Retinal Microvascular Signatures as Early Predictors of Cardiovascular Risk: Integrating Pathophysiology, Molecular...

Area of Science:

• Ophthalmology
• Cardiology
• Medical Imaging

Background:

• Cardiovascular diseases (CVDs) pose a major global health challenge, necessitating improved early risk prediction.
• Microvascular dysfunction, detectable in the retina, serves as an early indicator of vascular injury preceding overt CVD.
• The retinal microvasculature mirrors systemic circulation, offering a non-invasive window into vascular health.

Purpose of the Study:

• To explore the potential of retinal microvascular phenotyping for early cardiovascular risk assessment.
• To investigate the role of advanced retinal imaging and AI in identifying CVD risk factors.
• To highlight the utility of retinal imaging biomarkers in enhancing cardiovascular risk stratification.

Main Methods:

• Analysis of microvascular retinal changes (vascular caliber, tortuosity, perfusion patterns).
• Utilizing advanced retinal imaging techniques like optical coherence tomography angiography (OCT-A).
• Application of artificial intelligence (AI) and deep learning (DL) for automated feature extraction and risk prediction from retinal photographs.

Main Results:

• Microvascular retinal changes are associated with major cardiovascular conditions like hypertension, diabetes, stroke, and heart failure.
• AI-powered analysis of retinal images enables automated acquisition of vascular features for CVD risk prediction.
• Shared molecular pathways (endothelial dysfunction, inflammation) link retinal vascular remodeling to cardiovascular pathology.

Conclusions:

• Retinal microvascular phenotyping is a scalable, non-invasive tool for early cardiovascular risk assessment.
• Retinal imaging biomarkers can complement traditional methods for personalized cardiovascular risk stratification.
• This approach holds promise for future precision cardiology and preventive cardiovascular strategies.