An NMR metabolomics approach reveals a combined-biomarkers model in a wine interventional trial with validation in free-living individuals of the PREDIMED study

Abstract

The development of robust biomarkers of consumption would improve the classification of participants with regard to their dietary exposure. In addition, validation of them in free-living individuals remains an important challenge. The aim of this study is to assess wine intake biomarkers using an NMR metabolomic approach to measure the utility of these biomarkers in a wine interventional study (WIS, n = 56) and also to evaluate them in a free-living individuals (PREDIMED study, n = 91). Nine metabolites showed a significantly higher presence in urinary excretion in WIS after wine intake: five food metabolome metabolites (tartrate, ethyl glucuronide [EtG], 2,3-butanediol, mannitol, and ethanol); one related to the endogenous response to wine exposure (3-methyl-2-oxovalerate) and three unidentified compounds. Receiver operating characteristic (ROC) curve for each single metabolite were evaluated and exhibited areas under the curves (AUC) between 67.4 and 86.3 % when they were evaluated individually. Then, a logistic regression model was fitted to generate a combined-biomarkers model using these metabolites. The model generated which included tartrate-EtG, showed an AUC of 90.7 % in WIS. Similarly, the AUC in the PREDIMED study was 92.4 %. Results showed that a model combining tartrate-EtG is more useful for evaluating exposure to wine than single biomarkers, both in interventional studies and epidemiological data. To our knowledge, this is the first time that a combined-biomarker model using an NMR platform in wine biomarkers’ research has been generated and reproduced in a free-living population.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Abbreviations

AUC:

Area under the curve

CI:

Confidence interval

COSY:

Correlation spectroscopy

d:

Doublet

EtG:

Ethyl glucuronide

FFQ:

Food frequency questionnaires

FM:

Food metabolome

IQR:

Interquartile range

J:

J-coupling

m:

Multiplet

ROC:

Receiver operating characteristic

s:

Singlet

t:

Triplet

U:

Unassigned compound

WIS:

Wine interventional study

References

  1. Bahado-Singh, R. O., Akolekar, R., Mandal, R., et al. (2012). Metabolomics and first-trimester prediction of early-onset preeclampsia. The journal of maternal-fetal & neonatal medicine, 25, 1840–1847.

    CAS  Article  Google Scholar 

  2. Bartolomé, B., Monagas, M., Garrido, I., et al. (2010). Almond (Prunus dulcis (Mill.) D.A. Webb) polyphenols: From chemical characterization to targeted analysis of phenolic metabolites in humans. Archives of Biochemistry and Biophysics, 501, 124–133.

    PubMed  Article  Google Scholar 

  3. Beck, O., Stephanson, N., Böttcher, M., et al. (2007). Biomarkers to disclose recent intake of alcohol: potential of 5-hydroxytryptophol glucuronide testing using new direct UPLC-tandem MS and ELISA methods. Alcohol and Alcoholism, 42, 321–325.

    CAS  PubMed  Article  Google Scholar 

  4. Bemrah, N., Vin, K., Sirot, V., et al. (2012). Assessment of dietary exposure to annatto (E160b), nitrites (E249-250), sulphites (E220-228) and tartaric acid (E334) in the French population: the second French total diet study. Food additives and contaminants Part A, 29, 875–885.

    CAS  Article  Google Scholar 

  5. Chiva-Blanch, G., Urpi-Sarda, M., Llorach, R., et al. (2012). Differential effects of polyphenols and alcohol of red wine on the expression of adhesion molecules and inflammatory cytokines related to atherosclerosis: a randomized clinical trial. American Journal of Clinical Nutrition, 95, 326–334.

    CAS  PubMed  Article  Google Scholar 

  6. Darias-Martín, J. J., Rodríguez, O., Díaz, E., & Lamuela-Raventós, R. M. (2000). Effect of skin contact on the antioxidant phenolics in white wine. Food Chemistry, 71, 483–487.

    Article  Google Scholar 

  7. Donovan, J. L., Kasim-Karakas, S., German, J. B., & Waterhouse, A. L. (2002). Urinary excretion of catechin metabolites by human subjects after red wine consumption. British Journal of Nutrition, 87, 31–37.

    CAS  PubMed  Article  Google Scholar 

  8. Estruch, R. (2000). Wine and cardiovascular disease. Food Research International, 33, 219–226.

    Article  Google Scholar 

  9. Estruch, R., Martínez-González, M. A., Corella, D., et al. (2006). Effects of a Mediterranean-style diet on cardiovascular risk factors: a randomized trial. Annals of Internal Medicine, 145, 1–11.

    PubMed  Article  Google Scholar 

  10. Estruch, R., Ros, E., Salas-Salvadó, J., et al. (2013). Primary prevention of cardiovascular disease with a Mediterranean diet. New England Journal of Medicine, 368, 1279–1290.

    CAS  PubMed  Article  Google Scholar 

  11. Fernández-Ballart, J. D., Piñol, J. L., Zazpe, I., et al. (2010). Relative validity of a semi-quantitative food-frequency questionnaire in an elderly Mediterranean population of Spain. British Journal of Nutrition, 103, 1808–1816.

    PubMed  Article  Google Scholar 

  12. Garcia-Aloy, M., Llorach, R., Urpi-Sarda, M., et al. (2014). Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort. Metabolomics, 1–11. doi:10.1007/s11306-014-0682-6.

  13. Gonthier, M.-P., Cheynier, V., Donovan, J. L., et al. (2003). Microbial Aromatic Acid Metabolites Formed in the Gut Account for a Major Fraction of the Polyphenols Excreted in Urine of Rats Fed Red Wine Polyphenols. Journal of Nutrition, 133, 461–467.

    CAS  PubMed  Google Scholar 

  14. Heinzmann, S. S., Brown, I. J., Chan, Q., et al. (2010). Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. American Journal of Clinical Nutrition, 92, 436–443.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  15. Heinzmann, S. S., Merrifield, C. A., Rezzi, S., et al. (2011). Stability and Robustness of Human Metabolic Phenotypes in Response to Sequential Food Challenges. Journal of Proteome Research, 11, 643–655.

    PubMed  Article  Google Scholar 

  16. Helander, A., Böttcher, M., Fehr, C., Dahmen, N., & Beck, O. (2009). Detection Times for Urinary Ethyl Glucuronide and Ethyl Sulfate in Heavy Drinkers during Alcohol Detoxification. Alcohol and Alcoholism, 44, 55–61.

    CAS  PubMed  Article  Google Scholar 

  17. Høiseth, G., Bernard, J. P., Karinen, R., et al. (2007). A pharmacokinetic study of ethyl glucuronide in blood and urine: Applications to forensic toxicology. Forensic Science International, 172, 119–124.

    PubMed  Article  Google Scholar 

  18. Hwa, H.-L., Kuo, W.-H., Chang, L.-Y., et al. (2008). Prediction of breast cancer and lymph node metastatic status with tumour markers using logistic regression models. J Eval Clin Pract, 14, 275–280.

    PubMed  Article  Google Scholar 

  19. Jacobs, D. M., Deltimple, N., van Velzen, E., et al. (2008). (1)H NMR metabolite profiling of feces as a tool to assess the impact of nutrition on the human microbiome. NMR in Biomedicine, 21, 615–626.

    CAS  PubMed  Article  Google Scholar 

  20. Lande, R. G., & Marin, B. (2013). A Comparison of Two Alcohol Biomarkers in Clinical Practice: Ethyl Glucuronide Versus Ethyl Sulfate. Journal of Addictive Diseases, 32, 288–292.

    PubMed  Article  Google Scholar 

  21. Liu, S. Q. (2002). Malolactic fermentation in wine – beyond deacidification. Journal of Applied Microbiology, 92, 589–601.

    CAS  PubMed  Article  Google Scholar 

  22. Llorach, R., Garcia-Aloy, M., Tulipani, S., Vazquez-Fresno, R., & Andres-Lacueva, C. (2012). Nutrimetabolomic Strategies To Develop New Biomarkers of Intake and Health Effects. Journal of Agriculture and Food Chemistry, 60, 8797–8808.

    CAS  Article  Google Scholar 

  23. Lloyd, A. J., Beckmann, M., Haldar, S., et al. (2013). Data-driven strategy for the discovery of potential urinary biomarkers of habitual dietary exposure. American Journal of Clinical Nutrition, 97, 377–389.

    CAS  PubMed  Article  Google Scholar 

  24. López-Tamames, E., Puig-Deu, M. A., Teixeira, E., & Buxaderas, S. (1996). Organic acids, sugars, and glycerol content in white winemaking products determined by HPLC: Relationship to climate and varietal factors. American Journal of Enology and Viticulture, 47, 193–198.

    Google Scholar 

  25. Lord, R. S., Burdette, C. K., & Bralley, J. A. (2005). Significance of Urinary Tartaric Acid. Clinical Chemistry, 51, 672–673.

    CAS  PubMed  Article  Google Scholar 

  26. Mennen, L. I., Sapinho, D., Ito, H., et al. (2006). Urinary flavonoids and phenolic acids as biomarkers of intake for polyphenol-rich foods. British Journal of Nutrition, 96, 191–198.

    CAS  PubMed  Article  Google Scholar 

  27. Menni, C., Zhai, G., Macgregor, A., et al. (2013). Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics, 9, 506–514.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  28. Murabito, J. M., Keyes, M. J., Guo, C.-Y., et al. (2009). Cross-sectional relations of multiple inflammatory biomarkers to peripheral arterial disease: The Framingham Offspring Study. Atherosclerosis, 203, 509–514.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  29. O’Gorman, A., Gibbons, H., & Brennan, L. (2013). Metabolomics in the identification of biomarkers of dietary intake. Computational and Structural Biotechnology Journal, 4, e201301004. doi:10.5936/csbj.201301004.

  30. Odunsi, K., Wollman, R. M., Ambrosone, C. B., et al. (2005). Detection of epithelial ovarian cancer using 1H-NMR-based metabonomics. International Journal of Cancer, 113, 782–788.

    CAS  Article  Google Scholar 

  31. O’Sullivan, A., Gibney, M. J., & Brennan, L. (2011). Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. American Journal of Clinical Nutrition, 93, 314–321.

    PubMed  Article  Google Scholar 

  32. Pérez-Magariño, S., & González-San José, M. L. (2004). Evolution of Flavanols, Anthocyanins, and Their Derivatives during the Aging of Red Wines Elaborated from Grapes Harvested at Different Stages of Ripening. Journal of Agriculture and Food Chemistry, 52, 1181–1189.

    Article  Google Scholar 

  33. Pujos-Guillot, E., Hubert, J., Martin, J.-F., et al. (2013). Mass Spectrometry-based Metabolomics for the Discovery of Biomarkers of Fruit and Vegetable Intake: Citrus Fruit as a Case Study. Journal of Proteome Research, 12, 1645–1659.

    CAS  PubMed  Article  Google Scholar 

  34. Recamales, Á. F., Sayago, A., González-Miret, M. L., & Hernanz, D. (2006). The effect of time and storage conditions on the phenolic composition and colour of white wine. Food Research International, 39, 220–229.

    CAS  Article  Google Scholar 

  35. Robin, X., Turck, N., Hainard, A., et al. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77.

    PubMed Central  PubMed  Article  Google Scholar 

  36. Roowi, S., Stalmach, A., Mullen, W., et al. (2009). Green Tea Flavan-3-ols: Colonic Degradation and Urinary Excretion of Catabolites by Humans. Journal of Agriculture and Food Chemistry, 58, 1296–1304.

    Article  Google Scholar 

  37. Roussis, I. G., Lambropoulos, I., & Papadopoulou, D. (2005). Inhibition of the decline of volatile esters and terpenols during oxidative storage of Muscat-white and Xinomavro-red wine by caffeic acid and N-acetyl-cysteine. Food Chemistry, 93, 485–492.

    CAS  Article  Google Scholar 

  38. Salinas, M. R., Garijo, J., Pardo, F., Zalacain, A., & Alonso, G. L. (2005). Influence of prefermentative maceration temperature on the colour and the phenolic and volatile composition of rosé wines. Journal of the Science of Food and Agriculture, 85, 1527–1536.

    CAS  Article  Google Scholar 

  39. Scalbert, A., Brennan, L., Manach, C., et al. (2014). The food metabolome: a window over dietary exposure. American Journal of Clinical Nutrition, 99, 1286–1308.

    CAS  PubMed  Article  Google Scholar 

  40. Simonetti, P., Gardana, C., & Pietta, P. (2001). Caffeic acid as biomarker of red wine intake. Methods in Enzymology, 335, 122–130.

    CAS  PubMed  Google Scholar 

  41. Son, H.-S., Hwang, G.-S., Kim, K. M., et al. (2009). Metabolomic Studies on Geographical Grapes and Their Wines Using 1H NMR Analysis Coupled with Multivariate Statistics. Journal of Agriculture and Food Chemistry, 57, 1481–1490.

    CAS  Article  Google Scholar 

  42. Son, H. S., Kim, K. M., van den Berg, F., et al. (2008). 1H nuclear magnetic resonance-based metabolomic characterization of wines by grape varieties and production areas. Journal of Agriculture and Food Chemistry, 56, 8007–8016.

    CAS  Article  Google Scholar 

  43. Sousa, S. A. A., Magalhães, A., & Ferreira, M. M. C. (2013). Optimized bucketing for NMR spectra: Three case studies. Chemometrics and Intelligent Laborary Systems, 122, 93–102.

    CAS  Article  Google Scholar 

  44. Stalmach, A., Edwards, C. A., Wightman, J. D., & Crozier, A. (2013). Colonic catabolism of dietary phenolic and polyphenolic compounds from Concord grape juice. Food & Function, 4, 52–62.

    CAS  Article  Google Scholar 

  45. Teissedre, P.-L., & Landrault, N. (2000). Wine phenolics: contribution to dietary intake and bioavailability. Food Research International, 33, 461–467.

    CAS  Article  Google Scholar 

  46. Urpi-Sarda, M., Garrido, I., Monagas, M., et al. (2009a). Profile of Plasma and Urine Metabolites after the Intake of Almond [Prunus dulcis (Mill.) D.A. Webb] Polyphenols in Humans. Journal of Agriculture and Food Chemistry, 57, 10134–10142.

    CAS  Article  Google Scholar 

  47. Urpi-Sarda, M., Monagas, M., Khan, N., et al. (2009b). Epicatechin, procyanidins, and phenolic microbial metabolites after cocoa intake in humans and rats. Analytical and Bioanalytical Chemistry, 394, 1545–1556.

    CAS  PubMed  Article  Google Scholar 

  48. van Dorsten, F. A., Grün, C. H., van Velzen, E. J., et al. (2010). The metabolic fate of red wine and grape juice polyphenols in humans assessed by metabolomics. Molecular Nutrition & Food Research, 54, 897–908.

    Article  Google Scholar 

  49. Vázquez-Fresno, R., Llorach, R., Alcaro, F., et al. (2012). (1) H-NMR-based metabolomic analysis of the effect of moderate wine consumption on subjects with cardiovascular risk factors. Electrophoresis, 33, 2345–2354.

    PubMed  Article  Google Scholar 

  50. Weinmann, W., Schaefer, P., Thierauf, A., Schreiber, A., & Wurst, F. M. (2004). Confirmatory analysis of ethylglucuronide in urine by liquid-chromatography/electrospray ionization/tandem mass spectrometry according to forensic guidelines. Journal of the American Society for Mass Spectrometry, 15, 188–193.

    CAS  PubMed  Article  Google Scholar 

  51. Wojcik, M. H., & Hawthorne, J. S. (2007). Sensitivity of commercial ethyl glucuronide (ETG) testing in screening for alcohol abstinence. Alcohol and Alcoholism, 42, 317–320.

    CAS  PubMed  Article  Google Scholar 

  52. Wurst, F. M., Dresen, S., Allen, J. P., et al. (2006). Ethyl sulphate: a direct ethanol metabolite reflecting recent alcohol consumption. Addiction, 101, 204–211.

    PubMed  Article  Google Scholar 

  53. Xia, J., Broadhurst, D., Wilson, M., & Wishart, D. (2013). Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics, 9, 280–299.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  54. Xia, J., & Wishart, D. S. (2011). Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nature Protocols, 6, 743–760.

    CAS  PubMed  Article  Google Scholar 

  55. Zamora-Ros, R., Urpí-Sardà, M., Lamuela-Raventós, R. M., et al. (2009). Resveratrol metabolites in urine as a biomarker of wine intake in free-living subjects: The PREDIMED Study. Free Radical Biology and Medicine, 46, 1562–1566.

    CAS  PubMed  Article  Google Scholar 

Download references

Acknowledgments

Supported by the Spanish National Grants from Ministry of Economy and Competitiveness (MINECO) and cofounded by FEDER (Fondo Europeo de Desarrollo Regional): AGL2006-14228-C03-02/ALI, AGL2009-13906-C02-01, AGL2010-10084-E, the CONSOLIDER INGENIO 2010 Programme, FUN-C-FOOD (CSD2007-063), CIberOBN, as well as PI13/01172 Project, (Plan N de I+D+i 2013-2016) by ISCII-Subdirección General de Evaluación y Fomento de la Investigación. We also thank the award of 2014SGR1566 from the Generalitat de Catalunya’s Agency AGAUR. R.V.-F, O.K, M.U.-S and R. Ll. would like to thank the FPI fellowship, the “Juan de la Cierva” and the “Ramon y Cajal” programmes of the Spanish Government and the Fondo Social Europeo. We thank the participants for their collaboration in the study.

Conflict of interest

All the authors declare no competing financial interest.

Compliance with ethical requirements

WIS study. The study received the ethical approval Institutional Review Board of the Hospital Clinic of Barcelona. All participants had signed an informed consent. This trial has been registered in the Current Controlled Trials in London, International Standard Randomized Controlled Trial Number (ISRCTN88720134).

PREDIMED study. The trial protocol was conducted according to the Declaration of Helsinki and was approved by the institutional review boards of all the centres involved. All participants had signed an informed consent. This trial has been registered in the Current Controlled Trials in London, International Standard Randomized Controlled Trial Number (ISRCTN35739639).

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Rafael Llorach or Cristina Andres-Lacueva.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 24 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vázquez-Fresno, R., Llorach, R., Urpi-Sarda, M. et al. An NMR metabolomics approach reveals a combined-biomarkers model in a wine interventional trial with validation in free-living individuals of the PREDIMED study. Metabolomics 11, 797–806 (2015). https://doi.org/10.1007/s11306-014-0735-x

Download citation

Keywords

  • Biomarkers
  • Nutrimetabolomics
  • NMR
  • Wine
  • Interventional study
  • Cohort study