Endocrine hypertension in 2025: how AI and multiomics are revolutionizing detection

Endocrine hypertension (EH) represents a small yet clinically significant subset of secondary hypertension with curative potential. Recent advances in multiomics and artificial intelligence (AI) are transforming the diagnostic and therapeutic paradigms of EH, enabling earlier detection, precise subtyping, and personalized treatment strategies.

This review integrates evidence from PubMed, Scopus, and Web of Science (2000-July 2025) addressing the role of genomics, transcriptomics, proteomics, and metabolomics combined with AI in the diagnosis and management of EH, including primary aldosteronism, Cushing's syndrome, pheochromocytoma/paraganglioma, and thyroid-related hypertension. It highlights the shift toward molecularly informed clinical pathways, noninvasive biomarkers, and predictive algorithms for subtype differentiation and therapeutic optimization.

The convergence of multiomics and AI heralds a transformative era in EH care. While proof-of-concept studies demonstrate diagnostic accuracy comparable to invasive tests, translation into routine practice is limited by infrastructural inequities, lack of data harmonization, and gaps in clinician digital literacy. Future efforts should prioritize federated data systems, longitudinal multiomic integration, and hybrid models of human - machine collaboration. Within a decade, endocrine hypertension management will likely evolve from static, phenotype-based diagnosis to dynamic, data-driven systems medicine, integrating continuous biosensing and AI-guided decision support for truly individualized care.