Please use this identifier to cite or link to this item: https://doi.org/10.3389/fendo.2021.722656
Title: Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors
Authors: März, Juliane
Kurlbaum, Max
Roche-Lancaster, Oisin
Deutschbein, Timo
Peitzsch, Mirko
Prehn, Cornelia
Weismann, Dirk
Robledo, Mercedes
Adamski, Jerzy 
Fassnacht, Martin
Kunz, Meik
Kroiss, Matthias
Keywords: adrenal
catecholamines
feature selection
machine learning
mass spectronomy
paraganglioma
pheochromocytoma
targeted metabolomics
Issue Date: 7-Sep-2021
Publisher: Frontiers Media S.A.
Citation: März, 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
Rights: Attribution 4.0 International
Abstract: 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. © Copyright © 2021 März, Kurlbaum, Roche-Lancaster, Deutschbein, Peitzsch, Prehn, Weismann, Robledo, Adamski, Fassnacht, Kunz and Kroiss.
Source Title: Frontiers in Endocrinology
URI: https://scholarbank.nus.edu.sg/handle/10635/232019
ISSN: 1664-2392
DOI: 10.3389/fendo.2021.722656
Rights: Attribution 4.0 International
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