Integrated Oculomics and Lipidomics Reveal Microvascular Metabolic Signatures Associated with Cardiovascular Health in a Healthy Cohort

TL;DR

This study combines retinal imaging and serum lipidomics to identify early, non-invasive biomarkers of cardiovascular risk in healthy individuals, revealing microvascular and metabolic signatures linked to CVD.

Contribution

It introduces an innovative integrated omics framework combining deep learning-derived retinal traits with lipidomic profiles for early CVD biomarker discovery.

Findings

Strong correlations between retinal vessel traits and lipid subclasses.

Identification of microvascular remodeling markers associated with metabolic stress.

Potential for non-invasive early detection of cardiovascular risk.

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. By linking detailed vascular structural phenotypes to specific lipid species, this study fills a critical gap in the understanding of early CVD pathogenesis. This integration not only offers a novel perspective on microvascular metabolic associations but also presents a significant opportunity for the identification of robust, non-invasive biomarkers. Ultimately, these findings may support improved early detection, targeted prevention, and personalized approaches in cardiovascular healthcare.