Beyond blood pressure: AI-assisted multi-omics biomarker stratification for stroke risk in hypertension: clinical chemistry, analytical validation, and translational perspectives.

BACKGROUND: Hypertension remains a major contributor to stroke burden; however, conventional blood pressure measurements do not fully capture the heterogeneous biological processes underlying cerebrovascular risk. Residual risk may persist despite apparently controlled blood pressure due to vascular, inflammatory, metabolic, cardiac, renal, and neurovascular abnormalities. OBJECTIVE: This review summarizes emerging multi-omics biomarker strategies for biological stratification of stroke risk in hypertension, emphasizing mechanistic interpretation, analytical validation, artificial intelligence (AI)-assisted integration, and clinical translation. METHODS: Relevant literature on genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, extracellular vesicle biomarkers, clinical chemistry approaches, and AI-assisted integration was reviewed. Evidence was synthesized according to biological relevance, analytical readiness, validation status, and translational potential. KEY FINDINGS: Multi-omics approaches provide complementary information regarding vascular remodeling, endothelial dysfunction, inflammation, oxidative stress, thrombosis, metabolism, and target-organ injury. However, biological associations do not necessarily establish predictive validity, and predictive performance alone does not demonstrate clinical utility. Translation requires analytical validation, standardized workflows, external validation, and evidence of improved clinical decision-making. LIMITATIONS: Most proposed biomarkers remain in exploratory or early translational stages, with limitations including analytical variability, limited external validation, and insufficient prospective clinical utility studies. CONCLUSIONS: Multi-omics biomarkers offer a promising framework for understanding biological heterogeneity in hypertensive stroke risk. Future studies should prioritize standardized measurement, prospective validation, and demonstration of clinical utility before routine implementation.