Defining Personal Nutrition and Metabolic Health Through Metabonomics

  • S. Rezzi
  • F-P. J. Martin
  • S. Kochhar
Conference paper
Part of the Ernst Schering Foundation Symposium Proceedings book series (SCHERING FOUND, volume 2007/4)


A major charter for modern nutrition is to provide a molecular basis for health outcome resulting from different food choices and how this could be designed to maintain individual health free of disease. Nutrigenomic techniques have been developed to generate information at various levels of biological organization, i.e. genes, proteins, and metabolites. Within this frame, metabonomics targets the molecular characterization of a living system through metabolic profiling. The metabolic profiles are explored with sophisticated data mining techniques mainly based on multivariate statistics, which can recover key metabolic information to be further linked to biochemical processes and physiological events. The power of metabonomics relies on its unique ability to assess functional changes in the metabolism of complex organisms stemming from multiple influences such as lifestyle and environmental factors. In particular, metabolic profiles encapsulate information on the metabolic activity of symbiotic partners, i.e. gut microflora, in complex organisms, which represent a major determinant in nutrition and health. Therefore, applications of metabonomics to nutrition sciences led to the nutrimetabonomics approach for the classification of dietary responses in populations and the possibility of optimized or personalized nutritional management.


Irritable Bowel Syndrome Metabolic Profile Metabolic Phenotype Metabolic Health Personalized Healthcare 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Brindle JT, Antti H, Holmes E, Tranter G, Nicholson JK, Bethell HW, Clarke S, Schofield PM, McKilligin E, Mosedale DE, Grainger DJ (2002) Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med 8:1439–1444CrossRefPubMedGoogle Scholar
  2. Brindle JT, Nicholson JK, Schofield PM, Grainger DJ, Holmes E (2003) Application of chemometrics to 1H NMR spectroscopic data to investigate a relationship between human serum metabolic profiles and hypertension. Analyst 128:32–36CrossRefPubMedGoogle Scholar
  3. Clayton TA, Lindon JC, Cloarec O, Antti H, Charuel C, Hanton G, Provost JP, Le Net JL, Baker D, Walley RJ, Everett JR, Nicholson JK (2006) Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 440:1073–1077CrossRefPubMedGoogle Scholar
  4. Coen M, Hong YS, Clayton TA, Rohde CM, Pearce JT, Reily MD, Robertson DG, Holmes E, Lindon JC, Nicholson JK (2007) The mechanism of galactosamine toxicity revisited; a metabonomic study. J Proteome Res 6:2711–2719CrossRefPubMedGoogle Scholar
  5. Dumas ME, Barton RH, Toye A, Cloarec O, Blancher C, Rothwell A, Fearnside J, Tatoud R, Blanc V, Lindon JC, Mitchell SC, Holmes E, McCarthy MI, Scott J, Gauguier D, Nicholson JK (2006) Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc Natl Acad Sci U S A 103:12511–12516CrossRefPubMedGoogle Scholar
  6. Dunne C (2001) Adaptation of bacteria to the intestinal niche: probiotics and gut disorder. Inflamm Bowel Dis 7:136–145CrossRefPubMedGoogle Scholar
  7. Ebbels TM, Keun HC, Beckonert OP, Bollard ME, Lindon JC, Holmes E, Nicholson JK (2007) Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach. J Proteome Res 6:4407–4422CrossRefPubMedGoogle Scholar
  8. Gavaghan CL, Holmes E, Lenz E, Wilson ID, Nicholson JK (2004) An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences; application to the C57BL10J, Alpk:ApfCD mouse. FEBS Lett 484:169–174CrossRefGoogle Scholar
  9. Gibson GR, Fuller R (2000) Aspects of in vitro and in vivo research approaches directed toward identifying probiotics and prebiotics for human use. J Nutr 130:391S–395SPubMedGoogle Scholar
  10. Gill SR, Pop M, Deboy RT, Eckburg PB, Turnbaugh PJ, Samuel BS, Gordon JI, Relman DA, Fraser-Liggett CM, Nelson KE (2006) Metagenomic analysis of the human distal gut microbiome. Science 312:1355–1359CrossRefPubMedGoogle Scholar
  11. Lederberg J (2000) Infectious history. Science 288:287–293CrossRefPubMedGoogle Scholar
  12. Ley R, Turnbaugh P, Klein S, Gordon J (2006) Microbial ecology: human gut microbes associated with obesity. Nature 444:1022–1023CrossRefPubMedGoogle Scholar
  13. Lindon JC, Holmes E, Nicholson JK (2006) Metabonomics techniques and applications to pharmaceutical research and development. Pharm Res 23:1075–1088CrossRefPubMedGoogle Scholar
  14. Martin FP, Verdu EF, Wang Y, Dumas ME, Yap IK, Cloarec O, Bergonzelli GE, Corthesy-Theulaz I, Kochhar S, Holmes E, Lindon JC, Collins SM, Nicholson JK (2006) Transgenomic metabolic interactions in a mouse disease model: interactions of Trichinella spiralis infection with dietary Lactobacillus paracasei supplementation. J Proteome Res 5:2185–2193CrossRefPubMedGoogle Scholar
  15. Martin FP, Dumas ME, Wang Y, Legido-Quigley C, Yap IK, Tang H, Zirah S, Murphy GM, Cloarec O, Lindon JC, Sprenger N, Fay LB, Kochhar S, van BP, Holmes E, Nicholson JK (2007a) A top-down systems biology view of microbiome-mammalian metabolic interactions in a mouse model. Mol Syst Biol 3:112CrossRefPubMedGoogle Scholar
  16. Martin FP, Wang Y, Sprenger N, Holmes E, Lindon JC, Kochhar S, Nicholson JK (2007b) Effects of probiotic Lactobacillus paracasei treatment on the host gut tissue metabolic profiles probed via magic-angle-spinning NMR spectroscopy. J Proteome Res 6:1471–1481CrossRefPubMedGoogle Scholar
  17. Martin FP, Wang Y, Sprenger N, Yap IK, Lundstedt T, Lek P, Rezzi S, Ramadan Z, van Bladeren P, Fay LB, Kochhar S, Lindon JC, Holmes E, Nicholson JK (2008) Probiotic modulation of symbiotic gut microbial-host metabolic interactions in a humanized microbiome mouse model. Mol Syst Biol 4:157PubMedGoogle Scholar
  18. Nicholson JK, Wilson ID (2003) Opinion: understanding `global' systems biology: metabonomics and the continuum of metabolism. Nat Rev Drug Discov 2:668–676CrossRefPubMedGoogle Scholar
  19. Nicholson JK, Holmes E, Lindon JC, Wilson ID (2004) The challenges of modeling mammalian biocomplexity. Nat Biotechnol 22:1268–1274CrossRefPubMedGoogle Scholar
  20. Nicholson JK, Holmes E, Wilson ID (2005) Gut microorganisms, mammalian metabolism and personalized health care. Nat Rev Microbiol 3:431–438CrossRefPubMedGoogle Scholar
  21. Odunsi K, Wollman RM, Ambrosone CB, Hutson A, McCann SE, Tammela J, Geisler JP, Miller G, Sellers T, Cliby W, Qian F, Keitz B, Intengan M, Lele S, Alderfer JL (2005) Detection of epithelial ovarian cancer using 1H-NMR-based metabonomics. Int J Cancer 113:782–788CrossRefPubMedGoogle Scholar
  22. Rezzi S, Ramadan Z, Fay LB, Kochhar S (2007a) Nutritional metabonomics: applications and perspectives. J Proteome Res 6:513–525CrossRefPubMedGoogle Scholar
  23. Rezzi S, Ramadan Z, Martin FP, Fay LB, Bladeren PV, Lindon JC, Nicholson JK, Kochhar S (2007b) Human metabolic phenotypes link directly to specific dietary preferences in healthy individuals. J Proteome Res 6:4469–4477CrossRefPubMedGoogle Scholar
  24. Skordi E, Yap IK, Claus SP, Martin FP, Cloarec O, Lindberg J, Schuppe-Koistinen I, Holmes E, Nicholson JK (2007) Analysis of time-related metabolic fluctuations induced by ethionine in the rat. J Proteome Res 6:4572–4581CrossRefPubMedGoogle Scholar
  25. Stella C, Beckwith-Hall B, Cloarec O, Holmes E, Lindon JC, Powell J, vanderOuderaa F, Bingham S, Cross AJ, Nicholson JK (2006) Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res 5:2780–2788CrossRefPubMedGoogle Scholar
  26. Verdu EF, Bercik P, Bergonzelli GE, Huang XX, Blennerhasset P, Rochat F, Fiaux M, Mansourian R, Corthesy-Theulaz I, Collins SM (2004) Lactobacillus paracasei normalizes muscle hypercontractility in a murine model of postinfective gut dysfunction. Gastroenterology 127:826–837CrossRefPubMedGoogle Scholar
  27. Wang C, Kong H, Guan Y, Yang J, Gu J, Yang S, Xu G (2005) Plasma phospholipid metabolic profiling and biomarkers of type 2 diabetes mellitus based on high-performance liquid chromatography/electrospray mass spectrometry and multivariate statistical analysis. Anal Chem 77:4108–4116CrossRefPubMedGoogle Scholar
  28. Wang Y, Lawler D, Larson B, Ramadan Z, Kochhar S, Holmes E, Nicholson JK (2007) Metabonomic investigations of aging and caloric restriction in a life-long dog study. J Proteome Res 6:1846–1854CrossRefPubMedGoogle Scholar
  29. Yang J, Xu G, Hong Q, Liebich HM, Lutz K, Schmulling RM, Wahl HG (2004) Discrimination of type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles. J Chromatogr B Analyt Technol Biomed Life Sci 813:53–58CrossRefPubMedGoogle Scholar
  30. Yap IK, Clayton TA, Tang H, Everett JR, Hanton G, Provost JP, Le Net JL, Charuel C, Lindon JC, Nicholson JK (2006) An integrated metabonomic approach to describe temporal metabolic disregulation induced in the rat by the model hepatotoxin allyl formate. J Proteome Res 5:2675–2684CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.BioAnalytical Science, Metabonomics and BiomarkersNestlé Research CenterLausanne 26Switzerland

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