AI in Healthcare Virtual Summit Session Recordings-Endocrinology and Hypertension

Video Transcription

Video Summary

Maria-Christine Asenaro from INSERM and University Paris Cité discussed developing multi-omics biomarkers to diagnose endocrine hypertension. Arterial hypertension, affecting 40-50% of people over 40, is a significant risk factor for multiple serious health conditions and is often poorly controlled in patients. Identifying secondary hypertension forms, especially endocrine types like primary aldosteronism, is crucial for effective management. Primary aldosteronism is common in resistant hypertension cases, posing additional cardiovascular risks.<br /><br />Asenaro highlighted the challenges in diagnosing primary aldosteronism, which involves complex procedures and weak consensus on various testing aspects. This has resulted in a significant underdiagnosis and undertreatment, thereby increasing patient risk. The research aims to develop biomarkers based on multi-omics analyses (DNA, RNA, proteins, metabolites) to better identify and manage hypertension through stratified approaches. Using data from multiple European centers, they trained machine-learning models to generate predictive biomarkers, achieving promising diagnostic accuracy.<br /><br />Ongoing efforts involve Horizon Europe programs like HD-Advanced to validate these biomarkers through pragmatic trials, aiming for improved personalized medicine in hypertension. Addressing AI deployment in healthcare, Asenaro stresses the significance of ethical considerations, international collaboration, and real-world applicability.

Asset Subtitle

Maria-Christina Zennaro, MD, PhD
Inserm, Université Paris Cité, Paris Cardiovascular Research Center-PARCC

Keywords

multi-omics biomarkers

endocrine hypertension

primary aldosteronism

arterial hypertension

machine learning

diagnostic accuracy

personalized medicine

Horizon Europe

AI in healthcare


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