TY  - JOUR
A1  - Bliziotis, Nikolaos G.
A1  - Kluijtmans, Leo A. J.
A1  - Tinnevelt, Gerjen H.
A1  - Reel, Parminder
A1  - Reel, Smarti
A1  - Langton, Katharina
A1  - Robledo, Mercedes
A1  - Pamporaki, Christina
A1  - Pecori, Alessio
A1  - Van Kralingen, Josie
A1  - Tetti, Martina
A1  - Engelke, Udo F. H.
A1  - Erlic, Zoran
A1  - Engel, Jasper
A1  - Deutschbein, Timo
A1  - Nölting, Svenja
A1  - Prejbisz, Aleksander
A1  - Richter, Susan
A1  - Adamski, Jerzy
A1  - Januszewicz, Andrzej
A1  - Ceccato, Filippo
A1  - Scaroni, Carla
A1  - Dennedy, Michael C.
A1  - Williams, Tracy A.
A1  - Lenzini, Livia
A1  - Gimenez-Roqueplo, Anne-Paule
A1  - Davies, Eleanor
A1  - Fassnacht, Martin
A1  - Remde, Hanna
A1  - Eisenhofer, Graeme
A1  - Beuschlein, Felix
A1  - Kroiss, Matthias
A1  - Jefferson, Emily
A1  - Zennaro, Maria-Christina
A1  - Wevers, Ron A.
A1  - Jansen, Jeroen J.
A1  - Deinum, Jaap
A1  - Timmers, Henri J. L. M.
T1  - Preanalytical pitfalls in untargeted plasma nuclear magnetic resonance metabolomics of endocrine hypertension
JF  - Metabolites
N2  - 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.
KW  - confounders
KW  - metabolomics
KW  - multicenter
KW  - plasma NMR
KW  - preanalytical conditions
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-282930
SN  - 2218-1989
VL  - 12
IS  - 8
ER  - 
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  -