TY - JOUR A1 - März, Juliane A1 - Kurlbaum, Max A1 - Roche-Lancaster, Oisin A1 - Deutschbein, Timo A1 - Peitzsch, Mirko A1 - Prehn, Cornelia A1 - Weismann, Dirk A1 - Robledo, Mercedes A1 - Adamski, Jerzy A1 - Fassnacht, Martin A1 - Kunz, Meik A1 - Kroiss, Matthias T1 - Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors JF - Frontiers in Endocrinology N2 - Context Pheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome level have been described after surgical PPGL removal. The value of metabolomics for the diagnosis of PPGL has not been studied yet. Objective Evaluation of quantitative metabolomics as a diagnostic tool for PPGL. Design Targeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens and statistical modeling using ML-based feature selection approaches in a clinically well characterized cohort study. Patients Prospectively enrolled patients (n=36, 17 female) from the Prospective Monoamine-producing Tumor Study (PMT) with hormonally active PPGL and 36 matched controls in whom PPGL was rigorously excluded. Results Among 188 measured metabolites, only without considering false discovery rate, 4 exhibited statistically significant differences between patients with PPGL and controls (histidine p=0.004, threonine p=0.008, lyso PC a C28:0 p=0.044, sum of hexoses p=0.018). Weak, but significant correlations for histidine, threonine and lyso PC a C28:0 with total urine catecholamine levels were identified. Only the sum of hexoses (reflecting glucose) showed significant correlations with plasma metanephrines. By using ML-based feature selection approaches, we identified diagnostic signatures which all exhibited low accuracy and sensitivity. The best predictive value (sensitivity 87.5%, accuracy 67.3%) was obtained by using Gradient Boosting Machine Modelling. Conclusions The diabetogenic effect of catecholamine excess dominates the plasma metabolome in PPGL patients. While curative surgery for PPGL led to normalization of catecholamine-induced alterations of metabolomics in individual patients, plasma metabolomics are not useful for diagnostic purposes, most likely due to inter-individual variability. KW - adrenal KW - pheochromocytoma KW - paraganglioma KW - targeted metabolomics KW - mass spectronomy KW - catecholamines KW - machine learning KW - feature selection Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-245710 SN - 1664-2392 VL - 12 ER - 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 -