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Dietary exposure biomarker-lead discovery based on metabolomics analysis of urine samples

  • Manfred Beckmann (a1), Amanda J. Lloyd (a1), Sumanto Haldar (a2), Gaëlle Favé (a3), Chris J. Seal (a2), Kirsten Brandt (a2), John C. Mathers (a3) and John Draper (a1)...

Abstract

Although robust associations between dietary intake and population health are evident from conventional observational epidemiology, the outcomes of large-scale intervention studies testing the causality of those links have often proved inconclusive or have failed to demonstrate causality. This apparent conflict may be due to the well-recognised difficulty in measuring habitual food intake which may lead to confounding in observational epidemiology. Urine biomarkers indicative of exposure to specific foods offer information supplementary to the reliance on dietary intake self-assessment tools, such as FFQ, which are subject to individual bias. Biomarker discovery strategies using non-targeted metabolomics have been used recently to analyse urine from either short-term food intervention studies or from cohort studies in which participants consumed a freely-chosen diet. In the latter, the analysis of diet diary or FFQ information allowed classification of individuals in terms of the frequency of consumption of specific diet constituents. We review these approaches for biomarker discovery and illustrate both with particular reference to two studies carried out by the authors using approaches combining metabolite fingerprinting by MS with supervised multivariate data analysis. In both approaches, urine signals responsible for distinguishing between specific foods were identified and could be related to the chemical composition of the original foods. When using dietary data, both food distinctiveness and consumption frequency influenced whether differential dietary exposure could be discriminated adequately. We conclude that metabolomics methods for fingerprinting or profiling of overnight void urine, in particular, provide a robust strategy for dietary exposure biomarker-lead discovery.

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Corresponding author

* Corresponding author: J. Draper, fax +44 (0) 1970 621981, email jhd@aber.ac.uk

References

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1. Penn, L, Boeing, H, Boushey, CJ et al. (2010) Assessment of dietary intake: NuGO symposium report. Genes Nutr 5, 205213.
2. Bingham, SA, Gill, C, Welch, A et al. (1994) Comparison of dietary assessment methods in nutritional epidemiology – weighed records v 24-h recalls, food-frequency questionnaires and estimated-diet records. Br J Nutr 72, 619643.
3. Kristal, AR, Peters, U & Potter, JD (2005) Is it time to abandon the food-frequency questionnaire? Cancer Epidemiol Biomarkers Prev 14, 28262828.
4. Holmes, MD, Powell, IJ, Campos, H et al. (2007) Validation of a food-frequency questionnaire measurement of selected nutrients using biological markers in African–American men. Eur J Clin Nutr 61, 13281336.
5. Cade, J, Thompson, R, Burley, V et al. (2002) Development, validation and utilisation of food-frequency questionnaires – a review. Public Health Nutr 5, 567587.
6. Jia, X, Craig, LCA, Aucott, LS et al. (2008) Repeatability and validity of a food-frequency questionnaire in free-living older people in relation to cognitive function. J Nutr Health Aging 12, 735741.
7. Bingham, SA, Gill, C, Welch, A et al. (1997) Validation of dietary assessment methods in the UK arm of EPIC using weighed records, and 24-hour urinary nitrogen and potassium and serum vitamin C and carotenoids as biomarkers. Int J Epidemiol 26, S137S151.
8. Jenab, M, Slimani, N, Bictash, M et al. (2009) Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet 125, 507525.
9. Signorello, LB, Buchowski, MS, Cai, Q et al. (2010) Biochemical validation of food-frequency questionnaire-estimated carotenoid, alpha-tocopherol, and folate intakes among African–Americans and non-Hispanic whites in the Southern Community Cohort Study. Am J Epidemiol 171, 488497.
10. Mennen, LI, Sapinho, D, Ito, H et al. (2006) Urinary flavonoids and phenolic acids as biomarkers of intake for polyphenol-rich foods. Br J Nutr 96, 191198.
11. Favé, G, Beckmann, ME, Draper, J et al. (2009) Measurement of dietary exposure: a challenging problem which may be overcome thanks to metabolomics? Genes Nutr 4, 135141.
12. Scalbert, A, Brennan, L, Fiehn, O et al. (2009) Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics 5, 435458.
13. Holmes, E, Loo, RL, Stamler, J et al. (2008) Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453, 396400.
14. Walsh, MC, Brennan, L, Malthouse, JPG et al. (2006) Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans. Am J Clin Nutr 84, 531539.
15. Beckmann, M, Parker, D, Enot, DP et al. (2008) High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry. Nat Protoc 3, 486504.
16. Enot, DP, Lin, W, Beckmann, M et al. (2008) Preprocessing, classification modeling and feature selection using flow injection electrospray mass spectrometry metabolite fingerprint data. Nat Protoc 3, 446470.
17. Bertram, HC, Hoppe, C, Petersen, BO et al. (2007) An NMR-based metabonomic investigation on effects of milk and meat protein diets given to 8-year-old boys. Br J Nutr 97, 758763.
18. Bondia-Pons, I, Barri, T, Hanhineva, K et al. (2013) UPLC-QTOF/MS metabolic profiling unveils urinary changes in humans after a whole grain rye versus refined wheat bread intervention. Mol Nutr Food Res 57, 412–.422
19. 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.
20. Favé, G, Beckmann, M, Lloyd, AJ et al. (2011) Development and validation of a standardized protocol to monitor human dietary exposure by metabolite fingerprinting of urine samples. Metabolomics 7, 469484.
21. Heinzmann, SS, Merrifield, CA, Rezzi, S et al. (2012) Stability and robustness of human metabolic phenotypes in response to sequential food challenges. J Proteome Res 11, 643655.
22. Kahle, K, Kempf, M, Schreier, P et al. (2011) Intestinal transit and systemic metabolism of apple polyphenols. Eur J Nutr 50, 507522.
23. Llorach, R, Urpi-Sarda, M, Jauregui, O et al. (2009) An LC-MS-based metabolomics approach for exploring urinary metabolome modifications after cocoa consumption. J Proteome Res 8, 50605068.
24. Llorach-Asuncio, R, Jauregui, O, Urpi-Sarda, M et al. (2010) Methodological aspects for metabolome visualization and characterization: a metabolomic evaluation of the 24 h evolution of human urine after cocoa powder consumption. J Pharmaceut Biomed 51, 373381.
25. Lloyd, AJ, Favé, G, Beckmann, M et al. (2011) Use of mass spectrometry fingerprinting to identify urinary metabolites after consumption of specific foods. Am J Clin Nutr 94, 981991.
26. Lloyd, AJ, Beckmann, M, Favé, G et al. (2011) Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption. Br J Nutr 106, 812824.
27. 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.
28. Martin, F-PJ, Rezzi, S, Peré-Trepat, E et al. (2009) Metabolic effects of dark chocolate consumption on energy, gut microbiota, and stress-related metabolism in free-living subjects. J Proteome Res 8, 55685579.
29. 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.
30. 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.
31. Rasmussen, LG, Winning, H, Savorani, F et al. (2012) Assessment of the effect of high or low protein diet on the human urine metabolome as measured by NMR. Nutrients 4, 112131.
32. Stalmach, A, Mullen, W, Barron, D et al. (2009) Metabolite profiling of hydroxycinnamate derivatives in plasma and urine after the ingestion of coffee by humans: identification of biomarkers of coffee consumption. Drug Metabol Dispos 37, 17491758.
33. Stella, C, Beckwith-Hall, B, Cloarec, O et al. (2006) Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res 5, 27802788.
34. Tulipani, S, Llorach, R, Jáuregui, O et al. (2011) Metabolomics unveils urinary changes in subjects with metabolic syndrome following 12-week nut consumption. J Proteome Res 10, 50475058.
35. Walsh, MC, Brennan, L, Pujos-Guillot, E et al. (2008) Influence of acute phytochemical intake on human urinary metabolomic profiles. Am J Clin Nutr 86, 16871693.
36. Assfalg, M, Bertini, I, Colangiuli, D et al. (2008) Evidence of different metabolic phenotypes in humans. Proc Natl Acad Sci USA 105, 14201424.
37. Brownlee, IA, Moore, C, Chatfield, M et al. (2011) Markers of cardiovascular risk are not changed by increased whole grain-intake. The WHOLEheart study: a randomised, controlled dietary intervention. Br J Nutr 104, 125134.
38. Beckmann, M, Enot, DP, Overy, DP et al. (2010) Metabolite fingerprinting of urine suggests breed-specific dietary metabolism differences in domestic dogs. Br J Nutr 103, 11271138.
39. Draper, J, Enot, DP, Parker, D et al. (2009) Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour ‘rules’. BMC Bioinf 10, 24.
40. Abe, H (1983) Distribution of free L-histidine and related dipeptides in the muscle of fresh-water fishes. Comp Biochem Physiol Part B Biochem Mol Biol 76, 3539.
41. Abe, H, Okuma, E, Sekine, H et al. (1993) Human urinary-excretion of l-histidine-related compounds after ingestion of several meats and fish muscle. Int J Biochem 25, 12451249.
42. Dragsted, LO (2010) Biomarkers of meat intake and the application of nutrigenomics. Meat Sci 84, 301307.
43. Ito, H, Gonthier, MP, Manach, C et al. (2005) Polyphenol levels in human urine after intake of six different polyphenol-rich beverages. Br J Nutr 94, 500509.
44. Jacob, K, Periago, MJ, Boehm, V et al. (2008) Influence of lycopene and vitamin C from tomato juice on biomarkers of oxidative stress and inflammation. Br J Nutr 99, 137146.
45. Jaganath, IB, Mullen, W, Edwards, CA et al. (2006) The relative contribution of the small and large intestine to the absorption and metabolism of rutin in man. Free Radical Res 40, 10351046.
46. Lee, MB, Storer, MK, Blunt, JW et al. (2006) Validation of H-1 NMR spectroscopy as an analytical tool for methylamine metabolites in urine. Clin Chim Acta 365, 264269.
47. Mahaffey, KR (2004) Fish and shellfish as dietary sources of methylmercury and the omega-3 fatty acids, eicosahexaenoic acid and docosahexaenoic acid: risks and benefits. Environ Res 95, 414428.
48. Sánchez-Rodríguez, E, Ruiz, JM, Ferreres, F et al. (2012) Phenolic profiles of cherry tomatoes as influenced by hydric stress and rootstock technique. Food Chem 134, 775782.
49. Rechner, AR, Spencer, JPE, Kuhnle, G et al. (2001) Novel biomarkers of the metabolism of caffeic acid derivatives in vivo . Free Radical Biol Med 30, 12131222.
50. Vallverdu-Queralt, A, Jauregui, O, Medina-Remon, A et al. (2010) Improved characterization of tomato polyphenols using liquid chromatography/electrospray ionization linear ion trap quadrupole orbitrap mass spectrometry and liquid chromatography/electrospray ionization tandem mass spectrometry. Rapid Commun Mass Spectrom 24, 29862992.
51. Visciano, P, Perugini, M, Manera, M et al. (2009) Selected polycyclic aromatic hydrocarbons in smoked tuna, swordfish and Atlantic salmon fillets. Int J Food Sci Technol 44, 20282032.
52. Urpi-Sarda, M, Monagas, M, Khan, N et al. (2009) Targeted metabolic profiling of phenolics in urine and plasma after regular consumption of cocoa by liquid chromatography-tandem mass spectrometry. J Chromatogr A 1216, 72587267.

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