Genomic prediction of diabetic retinopathy via VEGF-A variants: insights from meta-analysis, machine learning and molecular docking approaches

Diabetic retinopathy (DR) is a complex ocular microvascular neurodegenerative condition resulting from chronic hyperglycemia and hypoxia. Genetic variants, particularly in the vascular endothelial growth factor A (VEGF-A) gene, play a key role in promoting pathological angiogenesis. This study conducted a systematic review and meta-analysis to identify key VEGF-A variants associated with DR development. Observational studies were retrieved from PubMed/Medline, Web of Science, Virtual Health Library, Embase, and Scopus, without time restrictions. Moreover, machine learning (ML) models were applied to enhance genetic risk prediction for DR. Additionally, molecular docking was performed to assess the impact of the −460T>C single-nucleotide variant (SNV) on the interaction between the VEGF-A promoter and the HIF1 transcription factor. Of the 20 studies included in the systematic review, 14 were eligible for meta-analysis. A significant association was found between the C allele of the rs833061 (−460T>C) variant and DR risk (OR = 1.49; 95% CI = 1.17–1.90; p = 0.0014), as well as in the dominant genotypic model (TT vs. TC + CC; OR = 1.76; 95% CI = 1.13–2.74; p = 0.0130). Although the −2549 I/D variant was not significant in the meta-analysis, ML models indicated its predictive value. Molecular docking revealed enhanced binding affinity of the −460C allele to HIF1, suggesting functional relevance. In conclusion, these findings support the potential of VEGF-A variants, particularly rs833061, as biomarkers for DR susceptibility and highlight the utility of integrating ML and molecular docking for predictive medicine.