 2019 to 2022. A total of 368 T2DM patients were included based on retinal exam findings classified into mild, moderate, severe non-proliferative retinopathy, and proliferative retinopathy. Descriptive statistics were used for categorical (frequency, percentage) and continuous variables (mean, SD), with chi-square/Fisher exact tests for categorical associations, ANOVA for continuous variables, and multiple logistic regression to identify DR risk factors, using OR, 95% CI, and p < 0.05 for significance. Results The prevalence of anemia (defined as hemoglobin levels ≤13 mg/dL for men and ≤12 mg/dL for women) was observed in 39.4% of individuals with DR aged between 40 and 88 years; 60.6% of the patients had normal hemoglobin, while 91 individuals (24.7%) exhibited mild anemia, 53 individuals (14.4%) showed moderate anemia, and only one individual (0.3%) had severe anemia. DR grading was as follows: mild non-proliferative DR (16.8%), moderate non-proliferative DR (30.2%), severe non-proliferative DR (13.3%), and proliferative DR (39.7%). Macular edema was present in 59.2% of patients, showing a statistically significant association with more severe DR stages (p < 0.0001). No significant association was found between hemoglobin levels and DR severity (p = 0.568). However, among males, a significant difference in mean hemoglobin levels across DR grades was observed (p = 0.009), with higher hemoglobin levels associated with lower odds of severe DR (OR = 0.775, p = 0.036). Macular edema strongly predicted DR severity, with significant odds ratios across all stages (p < 0.0001). Conclusions There is a significant prevalence of anemia among the examined population. DR severity was notably associated with lower hemoglobin levels in males, and macular edema was significantly linked to more severe stages of DR. Vigilant monitoring and integrated care for both anemia and DR are crucial to optimize patient outcomes and mitigate complications. Regular retinopathy screening is essential for early detection and timely intervention, particularly considering the challenges posed by anemia, such as delayed wound healing and increased infection risk post-screening.

Ultra-Widefield and Early Treatment Diabetic Retinopathy Study 7-Field Grading of Diabetic Retinopathy

High concordance in diabetic retinopathy (DR) outcomes between 7-field (7F) and ultra-widefield (UWF) images would allow for combining longitudinal assessments based on the 2 modalities both in clinical studies and clinical care. To compare 7F and UWF imaging with regard to DR severity and the associations of DR severity with risk factors, such as hemoglobin A1c, age, diabetes duration, and sex. This cross-sectional study describes the outcomes of the randomized clinical Diabetes Control and Complications Trial (DCCT) and its subsequent observational study, the Epidemiology of Diabetes Interventions and Complications (EDIC) study. Of the 1441 participants with type 1 diabetes in the DCCT, 1375 were enrolled in the EDIC study. Of the 1171 participants who were active between March 2019 and December 2021, 200° UWF color imaging and 7F fundus photographs were obtained for 785 participants once at the same visit. Central graders assessed 7F-UWF with a 7F template masking the retinal periphery and the full UWF image (UWF-global). Data were analyzed from January 2022 to March 2023. Hemoglobin A1c was assessed quarterly during the DCCT and annually during the EDIC study using high-performance liquid chromatography. Retinopathy was determined independently for all imaging as mild, moderate, or severe nonproliferative DR (SNPDR) using the Early Treatment Diabetic Retinopathy Study (ETDRS) grading scale for the 7F images and the global ETDRS grading scale for the UWF images. Panretinal and focal photocoagulation were self-reported or based on scarring location and pattern observed during grading. Proliferative DR (PDR) was defined by observed neovascularization or evidence of panretinal photocoagulation. Among the 785 participants included in this study, 420 (53%) were male and 365 (47%) were female. The mean (SD) age was 61 (7) years. DR grading between UWF-7F and 7F imaging was correlated for all outcomes, including for severe outcomes, such as SNPDR (κ, 0.73; concordance, 96%), PDR (κ, 0.74; concordance, 97%), scatter photocoagulation (κ, 0.97; concordance, 99%), and focal photocoagulation (κ, 0.71; concordance, 98%). Most DR severity scores were within 1 step (1410 of 1529 [92%]), and 3% (51 of 1529) were more than 2 steps apart (κ, 0.45; 95% CI, 0.42-0.49; weighted κ, 0.63; 95% CI, 0.60-0.67) on the ETDRS severity scale. DR severity assessed within the UWF-global area was higher compared to 7F (median [IQR] UWF-global score, 3 [2-3] vs median 7F level score, 2.0 [1-3]; P < .001), although the 2 modalities were correlated (1225 of 1508 [81%] 1-step agreement; weighted κ, 0.41). Standard ETDRS 7F and UWF evaluations of DR were comparable for ETDRS severity levels as previously reported by Diabetic Retinopathy Clinical Research Retina Network reports. In addition, these evaluations of DR were comparable for DCCT/EDIT study outcomes and major study conclusions, suggesting that use of UWF imaging is not likely to introduce relevant measurement biases in future longitudinal studies. ClinicalTrials.gov Identifiers: NCT00360815.

Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy.

This study aimed to investigate the impact of distinctive capillary-large vessel (CLV) analysis in optical coherence tomography angiography (OCTA) on the classification performance of diabetic retinopathy (DR). This multicenter study analyzed 212 OCTA images from 146 patients, including 28 controls, 36 diabetic patients without DR (NoDR), 31 with mild non-proliferative DR (NPDR), 28 with moderate NPDR, and 23 with severe NPDR. Quantitative features were derived from the whole image as well as the parafovea and perifovea regions. A support vector machine classifier was employed for DR classification. The accuracy and area under the receiver operating characteristic curve were used to evaluate the classification performance, utilizing features derived from the whole image and specific regions, both before and after CLV analysis. Differential CLV analysis significantly improved OCTA classification of DR. In binary classifications, accuracy improved by 11.81%, rising from 77.45% to 89.26%, when utilizing whole image features. For multiclass classifications, accuracy increased by 7.55%, from 78.68% to 86.23%. Incorporating features from the whole image, parafovea, and perifovea further improved binary classification accuracy from 83.07% to 93.80%, and multiclass accuracy from 82.64% to 87.92%. This study demonstrated that feature changes in capillaries are more sensitive during DR progression, and CLV analysis can significantly improve DR classification performance by extracting features that are specific to large vessels and capillaries in OCTA. Incorporating regional features further improves DR classification accuracy. Differential CLV analysis promises better disease screening, diagnosis, and treatment outcome assessment.

Causal association between cystatin C and diabetic retinopathy: A two-sample Mendelian randomization study.

To explore the causal relationship between cystatin C levels and different stages of diabetic retinopathy through Mendelian randomization (MR). The MRC Integrative Epidemiology Unit provided the Genome-wide association studies (GWAS) data related to cystatin C (exposure). GWAS data for outcomes [DR, proliferative diabetic retinopathy (PDR), severe non-proliferative background diabetic retinopathy (SNPBDR)] were sourced from the FinnGen. Adopted Inverse Variance Weighting (IVW), MR-Egger regression MR-PRESSO, Weighted Median, Constrained Maximum Likelihood and Model Averaging (cML-MA), Weighted model, Radial MR, and MR-Lasso to estimate the causal relationship between cystatin C and diabetic retinopathy. We conducted multivariable MR analysis to evaluate the independent causal effects of cystatin C levels on diabetic retinopathy. Based on the IVW method, we observed a causal relationship between cystatin C and diabetic retinopathy [odds ratio (OR)random effect = 1.137, 95% confidence interval (CI): 1.035-1.250]/PDR (ORrandom effect = 1.123, 95%CI: 1.004-1.255)/SNPBDR (ORfixed effect = 2.002, 95%CI: 1.343-2.986). Consistent findings were obtained through the cML-MA method. Cochran's Q test suggested the presence of heterogeneity between the cystatin C level and instrumental variables in relation to diabetic retinopathy and proliferative diabetic retinopathy, respectively. After adjusting for outliers using MR-PRESSO and Radial MR, it was observed that the statistical significance of the association between cystatin C level and diabetic retinopathy persists. Reverse MR analysis indicated that genetically related SNPBDR may influence the cystatin C level. In multivariable MR analysis, there were indications suggesting a causal relationship of cystatin C with the risk of DR/PDR/SNPBDR adjusting for confounders. This study utilizes Mendelian randomization analyses to establish a causal relationship between cystatin C and diabetic retinopathy, and reveals the impact of cystatin C on the risk of diabetic retinopathy, thus providing new evidence for clinical intervention of diabetic retinopathy.

VMseg: Using spatial variance to automatically segment retinal non-perfusion on OCT-angiography.

To develop and test VMseg, a new image processing algorithm performing automatic segmentation of retinal non-perfusion in widefield OCT-Angiography images, in order to estimate the non-perfusion index in diabetic patients. We included diabetic patients with severe non-proliferative or proliferative diabetic retinopathy. We acquired images using the PlexElite 9000 OCT-A device with a photomontage of 5 images of size 12 x 12 mm. We then developed VMseg, a Python algorithm for non-perfusion detection, which binarizes a variance map calculated through convolution and morphological operations. We used 70% of our data set (development set) to fine-tune the algorithm parameters (convolution and morphological parameters, binarization thresholds) and evaluated the algorithm performance on the remaining 30% (test set). The obtained automatic segmentations were compared to a ground truth corresponding to manual segmentation from a retina expert and the inference processing time was estimated. We included 51 eyes of 30 patients (27 severe non-proliferative, 24 proliferative diabetic retinopathy). Using the optimal parameters found on the development set to tune the algorithm, the mean dice for the test set was 0.683 (sd = 0.175). We found a higher dice coefficient for images with a higher area of retinal non-perfusion (rs = 0.722, p < 10-4). There was a strong correlation (rs = 0.877, p < 10-4) between VMseg estimated non-perfusion indexes and indexes estimated using the ground truth segmentation. The Bland-Altman plot revealed that 3 eyes (5.9%) were significantly under-segmented by VMseg. We developed VMseg, an automatic algorithm for retinal non-perfusion segmentation on 12 x 12 mm OCT-A widefield photomontages. This simple algorithm was fast at inference time, segmented images in full-resolution and for the OCT-A format, was accurate enough for automatic estimation of retinal non-perfusion index in diabetic patients with diabetic retinopathy.

Evaluation of an AI algorithm trained on an ethnically diverse dataset to screen a previously unseen population for diabetic retinopathy.

This study aimed to determine the generalizability of an artificial intelligence (AI) algorithm trained on an ethnically diverse dataset to screen for referable diabetic retinopathy (RDR) in the Armenian population unseen during AI development. This study comprised 550 patients with diabetes mellitus visiting the polyclinics of Armenia over 10 months requiring diabetic retinopathy (DR) screening. The Medios AI-DR algorithm was developed using a robust, diverse, ethnically balanced dataset with no inherent bias and deployed offline on a smartphone-based fundus camera. The algorithm here analyzed the retinal images captured using the target device for the presence of RDR (i.e., moderate non-proliferative diabetic retinopathy (NPDR) and/or clinically significant diabetic macular edema (CSDME) or more severe disease) and sight-threatening DR (STDR, i.e., severe NPDR and/or CSDME or more severe disease). The results compared the AI output to a consensus or majority image grading of three expert graders according to the International Clinical Diabetic Retinopathy severity scale. On 478 subjects included in the analysis, the algorithm achieved a high classification sensitivity of 95.30% (95% CI: 91.9%-98.7%) and a specificity of 83.89% (95% CI: 79.9%-87.9%) for the detection of RDR. The sensitivity for STDR detection was 100%. The study proved that Medios AI-DR algorithm yields good accuracy in screening for RDR in the Armenian population. In our literature search, this is the only smartphone-based, offline AI model validated in different populations.

Improving Access to Specialist Eye Care for Indigenous Australians via Telehealth: An Observational Cohort Study.

Background: Indigenous people are often neglected in eye health research and service delivery programs, despite having a greater burden of vision loss, most of which is avoidable. The objective of this work was to improve access to specialist eye care for Indigenous Australians living in rural and remote areas, by providing direct access to expert diagnostic services based in metropolitan areas through a tele-ophthalmology system. Methods: Over a four-year study period, 13 remote communities in Queensland and the Northern Territory were identified that had limited or no access to eye screening services. Relationships with health service providers in the communities were established to codesign a sustainable model of service delivery and referral pathways to ensure that patients identified with eye issues received appropriate treatment. Results: Over the course of the study, screening records from 378 patients were uploaded to a web-based telehealth system and diagnosed by ophthalmologists. From these examinations, 64 new cases of diabetic retinopathy (DR) were identified (including 2 cases of proliferative DR and 4 cases of severe nonproliferative DR), and diabetic macular edema was noted in 18 patients. The majority of participants screened had no eye problems, which enables the removal of these patients from the queues of overwhelmed specialist lists, improving service efficiency. The study also demonstrates capacity building of healthcare workers to perform eye screening and improved patient health awareness where the retinal cameras were used as an educational tool. Conclusions: A valuable screening service has been established in the target areas, where access to ophthalmic services has been improved for residents of the study screening locations. Routine eye examination (instead of opportunistic eye examination) is feasible for early detection of some eye diseases for remote and rural patients.

Linking Diabetic Retinopathy Severity to Coronary Artery Disease Risk Factors in Type 2 Diabetic Patients.

Diabetes mellitus (DM) is a common metabolic disorder characterized by hyperglycemia, leading to chronic complications, notably cardiovascular diseases such as coronary artery disease (CAD). Diabetic retinopathy (DR), a leading cause of blindness, may serve as a non-invasive marker for CAD. This study investigates the correlation between DR and CAD to explore its diagnostic potential in diabetic populations. A cross-sectional study was conducted over one year in a general hospital, involving 100 type 2 DM patients with retinopathy. DR was classified as mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, or proliferative retinopathy, based on fundus examinations. Data on age, duration of diabetes, cholesterol levels, glycated hemoglobin (HbA1C), and ECG (electrocardiography) findings were collected. Statistical analysis included frequency analysis, chi-square tests for association between categorical variables, and significance testing with p-values. Data were analyzed using IBM SPSS Statistics for Windows, Version 20.0 (Released 2011; IBM Corp., Armonk, New York, United States). Descriptive statistics were characterized by categorical and continuous variables. The chi-square test determined associations between qualitative variables, with significance set at p<0.05. The mean age of patients was 57.13 ± 8.51 years. Age and duration of diabetes were significant predictors of retinopathy severity (p<0.001). Proliferative retinopathy was found exclusively in patients over 70 years. Lower cholesterol levels (<200 mg/dL) were significantly associated with less severe retinopathy (p=0.033), whereas higher cholesterol levels (>200 mg/dL) did not show a statistically significant association with retinopathy severity (p=0.772). Patients with HbA1C levels between 6.5% and 8.5% predominantly had milder forms of retinopathy, as indicated by the significant p-value (<0.001). In contrast, patients with HbA1C levels above 8.5% are more likely to have severe NPDR or proliferative diabetic retinopathy (PDR), but this association was not statistically significant (p=0.582). ECG abnormalities increased with retinopathy severity (p=0.002). Hypertension was significantly linked to cardiac changes in retinopathy patients (p<0.001), while smoking and family history of CAD were not significant factors.This study's cross-sectional design limits causality inference. The single-center sample of 100 patients may not be broadly generalizable. Reliance on self-reported data introduces potential recall bias, and confounding factors such as diet, physical activity, and additional comorbidities were not accounted for. The lack of a control group further limits comparative analysis. Future longitudinal studies with larger, diverse samples are needed. Retinopathy in DM patients is significantly associated with cardiac changes and other risk factors such as hypertension, dyslipidemia, and poor glycemic control. Aggressive management of these factors is essential. Retinopathy can serve as a predictor of CAD in diabetic patients.

Multi-Plexus Nonperfusion Area Segmentation in Widefield OCT Angiography Using a Deep Convolutional Neural Network.

To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA). This cross-sectional study included 202 participants with a full range of diabetic retinopathy (DR) severities (diabetes mellitus without retinopathy, mild to moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR) and 39 healthy participants. Consecutive 6 × 6-mm OCTA scans at the central macula, optic disc, and temporal region in one eye from 202 participants in a clinical DR study were acquired with a 70-kHz OCT commercial system (RTVue-XR). Widefield OCTA en face images were generated by montaging the scans from these three regions. A projection-resolved OCTA algorithm was applied to remove projection artifacts at the voxel scale. A deep convolutional neural network with a parallel U-Net module was designed to detect NPAs and distinguish signal reduction artifacts from flow deficits in the superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Expert graders manually labeled NPAs and signal reduction artifacts for the ground truth. Sixfold cross-validation was used to evaluate the proposed algorithm on the entire dataset. The proposed algorithm showed high agreement with the manually delineated ground truth for NPA detection in three retinal vascular plexuses on widefield OCTA (mean ± SD F-score: SVC, 0.84 ± 0.05; ICP, 0.87 ± 0.04; DCP, 0.83 ± 0.07). The extrafoveal avascular area in the DCP showed the best sensitivity for differentiating eyes with diabetes but no retinopathy (77%) from healthy controls and for differentiating DR by severity: DR versus no DR, 77%; referable DR (rDR) versus non-referable DR (nrDR), 79%; vision-threatening DR (vtDR) versus non-vision-threatening DR (nvtDR), 60%. The DCP also showed the best area under the receiver operating characteristic curve for distinguishing diabetes from healthy controls (96%), DR versus no DR (95%), and rDR versus nrDR (96%). The three-plexus-combined OCTA achieved the best result in differentiating vtDR and nvtDR (81.0%). A deep learning network can accurately segment NPAs in individual retinal vascular plexuses and improve DR diagnostic accuracy. Using a deep learning method to segment nonperfusion areas in widefield OCTA can potentially improve the diagnostic accuracy of diabetic retinopathy by OCT/OCTA systems.