European journal of endocrinology · 2025
Identification of hypertension subtypes using microRNA profiles and machine learning.
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Reel Smarti, Reel Parminder S, Van Kralingen Josie, Larsen Casper K, Robertson Stacy, MacKenzie Scott M, Riddell Alexandra, McClure John D, Lamprou Stelios, Connell John M C, Amar Laurence, Pecori Alessio, Tetti Martina, Pamporaki Christina, Kabat Marek, Ceccato Filippo, Kroiss Matthias, Dennedy Michael C, Stell Anthony, Deinum Jaap, Mulatero Paolo, Reincke Martin, Gimenez-Roqueplo Anne-Paule, Assié Guillaume, Blanchard Anne, Beuschlein Felix, Rossi Gian Paolo, Eisenhofer Graeme, Zennaro Maria-Christina, Jefferson Emily, Davies Eleanor
Аннотация
OBJECTIVE: Hypertension is a major cardiovascular risk factor affecting about 1 in 3 adults. Although the majority of hypertension cases (∼90%) are classified as "primary hypertension" (PHT), endocrine hypertension (EHT) accounts for ∼10% of cases and is caused by underlying conditions such as primary aldosteronism (PA), Cushing's syndrome (CS), pheochromocytoma or paraganglioma (PPGL). EHT is often misdiagnosed as PHT leading to delays in treatment for the underlying condition, reduced quality of life and costly, often ineffective, antihypertensive treatment. MicroRNA (miRNA) circulating in the plasma is emerging as an attractive potential biomarker for various clinical conditions due to its ease of sampling, the accuracy of its measurement and the correlation of particular disease states with circulating levels of specific miRNAs.
METHODS: This study systematically presents the most discriminating circulating miRNA features responsible for classifying and distinguishing EHT and its subtypes (PA, PPGL, and CS) from PHT using 8 different supervised machine learning (ML) methods for the prediction.
RESULTS: The trained models successfully classified PPGL, CS, and EHT from PHT with area under the curve (AUC) of 0.9 and PA from PHT with AUC 0.8 from the test set. The most prominent circulating miRNA features for hypertension identification of different disease combinations were hsa-miR-15a-5p and hsa-miR-32-5p.
CONCLUSIONS: This study confirms the potential of circulating miRNAs to serve as diagnostic biomarkers for EHT and the viability of ML as a tool for identifying the most informative miRNA species.
METHODS: This study systematically presents the most discriminating circulating miRNA features responsible for classifying and distinguishing EHT and its subtypes (PA, PPGL, and CS) from PHT using 8 different supervised machine learning (ML) methods for the prediction.
RESULTS: The trained models successfully classified PPGL, CS, and EHT from PHT with area under the curve (AUC) of 0.9 and PA from PHT with AUC 0.8 from the test set. The most prominent circulating miRNA features for hypertension identification of different disease combinations were hsa-miR-15a-5p and hsa-miR-32-5p.
CONCLUSIONS: This study confirms the potential of circulating miRNAs to serve as diagnostic biomarkers for EHT and the viability of ML as a tool for identifying the most informative miRNA species.