Runmin Wei1,2, Alastair B Ross3,4, MingMing Su1, Jingye Wang1, Seu-Ping Guiraud5, Colleen Fogarty Draper5, Maurice Beaumont6, Wei Jia1, Francois-Pierre Martin5. 1. University of Hawaii Cancer Center (UHCC), Honolulu, HI, 96813, USA. 2. Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, 96822, USA. 3. Analytical Science Department, Nestlé Research Center, Lausanne, Switzerland. 4. Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden. 5. Nutrition and Metabolic health Department, Nestle Institute of Health Sciences (NIHS), Lausanne, Switzerland. 6. Clinical Development Unit, Nestlé Research Center, Lausanne, Switzerland.
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.
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.