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Identification of hypertension subtypes using microRNA profiles and machine learning
Smarti Reel1, Parminder S Reel1, Josie Van Kralingen2
1Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee DD2 4BF, United Kingdom.
Insights
This study shows that circulating microRNAs (miRNAs) can help distinguish endocrine hypertension (EHT) from primary hypertension (PHT). Machine learning identified specific miRNAs for accurate EHT subtype diagnosis.
Area of Science:
- Biomarker discovery
- Genomics
- Cardiovascular research
Background:
- Hypertension affects 1 in 3 adults, with primary hypertension (PHT) being most common.
- Endocrine hypertension (EHT), ~10% of cases, arises from conditions like primary aldosteronism (PA), Cushing's syndrome (CS), or pheochromocytoma/paraganglioma (PPGL).
- EHT is often misdiagnosed, delaying treatment and leading to ineffective management.
Purpose of the Study:
- To identify circulating microRNA (miRNA) biomarkers for distinguishing EHT and its subtypes from PHT.
- To evaluate the effectiveness of machine learning (ML) in classifying hypertension types using miRNA profiles.
Main Methods:
- Systematic analysis of circulating miRNA features.
- Application of 8 supervised machine learning methods for classification and prediction.
- Utilizing miRNA data to differentiate EHT subtypes (PA, CS, PPGL) from PHT.
Main Results:
- Machine learning models achieved high accuracy: AUC of 0.9 for classifying PPGL, CS, and EHT from PHT.
- Models achieved AUC of 0.8 for distinguishing PA from PHT.
- Key circulating miRNAs identified include hsa-miR-15a-5p and hsa-miR-32-5p.
Conclusions:
- Circulating miRNAs show significant potential as diagnostic biomarkers for EHT.
- Machine learning is a viable tool for identifying informative miRNAs for hypertension diagnosis.
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.
