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A metabolomics‐driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free‐living subjects from the PREDIMED study
Author(s) -
GarciaAloy Mar,
Llorach Rafael,
UrpiSarda Mireia,
Jáuregui Olga,
Corella Dolores,
RuizCanela Miguel,
SalasSalvadó Jordi,
Fitó Montserrat,
Ros Emilio,
Estruch Ramon,
AndresLacueva Cristina
Publication year - 2015
Publication title -
molecular nutrition and food research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.495
H-Index - 131
eISSN - 1613-4133
pISSN - 1613-4125
DOI - 10.1002/mnfr.201400434
Subject(s) - metabolomics , metabolite , biomarker , metabolome , population , food science , polyphenol , microbiology and biotechnology , medicine , chemistry , biology , bioinformatics , biochemistry , environmental health , antioxidant
Scope The aim of the current study was to apply an untargeted metabolomics strategy to characterize a model of cocoa intake biomarkers in a free‐living population. Methods and results An untargeted HPLC‐q‐ToF‐MS based metabolomics approach was applied to human urine from 32 consumers of cocoa or derived products (CC) and 32 matched control subjects with no consumption of cocoa products (NC). The multivariate statistical analysis (OSC‐PLS‐DA) showed clear differences between CC and NC groups. The discriminant biomarkers identified were mainly related to the metabolic pathways of theobromine and polyphenols, as well as to cocoa processing. Consumption of cocoa products was also associated with reduced urinary excretions of methylglutarylcarnitine, which could be related to effects of cocoa exposure on insulin resistance. To improve the prediction of cocoa consumption, a combined urinary metabolite model was constructed. ROC curves were performed to evaluate the model and individual metabolites. The AUC values (95% CI) for the model were 95.7% (89.8–100%) and 92.6% (81.9–100%) in training and validation sets, respectively, whereas the AUCs for individual metabolites were <90%. Conclusions The metabolic signature of cocoa consumption in free‐living subjects reveals that combining different metabolites as biomarker models improves prediction of dietary exposure to cocoa.