Retinal vessel imaging in inflammatory disease: From endothelial dysfunction to clinical evidence and translation.

Abstract

Inflammatory processes drive a heterogeneous spectrum of diseases, including cardiovascular (CV), neurodegenerative, autoimmune, rare, and viral disorders, which together account for a major global disease burden. Despite diverse clinical manifestations, these conditions share systemic endothelial dysfunction (ED) as a common pathophysiological hallmark. The retina, accessible through non-invasive imaging, provides a unique window into systemic microvascular health. Over the past decades, retinal vessel analysis (RVA), both static and dynamic, has emerged as a robust tool for detecting and predicting microvascular alterations in inflammatory diseases. Large population-based cohorts, including the Atherosclerosis Risk in Communities (ARIC, n > 9000 participants) study and the Rotterdam Study (n > 5000), have shown that retinal diameter changes independently predict incident CV events and all-cause mortality. Recent UK Biobank (n > 45,000) analyses further demonstrate incremental value in stroke prediction beyond traditional risk factors (AUC 0.739 to 0.752; p < 0.001). Other retinal imaging modalities, such as optical coherence tomography angiography (OCTA) and adaptive optics (AO), provide complementary high-resolution structural data on capillary architecture and perfusion integrity. The retinal vascular phenotype reflects both shared and disease-specific mechanisms of ED. Therefore, accurate interpretation of retinal biomarkers requires an understanding of the molecular pathways that shape ED across disease entities, thereby forming the conceptual foundation of oculomics. We synthesize current evidence linking systemic ED to retinal microvascular structure and function across major categories of inflammatory disease. We integrate findings from static and dynamic RVA, OCTA, and AO, discuss their mechanistic interpretation within the emerging framework of oculomics, and critically evaluate challenges for clinical translation. Finally, we outline how artificial intelligence (AI) may facilitate robust, scalable implementation of retinal biomarkers for risk stratification, disease monitoring, and outcome prediction. This review moves beyond modality-specific descriptions to propose a unified biological and translational framework for retinal biomarkers.