ReviewNutrikinetics: Concept, technologies, applications, perspectives
Highlights
► Nutrikinetics is an extension of the classical pharmacokinetic concept. ► Nutrikinetic signature summarize ADME characteristics in the human superorganism. ► Integration of profiling, multi-level analysis and population-based modelling. ► Understanding of modulation of bioavailability by phenotype and food matrix.
Introduction
Although several epidemiological studies have shown associations between intake of selected phytochemical classes and reduced disease risk there are still only a few randomized controlled human intervention studies which successfully showed causal exposure–effect relationships (Van Ommen et al., 2010). A number of scientific challenges are considered as limiting in establishing exposure–effect relationships in intervention studies. Firstly, food intake does in many cases not reflect systemic exposure as bioavailability and metabolic fate upon consumption are not taken into account (Spencer, Mohsen, Minihane, & Mathers, 2008). Secondly, in intervention studies phytochemicals are often not used in their original food matrix but extracts thereof and subsequently formulated in various dosage forms. Therefore, assessment of the absorption, distribution, metabolism and excretion (ADME) of phytochemicals is a critical first step in understanding their causality in exerting health effects in humans. ADME studies are generally focussed on the relative and in some cases on the absolute bioavailability and kinetic assessment of isolated nutritive and non-nutritive compounds, mainly secondary plant metabolites. Metabolism studies in particular aim at the discovery and identification of metabolite patterns after exposure to complex plant extracts (Manach et al., 2009, Scalbert et al., 2009). However, in studies aimed at assessment of complex phytochemical ingredients, their interaction with the gut microbiome (Box 1) and the host can result in a wide range of circulating exogenous metabolites (van Duynhoven et al., 2011, Gross et al., 2010, Wikoff et al., 2009), together constituting the food metabolome (Box 1) (van Duynhoven et al., 2011, Holmes et al., 2008a, van Ommen et al., 2008). One can now witness a drive towards quantitative and comprehensive description of the food metabolome (Rezzi et al., 2007, Wishart, 2008a), with the aim to describe total systemic exposure to phytochemicals. For selected cases one has been able to show that the food metabolome indeed reflects dietary intake (Heinzmann et al., 2010, O’Sullivan et al., 2011, Xu et al., 2010). The food metabolome is typically described by metabolic profiles derived from samples obtained at specific time-points (urine, plasma) or time-averaged (24 h urine) mode. Sofar this has not brought the expected progress in recognizing nutritional phenotypes (Box 1) that differ in systemic exposure of complex phytochemical food ingredients (Scalbert et al., 2009, Walsh et al., 2006, Winnike et al., 2009). Also progress in assessment of food matrix effects on systemic exposure of phytochemicals has been limited. For both areas it has been recognized that one needs to consider the kinetic nature of ADME characteristics of phytochemicals (de Vos, Castenmiller, Hamer, & Brummer, 2006). Pharmacokinetic (PK) approaches for modelling ADME of phytochemicals have been adequate in specific cases (Chen et al., 2005, Seeram et al., 2006) where important differences between nutritional vs. pharmaceutical interventions could be neglected (Gibney et al., 2005). Whereas drugs typically consist of single compounds with known chemical structures, phytochemicals are formulated as mixtures with high Compositional complexity. Discerning the metabolites of phytochemical interventions from those produced by the Dietary background can be challenging, in particular for gut microbial bioconversion products. This can be aggravated by the large Inter-individual variation in metabolic fate of phytochemicals when gut microbial bioconversions are involved (Gibney et al., 2005, Gross et al., 2010, de Vos et al., 2006). These bottlenecks have been presented in Fig. 1, where it is also shown how these can be overcome by integrating Study design, Metabolic profiling, Variable selection and Population-based modelling. This overall approach has been coined as nutrikinetics (NK, Box 1) (van Duynhoven et al., 2011, van Velzen et al., 2009). In the current review the technological requirements for NK will be explained, as well as its scope and application areas. Future perspectives will be outlined for considering multicompartmental models, and combining NK with nutridynamic (ND) approaches as a means to link dietary exposure to Biological effect (Van Ommen et al., 2010), by accounting for Matrix effects and Nutritional Phenotypes.
Section snippets
Gut metabolism and the concept of the human superorganism
Phytochemicals such as polyphenols (Jacobs et al., 2009, Selma et al., 2009) can to a certain extent persist to the colon, where they are exposed to the gut microbial community (Crozier, Del Rio, & Clifford, 2010). The resident colonic microbiota can be regarded as a separate compartment within the human system (Fig. 2) that performs functions of which the human host is incapable. These strong and symbiotic microbiota–host interactions have led to the recognition of humans as superorganisms, in
Metabolic profiling
Our phytochemical intake consists of thousands of compounds with widely varying concentration levels (Wishart, 2008a). When (part of) these phytochemicals are absorbed, secreted and/or digested within the human superorganism, analysis of the human food metabolome (Fardet et al., 2008, Manach et al., 2009, Wishart, 2008a) becomes a major challenge. Metabolic profiling provides an unbiased approach for discovering and identifying nutritional biomarkers (Fave et al., 2009, Manach et al., 2009,
Influence of the food matrix on systemic exposure
The food matrix may have a significant effect on the rate, on-set, and extent of absorption of (bioactive) food compounds after oral administration. Firstly, absorption of phytochemicals can be influenced by physico-chemical interactions, which can influence both gastrointestinal transit and absorption rates (Duchateau & Klaffke, 2008). Such interactions have been proposed between dairy and flavonoids (Roowi et al., 2009, Urpi-Sarda et al., 2010). Secondly, the food matrix can modulate the
Multicompartmental analysis
The current NK models particularly adapt to lumped, one-compartmental kinetic studies. We envisage extended NK models for the characterization and parameterization of multicompartmental (co-metabolome) interactions (van Duynhoven et al., 2011). Examples of in vivo multicompartmental studies have been reported earlier and focused on the ADME of quercetin and its metabolites in rats (Chen et al., 2005). Other multicompartmental nutritional studies used metabolic signatures (or metabotypes)
Acknowledgements
We are grateful to the European Commission for their financial support of the GutSystem project (MTKI-CT-2006-042786) under the Framework 6 Marie-Curie Transfer of Knowledge Industry-Academia Strategic Partnership scheme. Part of this project was carried out within the research program of the Netherlands Metabolomics Centre (NMC) which is part of the Netherlands Genomics Initiative / Netherlands Organization for Scientific Research. Ursula Garczarek, Ferdi van Dorsten, Sonja Peters, Elaine
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