Metabotypes Related to Meat and Vegetable Intake Reflect Microbial, Lipid and Amino Acid Metabolism in Healthy People
Journal article, 2018

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.

meat intake


bile acids

protein intake

amino acids


Runmin Wei

University of Hawaii

Alastair Ross

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Nestle S.A.

Ming Ming Su

University of Hawaii

Jingye Wang

University of Hawaii

S.P. Guiraud

Nestle S.A.

Colleen Fogarty Draper

Nestle S.A.

M. Beaumont

Nestle S.A.

Wei Jia

University of Hawaii

Francois Pierre Martin

Nestle S.A.

Molecular Nutrition and Food Research

1613-4125 (ISSN) 1613-4133 (eISSN)

Vol. 62 21 1800583

Subject Categories

Food Science

Environmental Health and Occupational Health

Nutrition and Dietetics

Areas of Advance

Life Science Engineering (2010-2018)





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