Please use this identifier to cite or link to this item: https://doi.org/10.3389/fendo.2021.722656
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dc.titlePlasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors
dc.contributor.authorMärz, Juliane
dc.contributor.authorKurlbaum, Max
dc.contributor.authorRoche-Lancaster, Oisin
dc.contributor.authorDeutschbein, Timo
dc.contributor.authorPeitzsch, Mirko
dc.contributor.authorPrehn, Cornelia
dc.contributor.authorWeismann, Dirk
dc.contributor.authorRobledo, Mercedes
dc.contributor.authorAdamski, Jerzy
dc.contributor.authorFassnacht, Martin
dc.contributor.authorKunz, Meik
dc.contributor.authorKroiss, Matthias
dc.date.accessioned2022-10-11T07:53:32Z
dc.date.available2022-10-11T07:53:32Z
dc.date.issued2021-09-07
dc.identifier.citationMärz, Juliane, Kurlbaum, Max, Roche-Lancaster, Oisin, Deutschbein, Timo, Peitzsch, Mirko, Prehn, Cornelia, Weismann, Dirk, Robledo, Mercedes, Adamski, Jerzy, Fassnacht, Martin, Kunz, Meik, Kroiss, Matthias (2021-09-07). Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors. Frontiers in Endocrinology 12 : 722656. ScholarBank@NUS Repository. https://doi.org/10.3389/fendo.2021.722656
dc.identifier.issn1664-2392
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232019
dc.description.abstractContext: 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. © Copyright © 2021 März, Kurlbaum, Roche-Lancaster, Deutschbein, Peitzsch, Prehn, Weismann, Robledo, Adamski, Fassnacht, Kunz and Kroiss.
dc.publisherFrontiers Media S.A.
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.subjectadrenal
dc.subjectcatecholamines
dc.subjectfeature selection
dc.subjectmachine learning
dc.subjectmass spectronomy
dc.subjectparaganglioma
dc.subjectpheochromocytoma
dc.subjecttargeted metabolomics
dc.typeArticle
dc.contributor.departmentBIOCHEMISTRY
dc.description.doi10.3389/fendo.2021.722656
dc.description.sourcetitleFrontiers in Endocrinology
dc.description.volume12
dc.description.page722656
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