High-Throughput UHPLC-MS to Screen Metabolites in Feces for Gut Metabolic Health

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  • Andressa de Zawadzki
  • Maja Thiele
  • Tommi Suvitaival
  • Asger Wretlind
  • Min Kim
  • Mina Ali
  • Annette F. Bjerre
  • Karin Stahr
  • Ismo Mattila
  • Hansen, Torben
  • Aleksander Krag
  • Cristina Legido-Quigley

Feces are the product of our diets and have been linked to diseases of the gut, including Chron's disease and metabolic diseases such as diabetes. For screening metabolites in heterogeneous samples such as feces, it is necessary to use fast and reproducible analytical methods that maximize metabolite detection. As sample preparation is crucial to obtain high quality data in MS-based clinical metabolomics, we developed a novel, efficient and robust method for preparing fecal samples for analysis with a focus in reducing aliquoting and detecting both polar and nonpolar metabolites. Fecal samples (n = 475) from patients with alcohol-related liver disease and healthy controls were prepared according to the proposed method and analyzed in an UHPLC-QQQ targeted platform in order to obtain a quantitative profile of compounds that impact liver-gut axis metabolism. MS analyses of the prepared fecal samples have shown reproducibility and coverage of n = 28 metabolites, mostly comprising bile acids and amino acids. We report metabolitewise relative standard deviation (RSD) in quality control samples, inter-day repeatability, LOD, LOQ, range of linearity and method recovery. The average concentrations for 135 healthy participants are reported here for clinical applications. Our high-throughput method provides a novel tool for investigating gut-liver axis metabolism in liver-related diseases using a noninvasive collected sample.

Original languageEnglish
Article number211
Issue number3
Number of pages16
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.Licensee MDPI, Basel, Switzerland

    Research areas

  • Bile acids, Fecal metabolomics, Gut-liver axis, Sample preparation, Targeted metabolomics

ID: 305688949