Role of retinal biomarkers in diabetes detection and risk prediction: A systematic scoping review

AIMS: This systematic scoping review was conducted to map and synthesize literature on retinal biomarkers associated with type 2 diabetes (T2D), including conventional and Artificial Intelligence(AI)-derived features, while considering ethnic/geographic diversity. METHODS: Following PRISMA-ScR statement, seven databases were searched up to January 28, 2026 for cross-sectional and longitudinal studies in adults (≥18 years) with or without T2D. Included studies required a normoglycemic comparator and quantitative retinal assessment, with or without AI. Two reviewers independently performed title/abstract screening, full-text review and quality appraisal (using Newcastle Ottawa scale(NOS)/modified NOS); disagreements were resolved by a third reviewer. Data extraction focused on biomarker type, methodology, and population characteristics. RESULT: Thirty-four studies (27 cross-sectional; 7 longitudinal) were included, mostly evaluating Caucasian/White [n = 18], Asian [n = 15] and Mixed/Latin American [n = 1] populations. Consistent vascular diabetes biomarkers included wider arteriolar and venular calibers. Arteriolar narrowing predicted incident diabetes in longitudinal analyses. Increased vessel tortuosity and altered fractal dimension were frequently observed. Ethnic variation was more pronounced in longitudinal studies; AI-based research was largely confined to China. CONCLUSIONS: Retinal biomarkers show promise for early diabetes detection, but research remains geographically skewed and requires multiethnic validation to ensure clinical impact. These findings provide the biological ground truth necessary for handcrafted feature extraction in the development of interpretative artificial intelligence.