NEI Intramural Biorepository for Retinal Diseases (12-EI-0042)

这项研究建立了一个临床数据库和生物谱系资料库，用于识别与各种视网膜疾病的发病机制、进展和治疗反应相关的新因素，特别是年龄相关性黄斑变性和糖尿病视网膜病变及其相关的系统性疾病。 目的：这项研究提供了纵向临床数据的标准化收集和各种生物样本的连续收集、处理和存储。临床数据集和生物谱库将用于识别与各种视网膜状况的发病机制、进展和治疗反应相关的新的遗传因素、生物标记物和实验模型，以及它们与疾病的相关系统相关。 研究人群：我们计划招募100名患有老年性黄斑变性(AMD)的参与者，200名患有糖尿病视网膜病变的参与者，多达200名患有其他视网膜疾病的参与者，以及150名没有任何视网膜疾病的参与者。 设计：这是一项针对多种视网膜疾病和适当对照的前瞻性观察性研究，包括： 1.眼科疾病的护理管理标准，以及统一的跟进和检测时间表；以及 2.为研究目的收集生物检疫剂，其抽样对参与者造成的风险不超过最低限度。 结果指标：结果指标包括表型的关键参数(如视力和眼部成像上的视网膜特征)与遗传变异和其他生物标记物之间的相互作用，以及眼睛健康和疾病的新实验模型的特征。 在2019财年，我们继续对参与者进行跟踪，并增收了少量参与者。目前的应计项目包括490多名参与者。Nida Sen博士(人工智能)批准了一项对对照队列参与者的彩色眼底图像进行分析的提案，用于鸟射脉络膜视网膜病变的深度学习研究(迄今尚未发表)。

This study establishes a clinical database and biospecimen repository for the identification of novel factors relevant to the pathogenesis, progression, and response to treatment of a variety of retinal conditions, particularly age-related macular degeneration and diabetic retinopathy and their associated systemic correlates of disease. Objectives: This study provides for standardized collection of longitudinal clinical data and for serial collection, processing, and storage of a variety of biospecimens. The clinical data set and biospecimen repository will be used to identify novel genetic factors, biomarkers, and experimental models associated with pathogenesis, progression, and response to treatment for various conditions of the retina and their associated systemic correlates of disease. Study Population: We plan to accrue 100 participants with age-related macular degeneration (AMD), 200 participants with diabetic retinopathy, up to 200 participants with other retinal diseases, and 150 participants without any retinal disease. Design: This is a prospective observational study of multiple retinal diseases and suitable controls incorporating: 1. Standard of care management of eye diseases with a standardized follow-up and testing schedule; and 2. Collection of biospecimens for research purposes for which sampling does not incur more than minimal risk to participants. Outcome Measures: Outcome measures include the interaction of key parameters of phenotype (such as visual acuity and retinal features on ocular imaging) with genetic variants and other biomarkers identified from biospecimens, and the characterization of new experimental models of eye health and disease. In fiscal year 2019, we continued follow up for participants, and enrolled a small number of additional participants. Present accrual includes over 490 participants. Dr. Nida Sen (AI) got approval for a proposal for analysis of color fundus images from participants in the control cohort for use in a deep-learning study on birdshot chorioretinopathy (no publication to date).