AbstractDiabetic retinopathy is a common complication of long-term diabetes and that could lead to vision loss. Unfortunately, early diabetic retinopathy remains poorly understood. There is no effective way to prevent or treat early diabetic retinopathy until patients develop later stages of diabetic retinopathy. Elevated acellular capillary density is considered a reliable quantitative trait present in the early development of retinopathy. Hence, in this study, we interrogated whole retinal vascular transcriptomic changes via a Nile rat model to better understand the early pathogenesis of diabetic retinopathy. We uncovered the complexity of associations between acellular capillary density and the joint factors of blood glucose, diet, and sex, which was modeled through a Bayesian network. Using segmented regressions, we have identified different gene expression patterns and enriched Gene Ontology (GO) terms associated with acellular capillary density increasing. We developed a random forest regression model based on expression patterns of 14 genes to predict the acellular capillary density. Since acellular capillary density is a reliable quantitative trait in early diabetic retinopathy, and thus our model can be used as a transcriptomic clock to measure the severity of the progression of early retinopathy. We also identified NVP-TAE684, geldanamycin, and NVP-AUY922 as the top three potential drugs which can potentially attenuate the early DR. Although we need more in vivo studies in the future to support our re-purposed drugs, we have provided a data-driven approach to drug discovery.