title: A metabolomics-driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free-living subjects from the PREDIMED study.
abstract: METHODS
  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.
  
  
  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.
authors.affiliation: Biomarkers & Nutrimetabolomic Lab, Nutrition and Food Science Department, XaRTA, INSA, Campus Torribera, Pharmacy Faculty, University of Barcelona, Spain**; INGENIO-CONSOLIDER Program, Fun-C-Food CSD2007-063, Spain.
authors.forename: Mar
authors.initials: M
authors.lastname: Garcia-Aloy
authors.forename: Rafael
authors.initials: R
authors.lastname: Llorach
authors.forename: Mireia
authors.initials: M
authors.lastname: Urpi-Sarda
authors.forename: Olga
authors.initials: O
authors.lastname: Jáuregui
authors.forename: Dolores
authors.initials: D
authors.lastname: Corella
authors.forename: Miguel
authors.initials: M
authors.lastname: Ruiz-Canela
authors.forename: Jordi
authors.initials: J
authors.lastname: Salas-Salvadó
authors.forename: Montserrat
authors.initials: M
authors.lastname: Fitó
authors.forename: Emilio
authors.initials: E
authors.lastname: Ros
authors.forename: Ramon
authors.initials: R
authors.lastname: Estruch
authors.forename: Cristina
authors.initials: C
authors.lastname: Andres-Lacueva
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D015415:Biomarkers / Q000652:urine*; D002099:Cacao / Q000737:chemistry*; D002851:Chromatography, High Pressure Liquid; D003430:Cross-Sectional Studies; D004032:Diet; D002149:Energy Intake; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D015999:Multivariate Analysis; D059808:Polyphenols / Q000378:metabolism*; D012372:ROC Curve; D016032:Randomized Controlled Trials as Topic; D013805:Theobromine / Q000378:metabolism*
chemical_list: D015415:Biomarkers; D059808:Polyphenols; D013805:Theobromine
keywords: Biomarker model; Cocoa; HPLC-q-ToF-MS; Metabolomics; Nutrition