Glycosylation-Associated Macrophage Signatures Define a Diagnostic Model for Diabetic Retinopathy via Single-Cell Analysis.

PURPOSE: Diabetic retinopathy (DR) remains a leading cause of vision loss, with macrophages and glycosylation dysregulation implicated in DR pathogenesis. However, the potential as diagnostic biomarkers has been rarely investigated. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) datasets to profile DR cell landscapes. The immune cell heterogeneity was dissected, and glycosylation-related transcriptional programs were delineated in DR. Machine learning-based diagnostic modeling was also conducted to identify macrophage differentiation-related glycosylation genes (MDRGGs). RESULTS: Macrophages exhibited elevated abundance in proliferative DR and showed intense interactions with other monocytes. Endothelial cells were subdivided into four subtypes, with Endo_KCNQ3 representing a dominant proliferative and highly glycosylated phenotype. Monocytes were clustered into three subtypes; Mono_RGS1 emerged as a transitional phenotype in the monocyte-to-macrophage trajectory. Macro_MIR181A1HG was identified as a proliferative and glycosylation-active macrophage subset. A total of 100 MDRGGs were identified. Among them, seven hub genes (AKAP13, SRGAP2, AFF1, ARHGAP24, RNF149, PTK2B, ATP1B3) were incorporated into a diagnostic model. The model achieved high predictive accuracy in both training and external validation cohorts (AUC > 0.85) and was further validated via nomogram and decision curve analyses. CONCLUSION: Glycosylation is closely associated with macrophage heterogeneity in DR. A seven-gene MDRGG-based diagnostic model demonstrated robust diagnostic performance in DR.