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Identifying the metabolomic fingerprint of high and low flavonoid consumers

  • Kerry L. Ivey (a1) (a2), Eric B. Rimm (a1) (a2) (a3), Peter Kraft (a2) (a4), Clary B. Clish (a3) (a5), Aedin Cassidy (a6), Jonathan Hodgson (a7), Kevin Croft (a7), Brian Wolpin (a3) (a8) and Liming Liang (a2) (a4)...

High flavonoid consumption can improve vascular health. Exploring flavonoid–metabolome relationships in population-based settings is challenging, as: (i) there are numerous confounders of the flavonoid–metabolome relationship; and (ii) the set of dependent metabolite variables are inter-related, highly variable and multidimensional. The Metabolite Fingerprint Score has been developed as a means of approaching such data. This study aims to compare its performance with that of more traditional methods, in identifying the metabolomic fingerprint of high and low flavonoid consumers. This study did not aim to identify biomarkers of intake, but rather to explore how systemic metabolism differs in high and low flavonoid consumers. Using liquid chromatography–tandem MS, 174 circulating plasma metabolites were profiled in 584 men and women who had complete flavonoid intake assessment. Participants were randomised to one of two datasets: (a) training dataset, to determine the models for the discrimination variables (n 399); and (b) validation dataset, to test the capacity of the variables to differentiate higher from lower total flavonoid consumers (n 185). The stepwise and full canonical variables did not discriminate in the validation dataset. The Metabolite Fingerprint Score successfully identified a unique pattern of metabolites that discriminated high from low flavonoid consumers in the validation dataset in a multivariate-adjusted setting, and provides insight into the relationship of flavonoids with systemic lipid metabolism. Given increasing use of metabolomics data in dietary association studies, and the difficulty in validating findings using untargeted metabolomics, this paper is of timely importance to the field of nutrition. However, further validation studies are required.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
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* Corresponding author: K. L. Ivey, email
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1. Jenkins, H, Hardy, N, Beckmann, M, et al. (2004) A proposed framework for the description of plant metabolomics experiments and their results. Nat Biotechnol 22, 16011606.
2. Fiehn, O (2002) Metabolomics – the link between genotypes and phenotypes. Plant Mol Biol 48, 155171.
3. Bro, R, Kamstrup-Nielsen, MH, Engelsen, SB, et al. (2015) Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics 11, 13761380.
4. Kuhara, T (2007) Noninvasive human metabolome analysis for differential diagnosis of inborn errors of metabolism. J Chromatogr B Analyt Technol Biomed Life Sci 855, 4250.
5. Kind, T, Tolstikov, V, Fiehn, O, et al. (2007) A comprehensive urinary metabolomic approach for identifying kidney cancer. Anal Biochem 363, 185195.
6. Mamas, M, Dunn, WB, Neyses, L, et al. (2011) The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch Toxicol 85, 517.
7. Wang, TJ, Larson, MG, Vasan, RS, et al. (2011) Metabolite profiles and the risk of developing diabetes. Nature Medicine 17, 448453.
8. Kenny, LC, Broadhurst, DI, Dunn, W, et al. (2010) Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers. Hypertension 56, 741749.
9. Altmaier, E, Kastenmüller, G, Römisch-Margl, W, et al. (2011) Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics. Eur J Epidemiol 26, 145156.
10. Kuo, C-H, Wang, K-C, Tian, T-F, et al. (2012) Metabolomic characterization of laborers exposed to welding fumes. Chem Res Toxicol 25, 676686.
11. Harnly, JM, Doherty, RF, Beecher, GR, et al. (2006) Flavonoid content of US fruits, vegetables, and nuts. J Agric Food Chem 54, 99669977.
12. Beecher, GR (2003) Overview of dietary flavonoids: nomenclature, occurrence and intake. J Nutr 133, 3248S3254S.
13. Hollman, PC, Gaag, MV, Mengelers, MJ, et al. (1996) Absorption and disposition kinetics of the dietary antioxidant quercetin in man. Free Radic Biol Med 21, 703707.
14. Erlund, I, Meririnne, E, Alfthan, G, et al. (2001) Plasma kinetics and urinary excretion of the flavanones naringenin and hesperetin in humans after ingestion of orange juice and grapefruit juice. J Nutr 131, 235241.
15. Déprez, S, Brezillon, C, Rabot, S, et al. (2000) Polymeric proanthocyanidins are catabolized by human colonic microflora into low-molecular-weight phenolic acids. J Nutr 130, 27332738.
16. Aura, AM, Martin-Lopez, P, O'Leary, KA, et al. (2005) In vitro metabolism of anthocyanins by human gut microflora. Eur J Nutr 44, 133142.
17. Stoupi, S, Williamson, G, Drynan, JW, et al. (2010) A comparison of the in vitro biotransformation of (–)-epicatechin and procyanidin B2 by human faecal microbiota. Mol Nutr Food Res 54, 747759.
18. Bondonno, CP, Yang, X, Croft, KD, et al. (2012) Flavonoid-rich apples and nitrate-rich spinach augment nitric oxide status and improve endothelial function in healthy men and women: a randomized controlled trial. Free Radic Biol Med 52, 95102.
19. Loke, WM, Hodgson, JM, Proudfoot, JM, et al. (2008) Pure dietary flavonoids quercetin and (−)-epicatechin augment nitric oxide products and reduce endothelin-1 acutely in healthy men. Am J Clin Nutr 88, 10181025.
20. Schroeter, H, Heiss, C, Balzer, J, et al. (2006) (–)-Epicatechin mediates beneficial effects of flavanol-rich cocoa on vascular function in humans. Proc Natl Acad Sci U S A 103, 10241029.
21. Manach, C, Hubert, J, Llorach, R, et al. (2009) The complex links between dietary phytochemicals and human health deciphered by metabolomics. Mol Nutr Food Res 53, 13031315.
22. Walsh, MC, Brennan, L, Pujos-Guillot, E, et al. (2007) Influence of acute phytochemical intake on human urinary metabolomic profiles. Am J Clin Nutr 86, 16871693.
23. 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.
24. 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.
25. Townsend, MK, Clish, CB, Kraft, P, et al. (2013) Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin Chem 59, 16571667.
26. Giovannucci, E, Ascherio, A, Rimm, EB, et al. (1995) Physical activity, obesity, and risk for colon cancer and adenoma in men. Ann Intern Med 122, 327334.
27. Colditz, GA & Hankinson, SE (2005) The Nurses’ Health Study: lifestyle and health among women. Nat Rev Cancer 5, 388396.
28. Yuan, C, Clish, CB, Wu, C, et al. (2016) Circulating metabolites and survival among patients with pancreatic cancer. J Natl Cancer Inst 108, djv409.
29. Cassidy, A, O'Reilly, ÉJ, Kay, C, et al. (2011) Habitual intake of flavonoid subclasses and incident hypertension in adults. Am J Clin Nutr 93, 338347.
30. Wei, EK, Giovannucci, E, Fuchs, CS, et al. (2005) Low plasma adiponectin levels and risk of colorectal cancer in men: a prospective study. J Natl Cancer Inst 97, 16881694.
31. Hankinson, SE, Willett, WC, Manson, JE, et al. (1995) Alcohol, height, and adiposity in relation to estrogen and prolactin levels in postmenopausal women. J Natl Cancer Inst 87, 12971302.
32. Bland, JM & Altman, DG (1995) Multiple significance tests: the Bonferroni method. BMJ 310, 170.
33. Benowitz, NL (1996) Cotinine as a biomarker of environmental tobacco smoke exposure. Epidemiol Rev 18, 188204.
34. Shrime, MG, Bauer, SR, McDonald, AC, et al. (2011) Flavonoid-rich cocoa consumption affects multiple cardiovascular risk factors in a meta-analysis of short-term studies. J Nutr 141, 19821988.
35. Ali, AH, Koutsari, C, Mundi, M, et al. (2011) Free fatty acid storage in human visceral and subcutaneous adipose tissue: role of adipocyte proteins. Diabetes 60, 23002307.
36. 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.
37. Menni, C, Zhai, G, MacGregor, A, et al. (2013) Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics 9, 506514.
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