Integrated oculomics and lipidomics reveal microvascular-metabolic signatures associated with cardiovascular health in a healthy cohort

Abstract

Cardiovascular disease (CVD) remains the leading global cause of mortality, yet current risk stratification methods often fail to detect early, subclinical changes. Previous studies have generally not integrated retinal microvasculature characteristics with comprehensive serum lipidomic profiles as potential indicators of CVD risk. In this study, an innovative imaging-omics framework was introduced, combining retinal microvascular traits-derived through deep learning-based image processing-with serum lipidomic data to highlight asymptomatic biomarkers of cardiovascular risk beyond the conventional lipid panel. This represents the first large-scale, covariate-adjusted and stratified correlation analysis conducted in a healthy population, which is essential for identifying early indicators of disease. Retinal phenotypes were quantified using automated image analysis tools, while serum lipid profiling was performed by Ultra-High-Performance Liquid Chromatography Electrospray ionization High-resolution mass spectrometry (UHPLC-ESI-HRMS). Strong, age-and sex-independent correlations were established, particularly between average artery width, vessel density, and lipid subclasses such as triacylglycerols (TAGs), diacylglycerols (DAGs), and ceramides (Cers). These associations suggest a converging mechanism of microvascular remodeling under metabolic stress.

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Identifiers

ORCID for Ernesto Elias Vidal Rosas: orcid.org/0000-0002-4486-7592

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