Nutritional metabolomics: progress in addressing complexity in diet and health

Abstract

Nutritional metabolomics is rapidly maturing to use small-molecule chemical profiling to support integration of diet and nutrition in complex biosystems research. These developments are critical to facilitate transition of nutritional sciences from population-based to individual-based criteria for nutritional research, assessment, and management. This review addresses progress in making these approaches manageable for nutrition research. Important concept developments concerning the exposome, predictive health, and complex pathobiology serve to emphasize the central role of diet and nutrition in integrated biosystems models of health and disease. Improved analytic tools and databases for targeted and nontargeted metabolic profiling, along with bioinformatics, pathway mapping, and computational modeling, are now used for nutrition research on diet, metabolism, microbiome, and health associations. These new developments enable metabolome-wide association studies (MWAS) and provide a foundation for nutritional metabolomics, along with genomics, epigenomics, and health phenotyping, to support the integrated models required for personalized diet and nutrition forecasting.

Figure 1. The nutritional metabolome as a component of the exposome

A. The summation of all chemicals found in an organism can be considered a “pan-metabolome”. Although no method is available to measure all chemicals, the pan-metabolome can be conceptualized to contain a core nutritional metabolome derived from required nutrients and related biochemicals derived from these nutrients in reactions catalyzed by enzymes encoded in the organism. The food metabolome contains many components of this core nutritional metabolome and also a large number of other non-nutritive chemicals. The pan-metabolome also contains microbiome-related chemicals derived from food metabolites, drugs and other environmental agents acted upon by the intestinal microbes. Other components of the pan-metabolome are derived from dietary supplements and pharmaceuticals, commercial products such as sun screen and face creams, and environmental chemicals. B. The exposome is defined as the cumulative exposures from conception onwards. Life cycle nutritional requirements can be viewed within the conceptual grid of the exposome, including the core nutritional metabolome and food metabolome as in Panel A. Together, Panels A and B provide a conceptual grid for the exposome. Consequently, nutritional metabolomics represents a central and critical component of exposome research, impacting expression of the genome and modification of the epigenome through the lifecycle.

Figure 2. Nutritional metabolomics to support personalized nutrition

A. Contemporary nutritional recommendations and interventions use a normative approach based upon the characteristics of a healthy population. Hypotheses are based upon experimental and epidemiologic studies, and tested in clinical trials to determine outcomes in individuals meeting certain phenotypic or nutritional criteria. Importantly, the criteria are based upon population-based norms. A person with a predefined deviation from the norm is prescribed an intervention based upon clinical trials which show that this intervention has a significant beneficial effect in at least some of the individuals in the trial. While cost-effective for the population, the approach does not work for all individuals. B. An integrated biosystems approach utilizes the hypotheses of Panel A along with information-rich nutritional metabolomics (systems data) for humans and model organisms to develop computational models. The computational models are tested and refined to correctly describe responses to differences in diet, genetics or other factors. The computational models are used with nutritional metabolomics data for an individual to provide personal health models for health prediction, risk profiling and treatments. The approach takes advantage of the knowledge base as in Panel A but refines this for personalized use. C. An artificial intelligence approach can take advantage of nutritional metabolomics (systems data) as in Panel B but does not require mechanistic development. In this case, artificial intelligence approaches are used to compare personal profiles to profiles and outcomes within a reference population to obtain the best matches for prediction. This has advantages that there is no delay in building models and the power increases with the size of the reference population. On the other hand, due to the correlative nature of these statistical models, there is has no scientific foundation to facilitate development of new interventional strategies. Based upon Voit and Brigham (89).

Figure 3. Development of multidimensional models for nutritional metabolomics

Recent use of unbiased pathway models (15) have revealed an important multidimensional character to nutritional metabolomics. Differences in plasma metabolite profiles in individuals with impaired glucose tolerance and normal glucose tolerance were associated with transporters functioning in mitochondrial/cytoplasmic balance of NAD⁺ and NADH (SLC25A13), and with cell membrane transporters involved in amino acid transport (System A and System L) and osmotic regulation (SLC6A12). In this schematic representation based upon the KEGG human metabolic pathway map, the plasma metabolome (bottom) is linked to KEGG biochemical pathways in the cytoplasm (middle) through System A, System L and SLC6A12, and a subset of metabolites in the cytoplasm is linked to the mitochondrial matrix though the glutamate-aspartate transporter (SCL25A13). Based upon findings of Deo et al (15). Some metabolites are not labeled due to lack of space.

Figure 4. Comparison of mass spectrometry (MS) based metabolic profiling approaches for chemicals have similar but not identical mass

A. Analysis with gas chromatography (GC) or liquid chromatography (LC) with a single low resolution mass detector requires separation of chemicals prior to detection. B. Analysis with a tandem mass spectrometer using either GC or LC often does not require complete separation because ion dissociation and detection of product ions supports identification without separation. However, quantification typically requires a stable isotopic form of chemicals of interest for internal standardization. C. LC coupled to high-resolution mass spectrometry supports high throughput analysis because chemicals are resolved by mass and have less demand for chromatographic separation. High resolution instruments include Fourier-transform ion cyclotron resonance, Orbitrap (Thermo) and newer time-of-flight (TOF) instruments (62).

Figure 5. Intermediary metabolites detected in 10-min liquid chromatography-Fourier transform mass spectrometry (LC-FTMS) analysis of human plasma

Matches for ions detected by LC-FTMS with high-resolution mass/charge (m/z) in human plasma to metabolites in the KEGG human metabolic pathways. MS data were extracted using apLCMS (104). Larger dots represent matches for 745 metabolites found in plasma from human, rhesus macaque, common marmoset, rat, mouse, pig and sheep. Metabolites are present for 136 out of 154 pathways in the database.