Skip to main content
×
×
Home

The role of metabolomics in determination of new dietary biomarkers

  • A. O'Gorman (a1) and L. Brennan (a1)
Abstract

Traditional methods for the assessment of dietary intake are prone to error; in order to improve and enhance these methods increasing interest in the identification of dietary biomarkers has materialised. Metabolomics has emerged as a key tool in the area of dietary biomarker discovery and to date the use of metabolomics has identified a number of putative biomarkers. Applications to identify novel biomarkers of intake have in general taken three approaches: (1) specific acute intervention studies to identify specific biomarkers of intake; (2) searching for biomarkers in cohort studies by correlating to self-reported intake of a specific food/food group(s); (3) analysing dietary patterns in conjunction with metabolomic profiles to identify biomarkers and nutritypes. A number of analytical technologies are employed in metabolomics as currently there is no single technique capable of measuring the entire metabolome. These approaches each have their own advantages and disadvantages. The present review will provide an overview of current technologies and applications of metabolomics in the determination of new dietary biomarkers. In addition, it will address some of the current challenges in the field and future outlooks.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      The role of metabolomics in determination of new dietary biomarkers
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      The role of metabolomics in determination of new dietary biomarkers
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      The role of metabolomics in determination of new dietary biomarkers
      Available formats
      ×
Copyright
Corresponding author
* Corresponding author: Dr A. O'Gorman, email aoife.ogorman@ucd.ie
References
Hide All
1. Rochfort, S (2005) Metabolomics reviewed: a new ‘omics’ platform technology for systems biology and implications for natural products research. J Nat Prod 68, 18131820.
2. Brennan, L (2013) Metabolomics in nutrition research: current status and perspectives. Biochem Soc Trans 41, 670673.
3. Putri, SP, Nakayama, Y, Matsuda, F et al. (2013) Current metabolomics: practical applications. J Biosci Bioeng 115, 579589.
4. Wang, TJ, Larson, MG, Vasan, RS et al. (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17, 448453.
5. Nicholson, JK, Holmes, E, Kinross, JM et al. (2012) Metabolic phenotyping in clinical and surgical environments. Nature 491, 384392.
6. Oresic, M, Seppanen-Laakso, T, Sun, D et al. (2012) Phospholipids and insulin resistance in psychosis: a lipidomics study of twin pairs discordant for schizophrenia. Genome Med 4, 1.
7. Dunn, WB, Bailey, NJ & Johnson, HE (2005) Measuring the metabolome: current analytical technologies. Analyst 130, 606625.
8. Goodacre, R, Vaidyanathan, S, Dunn, WB et al. (2004) Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol 22, 245252.
9. Lindon, JC, Holmes, E, Bollard, ME et al. (2004) Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 9, 131.
10. Sumner, LW, Mendes, P & Dixon, RA (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62, 817836.
11. Fiehn, O (2002) Metabolomics – the link between genotypes and phenotypes. Plant Mol Biol 48, 155171.
12. Dunn, WB & Ellis, DI (2005) Metabolomics: current analytical platforms and methodologies. Trac-Trends Anal Chem 24, 285294.
13. Alonso, A, Marsal, S & Julia, A (2015) Analytical methods in untargeted metabolomics: state of the art in 2015. Front Bioeng Biotechnol 3, 23.
14. Roberts, LD, Souza, AL, Gerszten, RE et al. (2012) Targeted metabolomics. Curr Protoc Mol Biol Chapter 30, Unit 30 2 124.
15. Vinayavekhin, N & Saghatelian, A (2010) Untargeted metabolomics. Curr Protoc Mol Biol Chapter 30, Unit 30 1 124.
16. Shulaev, V (2006) Metabolomics technology and bioinformatics. Brief Bioinformatics 7, 128139.
17. Malet-Martino, M & Holzgrabe, U (2011) NMR techniques in biomedical and pharmaceutical analysis. J Pharm Biomed Anal 55, 115.
18. Sebedio, JL & Brennan, L (2014) Metabolomics as a Tool in Nutrition Research, 1st ed. Cambridge, UK: Woodhead Publishing.
19. Ramautar, R, Demirci, A & de Jong, GJ (2006) Capillary electrophoresis in metabolomics. Trac-Trends Anal Chem 25, 455466.
20. Ravanbakhsh, S, Liu, P, Bjorndahl, TC et al. (2015) Accurate, fully-automated NMR spectral profiling for metabolomics. PLoS ONE 10, e0124219.
21. Brennan, L (2014) NMR-based metabolomics: from sample preparation to applications in nutrition research. Prog Nucl Magn Reson Spectrosc 83, 4249.
22. Wishart, DS, Knox, C, Guo, AC et al. (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37, D603D610.
23. Kobayashi, N, Harano, Y, Tochio, N et al. (2012) An automated system designed for large scale NMR data deposition and annotation: application to over 600 assigned chemical shift data entries to the BioMagResBank from the Riken Structural Genomics/Proteomics Initiative internal database. J Biomol NMR 53, 311320.
24. Pudakalakatti, SM, Dubey, A, Jaipuria, G et al. (2014) A fast NMR method for resonance assignments: application to metabolomics. J Biomol NMR 58, 165173.
25. Beckonert, O, Coen, M, Keun, HC et al. (2010) High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nat Protoc 5, 019032.
26. Beger, RD (2013) A review of applications of metabolomics in cancer. Metabolites 3, 552574.
27. Chan, EC, Koh, PK, Mal, M et al. (2009) Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res 8, 352361.
28. Putri, SP, Yamamoto, S, Tsugawa, H et al. (2013) Current metabolomics: technological advances. J Biosci Bioeng 116, 916.
29. Keun, HC, Beckonert, O, Griffin, JL et al. (2002) Cryogenic probe 13C NMR spectroscopy of urine for metabonomic studies. Anal Chem 74, 45884593.
30. Mercier, P, Lewis, MJ, Chang, D et al. (2011) Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra. J Biomol NMR 49, 307323.
31. Hao, J, Astle, W, De Iorio, M et al. (2012) BATMAN – an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics 28, 20882090.
32. Wang, Y, Liu, SY, Hu, YJ et al. (2015) Current state of the art of mass spectrometry-based metabolomics studies – a review focusing on wide coverage, high throughput and easy identification. RSC Adv 5, 7872878737.
33. Dettmer, K, Aronov, PA & Hammock, BD (2007) Mass spectrometry-based metabolomics. Mass Spectrom Rev 26, 5178.
34. Halket, JM, Waterman, D, Przyborowska, AM et al. (2005) Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS. J Exp Bot 56, 219243.
35. Castillo, S, Mattila, I, Miettinen, J et al. (2011) Data analysis tool for comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry. Anal Chem 83, 30583067.
36. Rocha, SM, Caldeira, M, Carrola, J et al. (2012) Exploring the human urine metabolomic potentialities by comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry. J Chromatogr A 1252, 155163.
37. Zhang, A, Sun, H, Wang, P et al. (2012) Modern analytical techniques in metabolomics analysis. Analyst 137, 293300.
38. Lei, ZT, Huhman, DV & Sumner, LW (2011) Mass spectrometry strategies in metabolomics. J Biol Chem 286, 2543525442.
39. Lind, MV, Savolainen, OI & Ross, AB (2016) The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples. Eur J Epidemiol 31, 717733.
40. Gonzalez-Dominguez, R, Garcia-Barrera, T & Gomez-Ariza, JL (2014) Using direct infusion mass spectrometry for serum metabolomics in Alzheimer's disease. Anal Bioanal Chem 406, 71377148.
41. Draper, J, Lloyd, AJ, Goodacre, R et al. (2013) Flow infusion electrospray ionisation mass spectrometry for high throughput, non-targeted metabolite fingerprinting: a review. Metabolomics 9, S4S29.
42. Wishart, DS (2016) Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 15, 473484.
43. Gowda, GA & Djukovic, D (2014) Overview of mass spectrometry-based metabolomics: opportunities and challenges. Methods Mol Biol 1198, 312.
44. Fave, G, Beckmann, ME, Draper, JH et al. (2009) Measurement of dietary exposure: a challenging problem which may be overcome thanks to metabolomics? Genes Nutr 4, 135141.
45. Bingham, SA (2002) Biomarkers in nutritional epidemiology. Public Health Nutr 5, 821827.
46. Kipnis, V, Midthune, D, Freedman, L et al. (2002) Bias in dietary-report instruments and its implications for nutritional epidemiology. Public Health Nutr 5, 915923.
47. Jenab, M, Slimani, N, Bictash, M et al. (2009) Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet 125, 507525.
48. Penn, L, Boeing, H, Boushey, CJ et al. (2010) Assessment of dietary intake: NuGO symposium report. Genes Nutr 5, 205213.
49. Heinzmann, SS, Brown, IJ, Chan, Q et al. (2010) Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am J Clin Nutr 92, 436443.
50. Beckmann, M, Lloyd, AJ, Haldar, S et al. (2013) Dietary exposure biomarker-lead discovery based on metabolomics analysis of urine samples. Proc Nutr Soc 72, 352361.
51. Andersen, MB, Kristensen, M, Manach, C et al. (2014) Discovery and validation of urinary exposure markers for different plant foods by untargeted metabolomics. Anal Bioanal Chem 406, 18291844.
52. Atkinson, W, Downer, P, Lever, M et al. (2007) Effects of orange juice and proline betaine on glycine betaine and homocysteine in healthy male subjects. Eur J Nutr 46, 446452.
53. Edmands, WMB, Beckonert, OP, Stella, C et al. (2011) Identification of human urinary biomarkers of cruciferous vegetable consumption by metabonomic profiling. J Proteome Res 10, 45134521.
54. Andersen, MBS, Reinbach, HC, Rinnan, A et al. (2013) Discovery of exposure markers in urine for Brassica-containing meals served with different protein sources by UPLC–qTOF–MS untargeted metabolomics. Metabolomics 9, 984997.
55. Stella, C, Beckwith-Hall, B, Cloarec, O et al. (2006) Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res 5, 27802788.
56. Cross, AJ, Major, JM & Sinha, R (2011) Urinary biomarkers of meat consumption. Cancer Epidemiol Biomarkers Prev 20, 11071111.
57. Heinzmann, SS, Holmes, E, Kochhar, S et al. (2015) 2-Furoylglycine as a candidate biomarker of coffee consumption. J Agric Food Chem 63, 86158621.
58. Lang, R, Wahl, A, Stark, T et al. (2011) Urinary N-methylpyridinium and trigonelline as candidate dietary biomarkers of coffee consumption. Mol Nutr Food Res 55, 16131623.
59. van Velzen, EJJ, Westerhuis, JA, van Duynhoven, JPM et al. (2009) Phenotyping tea consumers by nutrikinetic analysis of polyphenolic end-metabolites. J Proteome Res 8, 33173330.
60. Daykin, CA, Van Duynhoven, JPM, Groenewegen, A et al. TPJ (2005) Nuclear magnetic resonance spectroscopic based studies of the metabolism of black tea polyphenols in humans. J Agric Food Chem 53, 14281434.
61. Gibbons, H, McNulty, BA, Nugent, AP et al. (2015) A metabolomics approach to the identification of biomarkers of sugar-sweetened beverage intake. Am J Clin Nutr 101, 471477.
62. Jacobs, DM, Fuhrmann, JC, van Dorsten, FA et al. (2012) Impact of short-term intake of red wine and grape polyphenol extract on the human metabolome. J Agric Food Chem 60, 30783085.
63. Pujos-Guillot, E, Hubert, J, Martin, JF et al. (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, 16451659.
64. Gibbons, H & Brennan, L (2016) Metabolomics as a tool in the identification of dietary biomarkers. Proc Nutr Soc 112.
65. Brennan, L, Gibbons, H & O'Gorman, A (2015) An overview of the role of metabolomics in the identification of dietary biomarkers. Curr Nutr Reports 4, 304312.
66. Lloyd, AJ, Beckmann, M, Haldar, S et al. (2013) Data-driven strategy for the discovery of potential urinary biomarkers of habitual dietary exposure. Am J Clin Nutr 97, 377389.
67. Wittenbecher, C, Muhlenbruch, K, Kroger, J et al. (2015) Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes. Am J Clin Nutr 101, 12411250.
68. Myint, T, Fraser, GE, Lindsted, KD et al. (2000) Urinary 1-methylhistidine is a marker of meat consumption in black and in white California seventh-day Adventists. Am J Epidemiol 152, 752755.
69. Garcia-Aloy, M, Llorach, R, Urpi-Sarda, M et al. (2015) Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort. Metabolomics 11, 155165.
70. Garcia-Aloy, M, Llorach, R, Urpi-Sarda, M et al. (2014) Novel multimetabolite prediction of walnut consumption by a urinary biomarker model in a free-living population: the PREDIMED study. J Proteome Res 13, 34763483.
71. O'Sullivan, A, Gibney, MJ & Brennan, L (2011) Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am J Clin Nutr 93, 314321.
72. Pere-Trepat, E, Ross, AB, Martin, FP et al. (2010) Chemometric strategies to assess metabonomic imprinting of food habits in epidemiological studies. Chemometr Intell Lab Syst 104, 95100.
73. Menni, C, Zhai, G, Macgregor, A et al. (2013) Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics 9, 506514.
74. O'Gorman, A, Morris, C, Ryan, M et al. (2014) Habitual dietary intake impacts on the lipidomic profile. J Chromatogr B, Anal Technol Biomed Life Sci 966, 140146.
75. Floegel, A, von Ruesten, A, Drogan, D et al. (2013) Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam. Eur J Clin Nutr 67, 11001108.
76. Bouchard-Mercier, A, Rudkowska, I, Lemieux, S et al. (2013) The metabolic signature associated with the Western dietary pattern: a cross-sectional study. Nutr J 12, 158.
77. Andersen, MB, Rinnan, A, Manach, C et al. (2014) Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern. J Proteome Res 13, 14051418.
78. Scalbert, A, Brennan, L, Manach, C et al. (2014) The food metabolome: a window over dietary exposure. Am J Clin Nutr 99, 12861308.
79. Sebedio, JL, Martin, FP & Pujos, E (2008) Nutritional metabolomics: What are the perspective? OCL 15, 341345.
80. Wishart, DS, Tzur, D, Knox, C et al. (2007) HMDB: the human metabolome database. Nucleic Acids Res 35 (Database issue), D521D526.
81. Bouatra, S, Aziat, F, Mandal, R et al. (2013) The human urine metabolome. PLoS ONE 8, e73076.
82. Schauer, N, Steinhauser, D, Strelkov, S et al. (2005) GC–MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett 579, 13321337.
83. Psychogios, N, Hau, DD, Peng, J et al. (2011) The human serum metabolome. PLoS ONE 6, e16957.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Proceedings of the Nutrition Society
  • ISSN: 0029-6651
  • EISSN: 1475-2719
  • URL: /core/journals/proceedings-of-the-nutrition-society
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed