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Metabonomics in Clinical Practice

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Metabonomics and Gut Microbiota in Nutrition and Disease

Abstract

Metabonomics is recognized as a powerful top-down system biology approach to understand genetic-environment-health paradigm paving new avenues to identify clinically relevant biomarkers. It is nowadays commonly used in clinical applications shedding new light on physiological regulatory processes of complex mammalian systems as regards disease etiology, diagnostic stratification, and potentially mechanism of action of therapeutic solutions. It therefore offers opportunities to associate complex metabolic regulations to the etiology of multifactorial diseases and metabolic dysfunctions, which may subsequently lead to mechanistic hypotheses and targets for new nutritional concepts. This review aims at describing recent applications of metabonomics in clinical fields with insight into diseases diagnostics/monitoring and improvement of homeostasis metabolic regulation.

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References

  1. Tiret L. Gene-environment interaction: a central concept in multifactorial diseases. Proc Nutr Soc. 2002;61(4):457–63.

    PubMed  Google Scholar 

  2. Nicholson JK, Wilson ID. Opinion: understanding ‘global’ systems biology: metabonomics and the continuum of metabolism. Nat Rev Drug Discov. 2003;2(8):668–76.

    CAS  PubMed  Google Scholar 

  3. Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29(11):1181–9.

    CAS  PubMed  Google Scholar 

  4. Trujillo E, Davis C, Milner J. Nutrigenomics, proteomics, metabolomics, and the practice of dietetics. J Am Diet Assoc. 2006;106(3):403–13.

    CAS  PubMed  Google Scholar 

  5. Ordovas JM, Shen J. Gene-environment interactions and susceptibility to metabolic syndrome and other chronic diseases. J Periodontol. 2008;79(8 Suppl):1508–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Ordovas JM. Integrating environment and disease into “omic” analysis. Rev Esp Cardiol. 2009;62 Suppl 2:17–22.

    PubMed  Google Scholar 

  7. van der Greef J, Hankemeier T, McBurney RN. Metabolomics-based systems biology and personalized medicine: moving towards n = 1 clinical trials? Pharmacogenomics. 2006;7(7):1087–94.

    PubMed  Google Scholar 

  8. van der Greef J. Systems biology, connectivity and the future of medicine. Syst Biol (Stevenage). 2005;152(4):174–8.

    Google Scholar 

  9. Nicholson JK, Lindon JC. Systems biology: metabonomics. Nature. 2008;455(7216):1054–6.

    CAS  PubMed  Google Scholar 

  10. Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell. 2008;134(5):714–7.

    CAS  PubMed  Google Scholar 

  11. Keurentjes JJ. Genetical metabolomics: closing in on phenotypes. Curr Opin Plant Biol. 2009;12(2):223–30.

    CAS  PubMed  Google Scholar 

  12. Saito K, Matsuda F. Metabolomics for functional genomics, systems biology, and biotechnology. Annu Rev Plant Biol. 2010;61:463–89.

    CAS  PubMed  Google Scholar 

  13. Weckwerth W. Metabolomics in systems biology. Annu Rev Plant Biol. 2003;54:669–89.

    CAS  PubMed  Google Scholar 

  14. Beckonert O, Keun HC, Ebbels TMD, Bundy J, Holmes E, Lindon JC, Nicholson JK. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protocol. 2007;2(11):2692–703.

    CAS  Google Scholar 

  15. Keun HC, Beckonert O, Griffin JL, Richter C, Moskau D, Lindon JC, Nicholson JK. Cryogenic probe 13C NMR spectroscopy of urine for metabonomic studies. Anal Chem. 2002;74(17):4588–93.

    CAS  PubMed  Google Scholar 

  16. Beckonert O, Coen M, Keun HC, Wang Y, Ebbels TM, Holmes E, Lindon JC, Nicholson JK. High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nat Protoc. 2010;5(6):1019–32.

    CAS  PubMed  Google Scholar 

  17. Dumas ME, Maibaum EC, Teague C, Ueshima H, Zhou B, Lindon JC, Nicholson JK, Stamler J, Elliott P, Chan Q, Holmes E. Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. Anal Chem. 2006;78(7):2199–208.

    CAS  PubMed  Google Scholar 

  18. Álvarez-Sánchez B, Priego-Capote F, Castro MDLD. Metabolomics analysis II. Preparation of biological samples prior to detection. TrAC Trends Anal Chem. 2010;29(2):120–7.

    Google Scholar 

  19. Bojko B, Cudjoe E, Pawliszyn J, Wasowicz M. Solid-phase microextraction. How far are we from clinical practice? TrAC Trends Anal Chem. 2011;30(9):1505–12.

    CAS  Google Scholar 

  20. Bruce SJ, Tavazzi I, Parisod V, Rezzi S, Kochhar S, Guy PA. Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Anal Chem. 2009;81(9):3285–96.

    CAS  PubMed  Google Scholar 

  21. Römisch-Margl W, Prehn C, Bogumil R, Röhring C, Suhre K, Adamski J. Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics. Metabolomics. 2011;8(1):133–42.

    Google Scholar 

  22. Sellick CA, Hansen R, Maqsood AR BW, Stephens GM, Goodacre R, Dickson AJ, Dunn WB. Effective quenching processes for physiologically valid metabolite profiling of suspension cultured mammalian cells effective quenching processes for physiologically valid metabolite profiling of suspension cultured mammalian cells. Anal Chem. 2009;81:174–83.

    CAS  PubMed  Google Scholar 

  23. Villas-Bôas SG, Højer-Pedersen J, Åkesson M, Smedsgaard J, Nielsen J. Global metabolite analysis of yeast: evaluation of sample preparation methods. Yeast. 2005;22(14):1155–69.

    PubMed  Google Scholar 

  24. Buescher JM, Moco S, Sauer U, Zamboni N. Ultrahigh performance liquid chromatography-tandem mass spectrometry method for fast and robust quantification of anionic and aromatic metabolites. Anal Chem. 2010;82(11):4403–12.

    CAS  PubMed  Google Scholar 

  25. Wu L, Mashego MR, Van Dam JC, Proell AM, Vinke JL, Ras C, Van Winden WA, Van Gulik WM, Heijnen JJ. Quantitative analysis of the microbial metabolome by isotope dilution mass spectrometry using uniformly 13C-labeled cell extracts as internal standards. Anal Biochem. 2005;336(2):164–71.

    CAS  PubMed  Google Scholar 

  26. Kostiainen R, Kauppila TJ. Effect of eluent on the ionization process in liquid chromatography mass spectrometry. J Chromatogr A. 2009;1216(4):685–99.

    CAS  PubMed  Google Scholar 

  27. Nordström A, Want E, Northen T, Lehtiö J, Siuzdak G. Multiple ionization mass spectrometry strategy used to reveal the complexity of metabolomics. Anal Chem. 2008;80(2):421–9.

    PubMed  Google Scholar 

  28. Bruce SJ, Breton I, Decombaz J, Boesch C, Scheurer E, Montoliu I, Rezzi S, Kochhar S, Guy PA. A plasma global metabolic profiling approach applied to an exercise study monitoring the effects of glucose, galactose and fructose drinks during post-exercise recovery. J Chromatogr B. 2010;878(29):3015–23.

    CAS  Google Scholar 

  29. Tolstikov VV, Fiehn O. Analysis of highly polar compounds of plant origin: combination of hydrophilic interaction chromatography and electrospray ion trap mass spectrometry. Anal Biochem. 2002;301(2):298–307.

    CAS  PubMed  Google Scholar 

  30. Soga T, Igarashi K, Ito C, Mizobuchi K, Zimmermann HP, Tomita M. Metabolomic profiling of anionic metabolites by capillary electrophoresis mass spectrometry. Anal Chem. 2009;81(15):6165–74.

    CAS  PubMed  Google Scholar 

  31. Griffiths WJ, Ogundare M, Williams CM, Wang Y. On the future of “omics”: lipidomics. J Inherit Metab Dis. 2011;34(3):583–92.

    CAS  PubMed  Google Scholar 

  32. Wenk MR. The emerging field of lipidomics. Nat Rev Drug Discov. 2005;4(7):594–610.

    CAS  PubMed  Google Scholar 

  33. Liebisch G, Binder M, Schifferer R, Langmann T, Schulz B, Schmitz G. High throughput quantification of cholesterol and cholesteryl ester by electrospray ionization tandem mass spectrometry (ESI-MS/MS). Biochim Biophys Acta. 2006;1761(1):121–8.

    CAS  PubMed  Google Scholar 

  34. Liebisch G, Drobnik W, Reil M, Trumbach B, Arnecke R, Olgemoller B, Roscher A, Schmitz G. Quantitative measurement of different ceramide species from crude cellular extracts by electrospray ionization tandem mass spectrometry (ESI-MS/MS). J Lipid Res. 1999;40(8):1539–46.

    CAS  PubMed  Google Scholar 

  35. Liebisch G, Lieser B, Rathenberg J, Drobnik W, Schmitz G. High-throughput quantification of phosphatidylcholine and sphingomyelin by electrospray ionization tandem mass spectrometry coupled with isotope correction algorithm. Biochim Biophys Acta. 2004;1686(1–2):108–17.

    CAS  PubMed  Google Scholar 

  36. Schuhmann K, Herzog R, Schwudke D, Metelmann-Strupat W, Bornstein SR, Shevchenko A. Bottom-up shotgun lipidomics by higher energy collisional dissociation on LTQ Orbitrap mass spectrometers. Anal Chem. 2011;83(14):5480–7.

    CAS  PubMed  Google Scholar 

  37. Scherer M, Gnewuch C, Schmitz G, Liebisch G. Rapid quantification of bile acids and their conjugates in serum by liquid chromatography-tandem mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2009;877(30):3920–5.

    CAS  PubMed  Google Scholar 

  38. Scherer M, Leuthauser-Jaschinski K, Ecker J, Schmitz G, Liebisch G. A rapid and quantitative LC-MS/MS method to profile sphingolipids. J Lipid Res. 2010;51(7):2001–11.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Liebisch G, Schmitz G. Quantification of lysophosphatidylcholine species by high-throughput electrospray ionization tandem mass spectrometry (ESI-MS/MS). Methods Mol Biol. 2009;580:29–37.

    CAS  PubMed  Google Scholar 

  40. Sumner L, Amberg A, Barrett D, Beale M, Beger R, Daykin C, Fan T, Fiehn O, Goodacre R, Griffin J, Hankemeier T, Hardy N, Harnly J, Higashi R, Kopka J, Lane A, Lindon J, Marriott P, Nicholls A, Reily M, Thaden J, Viant M. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007;3(3):211–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Benton HP, Want E, Keun HC, Amberg A, Plumb RS, Goldfain-Blanc F, Walther B, Reily MD, Lindon JC, Holmes E, Nicholson JK, Ebbels TM. Intra- and interlaboratory reproducibility of ultra performance liquid chromatography-time-of-flight mass spectrometry for urinary metabolic profiling. Anal Chem. 2012;84(5):2424–32.

    CAS  PubMed  Google Scholar 

  42. Kamleh MA, Ebbels TM, Spagou K, Masson P, Want EJ. Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies. Anal Chem. 2012;84:2670–7.

    CAS  PubMed  Google Scholar 

  43. Brown M, Dunn WB, Dobson P, Patel Y, Winder CL, Francis-McIntyre S, Begley P, Carroll K, Broadhurst D, Tseng A, Swainston N, Spasic I, Goodacre R, Kell DB. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst. 2009;134(7):1322–32.

    CAS  PubMed  Google Scholar 

  44. Exarchou V, Godejohann M, van Beek TA, Gerothanassis IP, Vervoort J. LC-UV-solid-phase extraction-NMR-MS combined with a cryogenic flow probe and its application to the identification of compounds present in Greek oregano. Anal Chem. 2003;75(22):6288–94.

    CAS  PubMed  Google Scholar 

  45. Lommen A, Gerssen A, Oosterink JE, Kools HJ, Ruiz-Aracama A, Peters RJ, Mol HG. Ultra-fast searching assists in evaluating sub-ppm mass accuracy enhancement in U-HPLC/Orbitrap MS data. Metabolomics. 2011;7(1):15–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Moco S, Bino RJ, De Vos RCH, Vervoort J. Metabolomics technologies and metabolite identification. Trends Anal Chem. 2007;26:855–66.

    CAS  Google Scholar 

  47. Moco S, Forshed J, De Vos RCH, Bino RJ, Vervoort J. Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance. Metabolomics. 2008;4:202–15.

    CAS  Google Scholar 

  48. Tautenhahn R, Bottcher C, Neumann S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinforma. 2008;9:504.

    Google Scholar 

  49. Neumann S, Bocker S. Computational mass spectrometry for metabolomics: identification of metabolites and small molecules. Anal Bioanal Chem. 2010;398(7–8):2779–88.

    CAS  PubMed  Google Scholar 

  50. Coughlin SS. Ethical issues in epidemiologic research and public health practice. Emerg Themes Epidemiol. 2006;3:16.

    PubMed  PubMed Central  Google Scholar 

  51. Holmes E, Loo RL, Stamler J, Bictash M, Yap IK, Chan Q, Ebbels T, De Iorio M, Brown IJ, Veselkov KA, Daviglus ML, Kesteloot H, Ueshima H, Zhao L, Nicholson JK, Elliott P. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature. 2008;453(7193):396–400.

    CAS  PubMed  Google Scholar 

  52. Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, Chang D, Milburn MV, Gall WE, Weinberger KM, Mewes HW, Hrabe de Angelis M, Wichmann HE, Kronenberg F, Adamski J, Illig T. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010;5(11):e13953.

    PubMed  PubMed Central  Google Scholar 

  53. He Y, Yu Z, Giegling I, Xie L, Hartmann AM, Prehn C, Adamski J, Kahn R, Li Y, Illig T, Wang-Sattler R, Rujescu D. Schizophrenia shows a unique metabolomics signature in plasma. Transl Psychiatry. 2012;2:e149.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Yu Z, Zhai G, Singmann P, He Y, Xu T, Prehn C, Römisch-Margl W, Lattka E, Gieger C, Soranzo N, Heinrich J, Standl M, Thiering E, Mittelstraß K, Wichmann H-E, Peters A, Suhre K, Li Y, Adamski J, Spector TD, Illig T, Wang-Sattler R. Human serum metabolic profiles are age dependent. Aging Cell. 2012;11(6):960–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Haquin S, Oeuillet E, Pajon A, Harris M, Jones A, Tilbeurgh H, Markley J, Zolnai Z, Poupon A. Data management in structural genomics: an overview. In: Kobe B, Guss M, Huber T, editors. Structural proteomics, vol 426. Methods in Molecular Biology™. Humana Press; 2008. pp 49 – 79. doi:10.1007/978-1-60327-058-8_4.

  56. Holland NT, Smith MT, Eskenazi B, Bastaki M. Biological sample collection and processing for molecular epidemiological studies. Mutat Res. 2003;543(3):217–34.

    CAS  PubMed  Google Scholar 

  57. Lauridsen M, Hansen SH, Jaroszewski JW, Cornett C. Human urine as test material in 1H NMR-based metabonomics: recommendations for sample preparation and storage. Anal Chem. 2007;79(3):1181–6.

    CAS  PubMed  Google Scholar 

  58. Singh R, Kolvraa S, Rattan SI. Genetics of human longevity with emphasis on the relevance of HSP70 as candidate genes. Front Biosci. 2007;12:4504–13.

    CAS  PubMed  Google Scholar 

  59. De Benedictis G, Carotenuto L, Carrieri G, De Luca M, Falcone E, Rose G, Cavalcanti S, Corsonello F, Feraco E, Baggio G, Bertolini S, Mari D, Mattace R, Yashin AI, Bonafe M, Franceschi C. Gene/longevity association studies at four autosomal loci (REN, THO, PARP, SOD2). Eur J Hum Genet. 1998;6(6):534–41.

    PubMed  Google Scholar 

  60. Schmitt K, Grimm A, Kazmierczak A, Strosznajder JB, Gotz J, Eckert A. Insights into mitochondrial dysfunction: aging, amyloid-beta and tau – a deleterious trio. Antioxid Redox Signal. 2011;16:1456.

    Google Scholar 

  61. Castro MD, Suarez E, Kraiselburd E, Isidro A, Paz J, Ferder L, Ayala-Torres S. Aging increases mitochondrial DNA damage and oxidative stress in liver of rhesus monkeys. Exp Gerontol. 2011;47:29–32.

    Google Scholar 

  62. Radak Z, Zhao Z, Goto S, Koltai E. Age-associated neurodegeneration and oxidative damage to lipids, proteins and DNA. Mol Asp Med. 2011;32(4–6):305–15.

    CAS  Google Scholar 

  63. Xue H, Xian B, Dong D, Xia K, Zhu S, Zhang Z, Hou L, Zhang Q, Zhang Y, Han JD. A modular network model of aging. Mol Syst Biol. 2007;3:147.

    PubMed  PubMed Central  Google Scholar 

  64. Lawton KA, Berger A, Mitchell M, Milgram KE, Evans AM, Guo L, Hanson RW, Kalhan SC, Ryals JA, Milburn MV. Analysis of the adult human plasma metabolome. Pharmacogenomics. 2008;9(4):383–97.

    CAS  PubMed  Google Scholar 

  65. Nikkila J, Sysi-Aho M, Ermolov A, Seppanen-Laakso T, Simell O, Kaski S, Oresic M. Gender-dependent progression of systemic metabolic states in early childhood. Mol Syst Biol. 2008;4:197.

    PubMed  PubMed Central  Google Scholar 

  66. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith Jr SC, Spertus JA, Costa F. Diagnosis and management of the metabolic syndrome. An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Executive summary. Cardiol Rev. 2005;13(6):322–7.

    PubMed  Google Scholar 

  67. Wirfalt E, Hedblad B, Gullberg B, Mattisson I, Andren C, Rosander U, Janzon L, Berglund G. Food patterns and components of the metabolic syndrome in men and women: a cross-sectional study within the Malmo Diet and Cancer cohort. Am J Epidemiol. 2001;154(12):1150–9.

    CAS  PubMed  Google Scholar 

  68. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, Haqq AM, Shah SH, Arlotto M, Slentz CA, Rochon J, Gallup D, Ilkayeva O, Wenner BR, Yancy Jr WS, Eisenson H, Musante G, Surwit RS, Millington DS, Butler MD, Svetkey LP. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9(4):311–26.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Huffman KM, Shah SH, Stevens RD, Bain JR, Muehlbauer M, Slentz CA, Tanner CJ, Kuchibhatla M, Houmard JA, Newgard CB, Kraus WE. Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care. 2009;32(9):1678–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Fiehn O, Garvey WT, Newman JW, Lok KH, Hoppel CL, Adams SH. Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS ONE. 2010;5(12):e15234.

    PubMed  PubMed Central  Google Scholar 

  71. Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, Yang E, Farrell L, Fox CS, O’Donnell CJ, Carr SA, Vasan RS, Florez JC, Clish CB, Wang TJ, Gerszten RE. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121(4):1402–11.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O’Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17(4):448–53.

    PubMed  PubMed Central  Google Scholar 

  73. Oresic M, Gopalacharyulu P, Mykkanen J, Lietzen N, Makinen M, Nygren H, Simell S, Simell V, Hyoty H, Veijola R, Ilonen J, Sysi-Aho M, Knip M, Hyotylainen T, Simell O. Cord serum lipidome in prediction of islet autoimmunity and type 1 diabetes. Diabetes. 2013;62:3268–74.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Huffman KM, Slentz CA, Bateman LA, Thompson D, Muehlbauer MJ, Bain JR, Stevens RD, Wenner BR, Kraus VB, Newgard CB, Kraus WE. Exercise-induced changes in metabolic intermediates, hormones, and inflammatory markers associated with improvements in insulin sensitivity. Diabetes Care. 2011;34(1):174–6.

    PubMed  PubMed Central  Google Scholar 

  75. Lanza IR, Zhang S, Ward LE, Karakelides H, Raftery D, Nair KS. Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes. PLoS ONE. 2010;5(5):e10538.

    PubMed  PubMed Central  Google Scholar 

  76. Sysi-Aho M, Ermolov A, Gopalacharyulu PV, Tripathi A, Seppanen-Laakso T, Maukonen J, Mattila I, Ruohonen ST, Vahatalo L, Yetukuri L, Harkonen T, Lindfors E, Nikkila J, Ilonen J, Simell O, Saarela M, Knip M, Kaski S, Savontaus E, Oresic M. Metabolic regulation in progression to autoimmune diabetes. PLoS Comput Biol. 2011;7(10):e1002257.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Oresic M, Simell S, Sysi-Aho M, Nanto-Salonen K, Seppanen-Laakso T, Parikka V, Katajamaa M, Hekkala A, Mattila I, Keskinen P, Yetukuri L, Reinikainen A, Lahde J, Suortti T, Hakalax J, Simell T, Hyoty H, Veijola R, Ilonen J, Lahesmaa R, Knip M, Simell O. Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. J Exp Med. 2008;205(13):2975–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Fabbrini E, Sullivan S, Klein S. Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications. Hepatology. 2010;51(2):679–89.

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Johnson NA, Walton DW, Sachinwalla T, Thompson CH, Smith K, Ruell PA, Stannard SR, George J. Noninvasive assessment of hepatic lipid composition: advancing understanding and management of fatty liver disorders. Hepatology. 2008;47(5):1513–23.

    CAS  PubMed  Google Scholar 

  80. Tiniakos DG, Vos MB, Brunt EM. Nonalcoholic fatty liver disease: pathology and pathogenesis. Annu Rev Pathol. 2010;5:145–71.

    CAS  PubMed  Google Scholar 

  81. James OF, Day CP. Non-alcoholic steatohepatitis (NASH): a disease of emerging identity and importance. J Hepatol. 1998;29(3):495–501.

    CAS  PubMed  Google Scholar 

  82. Day CP, James OF. Steatohepatitis: a tale of two “hits”? Gastroenterology. 1998;114(4):842–5.

    CAS  PubMed  Google Scholar 

  83. Rull A, Vinaixa M, Angel RM, Beltran R, Brezmes J, Canellas N, Correig X, Joven J. Metabolic phenotyping of genetically modified mice: an NMR metabonomic approach. Biochimie. 2009;91(8):1053–7.

    CAS  PubMed  Google Scholar 

  84. Li H, Wang L, Yan X, Liu Q, Yu C, Wei H, Li Y, Zhang X, He F, Jiang Y. A proton nuclear magnetic resonance metabonomics approach for biomarker discovery in nonalcoholic fatty liver disease. J Proteome Res. 2011;10(6):2797–806.

    CAS  PubMed  Google Scholar 

  85. Barr J, Vazquez-Chantada M, Alonso C, Perez-Cormenzana M, Mayo R, Galan A, Caballeria J, Martin-Duce A, Tran A, Wagner C, Luka Z, Lu SC, Castro A, Le Marchand-Brustel Y, Martinez-Chantar ML, Veyrie N, Clement K, Tordjman J, Gual P, Mato JM. Liquid chromatography-mass spectrometry-based parallel metabolic profiling of human and mouse model serum reveals putative biomarkers associated with the progression of nonalcoholic fatty liver disease. J Proteome Res. 2010;9(9):4501–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Kalhan SC, Guo L, Edmison J, Dasarathy S, McCullough AJ, Hanson RW, Milburn M. Plasma metabolomic profile in nonalcoholic fatty liver disease. Metabolism. 2011;60(3):404–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Feldstein AE, Lopez R, Tamimi TA, Yerian L, Chung YM, Berk M, Zhang R, McIntyre TM, Hazen SL. Mass spectrometric profiling of oxidized lipid products in human nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. J Lipid Res. 2010;51(10):3046–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Yap IK, Angley M, Veselkov KA, Holmes E, Lindon JC, Nicholson JK. Urinary metabolic phenotyping differentiates children with autism from their unaffected siblings and age-matched controls. J Proteome Res. 2010;9(6):2996–3004.

    CAS  PubMed  Google Scholar 

  89. Han X, Rozen S, Boyle SH, Hellegers C, Cheng H, Burke JR, Welsh-Bohmer KA, Doraiswamy PM, Kaddurah-Daouk R. Metabolomics in early Alzheimer’s disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS ONE. 2011;6(7):e21643.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Oresic M, Tang J, Seppanen-Laakso T, Mattila I, Saarni SE, Saarni SI, Lonnqvist J, Sysi-Aho M, Hyotylainen T, Perala J, Suvisaari J. Metabolome in schizophrenia and other psychotic disorders: a general population-based study. Genome Med. 2011;3(3):19.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Xuan J, Pan G, Qiu Y, Yang L, Su M, Liu Y, Chen J, Feng G, Fang Y, Jia W, Xing Q, He L. Metabolomic profiling to identify potential serum biomarkers for schizophrenia and risperidone action. J Proteome Res. 2011;10(12):5433–43.

    PubMed  Google Scholar 

  92. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn GS, Ghosh D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature. 2009;457(7231):910–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Sawyers CL. The cancer biomarker problem. Nature. 2008;452(7187):548–52.

    CAS  PubMed  Google Scholar 

  94. Kind T, Tolstikov V, Fiehn O, Weiss RH. A comprehensive urinary metabolomic approach for identifying kidney cancer. Anal Biochem. 2007;363(2):185–95.

    CAS  PubMed  Google Scholar 

  95. Denkert C, Budczies J, Kind T, Weichert W, Tablack P, Sehouli J, Niesporek S, Konsgen D, Dietel M, Fiehn O. Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Res. 2006;66(22):10795–804.

    CAS  PubMed  Google Scholar 

  96. Pasikanti KK, Esuvaranathan K, Ho PC, Mahendran R, Kamaraj R, Wu QH, Chiong E, Chan EC. Noninvasive urinary metabonomic diagnosis of human bladder cancer. J Proteome Res. 2010;9(6):2988–95.

    CAS  PubMed  Google Scholar 

  97. Miyagi Y, Higashiyama M, Gochi A, Akaike M, Ishikawa T, Miura T, Saruki N, Bando E, Kimura H, Imamura F, Moriyama M, Ikeda I, Chiba A, Oshita F, Imaizumi A, Yamamoto H, Miyano H, Horimoto K, Tochikubo O, Mitsushima T, Yamakado M, Okamoto N. Plasma free amino acid profiling of five types of cancer patients and its application for early detection. PLoS ONE. 2011;6(9):e24143.

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Li M, Song Y, Cho N, Chang JM, Koo HR, Yi A, Kim H, Park S, Moon WK. An HR-MAS MR metabolomics study on breast tissues obtained with core needle biopsy. PLoS ONE. 2011;6(10):e25563.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Gu H, Pan Z, Xi B, Asiago V, Musselman B, Raftery D. Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer. Anal Chim Acta. 2011;686(1–2):57–63.

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Qiu Y, Zhou B, Su M, Baxter S, Zheng X, Zhao X, Yen Y, Jia W. Mass spectrometry-based quantitative metabolomics revealed a distinct lipid profile in breast cancer patients. Int J Mol Sci. 2013;14(4):8047–61.

    PubMed  PubMed Central  Google Scholar 

  101. Bathen TF, Geurts B, Sitter B, Fjosne HE, Lundgren S, Buydens LM, Gribbestad IS, Postma G, Giskeodegard GF. Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery. PLoS ONE. 2013;8(4):e61578.

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Moazzami AA, Zhang JX, Kamal-Eldin A, Aman P, Hallmans G, Johansson JE, Andersson SO. Nuclear magnetic resonance-based metabolomics enable detection of the effects of a whole grain rye and rye bran diet on the metabolic profile of plasma in prostate cancer patients. J Nutr. 2011;141(12):2126–32.

    CAS  PubMed  Google Scholar 

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Correspondence to Sebastiano Collino .

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Collino, S., Martin, FP., Moco, S. (2015). Metabonomics in Clinical Practice. In: Kochhar, S., Martin, FP. (eds) Metabonomics and Gut Microbiota in Nutrition and Disease. Molecular and Integrative Toxicology. Springer, London. https://doi.org/10.1007/978-1-4471-6539-2_2

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