Identification of hypertension subtypes using microRNA profiles and machine learning
Smarti Reel, Parminder Singh Reel, Josie Van Kralingen, Casper K. Larsen, Stacy Robertson, Scott M. MacKenzie, Alexandra Riddell, John McClure, Stelios Lamprou, John Connell, Laurence Amar, Alessio Pecori, Martina Tetti, Christina Pamporaki, Marek Kabat, Filippo Ceccato, Matthias Kroiß, Michael Conall Dennedy, Anthony Stell, Jaap Deinum, Paolo Mulatero, Martín Reincke, Anne‐Paule Gimenez‐Roqueplo, Guillaume Assié, Anne Blanchard, Felix Beuschlein, Gian Paolo Rossi, Graeme Eisenhofer, Maria‐Christina Zennaro, Emily Jefferson, Eleanor Davies
Abstract
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.