Adding Vitality to life Difference in metabolic phenotype and urinary kinetics of flavonoid metabolites in humans after a black tea and grape juice/red wine intervention E.J.J. van Velzen 1,2, J.A. Westerhuis 2, J.P.M. van Duynhoven 1, D.M. Jacobs 1, F.A. van Dorsten 1, M. Foltz 1, A.K. Smilde 2 1 Unilever R&D Vlaardingen, Vlaardingen, The Netherlands; 2 Biosystems Data Analysis, University Amsterdam, Amsterdam, The Netherlands Background & Objective Intervention studies suggest that flavonoids derived from various sources have an effect on vascular function. However, until now it is not clear whether formation of small phenolic acids in the colon is contributing to the vascular benefit. Several kinetic studies after a flavonoid intervention exhibit marked inter-individual differences in both, urinary phenolic acid concentrations suggesting a contribution of the gut microbial composition to this effect. We present here an exploratory analytical approach to investigate metabolic phenotype variation within one study population undergoing an acute black tea (BT) and grape juice/red wine (GW) intervention based on 1 H-NMR spectroscopy and GC-MS profiling. Aim of this study was to determine kinetics of flavonoid catabolites in urine in order to describe gut microbial phenotypes. Conclusions Multilevel PLS-DA on urine metabolic profiles has been applied to identify fermentation products of polyphenols. Pharmacokinetic parameters were estimated for each of these metabolites and used to investigate the major trends in the urinary clearance among the individuals. Based on the PK-model parameters different metabolic phenotypes can be identified. References (1) Scalbert, A., Williamson, G., J. Nutr., 2000, 130, 2073S-2085S. (2) van Velzen EJ et al. J Proteome Res (7): Modeling Gallic Acid Metabolism ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼▼ ▼▼ ▼ ▼ ▼ ▼▼▼ ▼ ▼ ▼▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼▼▼ Figure 1: Identified metabolites in pooled 48h urine of a single subject after BT intervention. In the urinary (A) GC- MS profile and (B-C) the 600 MHz 1 H NMR profile, various polyphenol metabolites can be annotated that were elevated after the treatments (BT:▼, RG:▼) and includes m-hydroxy-phenol (1); p-hydroxyphenol (2); 1,2,3- trihydroxy-benzene (5); m-hydroxyphenylacetic acid (7); 2,4-dihydroxybenzoic acid (12); hippuric acid (hip, 14); 3- (3-hydroxyphenyl)propionic acid (15); 4-O-methylgallic acid (16); 3-O-methylgallic acid (17); gallic acid (18); o- hydroxyhippuric acid (19); m-hydroxyhippuric acid (20); p- hydroxyhippuric acid (p-hhip, 22) and 1,3-dihydroxy- phenyl-2-O-sulphate (dps). Metabolite identification The multivariate metabolic effects in urine were investigated as a result of the BT and GW intake using multilevel PLS- DA, permutation testing and cross- model validation. Based on the permutations, significantly increased levels of a series of polyphenol catabolites could be identified (Fig. 1). The PK of the significantly increased metabolites were investigated using a simultaneous fit across the interventions (Fig. 2). Figure 2: Urinary excretion of hippuric acid obtained (A) after placebo (o) and BT intake (*). The green line represents the approximated base level in the intervention period. The estimated pk-model parameters are given. In panel (B) the net excretion is plotted from which the total 48h output of can be predicted. The (C) residual plot indicates that the model is a likely fit to the data. A B C PK modelling The metabolites identified in the NMR and GC-MS experiments may derive from two major metabolic pathways, i.e. the catechin pathway and the gallic acid pathway (Fig. 3). In Fig. 4 it is shown that the PK-model parameters provide direct insight in the activity of the gallic acid pathway on the individual level. The activity of the gallic acid pathway strongly differs across the test population (Fig. 5) and allow segmentation of phenotypes. Figure 4: Kinetic curves of 3/4-OMGA in plasma (A) and gallic acid (B), 4-OMGA (C) and pyrogallol (D) in urine. The PK-model parameters obtained across the pathway are summarized in bubble plots (E-H), whereby the lag time (  ) is plotted on the x-axis and the 1 st -order rate constant K e on the y-axis. The bubble size is equivalent to the total molar level. Figure 3: Gallic acid pathway. A B C D E F G H Figure 5: Bubble-plot representing the individual differences in PK-model parameters associated with the urinary excretion of pyrogallol after BT intake. Phenotypic differences in gallic acid metabolism is expressed in the individual variation in PK- model parameters of the catabolite pyrogallol (Fig. 5). Methods Study Design: See poster P130 for description of study design and sampling schedule. In brief, twenty male subjects on a low polyphenol diet received in the fasted state a single oral dose of 800 mg polyphenols in form of grape juice/red wine extract (GW) or black tea extract (BT) or placebo (sucrose) in a capsule. Urine samples were collected over 48h and 1H-NMR spectra as well as kinetic profiles of phenolic acids using GC-MS were determined. Pharmacokinetic modeling: A one compartment model with first order of excretion, lag time and baseline function was used to model urinary excretion profiles of 10 phenolic acids.