Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios

Smarti Reel (Lead / Corresponding author), Parminder Singh Reel, Zoran Erlic, Laurence Amar, Alessio Pecori, Casper K. Larsen, Martina Tetti, Christina Pamporaki, Cornelia Prehn, Jerzy Adamski, Michael C. Dennedy, Jaap Deinum, Graeme Eisenhofer, Katharina Langton, Paolo Mulatero, Martin Reincke, Gian Paolo Rossi, Livia Lenzini, Eleanor Davies, Maria Christina ZennaroFelix Beuschlein, Emily Jefferson (Lead / Corresponding author)

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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.

Original languageEnglish
Article number755
Number of pages20
JournalMetabolites
Volume12
Issue number8
DOIs
Publication statusPublished - 16 Aug 2022

Keywords

  • metabolomics
  • machine learning
  • hypertension
  • primary aldosteronism
  • pheochromocytoma/paraganglioma
  • Cushing syndrome
  • biomarkers

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