The Open Nutrition Journal
2018, 12 : 40-58Published online 2018 August 31. DOI: 10.2174/1874288201812010040
Publisher ID: TONUTRJ-12-40
RESEARCH ARTICLE
Effect of Macronutrient on Plasma, Liver and Pancreatic Metabolomics and their Hierarchic Weights in the Metabolic Network
2 Departments of Clinical Sciences, University of Kentucky, Lexington, KY 40536, USA
3 Drug Research Center, 12 Ady Endre Street, Balatonfüred, H-8230, Hungary
4 Centre for Bioinformatics, University of Veterinary Medicine, Budapest, István u. 2, 1078, Hungary
5 Department of Animal Sciences, University of Kentucky, Lexington, KY 40536, USA
* Address correspondence to the author at the Institute of Animal Breeding, Nutrition and Laboratory Animal Science, University of Veterinary Medicine, Budapest, István u. 2, 1078, Hungary; Tel: 3614784100; E-mail: szabo.jozsef@univer.hu
ABSTRACT
Background/Aims:
The aims of this study were to: 1) investigate the specific metabolomic effects of single macro nutrients in cold exposed rats; and 2) Using centrality analysis ascertain the correlations between these metabolomic parameter measurements.
Methods:
Fifty male Wistar rats were divided into 5 groups and individually housed under cold climatic conditions. Rats were either cold exposed fasted (negative control) or fed with the following commercially available single macronutrients: casein, POLYCOSE® and safflower oil; the positive control diets included all macronutrients. Samples from the plasma, liver and pancreas were collected and 33 different parameters were determined.
Results:
The primary correlation found between pancreatic hormones and the variables as measured, showed significant positive connections between centrality network node members. Heatmap analysis showed that the macronutrients fed have very differing effects on the metabolomics measured, i.e. casein has a high Heatmap Index on plasma corticosterone while POLYCOSE® and fat had a minimal impact.
Conclusion:
The cold exposed fasted animal model, in which nutrient catabolism is near maximal, serves as a useful “in vivo” tool for studying the relationships among the nutrients, hormones and digestive enzymes under cold stress conditions; feed intake, liver glucose, and small intestinal amylase hold a high position in centrality mapping and are highly imbedded in the metabolomic networks. For example, this model shed light on the relations amoung hormone and enzyme contents of duodenal pancreas, gastric+splenic pancreas and enzyme activities in small intestinal contents. These findings can be applied to optimizing feeding of animals under cold stress.