Dysbiosis, inflammation, and response to treatment: a longitudinal study of pediatric subjects with newly diagnosed inflammatory bowel disease

Beskrivning

Abstract Background Gut microbiome dysbiosis has been demonstrated in subjects with newly diagnosed and chronic inflammatory bowel disease (IBD). In this study we sought to explore longitudinal changes in dysbiosis and ascertain associations between dysbiosis and markers of disease activity and treatment outcome. Methods We performed a prospective cohort study of 19 treatment-naïve pediatric IBD subjects and 10 healthy controls, measuring fecal calprotectin and assessing the gut microbiome via repeated stool samples. Associations between clinical characteristics and the microbiome were tested using generalized estimating equations. Random forest classification was used to predict ultimate treatment response (presence of mucosal healing at follow-up colonoscopy) or non-response using patients’ pretreatment samples. Results Patients with Crohn’s disease had increased markers of inflammation and dysbiosis compared to controls. Patients with ulcerative colitis had even higher inflammation and dysbiosis compared to those with Crohn’s disease. For all cases, the gut microbial dysbiosis index associated significantly with clinical and biological measures of disease severity, but did not associate with treatment response. We found differences in specific gut microbiome genera between cases/controls and responders/non-responders including Akkermansia, Coprococcus, Fusobacterium, Veillonella, Faecalibacterium, and Adlercreutzia. Using pretreatment microbiome data in a weighted random forest classifier, we were able to obtain 76.5 % accuracy for prediction of responder status. Conclusions Patient dysbiosis improved over time but persisted even among those who responded to treatment and achieved mucosal healing. Although dysbiosis index was not significantly different between responders and non-responders, we found specific genus-level differences. We found that pretreatment microbiome signatures are a promising avenue for prediction of remission and response to treatment.
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Publiceringsår

2016

Typ av data

Upphovspersoner

Department of Computer Science

Abhiram Srivatsa - Medarbetare

Aleksandar D. Kostic - Medarbetare

Archana Kumar - Medarbetare

Cary Sauer - Medarbetare

Glen A. Satten - Medarbetare

Jarod Prince - Medarbetare

Jennifer G. Mulle - Medarbetare

Kelly A. Shaw - Medarbetare

Madeline Bertha - Medarbetare

Michael E. Zwick - Medarbetare

Pankaj Chopra - Medarbetare

Ramnik J. Xavier - Medarbetare

Subra Kugathasan - Medarbetare

Tatyana Hofmekler - Medarbetare

Tommi Vatanen - Upphovsperson

Centers for Disease Control and Prevention - Medarbetare

Children's Healthcare of Atlanta - Medarbetare

Emory University - Medarbetare

Harvard University - Medarbetare

Massachusetts General Hospital - Medarbetare

Xavier Lab in the Broad Institute - Medarbetare

figshare - Utgivare

Projekt

Övriga uppgifter

Vetenskapsområden

Data- och informationsvetenskap

Språk

Öppen tillgång

Öppet

Licens

Creative Commons Attribution 4.0 International (CC BY 4.0)

Nyckelord

Ämnesord

Temporal täckning

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