Jeffrey B Schwimmer1, Jethro S Johnson2, Jorge E Angeles3, Cynthia Behling4, Patricia H Belt5, Ingrid Borecki6, Craig Bross3, Janis Durelle3, Nidhi P Goyal3, Gavin Hamilton7, Mary L Holtz8, Joel E Lavine9, Makedonka Mitreva6, Kimberly P Newton1, Amy Pan10, Pippa M Simpson10, Claude B Sirlin7, Erica Sodergren2, Rahul Tyagi6, Katherine P Yates5, George M Weinstock2, Nita H Salzman11. 1. Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of California, San Diego School of Medicine, La Jolla, California; Department of Gastroenterology, Rady Children's Hospital San Diego, San Diego, California. 2. The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut. 3. Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of California, San Diego School of Medicine, La Jolla, California. 4. Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of California, San Diego School of Medicine, La Jolla, California; Department of Pathology, Sharp Medical Center, San Diego, California. 5. Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 6. The McDonnell Genome Institute, Washington University in St. Louis, St. Louis, Missouri. 7. Liver Imaging Group, Department of Radiology, University of California, San Diego, California. 8. Department of Pediatrics, Division of Gastroenterology, Medical College of Wisconsin, Milwaukee, Wisconsin; Center for Microbiome Research, Medical College of Wisconsin, Milwaukee, Wisconsin. 9. Department of Pediatrics, Division of Pediatric Gastroenterology, Hepatology and Nutrition, Columbia University, New York, New York. 10. Department of Pediatrics, Division of Quantitative Health Sciences, The Medical College of Wisconsin, Milwaukee, Wisconsin. 11. Department of Pediatrics, Division of Gastroenterology, Medical College of Wisconsin, Milwaukee, Wisconsin; Center for Microbiome Research, Medical College of Wisconsin, Milwaukee, Wisconsin. Electronic address: nsalzman@mcw.edu.
Abstract
BACKGROUND & AIMS: The intestinal microbiome might affect the development and severity of nonalcoholic fatty liver disease (NAFLD). We analyzed microbiomes of children with and without NAFLD. METHODS: We performed a prospective, observational, cross-sectional study of 87 children (age range, 8-17 years) with biopsy-proven NAFLD and 37 children with obesity without NAFLD (controls). Fecal samples were collected and microbiome composition and functions were assessed using 16S ribosomal RNA amplicon sequencing and metagenomic shotgun sequencing. Microbial taxa were identified using zero-inflated negative binomial modeling. Genes contributing to bacterial pathways were identified using gene set enrichment analysis. RESULTS: Fecal microbiomes of children with NAFLD had lower α-diversity than those of control children (3.32 vs 3.52, P = .016). Fecal microbiomes from children with nonalcoholic steatohepatitis (NASH) had the lowest α-diversity (control, 3.52; NAFLD, 3.36; borderline NASH, 3.37; NASH, 2.97; P = .001). High abundance of Prevotella copri was associated with more severe fibrosis (P = .036). Genes for lipopolysaccharide biosynthesis were enriched in microbiomes from children with NASH (P < .001). Classification and regression tree model with level of alanine aminotransferase and relative abundance of the lipopolysaccharide pathway gene encoding 3-deoxy-d-manno-octulosonate 8-phosphate-phosphatase identified patients with NASH with an area under the receiver operating characteristic curve value of 0.92. Genes involved in flagellar assembly were enriched in the fecal microbiomes of patients with moderate to severe fibrosis (P < .001). Classification and regression tree models based on level of alanine aminotransferase and abundance of genes encoding flagellar biosynthesis protein had good accuracy for identifying case children with moderate to severe fibrosis (area under the receiver operating characteristic curve, 0.87). CONCLUSIONS: In an analysis of fecal microbiomes of children with NAFLD, we associated NAFLD and NASH with intestinal dysbiosis. NAFLD and its severity were associated with greater abundance of genes encoding inflammatory bacterial products. Alterations to the intestinal microbiome might contribute to the pathogenesis of NAFLD and be used as markers of disease or severity.
BACKGROUND & AIMS: The intestinal microbiome might affect the development and severity of nonalcoholic fatty liver disease (NAFLD). We analyzed microbiomes of children with and without NAFLD. METHODS: We performed a prospective, observational, cross-sectional study of 87 children (age range, 8-17 years) with biopsy-proven NAFLD and 37 children with obesity without NAFLD (controls). Fecal samples were collected and microbiome composition and functions were assessed using 16S ribosomal RNA amplicon sequencing and metagenomic shotgun sequencing. Microbial taxa were identified using zero-inflated negative binomial modeling. Genes contributing to bacterial pathways were identified using gene set enrichment analysis. RESULTS: Fecal microbiomes of children with NAFLD had lower α-diversity than those of control children (3.32 vs 3.52, P = .016). Fecal microbiomes from children with nonalcoholic steatohepatitis (NASH) had the lowest α-diversity (control, 3.52; NAFLD, 3.36; borderline NASH, 3.37; NASH, 2.97; P = .001). High abundance of Prevotella copri was associated with more severe fibrosis (P = .036). Genes for lipopolysaccharide biosynthesis were enriched in microbiomes from children with NASH (P < .001). Classification and regression tree model with level of alanine aminotransferase and relative abundance of the lipopolysaccharide pathway gene encoding 3-deoxy-d-manno-octulosonate 8-phosphate-phosphatase identified patients with NASH with an area under the receiver operating characteristic curve value of 0.92. Genes involved in flagellar assembly were enriched in the fecal microbiomes of patients with moderate to severe fibrosis (P < .001). Classification and regression tree models based on level of alanine aminotransferase and abundance of genes encoding flagellar biosynthesis protein had good accuracy for identifying case children with moderate to severe fibrosis (area under the receiver operating characteristic curve, 0.87). CONCLUSIONS: In an analysis of fecal microbiomes of children with NAFLD, we associated NAFLD and NASH with intestinal dysbiosis. NAFLD and its severity were associated with greater abundance of genes encoding inflammatory bacterial products. Alterations to the intestinal microbiome might contribute to the pathogenesis of NAFLD and be used as markers of disease or severity.
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