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Metabotypes Related to Meat and Vegetable Intake Reflect Microbial, Lipid and Amino Acid Metabolism in Healthy People
Author(s) -
Wei Runmin,
Ross Alastair B.,
Su MingMing,
Wang Jingye,
Guiraud SeuPing,
Draper Colleen Fogarty,
Beaumont Maurice,
Jia Wei,
Martin FrancoisPierre
Publication year - 2018
Publication title -
molecular nutrition and food research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.495
H-Index - 131
eISSN - 1613-4133
pISSN - 1613-4125
DOI - 10.1002/mnfr.201800583
Subject(s) - food science , metabolism , population , lipid metabolism , gut flora , biology , feces , chemistry , biochemistry , medicine , microbiology and biotechnology , environmental health
Scope The objective of this study is to develop a new methodology to identify the relationship between dietary patterns and metabolites indicative of food intake and metabolism. Methods and results Plasma and urine samples from healthy Swiss subjects ( n = 89) collected over two time points are analyzed for a panel of host–microbial metabolites using GC– and LC–MS. Dietary intake is evaluated using a validated food frequency questionnaire. Dietary pattern clusters and relationships with metabolites are determined using Non‐Negative Matrix Factorization (NNMF) and Sparse Generalized Canonical Correlation Analysis (SGCCA). Use of NNMF allows detection of latent diet clusters in this population, which describes a high intake of meat or vegetables. SGCCA associates these clusters to i) diet‐host microbial and lipid associated bile acid metabolism, and ii) essential amino acid metabolism. Conclusion This novel application of NNMF and SGCCA allows detection of distinct metabotypes for meat and vegetable dietary patterns in a heterogeneous population. As many of the metabolites associated with meat or vegetable intake are the result of host–microbiota interactions, the findings support a role for microbiota mediating the metabolic imprinting of different dietary choices.