• search hit 9 of 64
Back to Result List

Preanalytical pitfalls in untargeted plasma nuclear magnetic resonance metabolomics of endocrine hypertension

Please always quote using this URN: urn:nbn:de:bvb:20-opus-282930
  • 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 106Despite 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.show moreshow less

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author: Nikolaos G. Bliziotis, Leo A. J. Kluijtmans, Gerjen H. Tinnevelt, Parminder Reel, Smarti Reel, Katharina Langton, Mercedes Robledo, Christina Pamporaki, Alessio Pecori, Josie Van Kralingen, Martina Tetti, Udo F. H. Engelke, Zoran Erlic, Jasper Engel, Timo Deutschbein, Svenja Nölting, Aleksander Prejbisz, Susan Richter, Jerzy Adamski, Andrzej Januszewicz, Filippo Ceccato, Carla Scaroni, Michael C. Dennedy, Tracy A. Williams, Livia Lenzini, Anne-Paule Gimenez-Roqueplo, Eleanor Davies, Martin Fassnacht, Hanna Remde, Graeme Eisenhofer, Felix Beuschlein, Matthias Kroiss, Emily Jefferson, Maria-Christina Zennaro, Ron A. Wevers, Jeroen J. Jansen, Jaap Deinum, Henri J. L. M. Timmers
URN:urn:nbn:de:bvb:20-opus-282930
Document Type:Journal article
Faculties:Medizinische Fakultät / Medizinische Klinik und Poliklinik I
Medizinische Fakultät / Comprehensive Cancer Center Mainfranken
Language:English
Parent Title (English):Metabolites
ISSN:2218-1989
Year of Completion:2022
Volume:12
Issue:8
Article Number:679
Source:Metabolites (2022) 12:8, 679. https://doi.org/10.3390/metabo12080679
DOI:https://doi.org/10.3390/metabo12080679
Sonstige beteiligte Institutionen:Zentraleinheit Klinische Massenspektrometrie
Tag:confounders; metabolomics; multicenter; plasma NMR; preanalytical conditions
Release Date:2023/08/18
Date of first Publication:2022/07/24
EU-Project number / Contract (GA) number:633983
OpenAIRE:OpenAIRE
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International