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Monitoring type 2 diabetes from volatile faecal metabolome in Cushing’s syndrome and single Afmid mouse models via a longitudinal study

  • Célia Lourenço
  • , Darren Kelly
  • , Jack Cantillon
  • , Michael Cauchi
  • , Marianne A. Yon
  • , Liz Bentley
  • , Roger Cox
  • , Claire Turner

    Research output: Contribution to JournalArticlepeer-review

    Abstract

    The analysis of volatile organic compounds (VOCs) as a non-invasive method for disease monitoring, such as type 2 diabetes (T2D) has shown potential over the years although not yet set in clinical practice. Longitudinal studies to date are limited and the understanding of the underlying VOC emission over the age is poorly understood. This study investigated longitudinal changes in VOCs present in faecal headspace in two mouse models of T2D – Cushing’s syndrome and single Afmid knockout mice. Longitudinal changes in bodyweight, blood glucose levels and plasma insulin concentration were also reported. Faecal headspace analysis was carried out using selected ion flow tube mass spectrometry (SIFT-MS) and thermal desorption coupled to gas chromatography-mass spectrometry (TD-GC-MS). Multivariate data analysis of the VOC profile showed differences mainly in acetic acid and butyric acid able to discriminate the groups Afmid and Cushing’s mice. Moreover, multivariate data analysis revealed statistically significant differences in VOCs between Cushing’s mice/wild-type (WT) littermates, mainly short-chain fatty acids (SCFAs), ketones, and alcohols, and longitudinal differences mainly attributed to methanol, ethanol and acetone. Afmid mice did not present statistically significant differences in their volatile faecal metabolome when compared to their respective WT littermates. The findings suggested that mice developed a diabetic phenotype and that the altered VOC profile may imply a related change in gut microbiota, particularly in Cushing’s mice. Furthermore, this study provided major evidence of age-related changes on the volatile profile of diabetic mice.
    Original languageEnglish
    Article number18779
    Number of pages13
    JournalScientific Reports
    Volume9
    DOIs
    Publication statusPublished - 11 Dec 2019

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Keywords

    • Type 2 Diabetes
    • Metabolomics
    • Prognostic Markers
    • SIFT-MS
    • VOCs
    • GC-MS
    • Pattern Recognition
    • Machine Learning

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