Karaman, İbrahim6; Nørskov, Natalja6; Yde, Christian Clement7; Måge, Ingrid4; Afseth, Nils Kristian5; Hedemann, Mette Skou6; Knudsen, Knud Erik Bach6; Martens, Harald5; Kohler, Achim5
1 Department of Animal Science - Molecular nutrition and reproduction, Department of Animal Science, Science and Technology, Aarhus University2 Department of Food Science - Plant, Food & Sustainability, Department of Food Science, Science and Technology, Aarhus University3 Department of Food Science - Differentiated & Biofunctional Foods, Department of Food Science, Science and Technology, Aarhus University4 Nofima Mat As, Centre for Biospectroscopy and Data Modelling5 2Nofima Mat As, Centre for Biospectroscopy and Data Modelling6 Department of Animal Science - Molecular nutrition and reproduction, Department of Animal Science, Science and Technology, Aarhus University7 Department of Food Science - Differentiated & Biofunctional Foods, Department of Food Science, Science and Technology, Aarhus University
Methods which can integrate more than two data matrices have been developed in and applied to the fields of psychometrics, consumer science, econometrics and process control. Recently these methods have been applied to multiple data sets in biosciences and proven to be very powerful in situations with many variables for the purpose of reducing over-fitting problems and providing useful interpretation tools. These tools have excellent possibilities for giving a graphical overview of sample and variation patterns. They handle co-linearity in an efficient way and make it possible to use different highly correlated data sets in one integrated approach. Due to the high number of variables in data sets from metabolomics (both raw data and after peak picking) the selection of important variables in an explorative analysis is difficult, especially when different data sets of metabolomics data need to be related. Tools for the handling of mental overflow minimising false discovery rates both by using statistical and biological validation in an integrative approach are needed. In this paper different strategies for variable selection were considered with respect to false discovery and the possibility for biological validation. The data set used in this study is metabolomics data from an animal intervention study. The aim of the metabolomics study was to investigate the metabolic profile in pigs fed various cereal fractions with special attention to the metabolism of lignans using NMR and LC-MS based metabolomic approaches. Whole grain consumption has been shown to be protective against cardiovascular diseases, certain types of cancers, and type II diabetes. However, the food factors responsible for the preventive effects of whole grain and fibre-rich cereal fractions and the underlying mechanisms associated with these effects are far from fully understood. This is due to the diversity of active constituents in whole grain and the complexity in the response to each of them. Metabolomic approaches being capable of describing the effects of diet on metabolism are ideally suited to address this issue. For the present study three experimental diets were formulated containing whole grain, wheat aleurone, and an aleurone enriched rye fraction, respectively. The diets were fed to six pigs in a repeated 3 x 3 latin square design and a standard wheat flour diet was used as wash-out diet between the experimental diets. The pigs were surgically fitted with 2 catheters, one in the portal vein and the second in the mesenteric artery. The pigs were fed the experimental diets Monday-Thursday and fasting blood samples were collected in morning and on Thursday blood samples were collected after feeding. Plasma was harvested and frozen until analysis with NMR and LC-MS instruments.