# From polygenic risk to digital twins: the future of personalised cardiovascular medicine

## TL;DR

This review explores how personalized cardiovascular medicine is evolving through genomics, digital tools, and AI to improve individualized care and outcomes.

## Contribution

The paper synthesizes recent advances and challenges in precision cardiology, emphasizing the need for equitable implementation and multidisciplinary collaboration.

## Key findings

- Polygenic risk scores and multi-omics data improve cardiovascular risk stratification and understanding of disease mechanisms.
- AI and machine learning enhance predictive models using imaging, health records, and wearable data.
- Digital twins and CRISPR-based technologies offer new frontiers in personalized cardiovascular care.

## Abstract

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. Traditional risk assessment and treatment approaches often follow generalised strategies that inadequately capture individual variability in disease susceptibility, progression, and therapeutic response. Precision cardiology seeks to overcome these limitations by leveraging genomic, molecular, and computational innovations to enable more individualised care. Advances in polygenic risk scores have improved our ability to stratify cardiovascular risk at a population level, though challenges remain in ensuring clinical utility across diverse populations. Integrating multi-omics platforms, including transcriptomics, proteomics, and metabolomics, offers a more comprehensive understanding of CVD pathophysiology and potential diagnostic or prognostic biomarkers. Pharmacogenomic insights increasingly guide the selection and dosing of cardiovascular therapies such as statins and antiplatelets, supporting the shift toward personalised pharmacologic strategies. Applying artificial intelligence and machine learning to cardiovascular imaging, electronic health records, and wearable data enables more accurate, scalable predictive models. Emerging technologies, including CRISPR-based gene editing, single-cell sequencing, and digital twin modelling, further expand the frontiers of personalised cardiovascular medicine. However, real-world implementation remains limited by regulatory uncertainty, data integration challenges, cost, and concerns about equity and access. This review synthesises advances across genomic, omics, digital, and therapeutic domains in cardiovascular precision medicine, discusses key translational gaps, and highlights ethical and implementation challenges. We emphasise the need for multidisciplinary collaboration, robust validation frameworks, and equitable infrastructure to ensure these innovations lead to meaningful clinical impact. Personalised cardiology is poised to redefine prevention, diagnosis, and treatment paradigms as the field matures, moving from reactive care to proactive, patient-specific strategies.