Progression of Age-Related Macular Degeneration Among Individuals Homozygous for Risk Alleles on Chromosome 1 (CFH-CFHR5) or Chromosome 10 (ARMS2/HTRA1) or Both

Importance: Age-related macular degeneration (AMD) is a common cause of irreversible vision loss among individuals older than 50 years. Although considerable advances have been made in our understanding of AMD genetics, the differential effects of major associated loci on disease manifestation and progression may not be well characterized. Objective: To elucidate the specific associations of the 2 most common genetic risk loci for AMD, the CFH-CFHR5 locus on chromosome 1q32 (Chr1) and the ARMS2/HTRA1 locus on chromosome 10q26 (Chr10)-independent of one another and in combination-with time to conversion to late-stage disease and to visual acuity loss. Design, Setting, and Participants: This case series study included 502 individuals who were homozygous for risk variants at both Chr1 and Chr10 (termed Chr1&10-risk) or at either Chr1 (Chr1-risk) or Chr10 (Chr10-risk) and who had enrolled in Genetic and Molecular Studies of Eye Diseases at the Sharon Eccles Steele Center for Translational Medicine between September 2009 and March 2020. Multimodal imaging data were reviewed for AMD staging, including grading of incomplete and complete retinal pigment epithelium and outer retinal atrophy. Main Outcomes and Measures: Hazard ratios and survival times for conversion to any late-stage AMD, atrophic or neovascular, and associated vision loss of 2 or more lines. Results: In total, 317 participants in the Chr1-risk group (median [IQR] age at first visit, 75.6 [69.5-81.7] years; 193 women [60.9%]), 93 participants in the Chr10-risk group (median [IQR] age at first visit, 77.5 [72.2-84.2] years; 62 women [66.7%]), and 92 participants in the Chr1&10-risk group (median [IQR] age at first visit, 71.7 [68.0-76.3] years; 62 women [67.4%]) were included in the analyses. After adjusting for age and AMD grade at first visit, compared with 257 participants in the Chr1-risk group, 56 participants in the Chr1&10-risk group (factor of 3.3 [95% CI, 1.6-6.8]; P < .001) and 58 participants in the Chr10-risk group (factor of 2.6 [95% CI, 1.3-5.2]; P = .007) were more likely to convert to a late-stage phenotype during follow-up. This difference was mostly associated with conversion to macular neovascularization, which occurred earlier in participants with Chr1&10-risk and Chr10-risk. Eyes in the Chr1&10-risk group (median [IQR] survival, 5.7 [2.1-11.1] years) were 2.1 (95% CI, 1.1-3.9; P = .03) times as likely and eyes in the Chr10-risk group (median [IQR] survival, 6.3 [2.7-11.3] years) were 1.8 (95% CI, 1.0-3.1; P = .05) times as likely to experience a visual acuity loss of 2 or more lines compared with eyes of the Chr1-risk group (median [IQR] survival, 9.4 [4.1-* (asterisk indicates event rate did not reach 75%)] years). Conclusions and Relevance: These findings suggest differential associations of the 2 major AMD-related risk loci with structural and functional disease progression and suggest distinct underlying biological mechanisms associated with these 2 loci. These genotype-phenotype associations may warrant consideration when designing and interpreting AMD research studies and clinical trials

A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography

Purpose - To develop and validate a deep learning (DL) framework for the detection and quantification of drusen and reticular pseudodrusen (RPD) on optical coherence tomography scans. Design - Development and validation of deep learning models for classification and feature segmentation. Methods - A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the ROC curve (AUC), kappa, accuracy and intraclass correlation coefficient (ICC). Results - The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification model). The mean ICC for drusen and RPD area vs. graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance. Conclusions - The models achieved high classification and segmentation performance, similar to human performance. Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings.Comment: 26 pages, 7 figure

A recommended “minimum data set” framework for SD-OCT retinal image acquisition and analysis from the Atlas of Retinal Imaging in Alzheimer’s Study (ARIAS)

Introduction: We propose a minimum data set framework for the acquisition and analysis of retinal images for the development of retinal Alzheimer\u27s disease (AD) biomarkers. Our goal is to describe methodology that will increase concordance across laboratories, so that the broader research community is able to cross‐validate findings in parallel, accumulate large databases with normative data across the cognitive aging spectrum, and progress the application of this technology from the discovery stage to the validation stage in the search for sensitive and specific retinal biomarkers in AD. Methods: The proposed minimum data set framework is based on the Atlas of Retinal Imaging Study (ARIAS), an ongoing, longitudinal, multi‐site observational cohort study. However, the ARIAS protocol has been edited and refined with the expertise of all co‐authors, representing 16 institutions, and research groups from three countries, as a first step to address a pressing need identified by experts in neuroscience, neurology, optometry, and ophthalmology at the Retinal Imaging in Alzheimer\u27s Disease (RIAD) conference, convened by the Alzheimer\u27s Association and held in Washington, DC, in May 2019. Results: Our framework delineates specific imaging protocols and methods of analysis for imaging structural changes in retinal neuronal layers, with optional add‐on procedures of fundus autofluorescence to examine beta‐amyloid accumulation and optical coherence tomography angiography to examine AD‐related changes in the retinal vasculature. Discussion: This minimum data set represents a first step toward the standardization of retinal imaging data acquisition and analysis in cognitive aging and AD. A standardized approach is essential to move from discovery to validation, and to examine which retinal AD biomarkers may be more sensitive and specific for the different stages of the disease severity spectrum. This approach has worked for other biomarkers in the AD field, such as magnetic resonance imaging; amyloid positron emission tomography; and, more recently, blood proteomics. Potential context of use for retinal AD biomarkers is discussed