Retinal Vascular Biomarkers for Neurovascular and Multiorgan Risk Assessment: From Fundus Imaging to Predictive Oculomics

Abstract

Retinal microcirculation provides a unique opportunity to observe vascular alterations directly and noninvasively. This study analyzed its potential as a biomarker of cerebral and systemic vascular damage and examined emerging applications of fundus photography, optical coherence tomography, optical coherence tomography angiography, and image-based predictive models. An integrative literature review was conducted using the scientific method and included 20 publications comprising prospective cohorts, cross-sectional studies, systematic reviews, meta-analyses, narrative reviews, and predictive investigations. Evidence was organized according to clinical application, methodological design, retinal biomarker, and systemic outcome. Cerebrovascular disease and stroke represented 50% of the publications, followed by cognitive decline and dementia with 25%. Prospective or longitudinal cohorts accounted for 50% of the methodological designs, while systematic reviews and meta-analyses represented 25%. Arteriolar or venular caliber was reported in 70% of the evidence, retinopathy and visible lesion signs in 65%, and arteriovenous ratio, focal narrowing, or arteriovenous nicking in 35%. The greatest concentration of evidence involved retinopathy in relation to cerebral small vessel disease and brain imaging abnormalities. Retinal findings were also associated with incident and lacunar stroke, silent cerebral infarction, white matter lesions, cognitive decline, dementia, coronary disease, biological aging, and mortality. Associations with renal dysfunction were less consistent. Retinal imaging may complement conventional vascular-risk assessment by identifying high-risk phenotypes and supporting targeted systemic evaluation. However, it should not replace neuroimaging or specialized cardiovascular, neurological, or renal assessment. Standardized acquisition, prospective validation, multimodal integration, and transparent predictive models are required before routine clinical implementation.