An individual's metabolic phenotype, and ultimately health, is significantly influenced by complex interactions between their genes and the diet. Studying these associations and their downstream biochemical consequences has proven extremely challenging using traditional hypothesis-led strategies. Metabonomics, a systems biology approach, allows the global metabolic response of biological systems to stimuli to be characterised. Through the application of this approach to nutritional-based research, nutrimetabonomics, the biochemical response to dietary inputs is being investigated at greater levels of resolution. This has allowed novel insights to be gained regarding intricate diet-gene interactions and their consequences for health and disease. In this review, we present some of the latest research exploring how nutrimetabonomics can assist in the elucidation of novel biomarkers of dietary behaviour and provide new perspectives on diet-health relationships. The use of this approach to study the metabolic interplay between the gut microbiota and the host is also explored.
WildC.P. (2005) Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol. Biomarkers Prev., 14, 1847–1850.
2.
ClausS.P., and SwannJ.R. (2013) Nutrimetabonomics: applications for nutritional sciences, with specific reference to gut microbial interactions. Ann. Rev. Food Sci. Technol., 4, 381–399.
3.
HeinzmannS.S., BrownI.J., ChanQ. (2010) Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am. J. Clin. Nutr., 92, 436–443.
4.
LloydA.J., BeckmannM., FavéG., MathersJ.C., and DraperJ. (2011) Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption. Br. J. Nutr., 106, 812–824.
5.
Pujos-GuillotE., HubertJ., MartinJ.-F. (2013) Mass Spectrometry-based metabolomics for the discovery of biomarkers of fruit and vegetable intake: citrus fruit as a case study. J. Proteome Res., 12, 1645–1659.
6.
EdmandsW.M.B., BeckonertO.P., StellaC. (2011) Identification of human urinary biomarkers of cruciferous vegetable consumption by metabonomic profiling. J. Proteome Res., 10, 4513–4521.
7.
HolmesE., LooR.-L., and StamlerJ. (2008) Human metabolic phenotype diversity and its association with diet and blood pressure. Nature, 453, 396–401.
8.
LloydA.J., FavéG., BeckmannM. (2011) Use of mass spectrometry fingerprinting to identify urinary metabolites after consumption of specific foods. Am. J. Clin. Nutr., 94, 981–991.
9.
RezziS., RamadanZ., FayL.B., and KochharS. (2007) Nutritional metabonomics: applications and perspectives. J. Proteome Res., 6, 513–525.
10.
LankinenM., SchwabU., ErkkiläA. (2009) Fatty fish intake decreases lipids related to inflammation and insulin signaling–a lipidomics approach. PLoS one, 4, e5258.
11.
FardetA., CanletC., GottardiG. (2007) Whole-grain and refined wheat flours show distinct metabolic profiles in rats as assessed by a 1H NMR-based metabonomic approach. J. Nutr., 137, 923–929.
12.
StellaC., Beckwith-HallB., CloarecO. (2006) Susceptibility of human metabolic phenotypes to dietary modulation. J. Proteome Res., 5, 2780–2788.
13.
WangY., TangH., NicholsonJ.K., HylandsP.J., SampsonJ., and HolmesE. (2005) A metabonomic strategy for the detection of the metabolic effects of chamomile (Matricaria recutita L.) ingestion. J. Agric. Food Chem., 53, 191–196.
14.
Van DorstenF.A., DaykinC.A., MulderT.P.J., Van DuynhovenJ.P.M. (2006) Metabonomics approach to determine metabolic differences between green tea and black tea consumption. J. Agric. Food Chem., 54, 6929–6938.
15.
DaykinC.A., Van DuynhovenJ.P.M., GroenewegenA., DachtlerM., Van AmelsvoortJ.M.M., and MulderT.P.J. (2005) Nuclear magnetic resonance spectroscopic based studies of the metabolism of black tea polyphenols in humans. J. Agric. Food Chem., 53, 1428–1434.
16.
SolankyK.S., BaileyN.J.C., Beckwith-HallB.M. (2003) Application of biofluid 1H nuclear magnetic resonance-based metabonomic techniques for the analysis of the biochemical effects of dietary isoflavones on human plasma profile. Anal. Biochem., 323, 197–204.
17.
MerrifieldC.A., LewisM.C., ClausS.P. (2012) Weaning diet induces sustained metabolic phenotype shift in the pig and influences host response to Bifidobacterium lactis NCC2818. Gut., 62, 842–851.
18.
SwannJ.R., TuohyK.M., LindforsP. (2011) Variation in antibiotic-induced microbial recolonisation impacts on the host metabolic phenotypes of rats. J. Proteome Res., 10, 3590–3603.
SwannJ.R., WantE.J., GeierF.M. (2011) Systemic gut microbial modulation of bile acid metabolism in host tissue compartments. Proc. Natl. Acad. Sci., 108, Suppl 1, 4523–4530.
21.
KokM.G.M., RuijkenM.M.A., SwannJ.R., WilsonI.D., SomsenG.W., and JongG.J. (2013) Anionic metabolic profiling of urine from antibiotic-treated rats by capillary electrophoresis–mass spectrometry. Anal. Bioanal. Chem., 405, 2585–2594.
22.
LeesH.J., SwannJ.R., WilsonI.D., NicholsonJ.K., and HolmesE. (2013) Hippurate: The Natural History of a Mammalian–Microbial Cometabolite. J. Proteome Res., 12, 1527–1546.
23.
TurnbaughP.J., LeyR.E., HamadyM., Fraser-LiggettC.M., KnightR., and GordonJ.I. (2007) The human microbiome project. Nature, 449, 804–810.
24.
SmithM.I., YatsunenkoT., ManaryM.J. (2013) Gut microbiomes of Malawian Twin pairs discordant for Kwashiorkor. Science, 339, 548–554.
25.
BoulangéC.L., ClausS.P., ChouC.J. (2013) Early metabolic adaptation in C57BL/6 mice resistant to high fat diet induced weight gain involves an activation of mitochondrial oxidative pathways. J. Proteome Res., 12, 1956–1968.
26.
DewulfE.M., CaniP.D., ClausS.P. (2012) Insight into the prebiotic concept: lessons from an exploratory, double blind intervention study with inulin-type fructans in obese women. Gut, doi:10.1136/gutjnl-2012-303304.
27.
CalvaniR., MiccheliA., CapuaniG. (2010) Gut microbiome-derived metabolites characterise a peculiar obese urinary metabotype. Int. J. Obes. (Lond.), 34, 1095–1098.
28.
ZuppiC., MessanaI., ForniF. (1997) 1H NMR spectra of normal urines: reference ranges of the major metabolites. Clin. Chim. Acta, 265, 85–97.
29.
AroraT., LooR.-L., AnastasovskaJ. (2012) Differential effects of two fermentable carbohydrates on central appetite regulation and body composition. PLoS one, 7, e43263.
30.
NicholsonJ.K., and WilsonI.D. (2003) Understanding “global” systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov., 2, 668–676.
31.
NicholsonJ., LindonJ., and HolmesE. (1999) “Metabonomics”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29, 1181–1189.
