Bacterial Taxa and Functions Are Predictive of Sustained Remission Following Exclusive Enteral Nutrition in Pediatric Crohn's Disease.

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  • Additional Information
    • Source:
      Publisher: Oxford University Press Country of Publication: England NLM ID: 9508162 Publication Model: Print Cited Medium: Internet ISSN: 1536-4844 (Electronic) Linking ISSN: 10780998 NLM ISO Abbreviation: Inflamm Bowel Dis Subsets: MEDLINE
    • Publication Information:
      Publication: 2018- : [Oxford] : Oxford University Press
      Original Publication: New York, NY : Raven Press, c1995-
    • Subject Terms:
      Enteral Nutrition*; Bacteria/*classification ; Bacterial Typing Techniques/*statistics & numerical data ; Crohn Disease/*microbiology ; Gastrointestinal Microbiome/*genetics; Adolescent ; Bacteria/genetics ; Bacterial Typing Techniques/methods ; Child ; Crohn Disease/therapy ; Feces/chemistry ; Feces/microbiology ; Female ; Follow-Up Studies ; Humans ; Leukocyte L1 Antigen Complex/analysis ; Machine Learning ; Male ; Metagenome ; Predictive Value of Tests ; Prospective Studies ; RNA, Ribosomal, 16S ; Recurrence ; Remission Induction ; Severity of Illness Index
    • Abstract:
      Background: The gut microbiome is extensively involved in induction of remission in pediatric Crohn's disease (CD) patients by exclusive enteral nutrition (EEN). In this follow-up study of pediatric CD patients undergoing treatment with EEN, we employ machine learning models trained on baseline gut microbiome data to distinguish patients who achieved and sustained remission (SR) from those who did not achieve remission nor relapse (non-SR) by 24 weeks.
      Methods: A total of 139 fecal samples were obtained from 22 patients (8-15 years of age) for up to 96 weeks. Gut microbiome taxonomy was assessed by 16S rRNA gene sequencing, and functional capacity was assessed by metagenomic sequencing. We used standard metrics of diversity and taxonomy to quantify differences between SR and non-SR patients and to associate gut microbial shifts with fecal calprotectin (FCP), and disease severity as defined by weighted Pediatric Crohn's Disease Activity Index. We used microbial data sets in addition to clinical metadata in random forests (RFs) models to classify treatment response and predict FCP levels.
      Results: Microbial diversity did not change after EEN, but species richness was lower in low-FCP samples (<250 µg/g). An RF model using microbial abundances, species richness, and Paris disease classification was the best at classifying treatment response (area under the curve [AUC] = 0.9). KEGG Pathways also significantly classified treatment response with the addition of the same clinical data (AUC = 0.8). Top features of the RF model are consistent with previously identified IBD taxa, such as Ruminococcaceae and Ruminococcus gnavus.
      Conclusions: Our machine learning approach is able to distinguish SR and non-SR samples using baseline microbiome and clinical data.
      (© 2020 Crohn’s & Colitis Foundation. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation.)
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    • Contributed Indexing:
      Keywords: exclusive enteral nutrition; gut microbiome; nutrition in pediatrics; pediatric Crohn’s disease
    • Accession Number:
      0 (Leukocyte L1 Antigen Complex)
      0 (RNA, Ribosomal, 16S)
    • Subject Terms:
      Pediatric Crohn's disease
    • Publication Date:
      Date Created: 20200122 Date Completed: 20210909 Latest Revision: 20210909
    • Publication Date:
      20231215
    • Accession Number:
      PMC7301407
    • Accession Number:
      10.1093/ibd/izaa001
    • Accession Number:
      31961432