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Related Experiment Video

Updated: Sep 20, 2025

Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291
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Gut metabolites predict Clostridioides difficile recurrence.

Jennifer J Dawkins1,2, Jessica R Allegretti3,4, Travis E Gibson1

  • 1Department of Pathology, Brigham & Woman's Hospital, Harvard Medical School, Boston, MA, USA.

Microbiome
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

Recurrent Clostridioides difficile infection (CDI) is linked to gut microbiome and metabolome changes. Metabolomic data accurately predicts CDI recurrence, offering potential for new diagnostic biomarkers.

Keywords:
C. difficileGastrointestinalHuman infectionLongitudinalMetabolomicsPredictive model

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Area of Science:

  • Microbiome research
  • Metabolomics
  • Infectious disease dynamics

Background:

  • Clostridioides difficile infection (CDI) is a leading hospital-acquired infection in the USA, with high recurrence rates exceeding 15%.
  • While gut microbial dysbiosis is linked to primary CDI, factors influencing recurrence remain poorly understood.
  • Previous studies using 16S rRNA sequencing alone have not reliably predicted CDI recurrence.

Purpose of the Study:

  • To investigate the role of gut microbiome and metabolome alterations in predicting Clostridioides difficile infection (CDI) recurrence.
  • To identify predictive biomarkers for CDI recurrence using a multi-omic approach.
  • To understand the dynamics of microbial and metabolic changes post-antibiotic treatment in primary CDI.

Main Methods:

  • A prospective, longitudinal study of 53 participants with primary CDI.
  • Stool sample analysis included 16S rRNA amplicon sequencing, metabolomics (1387 metabolites), and short-chain fatty acid profiling.
  • Predictive statistical/machine learning models were applied to multi-omic data.

Main Results:

  • Recurrent CDI cases showed delayed microbial diversity recovery and depletion of key anaerobic taxa (e.g., Clostridium clusters XIVa, IV).
  • Metabolomic analysis revealed delayed recovery and distinct functional abnormalities, including decreased deconjugation activity and altered endocannabinoid levels.
  • Metabolomic data, unlike other data sources, accurately predicted CDI recurrence at 1 and 2 weeks post-treatment (AUC 0.77).

Conclusions:

  • Longitudinal multi-omic analysis uncovered novel microbiome and host dynamics preceding CDI recurrence, particularly highlighting the gut metabolome.
  • A select panel of metabolites demonstrated high accuracy in predicting future CDI recurrence.
  • Findings support the development of metabolite-based diagnostic tests and targeted therapies to prevent CDI recurrence.