, Volume 11, Issue 1, pp 155–165 | Cite as

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

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


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.


Nutrimetabolomics Food metabolome Biomarkers Bread HPLC–q-TOF-MS Metabolic fingerprinting 



This research was supported by Spanish National Grants from the Ministry of Economy and Competitiveness (MINECO), as well as FEDER (Fondo Europeo de Desarrollo Regional): AGL2009-13906-C02-01, AGL2010-10084-E, CONSOLIDER INGENIO 2010 Programme: FUN-C-FOOD-CSD2007-063, and ISCIII-CIBEROBN; Merck Serono Research Grants 2010 from Fundación Salud 2000, and by “Pan cada día” open call promoted by the Scientific Committee of Bread and by INCERHPAN. The "CIBER de Fisiopatología de la Obesidad y Nutrición" (CIBEROBN) is an initiative of the Instituto de Salud Carlos III, Madrid, Spain. M. G. A. thanks the Generalitat de Catalunya’s Agency AGAUR for the predoctoral FI-DGR 2011 Fellowship. R. Ll. and M. U. S. thank the “Ramón y Cajal” (RYC-2010–07334 and RYC-2011-09677, respectively) and ST the “Juan de la Cierva” program, both programmes from MINECO and Fondo Social Europeo (FSE). MF was funded by a contract from the Catalan Government and the Instituto de Salud Carlos III FEDER (FIS CP06/00100). None of the funding sources had any involvement in the study or data analysis.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Mar Garcia-Aloy
    • 1
    • 2
  • Rafael Llorach
    • 1
    • 2
    Email author
  • 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
    Email author
  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

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