Identification of hypertension subtypes using microRNA profiles and machine learning.

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 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 microRNAs.

Methods: This study systematically presents the most discriminating circulating microRNA features responsible for classifying and distinguishing EHT and its subtypes (PA, PPGL, 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 AUC 0.9 and PA from PHT with AUC 0.8 from the test set. The most prominent circulating microRNA 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 microRNAs to serve as diagnostic biomarkers for EHT and the viability of machine learning as a tool for identifying the most informative microRNA species.