ASSOCIATIONS BETWEEN CHOROIDAL ALTERATIONS AND EARLY NEURODEGENERATION IN DIABETES WITHOUT DIABETIC RETINOPATHY: Insights From Ultra-Widefield Optical Coherence Tomography Angiography Imaging.

PURPOSE: To identify associations between choroidal alterations and the reduction of peripapillary retinal nerve fiber layer (pRNFL) thickness in diabetes without diabetic retinopathy (nondiabetic retinopathy, NDR). METHODS: This retrospective cross-sectional study included 143 eyes from 83 patients with NDR and 124 eyes from 82 matched healthy controls. Ultra-widefield swept-source optical coherence tomography angiography was used to automatically measure retinal and choroidal thickness (ChT), retinal vascular density, and choroidal vascular metrics. Data were analyzed using Student's t-tests, generalized estimating equations, and generalized linear mixed models. RESULTS: Patients with NDR exhibited significant reductions in perifoveal ChT (e.g., perifoveal inferior region: 253.42 ± 86.59 µm vs. 281.01 ± 80.25 µm, P = 0.026 in GEE test) compared with the controls. The NDR group showed a significant decrease in the choroidal vascular index (P = 0.012 in GEE test), and an increase in the choroidal stromal index (P = 0.012 in GEE test). The average pRNFL thickness significantly decreased in patients with NDR (114.58 ± 11.88 µm vs. 120.25 ± 16.36 µm, P = 0.005 in GEE test). The thickness of the outer nuclear layer and total retina significantly decreased in patients with NDR (P < 0.05). In multivariate models, ChT was significantly correlated with pRNFL thickness (ß = 0.041, P = 0.001), even after adjusting by confounding factors (ß = 0.056, P = 0.001). CONCLUSION: In NDR, there were decreases in ChT, choroidal vascular index, pRNFL thickness, and outer nuclear layer thickness. The reduction in ChT was independently associated with the reduction in pRNFL thickness, suggesting that ChT could serve as a predictor of retinal neurodegeneration in NDR.

High-Altitude Exposure and Diabetic Retinopathy: Unveiling the Impact and Mechanisms of Alleviation.

BACKGROUND: High altitude (HA) is an extremely challenging environment for millions of people who either travel to HA regions or inhabit there permanently. SUMMARY: Significant progress has been made over the past decades in the understanding of physiological adaptations in HA conditions, and recently, more studies regarding its influence on metabolic disease have been published. However, the effect of HA on diabetic retinopathy (DR), the leading cause of blindness, remains unclear. KEY MESSAGES: The present article provides an overview of the changes in the principal physiology and clinical characteristics related to DR after HA exposure. Despite conflicting evidence, this review synthesizes the available studies and explores the potential mechanisms, such as genetic adaptations, glucose homeostasis, and related physiological changes, by which long-term exposure to HA may alleviate the progression of DR. By shedding light on this complex relationship, it also provides insights into the interplay between HA and DR, offering valuable implications for clinical practice and further research.

Expression of the SARS-CoV-2 Receptor ACE2 and Protease TMPRSS2 in Ocular Hypertension Eyes of Nonhuman Primate and Human.

PURPOSE: Coronavirus disease 2019 is a disease caused by the novel severe acute respiratory syndrome coronavirus 2. The double-positive of angiotensin-converting enzyme 2 and transmembrane protease serine type 2 have a higher risk of being infected by severe acute respiratory syndrome coronavirus 2. The susceptibility of coronavirus disease 2019 in patients with chronic diseases, especially in different tissues of ocular hypertension eyes like glaucoma, is not yet known. METHODS: An ocular hypertension model was established by laser photocoagulation in rhesus monkeys. The expression of angiotensin-converting enzyme 2 and transmembrane protease serine type 2 in three ocular hypertension eyes and the three control eyes were analyzed using immunofluorescence. RESULTS: No difference was observed between ocular hypertension and control eyes in the expression of angiotensin-converting enzyme 2 and transmembrane protease serine type 2 in the conjunctival epithelium, corneal epithelium, and ciliary muscle. In ocular hypertension eyes and control eyes, angiotensin-converting enzyme 2 and transmembrane protease serine type 2 expression were both observed in the retina. Angiotensin-converting enzyme 2 staining of retinal ganglion cells was found to be significantly higher in ocular hypertension eyes than in control eyes. However, there was no difference in angiotensin-converting enzyme 2 and transmembrane protease serine type 2 expression in retinal vessels and choroidal vessels between ocular hypertension and control eyes. In our study, the expression and distribution of angiotensin-converting enzyme 2 and TMPREE2 in human retina were similar to that of non-human primates as expected. CONCLUSION: Our study confirmed that angiotensin-converting enzyme 2 and transmembrane protease serine type 2 were expressed widely in rhesus monkey eyes. When compared with controls eyes, the expression of angiotensin-converting enzyme 2 was higher in the retinal ganglion cells in ocular hypertension eyes, suggesting that high ocular pressure may affect the patients' ocular susceptibility to severe acute respiratory syndrome coronavirus 2 infection.

Association between choroidal thickness and diabetic macular edema: a meta-analysis.

AIMS: To evaluate the association between subfoveal choroidal thickness (SFCT) and diabetic macular edema (DME). DESIGN: A systematic review and meta-analysis. METHODS: A retrospective or prospective study comparing SFCT in diabetic retinopathy (DR) patients with and without DME was included. The data were collected from published studies retrieved from PubMed, Web of Science, Embase, Ovid Medline, and Cochrane Library. The final search was conducted on July 2, 2023. Heterogeneity was assessed using I2 statistics, and a random-effects model was used for the meta analysis. This study calculated the weighted mean difference (WMD) and 95% confidence interval (CI) for SFCT. RESULTS: A total of 26 relevant studies were identified, involving a combined sample size of 3201 eyes (1302 DR-DME eyes and 1899 DR-no DME eyes). The results showed no significance between DR-DME and DR-no DME (WMD = - 3.57 µm; 95% CI -26.54 to 19.41 µm; P = 0.76). Sub-analysis based on nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) subgroups showed that the SFCT of NPDR-DME was significantly thinner than that of NPDR-no DME eyes (WMD = - 19.80 µm; 95% CI - 34.55 to - 5.04 µm; P = 0.009), while there was no significance in SFCT between PDR-DME and PDR-no DME (WMD = - 26.45 µm; 95% CI - 104.00 to 51.11 µm; P = 0.50). CONCLUSION: The SFCT was thinner in NPDR-DME eyes compared to NPDR-no DME eyes. Thinning SFCT might cause retinal hypoxia, and play an important role in DME occurrence. Additionally, this study highlights the importance of considering DR grades and treatment history when evaluating SFCT between DME and no DME.

Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.

BACKGROUND: To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR) classification using both clinical data and optical coherence tomography angiography (OCTA). METHODS: In this cross-sectional observational study, clinical data and OCTA parameters from 203 diabetic patients (203 eye) were used to establish the ML models, and those from 169 diabetic patients (169 eye) were used for independent external validation. The random forest, gradient boosting machine (GBM), deep learning and logistic regression algorithms were used to identify the presence of DR, referable DR (RDR) and vision-threatening DR (VTDR). Four different variable patterns based on clinical data and OCTA variables were examined. The algorithms' performance were evaluated using receiver operating characteristic curves and the area under the curve (AUC) was used to assess predictive accuracy. RESULTS: The random forest algorithm on OCTA+clinical data-based variables and OCTA+non-laboratory factor-based variables provided the higher AUC values for DR, RDR and VTDR. The GBM algorithm produced similar results, albeit with slightly lower AUC values. Leading predictors of DR status included vessel density, retinal thickness and GCC thickness, as well as the body mass index, waist-to-hip ratio and glucose-lowering treatment. CONCLUSIONS: ML-based multiclass DR classification using OCTA and clinical data can provide reliable assistance for screening, referral, and management DR populations.