Multi-omics integrated analysis identifies causal risk factors and therapeutic targets for diabetic retinopathy

Abstract Background Diabetic retinopathy (DR) is the main cause of blindness worldwide, and its prevalence rate is constantly rising. More in-depth exploration of its risk factors and pathogenic mechanisms is needed. Methods This study systematically identified potential therapeutic targets for DR by evaluating causal effects of 16,989 genes and 2,923 proteins on DR/subtypes via two-sample Mendelian randomization (MR), validated with colocalization/Summary-data-based Mendelian randomization (SMR). National Health and Nutrition Examination Survey (NHANES) 1999–2010 cross-sectional data (weighted logistic/Restricted cubic spline (RCS)) pinpointed key risk factors; MR explored their links to DR subtypes. Bioinformatics (bulk and single-cell transcriptomics) analyzed molecular mechanisms of shared targets (gene expression, immune infiltration, pathway enrichment). Machine learning selected key targets for models. Finally, two-step mediation MR examined how targets regulate DR via risk factors. Results This study identified 64 core targets with causal links to DR. Subtype analysis revealed 2,128 causal genes and subtype-specific targets (e.g. 52 for background DR, 66 for proliferative DR). SMR validated these findings. NHANES data highlighted body mass index (BMI), stroke, hypertension (HBP), and C-reactive protein (CRP) as key DR risk factors, confirmed by MR. Transcriptomics identified 29 differentially expressed genes associated with both risk factors and DR, linked to immune cell regulation. Machine learning selected core targets ( LY9 , WWP2 , etc.) and built a nomogram for DR risk prediction. Functional enrichment showed these targets enriched in chemokine/cytokine and immune-inflammatory pathways. Two-step mediation MR further revealed LY9, ARHGAP1, and WWP2 influence DR subtypes via regulating BMI, CRP, and HBP. Conclusion This study systematically elucidates the key risk factors, potential molecular mechanisms, and core regulatory targets of DR through multi-omics integration, causal inference, and bioinformatics approaches. The results indicate that inflammation, immune dysregulation, and metabolic disorders play crucial roles in the pathogenesis of DR. Key genes such as LY9, ARHGAP1, and WWP2 could serve as potential intervention targets, offering theoretical foundations and strategic support for early warning and precision treatment of DR.