Age-related macular degeneration (AMD) is a common retina degenerative disease with a complex genetic and environmental background. This study aimed to determine the polygenic risk score (PRS) stratification between the AMD case and control patients. The PRS model was established on the targeted sequencing data of a cohort of 471 patients diagnosed with AMD and 167 healthy controls without symptoms of retinal degeneration. The highest predictive value to the target dataset was achieved for a 22-variant model with a p-value lower than threshold P_T = 0.0123. The median PRS for cases was higher by 1.1 than for control samples (95% CI: (−1.19; −0.85)). The patients in the highest quantile had a significantly higher relative risk of developing AMD than those in the lowest reference quantile (OR = 35.13, 95% CI: (7.9; 156.1), p < 0.001). The diagnostic ability was investigated using ROC analysis with AUC = 0.76 (95% CI: (0.72; 0.80)). The polygenic susceptibility to AMD may be the starting point to expand AMD diagnostics based on rare highly penetrant variants and investigate associations with disease progression and treatment response in Polish patients in future studies.

A subset of ophthalmic imaging examination results from 334 patients were subjected to reanalysis to identify a specific group of patients with pigment epithelial detachment (PED) in at least one eye. Overall, we found a subgroup of 47 patients manifesting PED and studied their genotypes in comparison to those of patients with age-related macular degeneration without PED and healthy controls. We established a polygenic risk score that allowed the explanation of 16.3% of the variation within the disease. The highest predictive value was achieved for a model consisting of six non-coding variants: rs760306 (BEST1), rs148662546 (BEST1), rs11569560 (C3), rs74600252 (GUCA1B), rs2240688 (PROM1), and rs185507582 (TCF4). The risk of PED occurrence was found to be the highest in the first tercile, showing a 7.89-fold higher risk compared to the third tercile for AMD without PED (95% CI: 2.87; 21.71, p < 0.001) and a 7.22-fold higher risk compared to the healthy controls (95% CI: 2.60; 20.06, p < 0.001). In addition, we focused on rare variants in targeted genes. The rare variants’ burden was compared among the groups, but no statistical significance was observed in the number of rare variants, predicted functional effects, or pathogenicity classification.

Background/Objectives: Real-world registries of neovascular age-related macular degeneration (nAMD) treatments provide critical insights for optimizing patient care and resource allocation. This study evaluates one-year outcomes of anti-VEGF therapy with aflibercept, ranibizumab, and brolucizumab in the Polish Therapeutic Program Monitoring System between 1 January 2016 and 31 October 2023. Methods: We analyzed data from 51,902 treatment-naïve patients with nAMD, comparing baseline characteristics and outcomes across drugs, as well as between those who discontinued therapy early and those treated for at least one year. Results: No significant baseline differences were found between drug groups. One-year follow-up was available for 40,396 eyes; 3184 were lost to follow-up, and 8322 discontinued treatment: 14.4% for those receiving aflibercept, 24.1% for those receiving brolucizumab, and 20.1% for those receiving ranibizumab. Early discontinuers were older and had higher baseline visual acuity (aflibercept, ranibizumab). Twelve-month treatment outcomes, particularly visual acuity gains and injection frequency (~6–7/year), were similar across agents. Only ~22% achieved at least 0.3 logMAR improvement, underscoring real-world treatment challenges. Conclusions: System-level support, appropriate treatment intensification, and strategic use of newer, durable agents like brolucizumab are crucial to narrowing the gap between clinical trial efficacy and real-world effectiveness, ultimately improving long-term outcomes in nAMD care.

Background: Age-related macular degeneration (AMD) is a complex eye disorder with an environmental and genetic origin, affecting millions worldwide. The study aims to explore the association between retinal morphology and the polygenic risk score (PRS) for AMD using fundus images and deep learning techniques. Methods: The study used and pre-processed 23,654 fundus images from 332 subjects (235 patients with AMD and 97 controls), ultimately selecting 558 high-quality images for analysis. The fine-tuned DenseNet121 deep learning model was employed to estimate PRS from single fundus images. After training, deep features were extracted, fused, and used in machine learning regression models to estimate PRS for each subject. The Grad-CAM technique was applied to examine the relationship between areas of increased model activity and the retina’s morphological features specific to AMD. Results: Using the hybrid approach improved the results obtained by DenseNet121 in 5-fold cross-validation. The final evaluation metrics for all predictions from the best model from each fold are MAE = 0.74, MSE = 0.85, RMSE = 0.92, R² = 0.18, MAPE = 2.41. Grad-CAM heatmap evaluation showed that the model decisions rely on lesion area, focusing mostly on the presence of drusen. The proposed approach was also shown to be sensitive to artifacts present in the image. Conclusions: The findings indicate an association between fundus images and AMD PRS, suggesting that deep learning models may effectively estimate genetic risk for AMD from retinal images, potentially aiding in early detection and personalized treatment strategies.

We want to bring our ophthalmology colleagues’ attention to a new microscope that we have found to be very useful in ocular surgery. During microsurgery, the view of tissue is limited to the surfac...