ena-STUDY-DALHOUSIE UNIVERSITY-19-07-2019-19:47:32:551-1723
A metagenomic approach to diagnosis, induction and maintenance of deep remission following exclusive enteral nutrition in pediatric Crohn's Disease
Induction of remission in pediatric Crohn's disease (CD) patients by exclusive enteral nutrition (EEN) extensively involves the gut microbiome. 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 those that achieved sustained remission (SR) and those who relapsed (non-SR) after 24 weeks. We collected robust clinical data from patients at each timepoint. A total of 139 fecal samples were obtained from 22 patients (age 8-15) 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 quantified by wPCDAI. We used microbial datasets in addition to clinical metadata in random forests (RF) models to classify treatment response and predict FCP levels.
Induction of remission in pediatric Crohn's disease (CD) patients by exclusive enteral nutrition (EEN) extensively involves the gut microbiome. 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 those that achieved sustained remission (SR) and those who relapsed (non-SR) after 24 weeks. We collected robust clinical data from patients at each timepoint. A total of 139 fecal samples were obtained from 22 patients (age 8-15) 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 quantified by wPCDAI. We used microbial datasets in addition to clinical metadata in random forests (RF) models to classify treatment response and predict FCP levels.