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      Gut Microbiome Predictors of Treatment Response and Recurrence in Primary Clostridium difficile Infection

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          Abstract

          Background

          Clostridium difficile infection (CDI) may not respond to initial therapy and frequently recurs but predictors of response and recurrence are inconsistent. The impact of specific alterations in the gut microbiota determining treatment response and recurrence in patients with CDI is unknown.

          Aim

          To assess microbial signatures as predictors of treatment response and recurrence in CDI.

          Methods

          Pre-treatment stool samples and clinical metadata including outcomes were collected prospectively from patients with their first CDI episode. Next generation 16s rRNA sequencing using MiSeq Illumina platform was performed and changes in microbial community structure were correlated with CDI outcomes.

          Results

          Eighty-eight patients (median age 52.7 years, 60.2% female) were included. Treatment failure occurred in 12.5% and recurrence after response in 28.5%. Patients who responded to treatment had an increase in Ruminococcaceae, Rikenellaceae, Clostridiaceae, Bacteroides, Faecalibacterium and Rothia compared to non-responders. A risk-index built from this panel of microbes differentiated responders (mean 0.07±0.24) from non-responders (0.52±0.42; p=0.0002). Receiver operating characteristic (ROC) curve demonstrated that risk-index was a strong predictor of treatment response with an area under the curve (AUC) of 0.85. Among clinical parameters tested, only proton-pump inhibitor use predicted recurrent CDI (OR 3.75, 95%CI 1.27-11.1, p=0.01). Patients with recurrent CDI had statistically significant increases in Veillonella, Enterobacteriaceae, Streptococci, Parabacteroides and Lachnospiraceae compared to patients without recurrence and a risk index was able to predict recurrence (AUC=0.78).

          Conclusions

          Gut microbiota signatures predict treatment response and recurrence potentially allowing identification of CDI patients that may benefit from early institution of alternate therapies.

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          Author and article information

          Journal
          8707234
          1160
          Aliment Pharmacol Ther
          Aliment. Pharmacol. Ther.
          Alimentary pharmacology & therapeutics
          0269-2813
          1365-2036
          19 July 2016
          02 August 2016
          October 2016
          01 October 2017
          : 44
          : 7
          : 715-727
          Affiliations
          [1 ]Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
          [2 ]EA 3826 Thérapeutiques Cliniques et Expérimentales des Infections, Faculté de Médecine, Université de Nantes, Nantes, France
          [3 ]Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
          [4 ]Division of Infectious Diseases, Mayo Clinic, Rochester, MN
          [5 ]Division of Clinical Microbiology, Department of Medicine, Mayo Clinic, Rochester, MN.
          Author notes
          [# ] Corresponding authors: Purna Kashyap, MBBS, Assistant Professor of Medicine, Darrell S. Pardi, MD, Professor of Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, 200 First Street SW, Rochester, MN. 55905, kashyap.purna@ 123456mayo.edu and pardi.darrell@ 123456mayo.edu
          Article
          PMC5012905 PMC5012905 5012905 nihpa802700
          10.1111/apt.13750
          5012905
          27481036
          b4e70359-8f41-4571-8112-49b7392c13de
          History
          Categories
          Article

          prediction models, Clostridium difficile infection,recurrence,treatment response,microbiome

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