Metabotypes Related to Meat and Vegetable Intake Reflect Microbial, Lipid and Amino Acid Metabolism in Healthy People. Issue 21 (28th August 2018)

• Record Type: Journal Article
• Authors: Wei, Runmin; Ross, Alastair B.; Su, MingMing; Wang, Jingye; Guiraud, Seu‐Ping; Draper, Colleen Fogarty; Beaumont, Maurice; Jia, Wei; Martin, Francois‐Pierre
• Is Part Of: Molecular nutrition & food research. Volume 62:Issue 21(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-28
• Subjects: amino acids -- bile acids -- meat intake -- metabolomics -- protein intake; Food -- Biotechnology -- Periodicals; Food -- Microbiology -- Periodicals; Nutrition -- Periodicals; Food -- Toxicology -- Periodicals; Nutrition -- Periodicals; Food Microbiology -- Periodicals; Food Technology -- Periodicals; Molecular Biology -- Periodicals; 664.0705
• DOI: 10.1002/mnfr.201800583
• Languages: English
• ISSNs: 1613-4125

Abstract

Abstract : 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. Abstract : Diet plays a major role in disease development, and different dietary patterns may reduce disease risk. The objective of this study is to use a new methodological approach to assess if dietary patterns could be linked to particular metabotypes in the urine or the blood plasma of healthy people, even in the case of a diverse population and uncontrolled dietary conditions. Dietary pattern clusters linked to i) diet‐host microbial and lipid‐associated bile acid metabolism, and ii) essential amino acid metabolism.