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Multi-omics approach to socioeconomic disparity in metabolic syndrome reveals roles of diet and microbiome
Authors:Faraz Bishehsari  Michael Drees  Darbaz Adnan  Deepak Sharma  Stefan Green  Jane Koshy  Leila B Giron  Aaron Goldman  Mohamed Abdel-Mohsen  Heather E Rasmussen  Gregory E Miller  Ali Keshavarzian
Institution:1. Rush Center for Integrated Microbiome and Chronobiology Research, Rush University Medical Center, Chicago, Illinois, USA;2. The Wistar Institute, Philadelphia, Pennsylvania, USA;3. University of Nebraska-Lincoln, Lincoln, Nebraska, USA;4. Institute for Policy Research and Dept of Psychology, Northwestern Univ, Evanston, Illinois, USA
Abstract:The epidemy of metabolic syndrome (MetS) is typically preceded by adoption of a “risky” lifestyle (e.g., dietary habit) among populations. Evidence shows that those with low socioeconomic status (SES) are at an increased risk for MetS. To investigate this, we recruited 123 obese subjects (body mass index BMI] ≥ 30) from Chicago. Multi-omic data were collected to interrogate fecal microbiota, systemic markers of inflammation and immune activation, plasma metabolites, and plasma glycans. Intestinal permeability was measured using the sugar permeability testing. Our results suggest a heterogenous metabolic dysregulation among obese populations who are at risk of MetS. Systemic inflammation, linked to poor diet, intestinal microbiome dysbiosis, and gut barrier dysfunction may explain the development of MetS in these individuals. Our analysis revealed 37 key features associated with increased numbers of MetS features. These features were used to construct a composite metabolic-inflammatory (MI) score that was able to predict progression of MetS among at-risk individuals. The MI score was correlated with several markers of poor diet quality as well as lower levels of gut microbial diversity and abnormalities in several species of bacteria. This study reveals novel targets to reduce the burden of MetS and suggests access to healthy food options as a practical intervention.
Keywords:dysbiosis  inflammation  metabolic syndrome  microbiome  multi-omics
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