TY  - JOUR
A1  - Reel, Smarti
A1  - Reel, Parminder S.
A1  - Erlic, Zoran
A1  - Amar, Laurence
A1  - Pecori, Alessio
A1  - Larsen, Casper K.
A1  - Tetti, Martina
A1  - Pamporaki, Christina
A1  - Prehn, Cornelia
A1  - Adamski, Jerzy
A1  - Prejbisz, Aleksander
A1  - Ceccato, Filippo
A1  - Scaroni, Carla
A1  - Kroiss, Matthias
A1  - Dennedy, Michael C.
A1  - Deinum, Jaap
A1  - Eisenhofer, Graeme
A1  - Langton, Katharina
A1  - Mulatero, Paolo
A1  - Reincke, Martin
A1  - Rossi, Gian Paolo
A1  - Lenzini, Livia
A1  - Davies, Eleanor
A1  - Gimenez-Roqueplo, Anne-Paule
A1  - Assié, Guillaume
A1  - Blanchard, Anne
A1  - Zennaro, Maria-Christina
A1  - Beuschlein, Felix
A1  - Jefferson, Emily R.
T1  - Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios
JF  - Metabolites
N2  - 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.
KW  - metabolomics
KW  - machine learning
KW  - hypertension
KW  - primary aldosteronism
KW  - pheochromocytoma/paraganglioma
KW  - Cushing syndrome
KW  - biomarkers
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286161
SN  - 2218-1989
VL  - 12
IS  - 8
ER  -