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

Progress in Retinal and Eye Research, vol. 112, pp. 101472

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.

Authors 13

1. Timon Wallraven corresponding

   TUM Klinikum · Technical University of Munich

   Affiliation as printed

   TUM School of Medicine and Health, Department of Nephrology, TUM University Hospital, Technical University of Munich, Munich, Germany. Electronic address: Timon.kuchler@mri.tum.de

2. Roman Günthner

   TUM Klinikum · Technical University of Munich

   Affiliation as printed

   TUM School of Medicine and Health, Department of Nephrology, TUM University Hospital, Technical University of Munich, Munich, Germany

3. Andrea Ribeiro

   TUM Klinikum · Technical University of Munich

   Affiliation as printed

   TUM School of Medicine and Health, Department of Nephrology, TUM University Hospital, Technical University of Munich, Munich, Germany

4. Momin S. Alnemer Aachen

   TUM Klinikum · Technical University of Munich · FH Aachen

   Affiliation as printed

   TUM School of Medicine and Health, Department of Nephrology, TUM University Hospital, Technical University of Munich, Munich, Germany; Aachen University of Applied Sciences, Heinrich-Mussmann-Str. 1, 52428, Jülich, Germany

5. Konstantin E. Kotliar Aachen

   FH Aachen

   Affiliation as printed

   Aachen University of Applied Sciences, Heinrich-Mussmann-Str. 1, 52428, Jülich, Germany

6. Maciej Lech

   LMU Klinikum · Ludwig-Maximilians-Universität München

   Affiliation as printed

   Department of Medicine IV, LMU University Hospital Munich, Munich, Germany

7. Nathalie Bleidißel

   TUM Klinikum · Technical University of Munich

   Affiliation as printed

   TUM School of Medicine and Health, Department of Ophthalmology, TUM University Hospital, Technical University of Munich, Munich, Germany

8. Rebecca Wicklein

   TUM Klinikum · Technical University of Munich

   Affiliation as printed

   TUM School of Medicine and Health, Department of Neurology, TUM University Hospital, Technical University of Munich, Munich, Germany

9. Christoph Hauser

   University of Basel

   Affiliation as printed

   Department of Sport, Exercise and Health, Preventive Sports Medicine and Systems Physiology, University of Basel, Basel, Switzerland

10. Lukas Streese

    Düsseldorf University Hospital · Heinrich Heine University Düsseldorf

    Affiliation as printed

    Faculty of Health Care, Therapeutic Sciences, Niederrhein University of Applied Sciences, Krefeld, Germany; Department of Nephrology, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany

11. Martin J. Menten

    Imperial College London · Technical University of Munich

    Affiliation as printed

    Chair for AI in Healthcare and Medicine, Technical University of Munich, Munich, Germany; BioMedIA, Imperial College London, London, United Kingdom

12. Henner Hanssen

    University of Basel

    Affiliation as printed

    Department of Sport, Exercise and Health, Preventive Sports Medicine and Systems Physiology, University of Basel, Basel, Switzerland

13. Christoph Schmaderer

    TUM Klinikum · German Center for Infection Research · Technical University of Munich

    Affiliation as printed

    TUM School of Medicine and Health, Department of Nephrology, TUM University Hospital, Technical University of Munich, Munich, Germany; German Centre for Infection Research (DZIF), Partner Site Munich, Munich, Germany