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