@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}
}
@article{BliziotisKluijtmansTinneveltetal.2022,
  author    = {Bliziotis, Nikolaos G. and Kluijtmans, Leo A. J. and Tinnevelt, Gerjen H. and Reel, Parminder and Reel, Smarti and Langton, Katharina and Robledo, Mercedes and Pamporaki, Christina and Pecori, Alessio and Van Kralingen, Josie and Tetti, Martina and Engelke, Udo F. H. and Erlic, Zoran and Engel, Jasper and Deutschbein, Timo and N{\"o}lting, Svenja and Prejbisz, Aleksander and Richter, Susan and Adamski, Jerzy and Januszewicz, Andrzej and Ceccato, Filippo and Scaroni, Carla and Dennedy, Michael C. and Williams, Tracy A. and Lenzini, Livia and Gimenez-Roqueplo, Anne-Paule and Davies, Eleanor and Fassnacht, Martin and Remde, Hanna and Eisenhofer, Graeme and Beuschlein, Felix and Kroiss, Matthias and Jefferson, Emily and Zennaro, Maria-Christina and Wevers, Ron A. and Jansen, Jeroen J. and Deinum, Jaap and Timmers, Henri J. L. M.},
  title     = {Preanalytical pitfalls in untargeted plasma nuclear magnetic resonance metabolomics of endocrine hypertension},
  series = {Metabolites},
  volume    = {12},
  journal   = {Metabolites},
  number    = {8},
  issn      = {2218-1989},
  doi       = {10.3390/metabo12080679},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-282930},
  year      = {2022},
  abstract  = {Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.},
  language  = {en}
}