Analysis of DR Progression to Identify Risks and Need for Treatment
Analysis of DR Progression to Identify Risks and Need for Treatment
The goal of this observational study is to understand whether vascular and structural changes in the eyes caused by diabetes can help predict which people are more likely to experience worsening diabetic retinopathy (a diabetes-related eye disease) and how these eye changes are related to cardiovascular complications.
The study will include about 1,000 people with type 2 diabetes, aged 35 to 90 years, and will take place over twelve months. It may also include a retrospective component, where existing medical and imaging data collected from previous visits (within the last 1 to 5 years) will be analyzed.
The main questions it aims to answer are:
Diabetic Retinopathy (DR) is a leading cause of vision loss in adults with type 2 diabetes (T2D) and is associated with systemic complications, including cardiovascular disease. Early identification of patients at high risk of DR progression and cardiovascular events is critical to optimizing clinical management. Retinal imaging biomarkers, combined with clinical and demographic data, provide an opportunity to better understand disease mechanisms, predict progression of DR and associated cardiovascular complications, and guide personalized interventions.
The aim of this study is to investigate the influence of central versus peripheral retinal lesions on DR progression and staging, characterize associations between retinal biomarkers and cardiovascular risk factors and major adverse cardiovascular events (MACE), and contribute to the understanding of pathophysiological mechanisms underlying DR in T2D patients. The study will also generate a high-quality, harmonized database to support the development of artificial intelligence (AI) models.
This study aims to determine the extent to which central and peripheral retinal lesions, together with quantitative imaging biomarkers, contribute to the progression and staging of DR and the occurrence of cardiovascular complications in patients with T2D. Additionally, in the scope of the ALERT project (supported by Fundação para Ciência e Tecnologia (FCT); COMPETE2030-FEDER-00921900), the data of this clinical study will be used to create a harmonized, high-quality database for the development of exploratory interpretable artificial intelligence models for predicting DR progression and stage as well as predict the values of cardiovascular risk factors, and the risk of developing cardiovascular complications.
Inclusion Criteria:
Exclusion Criteria:
alert_4c@aibili.pt239480137
alert_4c@aibili.pt239480115
Portugal, Coimbra District 3000-548, Portugal
ipmarques@aibili.pt239480124