Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. While glycemic control is crucial, the risk of developing DR varies significantly among individuals with type 2 diabetes (T2D), suggesting influences beyond glucose levels. Polygenic risk scores (PRS) for DR can help estimate inherited susceptibility. However, the performance of PRS may be modified by non-genetic factors, particularly exposure to different classes of anti-diabetic medications. Furthermore, clinical comorbidities such as hypertension and dyslipidemia are known risk factors, but their interplay with genetic risk and specific drug regimens remains poorly characterized.
By leveraging the extensive genetic, prescription, and phenotypic data from the UK Biobank cohort, we aim to: (1) evaluate the independent and joint associations of PRS for DR, anti-diabetic medication classes (e.g., metformin, SGLT2 inhibitors, GLP-1 RAs, insulin), and key clinical profiles (e.g., hypertension, BMI, lipid levels) with incident DR; (2) investigate the potential interactions between genetic susceptibility (PRS) and medication use in modifying DR risk; and (3) develop and validate an integrated risk prediction model that combines PRS, medication history, and clinical profiles to improve the stratification of T2D patients at high risk for DR.
We believe these findings will provide novel insights into the complex interplay between hereditary susceptibility, pharmacological exposure, and clinical conditions in DR pathogenesis. The improved prediction model could facilitate earlier identification of high-risk individuals, enabling targeted screening and personalized treatment strategies to prevent vision loss.
