Mar Garcia-Aloy, Rafael Llorach, Mireia Urpi-Sarda, Rosa Vázquez-Fresno, Olga Jáuregui, Cristina Andres-Lacueva
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1 Dietary exposure biomarkers in nutritional intervention and observational studies to discover biomarkers of intake and disease-risk through a HPLC-QToF-MS metabolomics approach Mar Garcia-Aloy, Rafael Llorach, Mireia Urpi-Sarda, Rosa Vázquez-Fresno, Olga Jáuregui, Cristina Andres-Lacueva Seminari de Recerca de la Facultat de Farmàcia Barcelona, 19 d abril de 2016 Department of Nutrition & Food Science Pharmacy School University of Barcelona BIOMARKERS AND NUTRITIONAL & FOOD METABOLOMICS RESEARCH GROUP IP Cristina Andrés-Lacueva Postdoctoral Scientists Rafael Llorach Mireia Urpi-Sardà Raúl Zamora-Ros Sara Tulipani Mar Garcia-Aloy Nina Görner Montse Rabassa Bonet PhD students Lyda Ximena Mora Enrique Almanza Aguilera Fco. Javier Madrid Gambín Sheila Estruel Amades Maria Trinidad Soria florido Magalí Palau Rodríguez Collaborators Olga Jauregui Alexandre Perera Lluna margarcia@ub.edu Location Dept. of Nutrition & Food Science Pharmacy School University of Barcelona (Spain) Diagonal Campus. Av. Diagonal, 643 Av. Joan XXIII s/n (Barcelona) 1
2 FUNDING COLLABORATIONS (Bingham Public Health Nutr, 2002; Livingstone & Black J Nutr, 2003; Tucker Nutr Metab Cardiovasc Dis, 2007) 2
3 (Bingham Public Health Nutr, 2002; Livingstone & Black J Nutr, 2003; Tucker Nutr Metab Cardiovasc Dis, 2007) (Potischman & Freudenheim J Nutr, 2003; van Ommen et al. Mol Nutr Food Res, 2009; Raiten et al. Am J Clin Nutr, 2011) Joint Programming Initiative A Healthy Diet for a Healthy Life (JPI HDHL),
4 (Andersen 2013, from Spencer et al. Br J Nutr 2008, Jenab et al. Hum Genet 2009, Manach et al. Mol Nutr Food Res 2009) (Nicholson et al. Nature, 2010; Patti et al. Nat Rev Mol Cell Biol, 2012) 4
5 (Bouatra,, Wishart. The human urine metabolome. Plos One, 2013; Wishart et al. Nucleic Acids Res, 2013; Scalbert et al. Am J Clin Nutr, 2014) Carbohydrates Proteins Tissue & Microbial Lipids Biotransformations Vitamins Oxidation, Minerals Reduction, Polyphenols Hydrolysis, Alkaloids Dehydrogenation, Carotenoids Methylation, Phytosterols Sulfation, Natural Volatiles Acetylation, Artificial Colorants Glucuronidation, Flavoring Additives Amino acid conjugation, Food Contaminants Glutathione conjugation, Maillard Reaction Products (Bouatra,, Wishart. The human urine metabolome. Plos One, 2013; Wishart et al. Nucleic Acids Res, 2013; Scalbert et al. Am J Clin Nutr, 2014) 5
6 Analysis of food metabolome Food surveys Age, gender, BMI, lifestyle, physical activity Genotype Phenotype Dietary assessment Food intake Nutritional exposure New metabolites New potential food bioactives Segmentation of poor or high absorbers & metabolizers (Manach, Glasgow 2013; Scalbert et al. Am J Clin Nutr, 2014) Non-Consumers versus High Consumers Clinical phenotyping Genetic phenotyping Microbiota phenotyping etc. Nutritional Consideration (Llorach R et al. J Agric Food Chem, 2012) 6
7 ACUTE INTERVENTION STUDY CHRONIC INTERVENTION STUDY OBSERVATIONAL STUDY (Llorach R et al. J Agric Food Chem, 2012; Pujos-Guillot et al. J Proteome Res, 2013) Orange Proline betaine (Scalbert et al. Am J Clin Nutr, 2014;99(6): ) 7
8 Distribution of compounds into various foods Confluence of various compounds in common metabolites Procyanidin B2 (+)-Catechin ELLAGITANNINS: Pedunculagin Sanguiin H6 Punicalagin Urolithins Hydroxyphenylvalerolactones Hydroxyphenylvaleric acids Garcia-Muñoz & Vaillant. Crit Rev Food Sci Nutr, 2014 (Llorach R et al. J Agric Food Chem, 2012; Scalbert et al. Am J Clin Nutr, 2014) Distribution of compounds into various foods Confluence of various compounds in common metabolites (Llorach R et al. J Agric Food Chem, 2012; Scalbert et al. Am J Clin Nutr, 2014) 8
9 Since metabolomics offers a new approach for the determination of biomarkers of dietary exposure, we will observe differences in metabolic fingerprints associated with the consumption of food, which will allow us to predict its intake. Identify biomarkers related to the intake of certain foods (markers of consumption) and its possible association with health (markers of effect) by the application of an HPLC-QToF-MS nontargeted metabolomic strategy in nutritional studies with different designs. Characterize urinary metabolic fingerprint associated with the intake of widely consumed foods: bread, nuts, cocoa. Replicate characterized biomarkers of exposure in controlled clinical trials in a free-living population. Develop predictive models for determining usual intake and compare its predictive ability with respect to the ability of the metabolites evaluated individually. 9
10 intensity intensity intensity 14-Apr-16 Samples Acquisition Analysis Biomarker Identification Interpretation Urine HPLC- QToF-MS processing Ion Characterization Marker detection Alignment Metabolite Identification Quality Control Analysis of Critical Points: ESI+ ESI Multivariate analysis Unsupervised techniques (PCA) Supervised techniques (PLS-DA) Robustness analysis Validation of multivariate models Multistep procedure (Llorach et al. J Pharm Biomed Anal, 2010; Want et al., Nature Protocols, 2010; Dunn et al. Nature Protocols, 2011; Llorach R et al. J Agric Food Chem, 2012) Samples Acquisition Analysis Biomarker Identification Interpretation HPLC-QToF-MS ESI+ ESI m/z [M + H CO] + m/z [M + H C 2H 3O] + [M + H] + m/z retention time (Llorach-Asuncion et al. J Pharm Biomed Anal, 2010) [M + Na]+ [M + K] + 10
11 Intensity m/z 14-Apr-16 Samples Acquisition Analysis Biomarker Identification Interpretation COELUTION & CORRELATION Chromatogram Mass spectrum (RT = 3.98 min) Time (min) Intensity (Llorach-Asuncion et al. J Pharm Biomed Anal, 2010; Fernández-Albert et al. Anal Chem, 2014) Samples Acquisition Analysis Biomarker Identification Interpretation Quality control QC1: Milli-Q water QC2: pool of phenolic compounds QC3: pool of endogenous compounds QC4: reinjection of opposite samples Equilibration between plates Randomization in plates (Llorach et al. J Proteome Res, 2009; Llorach et al. J Proteome Res, 2010; Llorach-Asuncion et al. J Pharm Biomed Anal, 2010) 11
12 Samples Acquisition Analysis Biomarker Identification Interpretation processing Marker detection & Alignment Multivariate analysis Unsupervised & supervised techniques (PCA; PLS-DA) R 2 Q 2 (cross-validation / permutation test) Visualization of results from metabolome (Llorach-Asuncion et al. J Pharm Biomed Anal, 2010) Samples Acquisition Analysis Biomarker Identification Interpretation MAIT (Garcia-Aloy M et al. J Protome Res, 2014; Garcia-Aloy M et al. Metabolomics, 2015; Semba et al. Andres-Lacueva JAMA Intern Med, 2014; Urpi-Sarda et al. Mol Nutr Food Res, 2014; Llorach et al. Mol Nutr Food Res, 2013; Tulipani et al. Anal Chem, 2013; Tulipani et al. J Proteome Res, 2011; Llorach et al. J Pharm Biomed Anal, 2010; Fernandez-Albert et al. Anal Chem, 2014; Fernandez-Albert et al. Bioinformatics, 2014;Llorach et al. J Proteome Res, 2010; Llorach et al. J Proteome Res, 2009) 12
13 Samples Acquisition Analysis Biomarker Identification Interpretation MAIT Level Confidence of identity Identified compounds Putatively annotated compounds Putatively characterized compound classes Unknown compounds Level of evidence Comparison of 2 orthogonal properties with an authentic compound analyzed under identical experimental conditions. Based upon property similarity with public/commercial databases, without chemical reference standard. Based upon properties of known compounds of a chemical class. Unidentified compounds. (Sumner et al. Proposed minimum reporting standards for chemical analysis (CAWG-MSI). Metabolomics, 2007) Samples Acquisition Analysis Biomarker Identification Interpretation (Llorach-Asuncion et al. J Pharm Biomed Anal, 2010) 13
14 Distribution of compounds into various foods Confluence of various compounds in common metabolites Procyanidin B2 (+)-Catechin ELLAGITANNINS: Pedunculagin Sanguiin H6 Punicalagin Urolithins Hydroxyphenylvalerolactones Hydroxyphenylvaleric acids Garcia-Muñoz & Vaillant. Crit Rev Food Sci Nutr, 2014 (Llorach R et al. J Agric Food Chem, 2012; Scalbert et al. Am J Clin Nutr, 2014) Distribution of compounds into various foods Confluence of various compounds in common metabolites Procyanidin B2 (+)-Catechin ELLAGITANNINS: Pedunculagin Sanguiin H6 Punicalagin Urolithins Hydroxyphenylvalerolactones Hydroxyphenylvaleric acids Garcia-Muñoz & Vaillant. Crit Rev Food Sci Nutr,
15 Sensitivity 14-Apr-16 ROC curve with perfect predictive capacity ROC curve with good predictive capacity ROC curve without predictive capacity 1 Specificity (Xia et al. Metabolomics, 2013) Non-Consumers versus High Consumers Clinical phenotyping Genetic phenotyping Microbiota phenotyping etc. Nutritional Consideration (Llorach R et al. J Agric Food Chem, 2012) 15
16 PREDIMED COHORT (N=7447) Subsample (n=275) BREAD consumption stratification Excluded (n=120): not meet the stratification criteria Non-consumers (n=56) White-bread consumers (n=48) Whole-grain bread consumers (n=51) (Garcia-Aloy M et al. Metabolomics, 2015) (Garcia-Aloy M et al. Metabolomics, 2015) 16
17 RT (min) DETECTED MASS (m/z) ASSIGNATION IDENTIFICATION NC vs PB NC vs PI PB vs PI [M H] - 2-Aminophenol sulphate [M + H] + HPAA glucuronide [M H] [M + H] + HHPAA [M + H] + HMBOA glucuronide [M H] [M H] - HBOA glycoside [M + H] + HPPA [M + H] + HMBOA [M H] [M H] - DHPPA glucuronide [M H] - 3,5-Dihydroxyphenylethanol sulphate [M H] - DHPPTA sulphate [M H] - Hydroxybenzoic acid glucuronide [M H] - Dihydroferulic acid sulphate [M + H GlcA] + Enterolactone glucuronide [M H] [M + H] + Pyrraline [M H] [M + H] + 3-Indolecarboxylic acid glucuronide [M H] [M + H] + Riboflavine [M + H] + N-α-Acetylcitrulline [M + H] + 2,8-Dihydroxyquinoline glucuronide [M H GlcA] - (Garcia-Aloy M et al. Metabolomics, 2015) vs vs vs HPAA glucuronide 73.5% (63.8%-83.2%) 64.0% (53.3%-74.6%) HHPAA 67.8% (57.7%-77.9%) 69.7% (59.3%-80.1%) HMBOA glucuronide 68.2% (57.8%-78.7%) HPPA 69.9% (59.8%-79.9%) HMBOA 68.4% (57.8%-79.0%) 66.3% (55.6%-77.0%) Enterolactone glucuronide 69.6% (59.7%-79.5%) 73.0% (63.0%-83.1%) Pyrraline 65.8% (55.6%-76.0%) 3-Indolecarboxylic acid glucuronide 67.2% (57.0%-77.4%) 65.5% (54.6%-76.5%) Riboflavin 64.2% (53.4%-75.0%) 73.2% (63.7%-82.8%) 62.9% (51.5%-74.4%) 2-Aminophenol sulphate 66.4% (56.0%-76.7%) 68.9% (59.0%-78.9%) HPAA glucuronide 62.0% (51.7%-72.4%) HMBOA glucuronide 66.1% (55.9%-76.3%) 61.0% (50.5%-71.5%) HBOA glycoside 73.0% (63.6%-82.4%) 63.4% (52.6%-74.2%) HMBOA 69.2% (59.2%-79.3%) 66.8% (56.8%-76.7%) DHPPA glucuronide 64.9% (54.4%-75.4%) 78.4% (69.8%-87.1%) 65.1% (54.5%-75.8%) 3,5-Dihidroxifeniletanol sulphate 67.0% (56.8%-77.2%) DHPPTA sulphate 76.7% (67.6%-85.7%) 76.1% (67.1%-85.1%) Hydroxybenzoic acid glucuronide 67.4% (57.2%-77.6%) 61.3% (50.8%-71.7%) Dihydroferulic acid sulphate 74.3% (65.0%-83.6%) 74.6% (65.0%-84.2%) Enterolactone glucuronide 65.6% (55.4%-75.7%) 62.8% (52.2%-73.4%) Pyrraline 64.8% (54.7%-75.0%) 62.5% (51.6%-73.3%) 3-Indolecarboxylic acid glucuronide 66.8% (56.9%-76.7%) 63.0% (52.3%-73.7%) (Garcia-Aloy M et al. Metabolomics, 2015) 17
18 vs Coef. Err. Est. p HPAA glucuronide 1,565 0,542 0,004 HHPAA HMBOA glucuronide HPPA HMBOA 1,639 0,556 0,003 Enterolactone glucuronide Pyrraline 3-Indolecarboxylic acid glucuronide Riboflavin 0,842 0,340 0,013 2-Aminophenol sulphate 1,359 0,401 0,001 HPAA glucuronide HMBOA glucuronide HBOA glycoside HMBOA 1,816 0,445 <0,001 DHPPA glucuronide 3,5-Dihidroxifeniletanol sulphate DHPPTA sulphate Hydroxybenzoic acid glucuronide Dihydroferulic acid sulphate Enterolactone glucuronide Pyrraline 3-Indolecarboxylic acid glucuronide (Garcia-Aloy M et al. Metabolomics, 2015) vs Coef. Err. Est. p HPAA glucuronide HHPAA 1,750 0,787 0,026 HMBOA glucuronide HPPA 1,361 0,579 0,019 HMBOA 1,362 0,674 0,043 Enterolactone glucuronide 1,642 0,559 0,003 Pyrraline 1,436 0,636 0,024 3-Indolecarboxylic acid glucuronide 1,617 0,556 0,004 Riboflavin 1,921 0,524 <0,001 2-Aminophenol sulphate HPAA glucuronide HMBOA glucuronide HBOA glycoside HMBOA 1,856 0,659 0,005 DHPPA glucuronide 1,289 0,439 0,003 3,5-Dihidroxifeniletanol sulphate DHPPTA sulphate 1,685 0,481 <0,001 Hydroxybenzoic acid glucuronide Dihydroferulic acid sulphate 0,911 0,438 0,037 Enterolactone glucuronide 1,157 0,581 0,047 Pyrraline 1,397 0,502 0,005 3-Indolecarboxylic acid glucuronide 0,980 0,449 0,029 (Garcia-Aloy M et al. Metabolomics, 2015) 18
19 vs Coef. Err. Est. p HPAA glucuronide HHPAA 2,923 0,924 0,002 HMBOA glucuronide HPPA HMBOA Enterolactone glucuronide 2,009 0,500 <0,001 Pyrraline 1,248 0,536 0,020 3-Indolecarboxylic acid glucuronide Riboflavin 2-Aminophenol sulphate HPAA glucuronide HMBOA glucuronide HBOA glycoside HMBOA DHPPA glucuronide 3,5-Dihidroxifeniletanol sulphate DHPPTA sulphate 1,159 0,327 <0,001 Hydroxybenzoic acid glucuronide Dihydroferulic acid sulphate 1,077 0,355 0,002 Enterolactone glucuronide Pyrraline 3-Indolecarboxylic acid glucuronide (Garcia-Aloy M et al. Metabolomics, 2015) Consumption data Benzoxazinoid-related compounds FFQ Alkylresorcinol metabolites Microbial-derived metabolites Markers of heattreated food products Metabolites related with bread composition Endogenous metabolites (Garcia-Aloy M et al. Metabolomics, 2015) 19
![AGL2009-13906-C02-01 RANDOMIZATION PREDIMED COHORT (n=7447) INTERVENTION PERIOD W0 (12 weeks) W12 n = 20 CONTROL GROUP Subsample 1 (n=275) [cross-sectional analysis] Subsample 2 (n=327)](/docs-images/93/112676169/images/20-2.jpg "[cross-sectional analysis] WALNUT consumption stratification n = 22 NUTS GROUP Excluded (n=80) Excluded (n=141) Nonconsumers (n=128) Habitual consumers (n=67) Nonconsumers (n=104) Habitual consumers")
20 AGL C02-01 RANDOMIZATION PREDIMED COHORT (n=7447) INTERVENTION PERIOD W0 (12 weeks) W12 n = 20 CONTROL GROUP Subsample 1 (n=275) [cross-sectional analysis] Subsample 2 (n=327) [cross-sectional analysis] WALNUT consumption stratification n = 22 NUTS GROUP Excluded (n=80) Excluded (n=141) Nonconsumers (n=128) Habitual consumers (n=67) Nonconsumers (n=104) Habitual consumers (n=82) (Tulipani S et al. J Proteome Res, 2011; Garcia-Aloy M et al. J Proteome Res, 2014) (Tulipani S et al. J Proteome Res, 2011; Garcia-Aloy M et al. J Proteome Res, 2014) 20
21 DETECTED RT MASS (min) (m/z) ASSIGNATION IDENTIFICATION [M H] - 10-Hydroxy-decene-4, [M H sulfate] - diynoic acid sulfate [M H] - Tridecadienoic/tridecynoic C[M H] - acid glucuronide [M + H] [M + H GlcA] [M + H GlcA H 2O] [M H] - Dodecanedioic acid C[M H] [M H H 2O] [M H H 2O CO 2] [M H] - Pyrogallol sulfate [HSO 3 H] [M H] - p-coumaryl alcohol C[M H] - glucuronide [M H] - Urolithin A glucuronide C[M H] [M H GlcA] [M + H] [M + H GlcA] [M H] - Urolithin A sulfoglucuronide [M H] - p-coumaryl alcohol sulfate C[M H] [M H sulfate] C[M H sulfate] [M H] - Urolithin A sulfate [M H] - N-Acetylserotonin sulfate [M H] - Hydroxyindoleacetic acid [M H CO 2] [M + H] [M + H H 2O] [M + H CH 2O 2] + RT (min) (Tulipani S et al. J Proteome Res, 2011; Garcia-Aloy M et al. J Proteome Res, 2014) MASA DETECTADA (m/z) ASIGNACIÓN IDENTIFICACIÓN [M H] - 10-Hydroxy-decene-4,6-diynoic acid sulfate [M H] - Tridecadienoic/tridecynoic acid C[M H] - glucuronide [M + H] C[M + H] [M + H GlcA] [M H] - Urolithin C glucuronide [M H] - Urolithin A glucuronide C[M H] [M H GlcA] C[M H GlcA] [M + H] [M + NH 4] [M + H GlcA] [M H] - Urolithin A sulfoglucuronide [M H] - Urolithin B glucuronide [M H GlcA] C[M H GlcA] [M + H] [M + H GlcA] [M H] - Enterolactone glucuronide C[M H] [M H GlcA] [M + NH 4] [M H sulfate] - Urolithin C sulfate [M H] - Urolithin A sulfate [M H sulfate] [M H] - 3-Indolecarboxylic acid [M + H] + glucuronide [M H] - Hydroxyindoleacetic acid sulfate [M H] - N-Acetylserotonin sulfate RT m/z ASSIGNATION IDENTIFICATION [M H] - Urolithin A C[M H] - glucuronide [M H GlcA] [M + H] [M + H GlcA] + RT m/z ASSIGNATION IDENTIFICATION [M H] - Urolithin A C[M H] - glucuronide [M H GlcA] C[M H GlcA] [M + H] [M + NH 4 ] [M + H GlcA] + (Tulipani S et al. J Proteome Res, 2011; Garcia-Aloy M et al. J Proteome Res, 2014) 21
22 10-Hydroxy-decene-4,6-diynoic acid sulfate 74.4% (66.4%-82.5%) 72.6% (65.0%-80.3%) Tridecadienoic/tridecynoic acid glucuronide 85.1% (79.8%-90.4%) 77.2% (70.4%-84.0%) Urolithin C glucuronide 75.4% (67.7%-83.0%) 71.4% (63.8%-79.0%) Urolithin A glucuronide 82.0% (75.7%-88.4%) 83.2% (77.3%-89.1%) Urolithin A sulfoglucuronide 70.4% (62.0%-78.7%) 79.0% (72.1%-85.9%) Urolithin B glucuronide 59.1% (50.6%-67.7%) 67.7% (59.6%-75.8%) Enterolactone glucuronide 62.3% (54.1%-70.5%) 66.3% (58.4%-74.2%) Urolithin C sulfate 69.7% (61.5%-78.0%) 73.3% (65.7%-80.9%) Urolithin A sulfate 78.7% (71.3%-86.1%) 79.2% (72.5%-85.9%) 3-Indolecarboxylic acid glucuronide 73.7% (66.2%-81.3%) 60.2% (52.0%-68.4%) Hydroxyindoleacetic acid sulfate 61.0% (52.5%-69.6%) 68.8% (61.2%-76.3%) N-Acetylserotonin sulfate 64.5% (56.2%-72.8%) 64.5% (56.5%-72.5%) (Garcia-Aloy M et al. J Proteome Res, 2014) AGL C02-01 PREDIMED COHORT (n=7447) Subsample 1 (n=275) [cross-sectional analysis] Subsample 2 (n=327) [cross-sectional analysis] WALNUT consumption stratification Excluded (n=80) Excluded (n=141) Nonconsumers (n=128) Habitual consumers (n=67) Nonconsumers (n=104) Habitual consumers (n=82) (Garcia-Aloy M et al. J Proteome Res, 2014) 22
23 10-Hydroxy-decene-4,6-diynoic acid sulfate 1,041 0,431 0,016 Tridecadienoic/tridecynoic acid glucuronide 2,212 0,491 <0,001 Urolithin C glucuronide Urolithin A glucuronide 0,778 0,305 0,011 Urolithin A sulfoglucuronide Urolithin B glucuronide Enterolactone glucuronide Urolithin C sulfate Urolithin A sulfate 0,812 0,395 0,040 3-Indolecarboxylic acid glucuronide 0,945 0,306 0,002 Hydroxyindoleacetic acid sulfate N-Acetylserotonin sulfate (Garcia-Aloy M et al. J Proteome Res, 2014) (Garcia-Aloy M et al. J Proteome Res, 2014) 23
![AGL2009-13906-C02-01 Subsample (n=275) [cross-sectional analysis] COCOA consumption stratification CONTROL GROUP COCOA GROUP Excluded (n=120): not meet](/docs-images/93/112676169/images/24-3.jpg "stratification criteria WASHOUT 1 COCOA GROUP WASHOUT 2 CONTROL GROUP Nonconsumers (n=192) Excluded (n=120): Case-control selection Habitual Consumers (n=32)")
24 RANDOMIZATION W0 INTERVENTION PERIOD (12 weeks) W12 FFQ n = 20 CONTROL GROUP n = 22 NUTS GROUP HPLC-QToF-MS Markers of fatty acids metabolism BIOMARKER PANEL Markers of microbial-derived metabolism of nuts Markers of the tryptophan/serotonin metabolic pathway AUC >90% [excellent] (Tulipani S et al. J Proteome Res, 2011; Garcia-Aloy M et al. J Proteome Res, 2014) PREDIMED COHORT (n=7447) RANDOMIZATION W2 INTERVENTION PERIOD ( 4 weeks) AGL C02-01 W6 W8 INTERVENTION PERIOD (4 weeks) W12 AGL C02-01 Subsample (n=275) [cross-sectional analysis] COCOA consumption stratification CONTROL GROUP COCOA GROUP Excluded (n=120): not meet stratification criteria WASHOUT 1 COCOA GROUP WASHOUT 2 CONTROL GROUP Nonconsumers (n=192) Excluded (n=120): Case-control selection Habitual Consumers (n=32) Nonconsumers (n=32) Habitual Consumers (n=32) (Llorach R et al. Mol Nutr Food Res, 2013; Garcia-Aloy M et al. Mol Nutr Food Res, 2015) 24
25 (Llorach R et al. Mol Nutr Food Res, 2013; Garcia-Aloy M et al. Mol Nutr Food Res, 2015) RT DETECTED MASS (min) (m/z) ASSIGNATION IDENTIFICATION RT DETECTED MASS (min) (m/z) ASSIGNATION IDENTIFICATION / [M + H] + /[M H] - AMMU [M + H] + 7-Methyluric acid / [M + H] + /[M AMMU isomer H] [M + H] + 3-Methyluric acid [M + H] + 7-Metilxanthine / [M + H] + /[M 3-Metilxanthine H] / [M + H] + /[M H] - 3,7-Dimethyluric acid [M + H] + Theobromine [M H] - Vanillic acid glucuronide / [M + H] + /[M Vanilloglycine H] [M H] - (Epi)catechin glucuronide [M H] - Vanillic acid sulfoglucuronide [M H] - (Epi)catechin sulfoglucurónido [M H] - Methyl(epi)catechin sulfate [M H] - Methyl(epi)catechin sulfate [M H] - HDHPVA glucuronide [M H] - HDHPVA glucuronide [M + H] + MHPV [M H] - HHMPVA sulfate / [M + H] + /[M DHPV glucuronide H] [M H] - MHPV glucuronide [M H] - DHPV sulfoglucuronide [M + H] + DHPV sulfate [M H] - HPV glucuronide [M H] - HDHPVA sulfate [M H] - MHPV glucuronide [M H] - DHPV sulfate [M H] - DHPV sulfate / [M + H] + /[M DHPV sulfate H] [M H] - HPVA sulfate [M H] - MHPV sulfate [M H] - DHPVA sulfate [M H] - HPVA sulfate [M + H] + Hydroxynicotinic acid [M + H] + Cyclo(propylalanyl) [M + H] + 3,5-Diethyl-2-methylpyrazine [M H] - N-[4 -Hydroxycinnamoyl]-L-aspartic acid [M H] - N-[4 -Hydroxy-3 -methoxy-cinnamoyl]-aspartic [M + H] + Tyrosine sulfate [M + H] + Butyrylcarnitine [M + H] + Methylglutarylcarnitine (Llorach R et al. Mol Nutr Food Res, 2013; Garcia-Aloy M et al. Mol Nutr Food Res, 2015) [M H] - Xanthine [M + H] + AMMU [M + H] + AMMU isomer / [M + H] + /[M H] - 3-Methyluric acid [M + H] + 7-Metilxanthine / [M + H] + /[M H] - 3-Metilxanthine / [M + H] + - /[M H] 3,7-Dimethyluric acid [M + H] + Theobromine [M H] - Vanillin sulfate [M + H] + (Epi)catechin glucuronide [M H] - Vanillic acid [M H] - (Epi)catechin sulfate [M H] - HDHPVA glucuronide [M H] - HHMPVA glucuronide [M + H] + MHPV [M H GlcA] - DHPV sulfoglucuronide [M H] - DHPV glucuronide [M H] - HDHPVA [M H] - HDHPVA sulfate / [M + H] + /[M H] - DHPV glucuronide [M H] - HHMPVA sulfate [M H] - MHPV glucuronide [M H] - HPV glucuronide [M + H] + DHPV sulfate [M H sulfato] - HPV sulfate [M H] - DHPVA sulfate [M H] - HPVA sulfate [M + H] + Furoylglycine [M H] - Cyclo(aspartyl-phenylalanyl) / [M + H] + /[M H] - Aspartyl-Phenylalanine [M + H] + Methylglutarylcarnitine 25
26 Untargeted metabolomics approach to obtain a metabolic footprint of regular dietary consumption by designing models of combined urinary biomarkers: Cocoa product intake in freeliving subjects from the PREDIMED study Mar Garcia-Aloy, Rafael Llorach, Mireia Urpi-Sarda, Olga Jáuregui, Dolores Corella, Miguel A. Martínez-González, Jordi Salas-Salvadó, Montserrat Fitó, Emilio Ros, Ramon Estruch, Cristina Andres-Lacueva. [submitted] Acute Intervention Long-term intervention isomer Free-living population (Garcia-Aloy M et al. Mol Nutr Food Res, 2015) (Garcia-Aloy M et al. Mol Nutr Food Res, 2015) 26
27 (Garcia-Aloy M et al. Mol Nutr Food Res, 2015) (Garcia-Aloy M et al. Mol Nutr Food Res, 2015) 27
28 RANDOMIZATION INTERVENTION PERIOD INTERVENTION PERIOD ( 4 weeks) (4 weeks) W2 W6 W8 W12 CONTROL GROUP COCOA GROUP WASHOUT 1 WASHOUT 2 COCOA GROUP CONTROL GROUP Consumption data FFQ Theobromine metabolism Biomarker panel Polyphenol metabolism Cocoa taste and flavour Endogenous markers (Llorach R et al. Mol Nutr Food Res, 2013; Garcia-Aloy M et al. Mol Nutr Food Res, 2015) 28
29 29
30 30
THE ROLE OF FOOD METABOLOME IN BIOMARKER DISCOVERY: EVIDENCE FROM CLINICAL AND OBSERVATIONAL STUDIES
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Recent Advances in Pharmaceutical Sciences VII, 2017: ISBN: Editors: Diego Muñoz-Torrero, Montserrat Riu and Carles Feliu
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