Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation

Area of Science:

• Cardiovascular research
• Bioinformatics
• Artificial Intelligence in Medicine

Background:

• Cardiovascular diseases (CVDs) are the leading cause of death globally.
• A significant translational gap exists, with conventional biomarkers failing to predict individual disease trajectories.
• Current single-omics studies lack a unified framework for integrating molecular data with computational intelligence.

Purpose of the Study:

• To evaluate the integration of multi-omics and AI for predictive precision cardiology.
• To transition from descriptive biomarkers to actionable risk prediction models.
• To address the translational gap in cardiovascular disease research.

Main Methods:

• Synthesizing evidence from non-coding RNA networks (miRNAs, lncRNAs) and exosomal trafficking.
• Assessing Machine Learning (ML) architectures, including supervised, unsupervised, and deep learning (DL) models.
• Delineating pipelines to deconvolute high-dimensional signatures into risk models.

Main Results:

• Identified lack of standardized bioinformatic workflows and model interpretability as key barriers to clinical translation.
• Demonstrated pathways to translate multi-omic signatures (e.g., TMAO, miRNAs) into risk models for heart failure and post-infarction outcomes.
• Highlighted the potential of multi-omics and AI to overcome limitations of single-omics approaches.

Conclusions:

• Proposes an integrated molecular-computational framework for precision cardiology.
• Emphasizes the need for Explainable AI (XAI) and standardized multi-omic protocols.
• Advocates for bridging the gap between high-dimensional biological data and clinical decision-making.