Anti-Retinal Autoantibodies As AMD Biomarkers

Wei Li, PhD University of Miami, Miller School of Medicine


New biomarker detection techniques could improve early diagnosis of age-related macular degeneration (AMD), which is important for early treatment to delay or reduce disease severity. Dr. Wei Li and colleagues will systematically identify the levels and identities of various “anti-retinal autoantibodies,” particular types of biomarkers that are found in AMD patients’ blood. These special types of self-reacting antibodies previously have been detected in AMD patient blood, but identifying a signature or fingerprint map of the complete autoantibody profile could potentially be used for early diagnosis of AMD.

Project Details

The goal of this project is to systematically identify autoantibodies as disease biomarkers for diagnosis of AMD by a newly developed antibody technology. In the future, this new technology could also help disease subclassification, deciding what therapy would be best for each patient and predicting the severity and outcome of the different forms of AMD.

Biomarkers potentially could facilitate early diagnosis and treatment of AMD, but they are not currently available in clinical settings. Anti-retinal autoantibody activities have been detected in AMD patient blood by various antibody technologies. These autoantibodies could be candidates for biomarkers. The barrier to harnessing their diagnostic potentials is the inability to identify a small number of unknown autoantibody biomarkers among a huge number of irrelevant antibodies.

Dr. Wei Li and colleagues have developed a new antibody technology with two unique advantages over all current technologies: 1) sufficient sensitivity to detect and quantify less abundant but potentially diagnostic autoantibodies; and 2) global identification and quantification of the entire blood autoantibody signature (fingerprint map) with the ability to follow thousands of autoantibody peaks at different activities. Li’s team will map the entire autoantibody fingerprint maps of multiple AMD patients and quantitatively compare them to healthy control individuals to identify AMD-specific autoantibody biomarkers.

When this project is complete, all possible autoantibody biomarkers for AMD in patient blood should be systematically identified. Although individual autoantibody biomarkers have limited diagnostic accuracy, this project will develop a model based on the combination of all identified autoantibody biomarkers and their activities, thereby improving the diagnostic accuracy. In combination with retinal imaging technologies, these biomarkers may, for the first time, allow doctors to reliably diagnose AMD based on its molecular profiles.