Abstract / Summary
Background: Genomic cardiology is an emerging field integrating genetic, molecular, imaging, and digital health data to improve cardiovascular disease (CVD) prevention, diagnosis, and treatment. The genetic architecture of CVD encompasses rare, high-impact variants as well as polygenic susceptibility, both of which influence disease risk, severity, and clinical outcomes.
Hypothesis: Integration of genomic information, including rare variants and polygenic risk scores (PRS), with clinical, imaging, biomarker, and artificial intelligence (AI)-based approaches may enhance personalized cardiovascular risk stratification and therapeutic decision-making.
Methods: This review summarizes current applications and emerging developments in genomic cardiology, including whole-genome and whole-exome sequencing, multigene panels, structural variant detection, PRS, multi-omics integration, and AI-assisted risk prediction. Current challenges and emerging therapeutic approaches, including RNA-based therapies and gene editing, are also discussed.
Results: Clinical genomic tools are increasingly being incorporated into the evaluation of inherited cardiac conditions. PRS can identify individuals with elevated lifelong CVD risk, although their current clinical utility is primarily as risk-enhancing markers rather than standalone diagnostic tests. Integration of genomic, clinical, imaging, and biomarker data using AI may further improve risk prediction and personalized treatment selection. However, ancestry bias, uncertainty in variant interpretation, limited accessibility, workflow integration barriers, and ethical and privacy concerns remain important challenges.
Conclusions: Genomic cardiology has substantial potential to advance precision cardiovascular medicine. Continued development of diverse genomic databases, standardized clinical implementation frameworks, and emerging genomic therapies will be important for translating genomic advances into equitable and clinically meaningful cardiovascular care.