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Purpose Pheochromocytomas and Paragangliomas (PPGL) result in chronic catecholamine excess and serious health complications. A recent study obtained a metabolic signature in plasma from PPGL patients; however, its targeted nature may have generated an incomplete picture and a broader approach could provide additional insights. We aimed to characterize the plasma metabolome of PPGL patients before and after surgery, using an untargeted approach, and to broaden the scope of the investigated metabolic impact of these tumors. Design A cohort of 36 PPGL patients was investigated. Blood plasma samples were collected before and after surgical tumor removal, in association with clinical and tumor characteristics. Methods Plasma samples were analyzed using untargeted nuclear magnetic resonance (NMR) spectroscopy metabolomics. The data were evaluated using a combination of uni- and multi-variate statistical methods. Results Before surgery, patients with a nonadrenergic tumor could be distinguished from those with an adrenergic tumor based on their metabolic profiles. Tyrosine levels were significantly higher in patients with high compared to those with low BMI. Comparing subgroups of pre-operative samples with their post-operative counterparts, we found a metabolic signature that included ketone bodies, glucose, organic acids, methanol, dimethyl sulfone and amino acids. Three signals with unclear identities were found to be affected. Conclusions Our study suggests that the pathways of glucose and ketone body homeostasis are affected in PPGL patients. BMI-related metabolite levels were also found to be altered, potentially linking muscle atrophy to PPGL. At baseline, patient metabolomes could be discriminated based on their catecholamine phenotype.

Following publication of the original article, the authors would like to correct a sentence in the paragraph “1H-NMR spectra were recorded at 298 K…” under the heading “NMR experiments”. The sentence currently reads: “The LED pulse sequence had the form -RD-901-G1- 1801-G1-901-G2-T-901-G1-1801-G1-901-G2-t-901-acquire FID, where RD is a relaxation delay, 901 is a 901 RF pulse, G1 is the pulsed-field gradient that is applied to allow editing, 1801 is a 1801 RF pulse, G2 is a spoil gradient applied to remove unwanted magnetization components. The diffusion delay Δ is the time during which the molecules are allowed to diffuse—this is the period (901-G1-1801-G1- 901-G2-T-); and t is a delay to allow the longitudinal eddy currents caused within the sample to decay (Beckonert et al. 2007).” The sentence should read: “The LED pulse sequence had the form -RD-90°-G1- 180°-G1-90°-G2-T-90°-G1-180°-G1-90°-G2-t-90°-acquire FID, where RD is a relaxation delay, 90° is a 90° RF pulse, G1 is the pulsed-field gradient that is applied to allow editing, 180° is a 180° RF pulse, G2 is a spoil gradient applied to remove unwanted magnetization components. The diffusion delay ? is the time during which the molecules are allowed to diffuse—this is the period (90°-G1-180°-G1-90°-G2 T-); and t is a delay to allow the longitudinal eddy currents caused within the sample to decay (Beckonert et al. 2007).” This has been corrected with this erratum.

Introduction When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr–Purcell–Meiboom–Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. Objectives Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. Methods We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares–Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. Results We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability, ~ 85% of all peaks had an adequately low coefficient of variation ( Conclusion Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery.