Prediction of Major Adverse Cardiovascular Events From Retinal, Clinical, and Genomic Data in Individuals With Type 2 Diabetes: A Population Cohort Study
Improved identification of individuals with type 2 diabetes at high cardiovascular risk could help in selection of newer cardiovascular risk-reducing therapies. The aim of this study was to determine whether retinal vascular parameters, derived from retinal screening photographs, alone and in combination with a genome-wide polygenic risk score for coronary heart disease (CHD PRS) would have independent prognostic value over traditional CV risk assessment in patients without prior cardiovascular disease.
Research Design and Methods
Patients in the GoDARTS study were linked to retinal photographs, prescriptions, and outcomes. Retinal photographs were analysed using VAMPIRE software, a semi-automated AI platform, to compute arterial and venous fractal dimension, tortuosity and diameter. CHD PRS was derived from previously published data. Multivariable Cox regression was used to evaluate the association between retinal vascular parameters and major adverse cardiovascular events (MACE) at 10 years compared to the pooled cohort equations (PCE) risk score.
5,152 individuals were included. 1,017 individuals suffered a MACE. Reduced arterial fractal dimension and diameter and increased venous tortuosity each independently predicted MACE. A risk score combining these parameters significantly predicted MACE after adjustment for age, sex, PCE and the CHD PRS (HR 1.11 per SD increase; 95% CI 1.04-1.18, p=0.002) with similar accuracy to PCE (AUC 0.663 vs. 0.658, p=0.33). A model incorporating retinal parameters and PRS improved MACE prediction compared to PCE (AUC 0.686 vs. 0.658, p<0.001).
Retinal parameters alone and in combination with genome-wide CHD PRS have independent and incremental prognostic value compared to traditional CV risk assessment in type 2 diabetes.