Clinical Report: AI, multiomics help advance endocrine HTN

Overview

The integration of multiomics profiling and artificial intelligence (AI) is enhancing the detection and subtyping of endocrine hypertension (EH), achieving diagnostic accuracy comparable to invasive methods. This narrative review highlights the underdiagnosis of EH and the potential for improved patient outcomes through precision diagnostics.

Background

Endocrine hypertension, accounting for 5% to 10% of hypertension cases, is often underdiagnosed due to complex diagnostic pathways. Primary aldosteronism (PA) is a significant contributor, with a prevalence potentially 3- to 5-fold higher than previously recognized. The application of multiomics and AI technologies may facilitate earlier detection and personalized treatment strategies, addressing a critical gap in hypertension management.

Data Highlights

Key Findings

• AI and multiomics can improve diagnostic accuracy for endocrine hypertension, comparable to invasive standards.
• Primary aldosteronism (PA) may account for 10% of hypertension cases, with under 1% of patients screened in routine practice.
• Somatic mutations in KCNJ5 are found in ≥40% of aldosterone-producing adenomas.
• Machine learning models show high diagnostic performance for identifying PA and predicting unilateral disease.
• Plasma free or 24-hour urinary fractionated metanephrines achieve approximately 97% sensitivity for pheochromocytoma.
• Integration of AI in clinical practice faces challenges such as data privacy and sharing.

Clinical Implications

Healthcare professionals should consider the integration of AI and multiomics in the evaluation of patients with suspected endocrine hypertension. Enhanced diagnostic tools may lead to earlier identification and treatment of conditions like primary aldosteronism, ultimately improving cardiovascular outcomes.

Conclusion

The convergence of AI and multiomics represents a significant advancement in the management of endocrine hypertension, with the potential to transform diagnostic practices and improve patient care.

References

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8. Primary Aldosteronism | Endocrine Society
9. Robustness of steroidomics-based machine learning for diagnosis of primary aldosteronism: a laboratory medicine perspective - PubMed
10. Pheochromocytoma and Paraganglioma - Endotext - NCBI Bookshelf