@article{ReelReelErlicetal.2022,
  author    = {Reel, Smarti and Reel, Parminder S. and Erlic, Zoran and Amar, Laurence and Pecori, Alessio and Larsen, Casper K. and Tetti, Martina and Pamporaki, Christina and Prehn, Cornelia and Adamski, Jerzy and Prejbisz, Aleksander and Ceccato, Filippo and Scaroni, Carla and Kroiss, Matthias and Dennedy, Michael C. and Deinum, Jaap and Eisenhofer, Graeme and Langton, Katharina and Mulatero, Paolo and Reincke, Martin and Rossi, Gian Paolo and Lenzini, Livia and Davies, Eleanor and Gimenez-Roqueplo, Anne-Paule and Assi{\´e}, Guillaume and Blanchard, Anne and Zennaro, Maria-Christina and Beuschlein, Felix and Jefferson, Emily R.},
  title     = {Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios},
  series = {Metabolites},
  volume    = {12},
  journal   = {Metabolites},
  number    = {8},
  issn      = {2218-1989},
  doi       = {10.3390/metabo12080755},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-286161},
  year      = {2022},
  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.},
  language  = {en}
}