Can nutrigenomics serve up the perfect diet?

By Ellie Fung

While it is disputed whether Hippocrates had ever uttered “let food be thy medicine”, there is no denying that food is inextricably linked to health. More recently, it was recognised that food can induce different responses in different individuals, and a subset of the population can benefit from a certain food that another subset is adversely affected by. The novel field of nutrigenomics, much like pharmacogenomics, seeks to elucidate the molecular basis driving differential responses to certain foods and how specific nutrients influence phenotypic outcome (German et al., 2011). 

In its infancy, research in the field revolved around gene-nutrient interactions, primarily on how single nucleotide polymorphisms (SNPs) shape response to diet. By affecting the quantity or structure of gene products, SNPs may lead to differential phenotypes  and thus alter the metabolic response to certain dietary constituents (Gaboon, 2011). A well-studied example involves MTHFR, a gene encoding for the methylenetetrahydrofolate reductase enzyme that converts homocysteine to methionine with a necessary folate cofactor. Homozygotes for the common C667T gene polymorphism produces an enzyme variant with greater heat sensitivity and lower capacity for folate use, consequently increasing the risk for neural tube defects and cardiovascular disease. Individuals with the SNP may thus require greater folate intake to maintain proper MTHFR function and avoid associated pathologies (Mead, 2007; Schneider et al., 1998). 

Although genomics has prompted a deeper understanding of the complex molecular relationship between food and phenotype, uncovering gene-nutrient interactions merely scratches the surface. Genome-wide association studies (GWAS) have revealed numerous SNPs associated with response to dietary fat intake, thus revealing genetic risk factors behind cardiovascular diseases. However, the variants only explained <20% of the total variation in post-meal serum levels of triacylglycerols and cholesterol (Ma & Ordovas, 2017). Similarly, a meta-analysis of 56 GWAS on adiposity found that 97 SNP variants found to be associated with body fat explained <3% of BMI variation (Livingstone et al., 2015). It is apparent that a broader perspective is required, especially since the molecular influence of the thousands of distinct compounds in food is not just restricted to individual genes. 

Spurred by recent progress in high-throughput omics technologies, the field has expanded to encompass interactions between different nutrients with the epigenome, proteome, transcriptome and metabolome (Mead, 2007; Sharma & Dwivedi, 2017). These complement genomic approaches by investigating how dietary components influence phenotype on a post-genome level, thus providing a more holistic view of how nutrition affects metabolic pathways and contributes to maintaining or perturbing cellular homeostasis (Mathers, 2017). 

For instance, by studying the complete RNA profile of tissues, transcriptomics can reveal specific changes in gene expression in response to certain diets and nutrients. With a microarray approach, Matualatupauw et al. (2017) demonstrated that a higher uptake of medium chain saturated fatty acids (MC-SFAs) may activate transcription factors responsible for upregulating expression of genes involved in energy metabolism, while simultaneously downregulating genes involved in inflammation. This suggests that MC-SFAs may confer multiple protective effects against obesity, by increasing energy expenditure to prevent adipogenesis and by reducing hyperinflammation to protect against insulin resistance. A more comprehensive perspective can be obtained by combining omics approaches, such as using transcriptomics and proteomics to reveal that a suboptimal selenium status can reduce inflammatory signalling and alter cytoskeleton remodelling to increase colorectal cancer risk  (Méplan et al., 2016). 

Yet, the methodologies used in nutrigenomics remain flawed. Investigations usually involve comparing differences in epigenetic, RNA, protein or metabolite profiles between subjects who have undergone varying dietary treatments. Such studies largely fail to control for human genotypic and environment variation, and while animal models can be used to resolve this issue, they in turn are not a completely accurate representation of humans (Mathers, 2017). Furthermore, these intervention studies often focus on just one or a few nutrients even though the human diet is diverse in foods each harbouring a multitude of nutrients in unique compositions. Classic observational studies, on the other hand, only demonstrates associations between diet and disease risk instead of establishing cause (Mead, 2007). These studies involve monitoring individual dietary intake with methods like diet diaries, 24-hour recalls, and food frequency questionnaires, which suffer from high levels of misreporting and may not be suitable for all population groups (Favé et al., 2009). 

However, metabolomics may provide an unbiased and highly precise measure of dietary exposure (German et al., 2011; Scalbert et al., 2014). Following digestion and metabolic processing, food leaves a unique composition of metabolites in bodily fluids such as blood and urine. Metabolite profiling, or the identification and quantification all metabolite biomarkers present in a sample of bodily fluids, can thus comprehensively and reliably characterise nutrient intake (Mathers, 2017). Though few in number, putative biomarkers have been identified for specific foods with high-throughput analytic chemistry technologies, such as caffeic acid sulfate for raspberries, tetronic acid for broccoli, O-acetylcarnitines for red meat and 7-methyluric acid for chocolate (Scalbert et al., 2014). A major obstacle lies in identifying unambiguous biomarkers for metabolites that can both be synthesised in tissues and derived from diet, such as sucrose, monounsaturated and saturated fatty acids (Mathers, 2017). As metabolomics continues to develop, the food metabolome could eventually be characterised and thus enrich other omics approaches in illuminating the associations between dietary factors and disease (Scalbert et al., 2014). 

No discussion of nutrigenomics can be complete without mention of personalised nutrition. By accounting for an individual’s unique genetic makeup and metabolic response, diets can be tailored to minimise disease risk while maximising health potential. The potential of personalised nutrition was clearly demonstrated by the European Food4Me trial. Compared to those who were given conventional one-size-fits-all dietary guidelines, individuals who received nutrition advice tailored to their metabolic phenotype and genotype of 5 diet-response variants showed a significantly greater improvement in diet 6 months after the advice was given, for instance with lower red meat and saturated fatty acid consumption and higher folate intake (Celis-Morales et al., 2017). As food is at the heart of every individual’s health and wellbeing, personalised nutrition harbours the potential to prevent disease, boost everyday performance and improve overall quality of life on a large scale (German et al., 2011). 

Given the immense complexity behind diet, nutrigenomics is far from reaching this ideal. Integrating omics technologies is a major stride in untangling the intricacies of food constituents, but even more is required to elucidate how the gut microbiome, physical activity and other environmental factors influence nutrient acquisition and use in parallel with the genome (German et al., 2011; Mathers, 2017). Nevertheless, we are already starting to get a taste of what nutrigenomics has to offer. 

References:

Celis-Morales, C., Livingstone, K. M., Marsaux, C. F. M., Macready, A. L., Fallaize, R., O’Donovan, C. B., Woolhead, C., Forster, H., Walsh, M. C., Navas-Carretero, S., San-Cristobal, R., Tsirigoti, L., Lambrinou, C. P., Mavrogianni, C., Moschonis, G., Kolossa, S., Hallmann, J., Godlewska, M., Surwillo, A., Traczyk, I., von, C. A., Bouwman, J., van Ommen, B., Grimaldi, K., Parnell, L. D., Matthews, J. N. S., Manios, Y., Daniel, H., AlfredoMartinez, J., Lovegrove, J. A., Gibney, E. R., Brennan, L., Saris, W. H. M., Gibney, M. & Mathers, J. C. (2017) Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. International Journal of Epidemiology. 46 (2), 578-588. Available from: doi: 10.1093/ije/dyw186. 

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Matualatupauw, J. C., Bohl, M., Gregersen, S., Hermansen, K. & Afman, L. A. (2017) Dietary medium-chain saturated fatty acids induce gene expression of energy metabolism-related pathways in adipose tissue of abdominally obese subjects. International Journal of Obesity. 41 (9), 1348-1354. Available from: doi: 10.1038/ijo.2017.120. 

Mead, M. N. (2007) Nutrigenomics: The Genome-Food Interface. Environmental Health Perspectives. 115 (12), A582-A589. Available from: doi: 10.1289/ehp.115-a582. 

Méplan, C., Johnson, I. T., Polley, A. C. J., Cockell, S., Bradburn, D. M., Commane, D. M., Arasaradnam, R. P., Mulholland, F., Zupanic, A., Mathers, J. C. & Hesketh, J. (2016) Transcriptomics and proteomics show that selenium affects inflammation, cytoskeleton, and cancer pathways in human rectal biopsies. The FASEB Journal. 30 (8), 2812-2825. Available from: doi: 10.1096/fj.201600251R. 

Scalbert, A., Brennan, L., Manach, C., Andres-Lacueva, C., Dragsted, L. O., Draper, J., Rappaport, S. M., van der Hooft, Justin J J & Wishart, D. S. (2014) The food metabolome: a window over dietary exposure. The American Journal of Clinical Nutrition. 99 (6), 1286-1308.. Available from: doi: 10.3945/ajcn.113.076133. 

Schneider, J. A., Rees, D. C., Liu, Y. & Clegg, J. B. (1998) Worldwide Distribution of a Common Methylenetetrahydrofolate Reductase Mutation. The American Journal of Human Genetics. 62 (5), 1258-1260. Available from: doi: https://doi.org/10.1086/301836

Sharma, P. & Dwivedi, S. (2017) Nutrigenomics and Nutrigenetics: New Insight in Disease Prevention and Cure. Indian Journal of Clinical Biochemistry. 32 (4), 371-373. Available from: doi: 10.1007/s12291-017-0699-5. 

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