Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors

• Main Authors: Juliane März, Max Kurlbaum, Oisin Roche-Lancaster, Timo Deutschbein, Mirko Peitzsch, Cornelia Prehn, Dirk Weismann, Mercedes Robledo, Jerzy Adamski, Martin Fassnacht, Meik Kunz, Matthias Kroiss
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-09-01
• Series: Frontiers in Endocrinology
• Subjects: adrenal; pheochromocytoma; paraganglioma; targeted metabolomics; mass spectronomy; catecholamines
• Online Access: https://www.frontiersin.org/articles/10.3389/fendo.2021.722656/full

ContextPheochromocytomas 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.ObjectiveEvaluation of quantitative metabolomics as a diagnostic tool for PPGL.DesignTargeted 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.PatientsProspectively 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.ResultsAmong 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.ConclusionsThe 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.