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Endocrine hypertension in 2025: how AI and multiomics are revolutionizing detection
Jenyfer M Fuentes-Mendoza1, Marcio J Concepción-Zavaleta1, Jeny J Mendoza-Godoy2
1Grupo de investigación de Neurociencias, Metabolismo, Efectividad Clínica y Sanitaria, Universidad Científica del Sur, Lima, Peru.
Expert Review of Cardiovascular Therapy
|February 18, 2026
Summary
Multiomics and artificial intelligence (AI) are revolutionizing endocrine hypertension (EH) diagnosis and treatment. These technologies promise earlier detection, precise subtyping, and personalized management for EH patients.
Area of Science:
- Endocrinology
- Genetics
- Artificial Intelligence
- Systems Medicine
Background:
- Endocrine hypertension (EH) is a significant subset of secondary hypertension with potential for cure.
- Advances in multiomics and AI are transforming EH diagnostics and therapeutics.
- Current paradigms enable earlier detection, precise subtyping, and personalized treatment strategies.
Purpose of the Study:
- To review the role of multiomics (genomics, transcriptomics, proteomics, metabolomics) combined with AI in EH diagnosis and management.
- To highlight the shift towards molecularly informed clinical pathways and noninvasive biomarkers.
- To discuss the integration of AI for EH subtype differentiation and therapeutic optimization.
Main Methods:
- Literature review of PubMed, Scopus, and Web of Science (2000-July 2025).
- Integration of evidence on multiomics data and AI applications in EH.
- Focus on specific EH subtypes: primary aldosteronism, Cushing's syndrome, pheochromocytoma/paraganglioma, and thyroid-related hypertension.
Main Results:
- Multiomics and AI demonstrate diagnostic accuracy comparable to invasive tests in proof-of-concept studies.
- A shift towards molecularly informed clinical pathways, noninvasive biomarkers, and predictive algorithms is evident.
- AI facilitates subtype differentiation and therapeutic optimization in various EH conditions.
Conclusions:
- The convergence of multiomics and AI is ushering in a transformative era for EH care.
- Translation into routine practice faces challenges including infrastructural inequities and data harmonization.
- Future directions include federated data systems, longitudinal multiomic integration, and human-AI collaboration for dynamic, individualized EH management.
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