An NMR metabolomics approach reveals a combined-biomarkers model in a wine interventional trial with validation in free-living individuals of the PREDIMED study

Abstract

The development of robust biomarkers of consumption would improve the classification of participants with regard to their dietary exposure. In addition, validation of them in free-living individuals remains an important challenge. The aim of this study is to assess wine intake biomarkers using an NMR metabolomic approach to measure the utility of these biomarkers in a wine interventional study (WIS, n=56) and also to evaluate them in a free-living individuals (PREDIMED study, n=91). Nine metabolites showed a significantly higher presence in urinary excretion in the WIS after wine intake: five food metabolome metabolites (tartrate, ethyl glucuronide [EtG], 2,3-butanediol, mannitol, and ethanol); one related to the endogenous response to wine exposure (3-methyl-2-oxovalerate) and three unidentified compounds. Receiver operating characteristic (ROC) curve for each single metabolite were evaluated and exhibited areas under the curves (AUC) between 67.4% -86.3% when they were evaluated individually. Then, a logistic regression model was fitted to generate a combined-biomarkers model using these metabolites. The model generated which included tartrate-EtG, showed an AUC of 90.7% in WIS. Similarly, the AUC in the PREDIMED study, the biomarker model was 92.4%. Results showed that a model combining tartrate-EtG is more useful for evaluating exposure to wine than single biomarkers, both in interventional studies and epidemiological data. To our knowledge, this is the first time that a combined-biomarker model using an NMR platform in wine biomarkers' research has been generated and reproduced in a free-living population.2

Comparing metabolite profiles of habitual diet in serum and urine

“…Our findings for specific food-metabolite associations replicate $31 associations previously reported from clinical and population studies, including for fruit and juice (13)(14)(15)(48)(49)(50)(51)(52), red meat (13,(53)(54)(55), fish (15,51,56), nuts (15,51), liquor and wine (15,57), coffee (15,16,51,58,59), and multivitamin supplement (15) intake. Our serum findings parallel those reported by the Atherosclerosis Risk in Communities study (n = 1977) (51) and the Prostate, Lung, Colorectal, and Ovarian Cancer screening trial (n = 502) (15) for metabolites associated with citrus (stachydrine, chiro-inositol, scyllo-inositol, and N-methyl proline), coffee (trigonelline, quinate, paraxanthine, 1-methylxanthine, and caffeine), fish (CMPF), nuts (tryptophan betaine), alcohol (ethyl glucuronide) and multivitamins (pyridoxate and pantothenate).…”

Recent Advances in the Application of Metabolomics for Nutrition and Health

“…Red wine (272mL/day) vs dealcoholized red wine (272mL/day) or gin (100mL/day) (Vazquez-Fresno, et al 2012) Effect of moderate wine intake on the metabolome of subjects with CDV risk, identifying both markers of consumption and endogenous changes RCT crossover 4 wk / High-risk subjects ≥55 y without documented CHD n=61Urine 1 H NMR -Metabolites from wine metabolism: mannitol in RWA and tartrate in RWA and RWD -Endogenous modifications after wine consumption: BCAA metabolites -Ethanol robust biomarker of alcohol consumption in GIN and RWA diets -4-hydroxyphenylacetate and hippurate different effect in combination with alcohol Functional beverage containing grape skin extract vs. a control beverage as a placebo (Khymenets, et al 2015) Impact of acute and sustained consumption of a functional beverage based on grape skin extracts on the urinary metabolome by applying an untargeted metabolomic approach…”

“…Cheese UPLC-qTOF/MS Urine Indoxyl sulfate; Xanthurenic acid; Tyramine sulfate; 4-hydroxyphenylacetic acid; Isovalerylglutamic acid; Acylglycines (Hjerpsted, et al 2014) Butter UPLC-qTOF/MS Urine 3-phenyllactic; alanine, proline; pyroglutamic acid (Hjerpsted, et al 2014) Butter UPLC-MS/MS Serum Methyl palmitate (15 or 2); Pentadecanoate (15:0); 10-Undecenoate (11:1n-1) (Guertin, et al 2014) Milk GC-MS 1 H NMR Urine Lactose; Galactose; Galactonate; Allantoin; Hippurate; Galactitol; galactono-1,5-lactone (Munger, et al 2017) Milk LC-MS GC-MS FIA-MS/MS Serum/ Plasma Urine Trimethyl-N-aminovalerate; Uridine; Hydroxysphingomyelin C14:1; Diacylphosphatidylcholine C28:1 (Pallister, et al 2017) NON-ALCOHOLIC BEVERAGES Sugarsweetened beverage 1 H NMR Urine Formate; Citrulline; Taurine; Isocitrate Coffee HPLC-ESI-MS/MS Urine Caffeic; Chlorogenic acid (Mennen, et al 2006) Coffee HPLC-PDA-MS Urine Dihydrocaffeic acid-3-O-sulfate; Feruloylglycine (Stalmach, et al 2009) Coffee LC-MS/MS Plasma Dimethoxycinnamic acids (Nagy, et al 2011) Coffee UPLC-qTOF-MS Urine Atractyligenin glucuronide; Diketopiperazine cyclo(isoleucyl-prolyl); Trigonelline; Paraxanthine; 1-methylxanthine, 1-methyluric acid, 1,7-dimethyluric acid, 1,3 or 3,7 dimethyluric acid; 1,3,7-trimethyluric acid; 5-acetylamino-6-formylamino-3-methyluracil (Rothwell, et al 2014) Coffee UPLC-MS/MS Serum Trigonelline (N'-methylnicotinate); Quinate; 1-Methylxanthine; Paraxanthine; N-2-furoyl-glycine; Catechol sulfate (Guertin, et al 2014) Coffee UPLC-qTOF Urine Dihydroferulic acid sulfate (Edmands, et al 2015) 1 H NMR Urine 2-furoylglycine (Heinzmann, et al 2015) Black tea 1 H NMR Urine Hippuric acid; 1,3-dihydroxyphenyl-2-O-sulfate (Daykin, et al 2005) Black tea HPLC-ESI-MS-MS Urine Gallic; 4-O-methylgallic acids (Mennen, et al 2006) Black tea/green tea HPLC-MS/MS Urine Hippuric acid (Mulder, et al 2005) Black tea/green tea 1 H NMR Urine Hippuric acid; 1,3-dihydroxyphenyl-2-O-sulfate (Van Dorsten, et al 2006) Black tea/green tea HPLC-FTMS(n) HPLC-TOFMS-SPE-NMR Urine Hippuric acid; Hydroxybenzoic glycine conjugate; Vanilloylglycine; Pyrogallol-2-O-sulfate (van der Hooft, et al 2012) Tea UPLC-qTOF Urine 4-O-methylgallic acid (Edmands, et al 2015) Wine 1 H NMR Urine Tartrate; Ethyl glucuronide; 2,3-butanedio; Mannitol; Ethanol; 3-Methyl-2-oxovalerate (Vazquez-Fresno, et al 2015) Wine HPLC-ESI-MS/MS Urine m-coumaric acid; Gallic acid; 4-O-methylgallic acid (Mennen, et al 2006) Wine UPLC-MS/MS Plasma Urine Gallic acid and ethylgallate metabolites; Resveratrol and resveratrol microbial metabolites; 2,4-Dihydroxybenzoic acid; (epi)catechin; Valerolactone metabolites Red wine UPLC-qTOF Urine Gallic acid ethyl ester…”

Novel strategies for improving dietary exposure assessment: Multiple-data fusion is a more accurate measure than the traditional single-biomarker approach

“…Our first study in this field identified an MBP that was highly predictive of walnut intake with an area under the curve [AUC (95% CI)] of 93.4% (90.1-96.8%) and 90.2% (85.9-94.6%) in the training and validation sets, respectively (Garcia-Aloy et al, 2014). In line with these results, a "tartrate-ethyl glucuronide" model showed an AUC of 90.7% (84.5-96.4%) in the training set composed of samples from volunteers that participated in a controlled clinical trial with a nutritional intervention with wine, and an AUC of 92.4% (84.1-100%) in the validation set composed of samples assessed at baseline from a subcohort of volunteers included in the PREDIMED study with a reported wine intake of ≥180 mL/day (Vázquez-Fresno et al, 2015). Additionally, this model showed promising performance in terms of its sensitivity, which enabled discernment of an intake of one glass of wine (three carotenoids determined in plasma) and data from dietary questionnaires (energy intake) (Gross et al, 1994).…”

“…As mentioned above, recent work from our laboratory applied a multivariate statistical approach to link dietary data with both targeted and untargeted metabolomics data to identify a series of MBPs (Garcia-Aloy et al, 2014;Urpi-Sarda et al, 2015 ;Vázquez-Fresno et al, 2015). In this approach, stepwise logistic regression analysis was used to include more than one metabolite in biomarker panels and regressed against dietary data to identify MBPs.…”