Search Results (185)

Microvascular complications of diabetes include retinopathy (DR), diabetic kidney disease (DKD), and neuropathy (DN), which play a crucial role in diabetes management, as they significantly impair the functionality of the patient and remain major causes of morbidity despite advances in glycaemic control. The aim of this review was to summarize multi-omics findings in DR, DKD, and DN. Multi-omics studies consist of genomic, epigenomic, transcriptomic, proteomic, and metabolomic research. These studies provided comprehensive insights into the complex mechanisms underlying microvascular complications of diabetes, such as inflammation, angiogenesis, and apoptosis in the retina, kidneys, and nervous system. They also enabled the search for emerging diagnostic, prognostic, and therapeutic biomarkers. Moreover, changes in microRNA levels were found to differentiate patients with non-proliferative and proliferative DR. In addition, different proteins and metabolites concentrations were noticed in diabetes macular oedema and tractional retinal detachment—serious complications of DR. Specific molecular signatures, such as miR-146a and miR-27 dysregulation, changes in levels of HLA-DRA, AGER, and HSPA1A proteins, and alterations in tyrosine, alanine, 2,4-dihydroxybutanoic acid, ribonic acid, myoinositol, ribitol, 3,4-dihydroxybutanoic acid, valine, glycine, and 2-hydroxyisovaleric acid, were found to be characteristic for all microvascular complications of diabetes. In the future, more studies in multi-omics are expected to help improve precision medicine approaches to treating diabetes, allowing for personalized prediction, prevention, and treatment of microvascular complications. Full article

Diabetic retinopathy (DR), a leading cause of vision loss in diabetic patients, involves complex pathological mechanisms including neurodegeneration, microvascular damage, inflammation, and oxidative stress. Recent studies have identified ferroptosis—a ferrodependent cell death mechanism—as playing a pivotal role in DR development. Existing evidence indicates that oxidative stress and mitochondrial dysfunction induced by hyperglycemia may contribute to retinal damage through the ferroptosis pathway in DR. Ferroptosis inhibitors such as Ferostatin-1 have demonstrated protective effects against DR in animal models. The core mechanisms of ferroptosis involve iron homeostasis imbalance and lipid peroxidation, with key regulatory pathways including GPX4-dependent and non-dependent mechanisms (such as FSP1-CoQ10). Within the signaling network, Nrf2 inhibits ferroptosis, p53 promotes it, while Hippo/YAP functions are environment-dependent. Non-coding RNAs and epigenetic modifications (e.g., DNA methylation and histone modifications) also participate in regulation. In DR, iron overload, GPX4 dysfunction, and p53 upregulation collectively induce ferroptosis in various types of retinal cells, making these pathways potential therapeutic targets. This review not only elaborates the role of iron metabolism imbalance and ferroptosis pathway in the occurrence and development of DR but also summarizes the new therapeutic approaches of DR targeting ferroptosis pathway. Investigating the relationship between ferroptosis and DR not only helps unravel its core pathophysiological mechanisms but also provides theoretical foundations for developing novel therapeutic approaches. Full article

Background: Laser Speckle Flowgraphy (LSFG) is a non-invasive imaging technology that quantitatively evaluates retinal and choroidal blood flow by analyzing speckle patterns generated by laser light scattering. This systematic review summarizes the application of LSFG in two major degenerative retinal diseases: age-related macular degeneration (AMD) and diabetic retinopathy (DR). Methods: A comprehensive literature search (2010–2025) was conducted in PubMed, Cochrane Library and EMBASE according to PRISMA guidelines. Twenty-three studies including a total of 974 eyes (191 AMD, 783 DR) were analyzed. Results: In AMD, LSFG detected baseline reductions in choroidal and retinal perfusion in non-exudative disease, often extending beyond atrophic regions. Anti-VEGF injections produced acute reductions in MBR, particularly with brolucizumab, with partial recovery over time; drug-specific differences suggest a potential impact on geographic atrophy progression. In DR, LSFG revealed early microvascular dysfunction even in asymptomatic eyes. Retinal and choroidal MBR and blowout score correlated with HbA1c, DR severity, and inflammatory mediators. Intravitreal anti-VEGF therapy consistently reduced retinal and choroidal MBR and RFV, while conventional panretinal photocoagulation decreased choroidal flow and vascular caliber more robustly than patterned laser, reflecting oxygenation-driven VEGF modulation. Low baseline MBR predicted higher central macular thickness and reduced therapeutic response in diabetic macular edema. Conclusions: LSFG provides reproducible, rapid, and non-invasive quantitative insights into ocular hemodynamics across degenerative retinal diseases. Its integration into multimodal imaging may facilitate early diagnosis, support personalized management, and assist in the prognostic assessment of retinal and choroidal vascular disorders. Full article

Objective: Diabetic retinopathy (DR) is a leading cause of blindness, and understanding its progression from non-proliferative (NPDR) to sight-threatening proliferative diabetic retinopathy (PDR) is crucial. Systemic lactate dehydrogenase (LDH) has been implicated in various disease processes. We investigated the association between systemic LDH levels at the time of NPDR diagnosis and the 1-year risk of progression to PDR and its complications. Methods: We conducted a retrospective, propensity-matched cohort study using the TriNetX US Collaborative Network. Patients with type 2 diabetes and a new diagnosis of NPDR were stratified into three groups based on a single LDH measurement taken within 6 months of the index date: low (<200 U/L), moderate (201–280 U/L), and high (≥281 U/L). Two separate analyses were performed: one comparing the low-LDH group to the moderate-LDH group, and another comparing the low-LDH group to the high-LDH group. The primary outcomes were the 1-year absolute risks and risk ratios (relative risk, RR) for PDR, tractional retinal detachment (TRD), and vitreous hemorrhage (VH). Results: Comparing the low-LDH cohort to the moderate-LDH cohort, the moderate-LDH group had a higher 1-year absolute risk of PDR (3.93% vs. 2.96%), TRD (1.35% vs. 0.99%), and VH (4.38% vs. 3.51%). Comparing the low-LDH group to the high-LDH group, the high-LDH cohort showed an increased risk for PDR (3.66% vs. 3.00%), TRD (1.27% vs. 0.96%), and VH (1.27% vs. 0.96%). Conclusions: Our findings demonstrate a consistent, dose-dependent relationship between higher systemic LDH levels and an increased risk of progression to PDR and its complications. Full article

Background and Objectives: To provide, for the first time, statistical data on risk factors for proliferative diabetic retinopathy (PDR) in young-onset type 1 diabetes (T1D) in Croatia, and to develop a predictive health economic model to evaluate the clinical and financial impact of implementing a national diabetic retinopathy (DR) screening. Materials and Methods: A cross-sectional study at University Hospital Split (June 2020–June 2022) analyzed 58 suitable T1D patients out of 562 screened. Patients were classified based on detailed fundus exams and photos into PDR and non-PDR groups. Clinical, demographic, and laboratory data were collected and analyzed using logistic regression. A health economic model was established to project the number of preventable PDR cases and the potential cost savings under various screening scenarios. Results: PDR was found in 47% of patients. Its presence was statistically significantly linked to longer diabetes duration, poor glycemic control, onset before 18 years of age, and irregular eye exams. Irregular ophthalmologic examinations increased the odds of PDR by nearly 30-fold. Health-economic modeling for 10,000 young-onset T1D patients showed that annual screening with 60% uptake could prevent about 1973 PDR cases and save €14.2 million annually. Screening remained cost-effective even with moderate uptake or less frequent intervals. Conclusions: Strict glycemic control and regular eye examinations are critical for preventing PDR in young T1D, and establishing a national screening program would be cost-effective, especially in resource-limited settings like Croatia, where providing appropriate, timely treatment may be challenging. Full article

Background: Diabetic retinopathy (DR) is a leading cause of vision-related disability worldwide. Evidence on how clinical and sociodemographic factors jointly shape vision-related quality of life (VRQoL) in Eastern European settings remains limited. Methods: We conducted a cross-sectional study of 151 adults with ophthalmologically confirmed DR attending a tertiary ophthalmology clinic in Northeastern Bulgaria (June 2023–February 2025). Best-corrected visual acuity (BCVA; decimal, better-seeing eye), glycated hemoglobin (HbA1c; ordinal categories), duration of diabetes, age, sex, DR subtype, and education were recorded. VRQoL was assessed using the NEI VFQ-25 questionnaire. Non-parametric tests were applied as appropriate; multiple linear regression identified independent predictors of NEI VFQ-25 composite scores. Results: Median age was 62 years (IQR 12.5); 53.0% were female. NEI VFQ-25 median was 77.2 (IQR 37.8). BCVA correlated positively with VRQoL (Spearman’s ρ = 0.455, p < 0.001). VRQoL differed by educational level (Kruskal–Wallis χ² = 37.3, p < 0.001, ε² = 0.249), but not by sex (Mann–Whitney U = 2740, p = 0.711); a trend was observed across DR subtypes (H = 5.386, p = 0.067). The multivariable model was significant (F(7, 132) = 10.64, p < 0.001; adjusted R² = 0.336). Higher VRQoL was independently associated with better BCVA (B = 35.38, 95% CI 25.81–44.95, p < 0.001), higher educational attainment (B = −10.15, 95% CI from −13.92 to −6.38, p < 0.001; coded such that lower education predicts lower scores), and DR subtype (B = 6.63, 95% CI 1.91–11.36, p = 0.007). Age, sex, HbA1c, and diabetes duration were not significant. Conclusions: In this Bulgarian cohort, functional vision (BCVA), education, and DR subtype are the principal determinants of VRQoL, highlighting the need for patient-centered strategies that integrate clinical and social factors. Full article

Background/Objectives: Evaluate outcomes and treatment patterns with 2 mg intravitreal aflibercept injection among patients who completed the phase 3 PANORAMA trial and enrolled in the VOYAGE (ClinicalTrials.gov identifier, NCT04708145; 12 January 2021) long-term extension study. Methods: During VOYAGE, patients were evaluated every 16 weeks and treated with 2 mg intravitreal aflibercept injection as needed depending on ophthalmoscopic examination findings. Those with no history of panretinal photocoagulation (PRP) received aflibercept if their clinician-determined diabetic retinopathy severity scale (DRSS) level was ≥47, corresponding to moderately severe non-proliferative diabetic retinopathy (NPDR). Patients with a history of PRP received aflibercept if active neovascularization was present. New or worsening diabetic retinopathy (DR) severity prompted more frequent treatment. Results: 320 patients (1 eye per patient) from 87 sites completed the PANORAMA trial. Of these, 41 patients (13% of PANORAMA completers) from 14 sites (16%) enrolled in VOYAGE after a mean interim period of 33.7 months, and 35 patients (85%) completed study visits through 1 year. At year 1 in VOYAGE, the mean number of anti-vascular endothelial growth factor (VEGF) injections increased from 1.1 per year during the interim period to 3.4 per year and was associated with stabilization or improvement in DRSS level in 81% (26/32) of patients. Mean best-corrected visual acuity (BCVA) remained relatively stable, and mean central subfield thickness (CST) improved by 24.4 µm to 269.5 μm through year 1 of VOYAGE. There were no unexpected safety events. Conclusions: Following a mean of 3 years of routine clinical care with associated declines in DRSS level, CST, and BCVA, stabilization of DRSS level and BCVA with reductions in CST was achieved through year 1 of the VOYAGE extension study, with a concurrent increase in aflibercept dosing frequency. Full article

Background and Objectives: Diabetic retinopathy (DR) is a major microvascular complication of diabetes mellitus (DM) and a leading cause of blindness among working-age adults. Advanced glycation end products (AGEs) contribute to DR pathogenesis through oxidative stress and inflammation. This study aimed to evaluate the association between AGE levels and DR severity in Lithuanian patients with type 1 (T1D) and type 2 (T2D) diabetes. Materials and Methods: An observational cross-sectional study enrolled 152 patients with T1D and T2D from a tertiary university hospital. AGE values were measured with a non-invasive AGE Reader device. DR severity was assessed through ophthalmological examination. Results: The overall DR prevalence was 54.6%. AGE values increased with the severity of DR, though not significantly between the DR groups (p = 0.386). The mean AGE value was higher in T2D compared with the T1D group (2.429 vs. 2.217, p = 0.011). In T1D, AGE values were higher in the advanced DR group compared with the non-DR patients (2.445 vs. 1.905, p = 0.007), whereas no significant difference was found in the T2D subgroups. In T1D, AGEs correlated positively with both DM duration and higher HbA1c (p < 0.05). Patients with AGE values > 2.35 in T1D and 2.15 in T2D had an increased likelihood of having advanced DR. Patients in the non-DR group had significantly lower AGE values compared to those in the advanced DR group (OR = 0.118, 95% CI 0.025–0.566, p = 0.008). Conclusions: AGEs measured using skin autofluorescence may indicate DR severity in T1D but not in T2D and might not be a suitable standalone risk predictor. DM duration remains a significant determinant of advanced DR, highlighting the importance of early monitoring and timely intervention. Full article

This study analyzes retinal fundus images to distinguish healthy retinas from those affected by diabetic retinopathy (DR) and glaucoma using a dual-framework approach: vascular texture analysis and global color distribution analysis. The texture-based approach involved segmenting the retinal vasculature and extracting eight Haralick texture features from the Gray-Level Co-occurrence Matrix. Significant differences in features such as energy, contrast, correlation, and entropy were found between healthy and pathological retinas. Pathological retinas exhibited lower textural complexity and higher uniformity, which correlates with vascular thinning and structural changes observed in DR and glaucoma. In parallel, the global color distribution of the full fundus area was analyzed without segmentation. RGB intensity histograms were calculated for each channel and averaged across groups. Statistical tests revealed significant differences, particularly in the green and blue channels. The Mahalanobis distance quantified the separability of the groups per channel. These results indicate that pathological changes in retinal tissue can also lead to detectable chromatic shifts in the fundus. The findings underscore the potential of both vascular texture and color features as non-invasive biomarkers for early retinal disease detection and classification. Full article

Background/Objectives: Diabetic retinopathy is a leading cause of blindness in diabetic patients, with disease susceptibility influenced by both genetic and environmental factors. This study aimed to identify novel genetic variants associated with DR and evaluate interactions between polygenic risk scores (PRS) and lifestyle factors in a Korean diabetic cohort. Methods: After excluding subjects with non-diabetic retinopathy eye diseases (n = 2519), we analyzed data from 50,361 non-diabetic controls, 4873 diabetic participants without retinopathy (DM-NR), and 165 with diabetic retinopathy (DM-DR). We conducted genome-wide association studies comparing DM-NR and DM-DR groups, performed generalized multifactor dimensionality reduction (GMDR) analysis for epistatic interactions, developed unweighted PRS models, and examined PRS–lifestyle interactions using two-way analysis of covariance. Results: DM-DR prevalence showed strong associations with metabolic syndrome and its components. Five novel genetic variants were identified: ABCA4_rs17110929, MMP2-AS1_rs2576531, FOXP1_rs557869288, MRPS33_rs1533933, and DRD2_rs4936270. A significant three-way epistatic interaction among the first three variants was discovered through GMDR analysis. High-PRS individuals (scores 5–6) showed a 49-fold higher odds ratio of DM-DR compared to low-PRS individuals (scores 0–2; p < 0.0001). MAGMA analysis revealed enrichment in pathways related to protein degradation, vascular function, and neuronal signaling, with predominant upregulation in brain tissues. Significant PRS × lifestyle interactions were identified for fruit intake, coffee consumption, alcohol intake, eating duration, and physical activity, with lifestyle factors modifying genetic risk effects (all p < 0.003). Conclusions: These findings identify novel genetic variants and epistatic interactions in DM-DR pathogenesis, supporting the use of PRS-based risk stratification for intensive monitoring and personalized lifestyle interventions. The discovery of brain tissue-enriched pathways suggests DM-DR shares mechanisms with neurodegenerative diseases, expanding therapeutic targets beyond traditional vascular approaches. Full article

Databases play a crucial role in training, validating, and comparing AI models for detecting retinal diseases, as well as in clinical research, technology development, and healthcare professional training. Diabetic retinopathy (DR), a common diabetes complication, is a leading cause of vision impairment and blindness worldwide. Early detection and management are essential to prevent irreversible vision loss. Fundus photography, known for being economical and non-contact, is a widely applicable gold standard method that offers a convenient way to diagnose and grade DR. This paper presents a comprehensive review of 22 open-source fundus retinal image databases commonly used in DR research, highlighting their main characteristics and key features. Most of these datasets were released between 2000 and 2022. These databases are analyzed through an in-depth examination of their images, enabling objective comparison using color space distances and Principal Component Analysis (PCA) based on 16 key statistical features. Finally, this review aims to support informed decision-making for researchers and practitioners involved in DR diagnosis and management, ultimately improving patient outcomes. Full article

The focus in this study was to investigate the proteolytic functions of matrix metalloproteinases (MMPs) and a disintegrin and metalloprotease (ADAM) proteins in the progression of diabetic retinopathy (DR) and to evaluate their potential as therapeutic targets for eye diseases. This study involved three groups: non-proliferative diabetic retinopathy patients (NPDR) (n = 27), proliferative diabetic retinopathy patients (PDR) (n = 32), and a control group of 30 healthy individuals. Serum levels of ADAM9 and MMP9 were measured across these groups for comparative analysis. Serum ADAM9 levels were significantly lower in the NPDR and PDR groups than in the control group (p = 0.031, p < 0.001). Although ADAM9 levels were lower in the PDR group than in the NPDR group, this difference was not significant (p = 0.142). Serum MMP9 levels in the PDR group were significantly lower than those in both the control and NPDR groups (p = 0.039, p = 0.013). The findings of this study indicate that ADAM9, MMP9, and left-eye ocular parameters may have potential value in the assessment of DR. The notable variation in the MMP9 marker in the proliferative stage, as opposed to its stability in the non-proliferative stage, suggests a distinct role in retinopathy staging. This underscores the specific importance of MMP9 in the proliferative stage. Full article

Ocular disease (OD) represents a complex medical condition affecting humans. OD diagnosis is a challenging process in the current medical system, and blindness may occur if the disease is not detected at its initial phase. Recent studies showed significant outcomes in the identification of OD using deep learning (DL) models. Thus, this work aims to develop a multi-classification DL-based model for the classification of seven ODs, including normal (NOR), age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma (GLU), maculopathy (MAC), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR), using color fundus images (CFIs). This work proposes a custom model named the ocular disease detection model (ODDM) based on a CNN. The proposed ODDM is trained and tested on a publicly available ocular disease dataset (ODD). Additionally, the SMOTE Tomek (SM-TOM) approach is also used to handle the imbalanced distribution of the OD images in the ODD. The performance of the ODDM is compared with seven baseline models, including DenseNet-201 (R₁), EfficientNet-B0 (R₂), Inception-V3 (R₃), MobileNet (R₄), Vgg-16 (R₅), Vgg-19 (R₆), and ResNet-50 (R₇). The proposed ODDM obtained a 98.94% AUC, along with 97.19% accuracy, a recall of 88.74%, a precision of 95.23%, and an F1-score of 88.31% in classifying the seven different types of OD. Furthermore, ANOVA and Tukey HSD (Honestly Significant Difference) post hoc tests are also applied to represent the statistical significance of the proposed ODDM. Thus, this study concludes that the results of the proposed ODDM are superior to those of baseline models and state-of-the-art models. Full article

Objective: Retinal capillary dropout, characterized by acellular capillaries or “ghost vessels,” is an early pathological sign of diabetic retinopathy (DR) that remains undetectable through standard clinical imaging techniques until visible morphological changes, such as microaneurysms or hemorrhages, occur. This study aims to develop a non-destructive artificial intelligence (AI)-based method using fluorescein angiography (FA) images to detect early-stage, silent retinal capillary dropout. Methods: We utilized 94 FA images and corresponding destructive retinal capillary density measurements obtained through retinal trypsin digestion from 51 Nile rats. Early capillary dropout was defined as having an acellular capillary density of ≥18 counts per mm². A DenseNet based deep learning model was trained to classify images into early capillary dropout or normal. A Bayesian framework incorporating diabetes duration was used to enhance model predictions. RNA sequencing was conducted on retinal vasculature to identify molecular markers associated with capillary early dropout. Results: The AI-based FA imaging model demonstrated an accuracy of 80.85%, sensitivity of 84.21%, specificity of 75.68%, and an AUC of 0.86. Integration of diabetes duration into a Bayesian predictive framework further improved the model’s performance (AUC = 0.90). Transcriptomic analysis identified 43 genes significantly upregulated in retinal tissues preceding capillary dropout. Notably, inflammatory markers such as Bcl2a1, Birc5, and Il20rb were among these genes, indicating that inflammation might play a critical role in early DR pathogenesis. Conclusions: This study demonstrates that AI-enhanced FA imaging can predict silent retinal capillary dropout before conventional clinical signs of DR emerge. Combining AI predictions with diabetes duration data significantly improves diagnostic performance. The identified gene markers further highlight inflammation as a potential driver in early DR, offering novel insights and potential therapeutic targets for preventing DR progression. Full article

Background/Objectives: To investigate the incidence of new-onset diabetic retinopathy (DR) in individuals with pre-existing type 2 diabetes (T2D) up to 3 years post SARS-CoV-2 infection. Methods: This retrospective study consisted of 5151 COVID-19 and 5151 propensity-matched non-COVID-19 patients with T2D in the Montefiore Health System between 1 March 2020 and 17 January 2023. The primary outcome was new-onset DR at least 2 months after the index date up to 17 January 2023. Matching for index date between groups was also used to ensure the same follow-up duration. Hazard ratios (HRs) were computed, adjusted for competing risks. Results: T2D patients with COVID-19 had a higher cumulative incidence of DR than T2D patients. The unadjusted HR for COVID-19 status for developing new DR was 2.44 [1.60, 3.73], p < 0.001. The adjusted HR was 1.70 [1.08, 2.70], p < 0.05, and the adjusted HR for prior insulin use was 3.28 [2.10, 5.12], p < 0.001. Sex, ethnicity, and major comorbidities had no significant association with outcome. Conclusions: T2D patients who contracted COVID-19 exhibited a significantly higher risk of developing DR within three years post infection compared to propensity-matched controls. The increased incidence was primarily driven by greater pre-existing insulin usage and SARS-CoV-2 infection in the COVID-19 positive cohort. Full article