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Plasma metabolome profiling for the diagnosis of catecholamine producing tumors.
Front. Endocrin. 12:722656 (2021)
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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Adrenal ; Catecholamines ; Feature Selection ; Machine Learning ; Mass Spectronomy ; Paraganglioma ; Pheochromocytoma ; Targeted Metabolomics
ISSN (print) / ISBN
1664-2392
e-ISSN
1664-2392
Journal
Frontiers in Endocrinology
Quellenangaben
Volume: 12,
Article Number: 722656
Publisher
Frontiers
Publishing Place
Lausanne
Non-patent literature
Publications
Reviewing status
Peer reviewed
Grants
Deutsche Forschungsgemeinschaft
Schickedanz Kinderkrebsstiftung
Schickedanz Kinderkrebsstiftung