title: Stability and robustness of human metabolic phenotypes in response to sequential food challenges.
abstract: High-resolution spectroscopic profiles of biofluids can define metabolic phenotypes, providing a window onto the impact of diet on health to reflect gene-environment interactions. (1)H NMR spectroscopic profiling was used to characterize the effect of nutritional intervention on the stability of the metabolic phenotype of 7 individuals following a controlled 7 day dietary protocol. Inter-individual metabolic differences influenced proportionally more of the spectrum than dietary modulation, with certain individuals displaying a greater stability of metabolic phenotypes than others. Correlation structures between urinary metabolites were identified and used to map inter-individual pathway differences. Choline degradation was the pathway most affected by the individual, suggesting that the gut microbiota influence host metabolic phenotypes. This influence was further emphasized by the highly correlated excretion of the microbial-mammalian co-metabolites phenylacetylglutamine, 4-cresylsulfate (r = 0.87), and indoxylsulfate (r = 0.67) across all individuals. Above the background of inter-individual differences, clear biochemical effects of single type dietary interventions, animal protein, fruit and wine intake, were observed; for example, the spectral variance introduced by fruit ingestion was attributed to the metabolites tartrate, proline betaine, hippurate, and 4-hydroxyhippurate. This differential metabolic baseline and response to selected dietary challenges highlights the importance of understanding individual differences in metabolism and provides a rationale for evaluating dietary interventions and stratification of individuals with respect to guiding nutrition and health programmes.
authors: Biomolecular Medicine, Department of Surgery & Cancer, Imperial College London , Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom.
authors: Silke S
authors: SS
authors: Heinzmann
authors: Claire A
authors: CA
authors: Merrifield
authors: Serge
authors: S
authors: Rezzi
authors: Sunil
authors: S
authors: Kochhar
authors: John C
authors: JC
authors: Lindon
authors: Elaine
authors: E
authors: Holmes
authors: Jeremy K
authors: JK
authors: Nicholson
mesh_terms: D000328:Adult; D002794:Choline / Q000378:metabolism; D016000:Cluster Analysis; D004032:Diet; D004044:Dietary Proteins / Q000378:metabolism; D016002:Discriminant Analysis; D005260:Female; D005638:Fruit; D006801:Humans; D008297:Male; D053858:Metabolic Networks and Pathways / Q000502:physiology; D055442:Metabolome / Q000502:physiology*; D055432:Metabolomics / Q000379:methods*; D054892:Metagenome; D008875:Middle Aged; D015999:Multivariate Analysis; D019906:Nuclear Magnetic Resonance, Biomolecular; D009747:Nutritional Physiological Phenomena; D010363:Pattern Recognition, Automated; D010641:Phenotype; D014556:Urine / Q000737:chemistry; D014920:Wine
chemical_list: D004044:Dietary Proteins; D002794:Choline