Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension.

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

Pre- versus post-operative untargeted plasma nuclear magnetic resonance spectroscopy metabolomics of pheochromocytoma and paraganglioma.

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.

Prevalence of coeliac disease in Northwest China: heterogeneity across Northern Silk road ethnic populations.

BACKGROUND: Epidemiological data of coeliac disease are lacking from the central Asian region. AIMS: To verify the occurrence of coeliac disease amongst four major ethnic groups of Xinjiang Uyghur Autonomus Region, China. METHODS: 2277 in-patients with gastrointestinal symptoms (1391 Han, 608 Uyghur, 146 Kazakh and 132 Hui; mean age: 54 ± 12.8 years) were included. Total IgA, anti-deamidated gliadin peptide (DGP)-IgG, and anti-tissue transglutaminase (anti-tTG)-IgA were analysed. All antibody-positive subjects were further tested for endomysial (EMA) antibodies and were HLA genotyped. All subjects with antibody positivity were asked to undergo intestinal biopsy. In addition, a subset of antibody-negative subjects were tested for HLA-DQA1and DQB1. RESULTS: Among the 2277 subjects, 29 subjects were defined as coeliac disease autoimmune (positive results for anti-tTG IgA and EMA-IgA) (1.27%; 95% confidence interval, 0.81%-1.73%), eight of them underwent biopsy and all showed coeliac disease histology (0.35%; 95% Cl, 0.11%-0.59%). The frequency of coeliac disease autoimmunity was lowest among the Han (0.79%), followed by the Uyghur (1.81%), the Kazakh (2.05%) and the Hui (3.03%). The frequency of the HLA-DQ2 and/or DQ8 haplotype was highest in the Uyghur (52.1%), followed by the Hui (44.4%), the Kazakh (40.0%) and the Han (39.4%). Besides, a three times higher frequency of coeliac disease autoimmunity was found among rural living subjects with significantly higher wheat consumption compared to urban living subjects (3.16% vs 0.97%, P < 0.01). CONCLUSIONS: In Xinjiang, coeliac disease does occur, especially in the rural area. The HLA haplotype and environment play key roles in the development of coeliac disease.

A complete workflow for high-resolution spectral-stitching nanoelectrospray direct-infusion mass-spectrometry-based metabolomics and lipidomics.

Metabolomic and lipidomic studies measure and discover metabolic and lipid profiles in biological samples, enabling a better understanding of the metabolism of specific biological phenotypes. Accurate biological interpretations require high analytical reproducibility and sensitivity, and standardized and transparent data processing. Here we describe a complete workflow for nanoelectrospray ionization (nESI) direct-infusion mass spectrometry (DIMS) metabolomics and lipidomics. After metabolite and lipid extraction from tissues and biofluids, samples are directly infused into a high-resolution mass spectrometer (e.g., Orbitrap) using a chip-based nESI sample delivery system. nESI functions to minimize ionization suppression or enhancement effects as compared with standard electrospray ionization (ESI). Our analytical technique-named spectral stitching-measures data as several overlapping mass-to-charge (m/z) windows that are subsequently 'stitched' together, creating a complete mass spectrum. This considerably increases the dynamic range and detection sensitivity-about a fivefold increase in peak detection-as compared with the collection of DIMS data as a single wide mass-to-charge (m/z ratio) window. Data processing, statistical analysis and metabolite annotation are executed as a workflow within the user-friendly, transparent and freely available Galaxy platform (galaxyproject.org). Generated data have high mass accuracy that enables molecular formulae peak annotations. The workflow is compatible with any sample-extraction method; in this protocol, the examples are extracted using a biphasic method, with methanol, chloroform and water as the solvents. The complete workflow is reproducible, rapid and automated, which enables cost-effective analysis of >10,000 samples per year, making it ideal for high-throughput metabolomics and lipidomics screening-e.g., for clinical phenotyping, drug screening and toxicity testing.