# An Integrative Review of the Cardiovascular Disease Spectrum: Integrating Multi-Omics and Artificial Intelligence for Precision Cardiology

## TL;DR

This paper reviews how combining multi-omics data and AI can improve understanding and treatment of cardiovascular diseases as a connected spectrum.

## Contribution

The paper integrates multi-omics and AI for a mechanism-driven approach to precision cardiology.

## Key findings

- Multi-omics and AI improve risk prediction and clinical decision-making across cardiovascular conditions.
- Common biological pathways link heterogeneous clinical phenotypes in CVDs.
- Precision cardiology benefits from data-driven approaches but requires attention to data quality and equity.

## Abstract

Background/Objectives: Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide and increasingly are recognized as a continuum of interconnected conditions rather than isolated entities. Methods: A structured narrative literature search was performed in PubMed, Scopus, and Google Scholar for publications from 2015 to 2025 using combinations of different keywords: “cardiovascular disease spectrum”, “multi-omics”, “precision cardiology”, “machine learning”, and “artificial intelligence in cardiology”. Results: Evidence was synthesized across seven major clusters of cardiovascular conditions, and across these domains, common biological pathways were mapped onto heterogeneous clinical phenotypes, and we summarize how multi-omics integration, AI-enabled imaging and digital tools contribute to improved risk prediction and more informed clinical decision-making within this spectrum. Conclusions: Interpreting cardiovascular conditions as components of a shared disease spectrum clarifies cross-disease interactions and supports a shift from organ- and syndrome-based classifications toward mechanism- and data-driven precision cardiology. The convergence of multi-omics, and AI offers substantial opportunities for earlier detection, individualized prevention, and tailored therapy, but requires careful attention to data quality, equity, interpretability, and practical implementation in routine care.