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miércoles, 16 de marzo de 2016

ISCHOM II: Metabolomics, cocoa & chocolate. GARCIA-ALOY, M



2.5. What Can Chocolate and Cocoa Learn from Metabolomics? 



Garcia-Aloy, M.; Llorach, R.; Urpi-Sarda, M.; Vázquez-Fresno, R.; Jáuregui, O.; Andres-Lacueva, C. * 


There is a growing body of evidence of the beneficial cardiovascular effects of cocoa consumption. Untargeted metabolomics is used as a hypothesis-generating tool. The main aim of this work was to contribute to the identification of biomarkers related to food ingestion (biomarkers of intake), as well as their potential association with health (biomarkers of effect) in a population at high-risk of cardiovascular disease, using an untargeted High-Performance Liquid Chromatography coupled to Quadropole Time-of-Flight Mass Spectrometry (HPLC-Q-ToF-MS) metabolomics strategy in acute and short-term clinical trials, as well as in observational studies. Dietary cocoa fingerprinting was characterized by a complex metabolic pattern linked to cocoa phytochemicals (alkaloids and polyphenols) and processing-derived compounds, as well as endogenous metabolites. A large proportion of metabolites were characteristic of cocoa exposure independently of the study design. They belong both to theobromine metabolism and to microbial-derived metabolism of polyphenols. With respect to the endogenous metabolome, methylglutarylcarnitine showed reduced levels associated with cocoa consumption, both in the short-term clinical trial and in the observational study. Because of the potential role of acylcarnitines in insulin resistance, this observation could provide a mechanistic insight into the beneficial effects of cocoa consumption on insulin sensitivity previously described in epidemiological studies. 

Finally, to improve the prediction of cocoa consumption, a combined urinary metabolite model was constructed. Receiver operating characteristic (ROC) curves were performed to evaluate the model and individual metabolites. The area under curve values (95% confidence interval) 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%. Discriminating metabolites of cocoa exposure were replicated in three studies with different designs, increasing the level of evidence from observed associations. Since some of the discriminating compounds are produced by gut microbiota this reinforces the hypothesis that the microbial food metabolome is an important source of dietary biomarkers. The predictive capacity of dietary exposition was improved using multimetabolite combined models compared with the same compounds individually. 

Acknowledgement:
This work has been supported by MINECO and co-founded by FEDER: AGL2009-13906-C02-01; CONSOLIDER-INGENIO 2010 Program, FUN-C-FOOD (CSD2007-063); and PCIN-2014-133 JPI HDHL_Biomarkers.
We also thank the award of 2014SGR1566 from AGAUR. M. Urpi-Sarda thanks the “Ramón y Cajal” program (RYC-2011-09677), and all authors thank MINECO and Fondo Social Europeo.

ISCHOM II: Barcelona 2015.

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