Precision Medicine for Diabetic Retinopathy: Integrating Genetics, Biomarkers, Lifestyle, and AI

Diabetic retinopathy (DR) is a common sight-threatening complication of diabetes. Overall, 26% of the 37 million Americans with diabetes suffer from DR, and 5% of people with diabetes suffer from vision-threatening DR. DR is a heterogeneous disease; thus, it is essential to acknowledge this diversity as we advance toward precision medicine. The current classification for DR primarily focuses on the microvascular component of disease progression, which does not fully capture the heterogeneity of disease etiology in different patients. Further, researchers in the field have suggested renewed interest in improving diagnosis and treatment modalities for personalized care in DR management. Moreover, genetic factors, lifestyle, and environmental variation strongly affect the disease outcome. It is important to emphasize that various ocular and peripheral biomarkers, along with imaging techniques, significantly influence the diagnosis of DR. Therefore, in this review, we explore the heterogeneity of DR, genetic variations or polymorphism, lifestyle and environmental factors, and how these factors may affect the development of precision medicine for DR. First, we provide a rationale for the necessity of a multifaceted understanding of disease etiology. Next, we discuss different aspects of DR diagnosis, emphasizing the need for further stratification of patient populations to facilitate personalized treatment. We then discuss different genetics, race, sex, lifestyle, and environmental factors that could help personalize treatments for DR. Lastly, we summarize the available literature to elaborate how artificial intelligence can enhance diagnostics and disease classification and create personalized treatments, ultimately improving disease outcomes in DR patients who do not respond to first-line care.