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Guiding longitudinal sampling in IBD cohorts
  1. Yoshiki Vázquez-Baeza1,
  2. Antonio Gonzalez2,
  3. Zhenjiang Zech Xu2,
  4. Alex Washburne3,
  5. Hans H Herfarth4,5,
  6. R Balfour Sartor4,5,6,
  7. Rob Knight1,2
  1. 1 Department of Computer Science and Engineering, University of California, San Diego, California, USA
  2. 2 Department of Pediatrics, University of California, San Diego, California, USA
  3. 3 Department of Microbiology and Immunology, Montana State University System, Bozeman, Montana, USA
  4. 4 Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina, USA
  5. 5 Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
  6. 6 Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, North Carolina, USA
  1. Correspondence to Professor Rob Knight, Department of Computer Science and Engineering, University of California, La Jolla, CA 92093-0763, USA; robknight{at}ucsd.edu

https://doi.org/10.1136/gutjnl-2017-315352

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    We read with interest the work by Pascal et al published recently in Gut.1 Here, they report the volatile microbial signatures of patients with Crohn’s disease (CD), a quality that greatly hinders our ability to classify healthy from affected subjects using 16S rRNA profiles from stool. Nonetheless, their work overcame these and other complications,2 producing a decision tree that classifies subjects with CD, UC, irritable bowel syndrome and anorexia. Although the authors note that both subtypes of IBD, particularly CD, have increased microbial community instability, this information is not used as a feature to improve classifier accuracy. Could microbiome instability become actionable by creating a new classifier that benefits from repeated measurements? If so, how many samples per individual are needed to assess instability?

    We collected daily stool samples for up to 6 weeks from 19 CD subjects and 12 controls (see the analysis notebook for cohort description, methods and data, https://github.com/knightlab-analyses/longitudinal-ibd) over two separate periods of 2 or 4 weeks spread over 2 and 5 months, for a total of 960 samples. We believe that this is the most densely sampled longitudinal study …

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