Abstract
BACKGROUND: Hypertension, a major modifiable risk factor for cardiovascular disease, exhibits significant heterogeneity due to genetic, metabolic, and environmental influences. Traditional one-size-fits-all management is inadequate, necessitating predictive, preventive, and personalized medicine (3PM) approaches. STUDY OBJECTIVE: This review critically evaluates 3PM's application in hypertension, focusing on leveraging biomarkers, artificial intelligence (AI), and digital health for risk stratification, early intervention, and tailored therapies. KEY DISCUSSION: The 3PM framework leverages AI-driven integration of multi-omics, retinal imaging like ViT models, and hemodynamic profiling for risk prediction and treatment response forecasting; genetic profiling such as MTHFR, UMOD variants, urinary proteomics (CKD273 classifier), and microbiome-guided nutrition for early intervention; and pharmacogenomics, digital phenotyping like smartphone-guided dosing, and novel therapies such as aprocitentan and finerenone for personalized efficacy. Specific findings include aprocitentan reducing systolic BP by -15.3 mmHg in resistant hypertension, UMOD-guided torasemide use lowering BP by 8.5 mmHg in carriers, and microbiome-based nutrition reducing systolic BP by 14% in hyperglycemic patients. Key challenges include limited biomarker validation, "black box" AI algorithms, high costs, interoperability gaps, and equity barriers. CONCLUSION: 3PM transforms hypertension management by enabling proactive, individualized care. However, rigorous validation, affordable diagnostics, pragmatic trials, and equitable access are essential to bridge translational gaps and achieve personalized cardiology's full potential.