Metabolomics

, Volume 11, Issue 1, pp 155–165

Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort

  • Mar Garcia-Aloy
  • Rafael Llorach
  • Mireia Urpi-Sarda
  • Sara Tulipani
  • Jordi Salas-Salvadó
  • Miguel Angel Martínez-González
  • Dolores Corella
  • Montserrat Fitó
  • Ramon Estruch
  • Lluis Serra-Majem
  • Cristina Andres-Lacueva
Original Article

DOI: 10.1007/s11306-014-0682-6

Cite this article as:
Garcia-Aloy, M., Llorach, R., Urpi-Sarda, M. et al. Metabolomics (2015) 11: 155. doi:10.1007/s11306-014-0682-6

Abstract

Bread is one of the most widely consumed foods. Its impact on human health is currently of special interest for researchers. We aimed to identify biomarkers of bread consumption by applying a nutrimetabolomic approach to a free-living population. An untargeted HPLC–q-TOF-MS and multivariate analysis was applied to human urine from 155 subjects stratified by habitual bread consumption in three groups: non-consumers of bread (n = 56), white-bread consumers (n = 48) and whole-grain bread consumers (n = 51). The most differential metabolites (variable importance for projection ≥1.5) included compounds originating from cereal plant phytochemicals such as benzoxazinoids and alkylresorcinol metabolites, and compounds produced by gut microbiota (such as enterolactones, hydroxybenzoic and dihydroferulic acid metabolites). Pyrraline, riboflavin, 3-indolecarboxylic acid glucuronide, 2,8-dihydroxyquinoline glucuronide and N-α-acetylcitrulline were also tentatively identified. In order to combine multiple metabolites in a model to predict bread consumption, a stepwise logistic regression analysis was used. Receiver operating curves were constructed to evaluate the global performance of individual metabolites and their combination. The area under the curve values [AUC (95 % CI)] of combined models ranged from 77.8 % (69.1–86.4 %) to 93.7 % (89.4–98.1 %), whereas the AUC for the metabolites included in the models had weak values when they were evaluated individually: from 58.1 % (46.6–69.7 %) to 78.4 % (69.8–87.1 %). Our study showed that a daily bread intake significantly impacted on the urinary metabolome, despite being examined under uncontrolled free-living conditions. We further concluded that a combination of several biomarkers of exposure is better than a single biomarker for the predictive ability of discriminative analysis.

Keywords

NutrimetabolomicsFood metabolomeBiomarkersBreadHPLC–q-TOF-MSMetabolic fingerprinting

Supplementary material

11306_2014_682_MOESM1_ESM.pdf (568 kb)
Supplementary material 1 (PDF 568 kb)

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Mar Garcia-Aloy
    • 1
    • 2
  • Rafael Llorach
    • 1
    • 2
  • Mireia Urpi-Sarda
    • 1
    • 2
  • Sara Tulipani
    • 2
    • 3
  • Jordi Salas-Salvadó
    • 4
    • 5
  • Miguel Angel Martínez-González
    • 5
    • 6
  • Dolores Corella
    • 5
    • 7
  • Montserrat Fitó
    • 5
    • 8
  • Ramon Estruch
    • 5
    • 9
  • Lluis Serra-Majem
    • 5
    • 10
  • Cristina Andres-Lacueva
    • 1
    • 2
  1. 1.Biomarkers & Nutrimetabolomic Lab., Nutrition and Food Science Department, XaRTA, INSA, Campus Torribera, Pharmacy FacultyUniversity of BarcelonaBarcelonaSpain
  2. 2.INGENIO-CONSOLIDER Program, Fun-C-Food CSD2007-063Ministry of Science and InnovationBarcelonaSpain
  3. 3.Biomedical Research Institute (IBIMA), Service of Endocrinology and Nutrition, Hospital Complex (Virgen de la Victoria), Campus de Teatinos s/nUniversity of MálagaMálagaSpain
  4. 4.Human Nutrition Unit, Hospital Universitari de Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV)Universitat Rovira i VirgiliReusSpain
  5. 5.CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn)Instituto de Salud Carlos III (ISCIII)MadridSpain
  6. 6.Department of Preventive Medicine and Public Health, Medical School-ClinicaUniversity of NavarraPamplonaSpain
  7. 7.Department of Preventive Medicine and Public HealthUniversity of ValenciaValenciaSpain
  8. 8.Cardiovascular Risk and Nutrition Research GroupIMIM-Institut de Recerca del Hospital del MarBarcelonaSpain
  9. 9.Department of Internal Medicine, Hospital ClinicInstitut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS)BarcelonaSpain
  10. 10.Research Institute of Biomedical and Health SciencesUniversity of Las Palmas de Gran CanariaLas PalmasSpain