Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios

Reel, S. et al. (2022) Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios. Metabolites, 12(8), 755. (doi: 10.3390/metabo12080755) (PMID:36005627) (PMCID:PMC9416693)

Abstract

Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing’s syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification.

• Item Type: Articles
• Keywords: Metabolomics, machine learning, hypertension, primary aldosteronism, pheochromocytoma/paraganglioma, Cushing syndrome, biomarkers.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jefferson, Professor Emily and Davies, Professor Eleanor

Creator Roles

Davies, E.: Data curation, Writing – review and editing
Jefferson, E. R.: Data curation, Writing – review and editing, Conceptualization, Resources, Writing – original draft, Supervision, Project administration, Funding acquisition

• Authors: Reel, S., Reel, P. S., Erlic, Z., Amar, L., Pecori, A., Larsen, C. K., Tetti, M., Pamporaki, C., Prehn, C., Adamski, J., Prejbisz, A., Ceccato, F., Scaroni, C., Kroiss, M., Dennedy, M. C., Deinum, J., Eisenhofer, G., Langton, K., Mulatero, P., Reincke, M., Rossi, G. P., Lenzini, L., Davies, E., Gimenez-Roqueplo, A.-P., Assié, G., Blanchard, A., Zennaro, M.-C., Beuschlein, F., and Jefferson, E. R.
• College/School: College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health; College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Public Health
• Journal Name: Metabolites
• Publisher: MDPI
• ISSN: 2218-1989
• ISSN (Online): 2218-1989
• Published Online: 16 August 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Metabolites 12(8): 755
• Publisher Policy: Reproduced under a Creative Commons License