A taxonomic signature of obesity in a large study of American adults

Brandilyn A. Peters, Jean A. Shapiro, Timothy R. Church, George Miller, Chau Trinh-Shevrin, Elizabeth Yuen, Charles Friedlander, Richard B. Hayes, Jiyoung Ahn

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

Animal models suggest that gut microbiota contribute to obesity; however, a consistent taxonomic signature of obesity has yet to be identified in humans. We examined whether a taxonomic signature of obesity is present across two independent study populations. We assessed gut microbiome from stool for 599 adults, by 16S rRNA gene sequencing. We compared gut microbiome diversity, overall composition, and individual taxon abundance for obese (BMI ≥ 30 kg/m2), overweight (25 ≤ BMI < 30), and healthy-weight participants (18.5 ≤ BMI < 25). We found that gut species richness was reduced (p = 0.04), and overall composition altered (p = 0.04), in obese (but not overweight) compared to healthy-weight participants. Obesity was characterized by increased abundance of class Bacilli and its families Streptococcaceae and Lactobacillaceae, and decreased abundance of several groups within class Clostridia, including Christensenellaceae, Clostridiaceae, and Dehalobacteriaceae (q < 0.05). These findings were consistent across two independent study populations. When random forest models were trained on one population and tested on the other as well as a previously published dataset, accuracy of obesity prediction was good (70%). Our large study identified a strong and consistent taxonomic signature of obesity. Though our study is cross-sectional and causality cannot be determined, identification of microbes associated with obesity can potentially provide targets for obesity prevention and treatment.

Original languageEnglish (US)
Article number9749
JournalScientific reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018

Bibliographical note

Funding Information:
Research reported in this publication was supported in part by the US National Cancer Institute under award numbers R01CA159036, U01CA182370, R01CA164964, R03CA159414, P30CA016087, and R21CA183887. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Samples were sequenced at the NYUMC Genome Technology Center. The NYUMC Genome Technology Center is partially supported by the Cancer Center Support Grant, P30CA016087, at the Laura and Isaac Perlmutter Cancer Center.

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