Hyperreflective choroidal foci in diabetic eyes with and without macular edema: Novel insights on diabetic choroidopathy.

Histopathologic studies of diabetic choroid suggest that diabetic choroidopathy is a key aspect secondary to diabetes. Recently, hyperreflective choroidal foci (HCF) have been introduced as novel optical coherence tomography (OCT) parameter. The aim of this study was to identify and quantify HCF in diabetic subjects with retinopathy, with or without diabetic macular edema (DME). Eighty-five diabetic subjects with different degrees of DR were enrolled: 37 without DME and 48 with DME. All subjects underwent full ophthalmologic examination including spectral domain optical coherence tomography (OCT). OCT images were analyzed to quantify and localize HCF. Each image was analyzed by two independent, masked examiners. OCT images showed that all subjects (100%) had HCF in the different layers of the choroid. The number of HCF was significantly higher in diabetics with DME versus those without DME (p < 0.0001). HCF showed variable size, shape and location inside the choroid. They were mainly located in choriocapillaris and Sattler's layer, on the edges of blood vessels. The intraobserver and interobserver agreement was almost perfect (ICC >0.9). This study suggests that hyperreflective foci in the choroid of subjects with DR may be accurately identified with structural OCT. Their number significantly increases with the progression of DME. These HCF may represent, as in the retina, a sign of infiltration of inflammatory cells (mainly migrating microglia) into the choroid, according to the hypothesis raised by Jerry Lutty. HCF may confirm in vivo the histopathologic findings suggesting that diabetic choroidopathy may be primarily a neuroinflammatory disorder.

Type 2 diabetes: is obesity for diabetic retinopathy good or bad? A cross-sectional study.

BACKGROUND: The relationship between obesity and diabetic retinopathy (DR) remains controversial, and the relationship between sarcopenic obesity and DR is still unclear. The purpose of this study is to investigate the relationship between obesity, sarcopenic obesity, and DR in patients with type 2 diabetes mellitus (T2DM). METHODS: A cross-sectional study was conducted on patients with T2DM. Obesity was assessed by body mass index (BMI), fat mass index (FMI), android fat mass, gynoid fat mass, and visceral adipose tissue (VAT) mass. Sarcopenia was defined according to the criteria of Consensus of the Asian Working Group for Sarcopenia (AWGS 2019). Sarcopenic obesity was defined as the coexistence of sarcopenia and obesity. The association between obesity, sarcopenic obesity, and DR was examined using univariable and multivariable logistic regression models. RESULTS: A total of 367 patients with T2DM (mean age 58.3 years; 57.6% male) were involved in this study. The prevalence of DR was 28.3%. In total patients, significant adverse relationships between obesity and DR were observed when obesity was assessed by BMI (adjusted odds ratio [aOR] 0.54, 95% confidence interval [CI] 0.31 to 0.96, p = 0.036), FMI (aOR 0.49, 95% CI 0.28 to 0.85, p = 0.012), android fat mass (aOR 0.51, 95% CI 0.29 to 0.89, p = 0.019), gynoid fat mass (aOR 0.52, 95% CI 0.30 to 0.91, p = 0.021) or VAT mass (aOR 0.45, 95% CI 0.25 to 0.78, p = 0.005). In patients with T2DM and obesity, the prevalence of sarcopenic obesity was 14.8% (n = 23) when obesity was assessed by BMI, 30.6% (n = 56) when assessed by FMI, 27.9% (n = 51) when assessed by android fat mass, 28.4% (n = 52) when assessed by gynoid fat mass, and 30.6% (n = 56) when assessed by VAT mass. Sarcopenic obesity was associated with DR when obesity was assessed by BMI (aOR 2.61, 95% CI 1.07 to 6.37, p = 0.035), android fat mass (aOR 3.27, 95% CI 1.37 to 7.80, p = 0.007), or VAT mass (aOR 2.50, 95% CI 1.06 to 5.92, p = 0.037). CONCLUSIONS: Patients with T2DM showed a substantial inverse relationship between DR and obesity, and sarcopenic obesity was considerably favorably associated with DR. Detection of sarcopenia in patients with T2DM, especially in obese T2DM, is essential to guide clinical intervention in DR.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy.

Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is relatively underexplored. In this study, using retinal photographs, we evaluated the comparative performances of ViTs and CNNs on detection of referable DR. Design: Retrospective study. Participants: A total of 48 269 retinal images from the open-source Kaggle DR detection dataset, the Messidor-1 dataset and the Singapore Epidemiology of Eye Diseases (SEED) study were included. Methods: Using 41 614 retinal photographs from the Kaggle dataset, we developed 5 CNN (Visual Geometry Group 19, ResNet50, InceptionV3, DenseNet201, and EfficientNetV2S) and 4 ViTs models (VAN_small, CrossViT_small, ViT_small, and Hierarchical Vision transformer using Shifted Windows [SWIN]_tiny) for the detection of referable DR. We defined the presence of referable DR as eyes with moderate or worse DR. The comparative performance of all 9 models was evaluated in the Kaggle internal test dataset (with 1045 study eyes), and in 2 external test sets, the SEED study (5455 study eyes) and the Messidor-1 (1200 study eyes). Main Outcome Measures: Area under operating characteristics curve (AUC), specificity, and sensitivity. Results: Among all models, the SWIN transformer displayed the highest AUC of 95.7% on the internal test set, significantly outperforming the CNN models (all P < 0.001). The same observation was confirmed in the external test sets, with the SWIN transformer achieving AUC of 97.3% in SEED and 96.3% in Messidor-1. When specificity level was fixed at 80% for the internal test, the SWIN transformer achieved the highest sensitivity of 94.4%, significantly better than all the CNN models (sensitivity levels ranging between 76.3% and 83.8%; all P < 0.001). This trend was also consistently observed in both external test sets. Conclusions: Our findings demonstrate that ViTs provide superior performance over CNNs in detecting referable DR from retinal photographs. These results point to the potential of utilizing ViT models to improve and optimize retinal photo-based deep learning for referable DR detection. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Clinical management of eye diseases: carotenoids and their nanoformulations as choice of therapeutics.

Eye diseases, such as age-related macular degeneration (AMD) and diabetic retinopathy (DR), impose a substantial health cost on a worldwide scale. Carotenoids have emerged as intriguing candidates for pharmacological treatment of various disorders. Their therapeutic effectiveness, however, is hindered by poor solubility and vulnerability to degradation. Nanocarriers, such as nanoparticles, liposomes, and micelles, provide a transformational way to overcome these limits. This review explores the pharmacological potential of carotenoids, namely lutein, zeaxanthin, and astaxanthin, to treat several ocular disorders. The main emphasis is on their anti-inflammatory and antioxidant actions, which help to counteract inflammation and oxidative stress, crucial factors in the development of AMD and DR. The review evaluates the significant benefits of nano-formulated carotenoids, such as improved bioavailability, higher cellular absorption, precise administration to particular ocular tissues, and greater biostability, which make them superior to conventional carotenoids. Some clinical studies on the beneficial properties of carotenoids in eye diseases are discussed. Furthermore, safety and regulatory concerns are also taken into account. Ultimately, carotenoids, especially when created in their nano form, have significant potential for safeguarding eyesight and enhancing the overall well-being of several individuals afflicted with vision-endangering eye diseases.

The STAT1-SLC31A1 axis: Potential regulation of cuproptosis in diabetic retinopathy.

BACKGROUND: By identifying molecular biological markers linked to cuproptosis in diabetic retinopathy (DR), new pathobiological pathways and more accessible diagnostic markers can be developed. METHODS: The datasets related to DR were acquired from the Gene Expression Omnibus database, while genes associated with cuproptosis were sourced from previously published compilations. Consensus clustering was conducted to delineate distinct DR subclasses. Feature genes were identified utilizing weighted correlation network analysis (WGCNA). Additionally, two machine-learning algorithms were employed to refine the selection of feature genes. Finally, we conducted preliminary validation experiments to ascertain the involvement of cuproptosis in DR development and the transcriptional regulation of critical genes using both the streptozotocin-induced diabetic mouse model and the high glucose-induced BV2 model. RESULTS: In the STZ-induced diabetic mouse retinas, a decrease in the expression of cuproptosis signature proteins (FDX1, DLAT, and NDUFS8) suggested the occurrence of cuproptosis in DR. Subsequently, the expression of eight cuproptosis differential genes was validated through the STZ-induced diabetes and oxygen-induced retinopathy (OIR) models, with the key gene SLC31A1 showing upregulation in both models and dataset species. Further analyses, including weighted gene co-expression network analysis, GSVA, and immune infiltration analysis, indicated a close correlation between cuproptosis and microglia function. Additionally, validation in an in vitro model of microglia indicated the occurrence of cuproptosis in microglia under high glucose conditions, alongside abnormal expression of STAT1 with SLC31A1. CONCLUSION: Our findings suggest that STAT1/SLC31A1 may pave the way for a deeper comprehension of the mechanistic basis of DR and offer potential therapeutic avenues.

Visual motion sensitivity as an indicator of diabetic retinopathy in type 2 diabetes mellitus.

Objectives: This current study is based on a set of visual motion sensitivity tests, investigating the correlation between visual motion sensitivity and diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM), thereby furnishing a scientific rationale for preventing and controlling DR. Methods: This research was conducted by a combination of questionnaire collection and on-site investigation that involved 542 T2DM recruited from a community. The visual motion sensitivity determined the visual motion perception of the participants across three spatial frequencies (low, medium, and high) for both the first- and second-order contrast. The logistic regression model was adopted to investigate the relationship between visual motion sensitivity and DR prevalence. Besides, the Pearson correlation analysis was used to analyze the factors influencing visual motion sensitivity and restricted cubic spline (RCS) functions to assess the dose-response relationship between visual motion sensitivity and glycated hemoglobin. Results: Among 542 subjects, there are 162 cases of DR, with a prevalence rate of 29.89%. After adjusting factors of age, gender, glycated hemoglobin, duration of diabetes, BMI, and hypertension, we found that the decline in first- and second-order high spatial frequency sensitivity increased the risk for DR [odds ratio (OR): 1.519 (1.065, 2.168), 1.249 (1.068, 1.460)]. The decline in perceptual ability of second-order low, medium, and high spatial frequency sensitivity is a risk factor for moderate to severe DR [OR: 1.556 (1.116, 2.168), 1.388 (1.066, 1.806), 1.476 (1.139, 1.912)]. The first-order and the second-order high spatial frequency sensitivity are significantly positively correlated with glycated hemoglobin (r = 0.105, p = 0.015 and r = 0.119, p = 0.005, respectively). Conclusion: Visual motion sensitivity especially for the second-order high spatial frequency stimuli emerges as a significant predictor of DR in T2DM, offering a sensitive diagnostic tool for early detection.

Key genes and regulatory networks for diabetic retinopathy based on hypoxia-related genes: a bioinformatics analysis.

AIM: To prevent neovascularization in diabetic retinopathy (DR) patients and partially control disease progression. METHODS: Hypoxia-related differentially expressed genes (DEGs) were identified from the GSE60436 and GSE102485 datasets, followed by gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Potential candidate drugs were screened using the CMap database. Subsequently, a protein-protein interaction (PPI) network was constructed to identify hypoxia-related hub genes. A nomogram was generated using the rms R package, and the correlation of hub genes was analyzed using the Hmisc R package. The clinical significance of hub genes was validated by comparing their expression levels between disease and normal groups and constructing receiver operating characteristic curve (ROC) curves. Finally, a hypoxia-related miRNA-transcription factor (TF)-Hub gene network was constructed using the NetworkAnalyst online tool. RESULTS: Totally 48 hypoxia-related DEGs and screened 10 potential candidate drugs with interaction relationships to upregulated hypoxia-related genes were identified, such as ruxolitinib, meprylcaine, and deferiprone. In addition, 8 hub genes were also identified: glycogen phosphorylase muscle associated (PYGM), glyceraldehyde-3-phosphate dehydrogenase spermatogenic (GAPDHS), enolase 3 (ENO3), aldolase fructose-bisphosphate C (ALDOC), phosphoglucomutase 2 (PGM2), enolase 2 (ENO2), phosphoglycerate mutase 2 (PGAM2), and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3). Based on hub gene predictions, the miRNA-TF-Hub gene network revealed complex interactions between 163 miRNAs, 77 TFs, and hub genes. The results of ROC showed that the except for GAPDHS, the area under curve (AUC) values of the other 7 hub genes were greater than 0.758, indicating their favorable diagnostic performance. CONCLUSION: PYGM, GAPDHS, ENO3, ALDOC, PGM2, ENO2, PGAM2, and PFKFB3 are hub genes in DR, and hypoxia-related hub genes exhibited favorable diagnostic performance.

Differential distribution of fibrovascular proliferative membranes in 25-gauge vitrectomy for proliferative diabetic retinopathy.

AIM: To analyze the distribution of fibrovascular proliferative membranes (FVPMs) in proliferative diabetic retinopathy (PDR) patients that treated with pars plana vitrectomy (PPV), and to evaluate the outcomes separately. METHODS: This was a retrospective and cross-sectional study. Consecutive 25-gauge (25-G) PPV cases operated for PDR from May 2018 to April 2020. According to the FVPMs images outlined after operations, subjects were assigned into three groups: arcade type group, juxtapapillary type group, and central type group. All patients were followed up for over one year. General characteristics, operation-related variables, postoperative parameters and complications were recorded. RESULTS: Among 103 eyes recruited, the FVPMs distribution of nasotemporal and inferiosuperioral was significantly different (both P<0.01), with 95 (92.23%) FVPMs located in the nasal quadrants, and 74 (71.84%) in the inferior. The eyes with a central FVPM required the longest operation time, with silicon oil used in most patients, generally combined with tractional retinal detachment (RD) and rhegmatogenous RD, the worst postoperative best-corrected visual acuity (BCVA) and the highest rates of recurrent RD (all P<0.05). FVPM type, age of onset diabetes mellitus, preoperative BCVA, and combined with tractional RD and rhegmatogenous RD were significantly associated with BCVA improvement (all P<0.05). Compared with the central type group, the arcade type group had higher rates of BCVA improvement. CONCLUSION: FVPMs are more commonly found in the nasal and inferior mid-peripheral retina in addition to the area of arcade vessels. Performing 25-G PPV for treating PDR eyes with central FVPM have relatively worse prognosis.

Knockdown of fibrillin-1 suppresses retina-blood barrier dysfunction by inhibiting vascular endothelial apoptosis under diabetic conditions.

AIM: To investigate the effects of fibrillin-1 (FBN1) deletion on the integrity of retina-blood barrier function and the apoptosis of vascular endothelial cells under diabetic conditions. METHODS: Streptozotocin (STZ)-induced diabetic mice were used to simulate the diabetic conditions of diabetic retinopathy (DR) patients, and FBN1 expression was detected in retinas from STZ-diabetic mice and controls. In the Gene Expression Omnibus (GEO) database, the GSE60436 dataset was selected to analyze FBN1 expressions in fibrovascular membranes from DR patients. Using lentivirus to knock down FBN1 levels, vascular leakage and endothelial barrier integrity were detected by Evans blue vascular permeability assay, fluorescein fundus angiography (FFA) and immunofluorescence labeled with tight junction marker in vivo. High glucose-induced monkey retinal vascular endothelial cells (RF/6A) were used to investigate effects of FBN1 on the cells in vitro. The vascular endothelial barrier integrity and apoptosis were detected by trans-endothelial electrical resistance (TEER) assay and flow cytometry, respectively. RESULTS: FBN1 mRNA expression was increased in retinas of STZ-induced diabetic mice and fibrovascular membranes of DR patients (GSE60436 datasets) using RNA-seq approach. Besides, knocking down of FBN1 by lentivirus intravitreal injection significantly inhibited the vascular leakage compared to STZ-DR group by Evans blue vascular permeability assay and FFA detection. Expressions of tight junction markers in STZ-DR mouse retinas were lower than those in the control group, and knocking down of FBN1 increased the tight junction levels. In vitro, 30 mmol/L glucose could significantly inhibit viability of RF/6A cells, and FBN1 mRNA expression was increased under 30 mmol/L glucose stimulation. Down-regulation of FBN1 reduced high glucose (HG)-stimulated retinal microvascular endothelial cell permeability, increased TEER, and inhibited RF/6A cell apoptosis in vitro. CONCLUSION: The expression level of FBN1 increases in retinas and vascular endothelial cells under diabetic conditions. Down-regulation of FBN1 protects the retina of early diabetic rats from retina-blood barrier damage, reduce vascular leakage, cell apoptosis, and maintain vascular endothelial cell barrier function.

High-normal serum bilirubin is a useful indicator to assess the risk of diabetic retinopathy in type 2 diabetes: A real-world study.

Background: To investigate the association of serum bilirubin within normal range, especially unconjugated bilirubin (UCB), with diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). Methods: In this cross-sectional, real-world study, 7617 T2DM patients were stratified into quartiles based on serum UCB levels. DR was determined by digital fundus photography and further classified into non-proliferative diabetic retinopathy (NPDR) and PDR. The associations of serum bilirubin levels and UCB quartiles with DR were investigated by logistic regression analysis. Results: After controlling for age, sex, and diabetes duration, the DR prevalence was significantly decreased across the serum UCB quartiles (40.4 %, 33.4 %, 29.7 %, 26.6 % for each quartile, respectively, p < 0.001 for trend). The subjects with DR had lower serum total bilirubin (TB) and UCB, rather than conjugated bilirubin (CB), compared with those without DR (p = 0.003 for TB, p < 0.001 for UCB, and p = 0.528 for CB, respectively), while all three types of serum bilirubin in the subjects with PDR were obviously lower than those with NPDR (p = 0.006 for TB, and p < 0.001 for UCB and CB, respectively). After adjustment for confounding factors, logistic regression demonstrated negative associations of serum TB and UCB levels, rather than CB, with the presence of DR (OR: 0.844, 95%CI: 0.774-0.920, p < 0.001 for TB; OR: 0.828, 95%CI: 0.763-0.899, p < 0.001 for UCB; and OR: 0.984, 95%CI: 0.900-1.074, p = 0.713 for CB, respectively). Additionally, a fully-adjusted analysis revealed a negative correlation between UCB quartiles and DR (p < 0.001). Conclusion: High-normal serum TB and UCB were closely associated with the decreased odds of DR, while all types of serum bilirubin were negatively correlated with the severity of DR in T2DM patients. Serum bilirubin may be used as a potential indicator to assess the risk and severity of DR in T2DM.

Association between gut microbiota and diabetic microvascular complications: a two-sample Mendelian randomization study.

Background: Gut microbiota (GM) homeostasis in the human body is closely associated with health, which can be used as a regulator for preventing the onset and progression of disease. Diabetic microvascular complications bring about not only a huge economic burden to society, but also miserable mental and physical pain. Thus, alteration of the GM may be a method to delay diabetic microvascular complications. Objective: A two-sample Mendelian randomization (MR) analysis was conducted to reveal the causal inference between GM and three core diabetic microvascular complications, namely, diabetic kidney disease (DKD), diabetic retinopathy (DR), and diabetic neuropathy (DNP). Methods: First, genome-wide association study (GWAS) summary statistics for GM from the MiBioGen consortium and three main diabetic microvascular complications acquired from the FinnGen research project were assessed. Second, a forward MR analysis was conducted to assess the causality of GM on the risk of DKD, DR, and DNP. Third, a series of sensitivity studies, such as heterogeneity tests, pleiotropy evaluations, and leave-one-out analyses, were further conducted to assess the accuracy of MR analysis. Finally, Steiger tests and reverse MR analyses were performed to appraise the possibility of reverse causation. Results: A total of 2,092 single-nucleotide polymorphisms related to 196 bacterial traits were selected as instrumental variables. This two-sample MR analysis provided strongly reasonable evidence that 28 genetically predicted abundance of specific GM that played non-negligible roles in the occurrence of DKD, DR, and DNP complications were causally associated with 23 GM, the odds ratio of which generally ranged from 0.9 to 1.1. Further sensitivity analysis indicated low heterogeneity, low pleiotropy, and high reliability of the causal estimates. Conclusion: The study raised the possibility that GM may be a potential target to prevent and delay the progression of diabetic microvascular complications. Further experiments of GM therapy on diabetic microvascular complications are warranted to clarify their effects and specific mechanisms.

Diagnostic application in streptozotocin-induced diabetic retinopathy rats: A study based on Raman spectroscopy and machine learning.

Vision impairment caused by diabetic retinopathy (DR) is often irreversible, making early-stage diagnosis imperative. Raman spectroscopy emerges as a powerful tool, capable of providing molecular fingerprints of tissues. This study employs RS to detect ex vivo retinal tissue from diabetic rats at various stages of the disease. Transmission electron microscopy was utilized to reveal the ultrastructural changes in retinal tissue. Following spectral preprocessing of the acquired data, the random forest and orthogonal partial least squares-discriminant analysis algorithms were employed for spectral data analysis. The entirety of Raman spectra and all annotated bands accurately and distinctly differentiate all animal groups, and can identify significant molecules from the spectral data. Bands at 524, 1335, 543, and 435 cm-1 were found to be associated with the preproliferative phase of DR. Bands at 1045 and 1335 cm-1 were found to be associated with early stages of DR.

Choroidal thickening induced by pioglitazone in diabetic patients.

Purpose: This study aimed to determine the changes in choroidal thickness induced by pioglitazone in diabetic patients. Methods: A total of 261 patients diagnosed with type 2 diabetes who had taken oral pioglitazone for more than 6 months were included in the study. After excluding patients who did not undergo regular eye examinations or who had ophthalmic surgery/interventions during the treatment period, a total of 40 eyes were included. The study examined the duration and dosage of pioglitazone, patient age, ocular axial length, refraction, glycated hemoglobin, systolic blood pressure, corrected visual acuity, macular thickness, choroidal thickness, and choroid vascular index. Patients were categorized into a high dose group if their pioglitazone dose was 30mg or more per day, and a low dose group if it was 15mg or less. Choroidal thickness was measured below the subfovea and a 500 µm radius nasal and temporal to that location. Results: Choroidal thickness significantly increased after 6 and 12 months of pioglitazone in all subjects (6.70µm, 13.65µm, each). When stratified by pioglitazone dosage, choroidal thickness increased at 6 and 12 months in both the high (4.48µm, 0.84µm, each) and low dose groups (6.85µm, 21.45µm, each), with a greater change observed in low dose group (p<0.05, respectively). Based on the location of choroidal thickness measurements, a significant increase in choroidal thickness was observed at 6 and 12 months of pioglitazone treatment in the subfoveal (7.00µm, 13.15µm, each) and nasal regions (6.43µm, 19.24µm, each), while a significant increase was only observed after 6 months of treatment in the temporal region (8.53µm) (p<0.05, respectively). The largest increase in choroidal thickness was observed in the nasal side. Conclusion: This study found that choroidal thickness increased in diabetic patients after taking pioglitazone. Regular eye examinations are recommended for diabetic patients who are on pioglitazone.

Serum biomarkers for predicting microvascular complications of diabetes mellitus.

INTRODUCTION: Diabetic microvascular complications such as retinopathy, nephropathy, and neuropathy are primary causes of blindness, terminal renal failure, and neuropathic disorders in type 2 diabetes mellitus patients. Identifying reliable biomarkers promptly is pivotal for early detection and intervention in these severe complications. AREAS COVERED: This review offers a thorough examination of the latest research concerning serum biomarkers for the prediction and assessment of diabetic microvascular complications. It encompasses biomarkers associated with glycation, oxidative stress, inflammation, endothelial dysfunction, basement membrane thickening, angiogenesis, and thrombosis. The review also highlights the potential of emerging biomarkers, such as microRNAs and long non-coding RNAs. EXPERT OPINION: Serum biomarkers are emerging as valuable tools for the early assessment and therapeutic guidance of diabetic microvascular complications. The biomarkers identified not only reflect the underlying pathophysiology but also align with the extent of the disease. However, further validation across diverse populations and improvement of the practicality of these biomarkers in routine clinical practice are necessary. Pursuing these objectives is essential to advance early diagnosis, risk assessment, and individualized treatment regimens for those affected by diabetes.

Clinical Profile and Ocular Morbidities in Patients with Both Diabetic Retinopathy and Uveitis.

Purpose: To describe the clinical profile and complications of diabetic retinopathy (DR) and uveitis in patients with coexisting conditions and to derive associations based on site of primary inflammation, stage of DR, and complications of each. Design: Single-center, cross-sectional observational study. Participants: Sixty-six patients with coexisting DR and uveitis. Methods: Electronic medical records of 66 such cases were evaluated. The demographic data, diabetic status, clinical characteristics, and complications of DR and uveitis on the final follow-up were recorded. Main Outcome Measures: Associations between best corrected visual acuity (BCVA), prevalence of various stages, and complications of DR among eyes with and without uveitis, and correlation between the intensity and primary sites of inflammation among eyes with proliferative and nonproliferative changes. Results: Of the 132 eyes, all had DR and 97 eyes had uveitis (35 unilateral and 31 bilateral cases). Mean age of patients was 53.4 ± 8.7 years, duration of diabetes was 10.5 ± 6.9 years, and duration of uveitis was 61.3 ± 68.8 months. Of uveitis patients, 54.6% had anterior uveitis (AU), 20.6% had intermediate, 10.3% posterior, and 14.4% panuveitis. Forty-nine point five percent of eyes had proliferative DR (PDR) changes. There was a higher proportion PDR cases among anterior (56.6%), posterior (70%), and panuveitis (64.3%), with difference in AU cases approaching statistical significance (P = 0.067). Conversely, significant (P < 0.001) intermediate uveitis cases had nonproliferative changes (80%). Final BCVA was significantly poorer in the group with uveitis (P = 0.045). The proportion of fibrovascular proliferations, tractional detachments. and iris neovascularization among proliferative retinopathy eyes with uveitis (14.6%, 18.8%, and 12.5% respectively) was higher than those without uveitis (5.3%, 10.5%, and 5.3%). Among uveitis cases, 58.5% eyes developed cataracts, 44.3% had posterior synechiae, 12.3% developed secondary glaucoma, 4.1% had epiretinal membrane, 4.1% had band-shaped keratopathy, and 1.0% developed macular neovascularization. Conclusions: Eyes with coexisting DR and uveitis have a higher prevalence of neovascular and uveitis complications along with a risk of poorer visual outcomes. Treatment should aim at limiting the duration and intensity of inflammation. Strict glycemic control is essential for inflammation control and preventing the progression of DR to more advanced stages. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The Effect of Glucagon-Like Peptide-1 Receptor Agonists on Diabetic Retinopathy at a Tertiary Care Center.

Objective: The potential association between diabetic retinopathy (DR) worsening and glucagon-like peptide-1 receptor agonists (GLP-1RA) has affected therapeutic management of diabetic patients but remains controversial. This study compared rates of DR development or progression in patients on GLP-1RA to those on SGLT-2 inhibitors (SGLT-2I). Design: Retrospective cohort study. Subjects: Nine hundred eighty-one patients with diabetes mellitus taking GLP-1RA or SGLT-2I, the latter serving as controls, between 2012 and 2023. Methods: Patients were one-to-one greedy matched by propensity scores on race/ethnicity, age, smoking status, baseline body mass index and hemoglobin A1c %, type of diabetes mellitus, baseline DR status and history of DR procedures, duration of drug use, whether they had taken both drug types, and change in hemoglobin A1c % after 1 year on the drug. Main Outcome Measures: The primary outcome was clinical DR development or progression (termed "worsening") detected by International Classification of Diseases (ICD), 10th edition codes, confirmed by manual review, on GLP-1RA compared with SGLT-2I after propensity score matching. Secondary outcomes included DR worsening indicated by need for procedures due to complications, and time-to-first DR worsening event. Results: The study included 692 GLP-1RA users and 289 SGLT-2I users. The mean follow-up periods for GLP-1RA versus SGLT-2I use were 1.54 (standard deviation [SD] 1.82) years and 1.38 (SD 1.56) years, respectively. The rates of clinical worsening were 2.3% and 2.8%, respectively. After propensity score matching, an association was not identified between GLP1-RA and DR worsening neither clinically by ICD-10 codes (odds ratio [OR] = 0.33, 95% confidence interval [CI]: 0.11-1.03) nor by indication for procedures (OR = 0.50, 95% CI 0.13-2.00). Time-to-first DR worsening did not differ between the groups in Kaplan-Meier analysis. The most common type of clinical worsening event for both drug types was vitreous hemorrhage (43.7% and 50% of worsening events in GLP-1RA and SGLT-2I users, respectively). The most common DR procedure indicated was anti-VEGF injections (34% and 35% of GLP-1RA and SGLT-2I events, respectively). Conclusions: Diabetic retinopathy worsening, either clinically or by procedures, was not associated with GLP-1RA compared with SGLT-2I, both before and after propensity score matching on all analyses, including time-to-first worsening event. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Sulforaphane and ophthalmic diseases.

Sulforaphane (SFN) is an organosulfur compound categorized as an isothiocyanate (ITC), primarily extracted from cruciferous vegetables like broccoli and cabbage. The molecular formula of sulforaphane (SFN) is C6H11NOS2. SFN is generated by the hydrolysis of glucoraphanin (GRP) through the enzyme myrosinase, showing notable properties including anti-diabetic, anti-inflammatory, antimicrobial, anti-angiogenic, and anticancer attributes. Ongoing clinical trials are investigating its potential in diseases such as cancer, neurodegenerative diseases, diabetes-related complications, chronic kidney disease, cardiovascular disease, and liver diseases. Several animal carcinogenesis models and cell culture models have shown it to be a very effective chemopreventive agent, and the protective effects of SFN in ophthalmic diseases have been linked to multiple mechanisms. In murine models of diabetic retinopathy and age-related macular degeneration, SFN delays retinal photoreceptor cell degeneration through the Nrf2 antioxidative pathway, NF-κB pathway, AMPK pathway, and Txnip/mTOR pathway. In rabbit models of keratoconus and cataract, SFN has been shown to protect corneal and lens epithelial cells from oxidative stress injury by activating the Keap1-Nrf2-ARE pathway and the Nrf-2/HO-1 antioxidant pathway. Oral delivery or intraperitoneal injection at varying concentrations are the primary strategies for SFN intake in current preclinical studies. Challenges remain in the application of SFN in eye disorders due to its weak solubility in water and limited bioavailability because of the presence of blood-ocular barrier systems. This review comprehensively outlines recent research on SFN, elucidates its mechanisms of action, and discusses potential therapeutic benefits for eye disorders such as age-related macular degeneration (AMD), diabetic retinopathy (DR), cataracts, and other ophthalmic diseases, while also indicating directions for future clinical research to achieve efficient SFN treatment for ophthalmic diseases.

Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study.

BACKGROUND: For medical artificial intelligence (AI) training and validation, human expert labels are considered the gold standard that represents the correct answers or desired outputs for a given data set. These labels serve as a reference or benchmark against which the model's predictions are compared. OBJECTIVE: This study aimed to assess the accuracy of a custom deep learning (DL) algorithm on classifying diabetic retinopathy (DR) and further demonstrate how label errors may contribute to this assessment in a nationwide DR-screening program. METHODS: Fundus photographs from the Lifeline Express, a nationwide DR-screening program, were analyzed to identify the presence of referable DR using both (1) manual grading by National Health Service England-certificated graders and (2) a DL-based DR-screening algorithm with validated good lab performance. To assess the accuracy of labels, a random sample of images with disagreement between the DL algorithm and the labels was adjudicated by ophthalmologists who were masked to the previous grading results. The error rates of labels in this sample were then used to correct the number of negative and positive cases in the entire data set, serving as postcorrection labels. The DL algorithm's performance was evaluated against both pre- and postcorrection labels. RESULTS: The analysis included 736,083 images from 237,824 participants. The DL algorithm exhibited a gap between the real-world performance and the lab-reported performance in this nationwide data set, with a sensitivity increase of 12.5% (from 79.6% to 92.5%, P<.001) and a specificity increase of 6.9% (from 91.6% to 98.5%, P<.001). In the random sample, 63.6% (560/880) of negative images and 5.2% (140/2710) of positive images were misclassified in the precorrection human labels. High myopia was the primary reason for misclassifying non-DR images as referable DR images, while laser spots were predominantly responsible for misclassified referable cases. The estimated label error rate for the entire data set was 1.2%. The label correction was estimated to bring about a 12.5% enhancement in the estimated sensitivity of the DL algorithm (P<.001). CONCLUSIONS: Label errors based on human image grading, although in a small percentage, can significantly affect the performance evaluation of DL algorithms in real-world DR screening.