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

2025-07-16

AI Summary

Current cardiovascular disease (CVD) risk stratification methods fail to detect early subclinical changes and lack integrated analysis of retinal microvascular features with systemic lipid metabolism. To address this, we conducted the first large-scale, covariate-adjusted association study in a healthy population, integrating deep learning–driven retinal radiomics—quantifying arterial caliber, vessel density, and other microvascular parameters—with untargeted serum lipidomics profiling via UHPLC-ESI-HRMS. We identified age- and sex-independent associations between retinal microvascular metrics—particularly mean arterial width and vessel density—and specific lipid subclasses, including triacylglycerols (TAGs), diacylglycerols (DAGs), and ceramides (Cers). These findings reveal a pathophysiological convergence between microvascular remodeling and metabolic stress. Our work establishes a novel, noninvasive biomarker framework for dynamic, subclinical CVD risk assessment and provides mechanistic insights into early vascular–metabolic crosstalk.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Kernel Methods
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Identify early CVD biomarkers via retinal and lipidomic integration
• Link microvascular traits to lipid profiles for risk prediction
• Develop non-invasive methods for early cardiovascular disease detection

Innovation

• Deep learning analyzes retinal microvascular traits
• UHPLC ESI HRMS profiles serum lipidomics
• Integrated oculomics and lipidomics reveal CVD biomarkers