ReviewChronic diseases and translational medicine2026
Systemic Diseases and the Eye: A Translational Perspective on Diabetes.
Review in Chronic diseases and translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Diabetes mellitus is a global chronic disease that affects the whole body and not just glucose metabolism. In diabetes, non-invasive ocular changes can help understand microvascular, neurodegenerative, and inflammatory processes. This analysis looks at the eye as a biomarker platform for diabetes-related disease, with retinal imaging informing systemic pathophysiology and the emergence of AI-assisted approaches. With improvements in optical coherence tomography (OCT) and OCT angiography, it is possible to observe neurovascular changes that can occur before the clinical onset of diabetic retinopathy. Issues in the retina have also been connected to problems with the heart, brain, kidneys, and cognition. However, much of the evidence for problems involving the heart and cognition comes from studies on people without diabetes. Also, most of the available data are observational, rather than coming from controlled studies. As a result, retinal measurements should be considered candidate biomarkers rather than clinically validated predictors or surrogate endpoints for systemic outcomes. Artificial Intelligence (AI) has reached a phase of clinical maturity such that it can be used in the automated screening of diabetic retinopathy. However, it remains investigational for systemic risk prediction, such as for chronic kidney disease, cardiovascular outcomes, and cognitive decline. Equally, the therapeutic implications of retinal neurodegeneration and other imaging biomarkers are under-validated for systemic decisions. Before retinal biomarkers can be used in precision diabetes care, we need more diabetes-specific prospective studies to establish standardized measurements, the clinical meaning of thresholds, incremental predictive value above and beyond established risk factors, reproducibility, cost-effectiveness, and evidence of improved outcomes.
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