A metabolomics-driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free-living subjects from the PREDIMED study

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 d...

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Bibliographic Details
Authors: Garcia Aloy, Mar, Llorach, Rafael, Urpí Sardà, Mireia, Jáuregui Pallarés, Olga, Corella Piquer, Dolores, Ruiz-Canela, Miguel, Salas Salvadó, Jordi, Fitó Colomer, Montserrat, Ros Rahola, Emilio, Estruch Riba, Ramon, Andrés Lacueva, Ma. Cristina
Format: article
Status:Versión aceptada para publicación
Publication Date:2015
Country:España
Institution:Universidad de Barcelona
Repository:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/108943
Online Access:https://hdl.handle.net/2445/108943
Access Level:Open access
Keyword:Marcadors bioquímics
Cacau
Cromatografia de líquids d'alta resolució
Nutrició
Polifenols
Metabolisme
Biochemical markers
Cocoa
High performance liquid chromatography
Nutrition
Polyphenols
Metabolism
Description
Summary: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 constructed 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.

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