Nutrition and Genomics: Issues of Ethics, Law, Regulation and Communication

By David Castle

Nutrigenomics is the rapidly developing field of science that studies nutrient-gene interaction. This field has broad implications for understanding the interaction of human genomics and nutrition, but can also have very specific implications for individual dietary recommendations in light of personal genetics. Predicted applications for nutrigenomics include genomics-based dietary guidelines and personalized nutrition based on individual genetic tests. These developments have sweeping ethical, legal and regulatory implications for individuals, corporations and governments.This book brings together experts in ethics, law, regulatory analysis, and communication studies to identify and address relevant issues in the emerging field of nutritional genomics. Contributing authors are experts in the social aspects of biotechnology innovation, with expertise in nutrigenomics. From addressing the concern that nutrigenomics will transform food into medicine and undermine pleasures associated with eating to the latest in the science of nutrigenomics, this book provides a world-wide perspective on the potential impact of nutrigenomics on our association with food.

• Explores the rapidly developing, yet not fully understood, impact of nutrigenomics on the relationship to food medicalization, genetic privacy, nutrition and health
• Provides ground for further exploration to identify issues and provide analysis to aid in policy and regulation development
• Provides ethical and legal insights into this unfolding science, as well as serving as a model for thinking about issues arising in other fields of science and technology

• Language: English
• Publisher: Academic Press
• Release date: Apr 14, 2009
• ISBN: 9780080920825

Book preview

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Gene–Environment Interactions: Where are we and where should we be Going?

Jose M. Ordovas and E. Shyong Tai

Summary

Introduction

Genes or environment? Limitations of the traditional approach to disease risk assessment

Cardiovascular risk factors

Gene–Smoking Interactions

Gene–Alcohol Interactions

Gene–Physical Activity Interactions

Gene–Diet Interactions

Statistical Methods for gene–environment interactions

Personalized nutrition and the consumer

Summary

Acknowledgment

References

Summary

This chapter examines the reasons for studying gene–environment interactions and evaluates recent reports of interactions between genes and environmental modulators in relation to cardiovascular disease and its common risk factors. Recent studies focusing on smoking, alcohol, physical activity and coffee are all observational and include relatively large sample sizes. They tend to examine a single gene and fail to address interactions with other genes as well as other correlated environmental factors. Studies examining gene–diet interactions include both observational and interventional designs, and are of smaller scale, especially those including dietary interventions. Among the reported gene–diet interactions, it is important to highlight the strengthening evidence for APOA5 as a major gene involved in triglyceride metabolism and modulated by dietary factors and the identification of APOA2 as a modulator of food intake and obesity risk. This chapter concludes that, overall, the study of gene–environment interactions is an active and much needed area of research. While technical barriers in genetic studies are being quickly overcome, the inclusion of comprehensive and reliable environmental information represents a significant shortcoming to genetic studies. Progress depends on larger study populations being included and also more comprehensive, standardized and precise approaches to capture environmental information.

Introduction

After more than two decades of great expectations but few deliverables, the field of genetics related to common and complex disorders has made remarkable progress towards the identification of novel loci and genetic variants associated with these diseases. This has been possible thanks to the combination of more robust experimental approaches, including large population studies, with the availability of high density genotyping (>1 million single nucleotide polymorphisms (SNPs)). At the time of writing, there have been both consolidation of some of the traditional candidate genes, especially in the area of lipid metabolism, and, more exciting, identification of new lipid-related loci. These findings will provide us with a more complete understanding of the metabolic landscape and new insights into the pathogenesis of disease (Smith, 2007; Zeggini and McCarthy, 2007). The search is far from over, however, and for the newly discovered and for well-known candidate genes, current knowledge needs to be augmented by using deep re-sequencing and phenotyping of individuals carrying functional variants at these loci to understand the pathways and metabolic fluxes affected by these genetic variants and to provide greater insight into the physiologic basis of disease (Tracy, 2008). Based on current knowledge, however, many of the observed gene effects will not be insulated from environmental modulation. Therefore, there is a compelling need to further the initial association studies with well-designed investigation of gene–environment interactions.

Genes or environment? Limitations of the traditional approach to disease risk assessment

From an epidemiologic perspective, studies of genetic and environmental factors will continue to underestimate the population-attributable risk associated with either genetic or environmental factors. In fact, consideration of the joint effects of genetic and environmental factors strengthens their associations with disease, permitting the identification of risk factors that have small marginal effects. Even the best well established genetic markers for common traits show inter-population differences. For example, the recently identified fat mass and obesity-associated (FTO) gene has been heralded as the most solid locus for obesity risk. Yet there are conflicting reports of lack of association in African Americans or Han Chinese (Li et al., 2008). It remains unclear whether these are due to genetic differences between the different populations, or whether a gene–environment interaction may be masking the effect in these other ethnic groups. Findings from a Danish population, for example, indicate that physical activity may attenuate the effects of the FTO genetic variants in support of gene–environment interactions (Andreasen et al., 2008).

Reliably capturing environmental measures and the complexity of the dietary ‘environment’ present major difficulties in studying gene–environment interactions. Methods to capture dietary information from observational studies have been criticized for years for a lack of accuracy, precision and objectivity. Moreover, foods are very complicated mixtures and we may attribute an observed effect to a specific nutrient because we know about it but, in reality, it may be due to another component of the food that we may not know or pay attention to. For example, coffee consumption shows a well-replicated association with the risk of type 2 diabetes mellitus (van Dam and Hu, 2005; Pereira et al., 2006). But coffee is a complex mixture of compounds and it is common to equate coffee with caffeine. Still, the specific compound in coffee that produces this effect is unclear. In fact, caffeine may not be important after all, since the association is also seen with decaffeinated coffee (van Dam et al., 2006). Therefore, integration of genetic variability with gene expression studies will pinpoint pathways that the environmental exposures may act through and will provide some guidance as to the specific environmental components that warrant further investigation.

From a public health perspective, it has been suggested that the identification of genetic variants that encode susceptibility to disease could be used in risk algorithms to identify individuals at high risk of disease. The identification of gene–environment interactions may suggest specific interventions that may attenuate the risk in these individuals. Taken to an extreme, it has been suggested that these studies could lead to individualized health plans based on a person’s genetic make-up (Subbiah, 2007). In this chapter, we summarize current knowledge related to gene–environment interactions as they pertain to cardiovascular disease risk factors, particularly those related to metabolic phenotypes. In addition, we raise some of the issues that need to be addressed to advance this field of research and to develop applications for the public.

Cardiovascular risk factors

The scientific literature is heavily populated with papers reporting cardiovascular risk factors. The latest catalog published over one decade ago listed 177 of them with many of them falling in the categories of ‘Nutrition-Related’ and ‘Environmental’ (Omura et al., 1996). Many of the reported risk factors are rather questionable, however. Updating this catalog today would probably add a few hundred more to the list. Besides diet, the best characterized environmental risk factors include smoking, inadequate physical activity, alcohol and, more recently, coffee drinking (which, given its popularity and widespread use, has received increased attention as a modifier of cardiovascular disease risk) (Campos and Baylin, 2007). Those common and well established behavioral risk factors are the focus of this section.

Gene–Smoking Interactions

The study of interactions between genetic factors and smoking has been an active area of research primarily in the fields of cancer and neurodegenerative diseases (Wang and Wang, 2005; Elbaz et al., 2007), but it also caught the early attention of cardiovascular researchers (Kondo et al., 1989) and a substantial body of evidence has accumulated during the last two decades, as summarized by recent reviews (Talmud, 2007). Considering what we know about the risk associated with tobacco smoking, it is obvious that all the reports are supported by observational data and there are no randomized intervention studies. Of the behavioral factors contemplated in this section, tobacco smoking may be the most reliable in terms of validity of reporting. Moreover, it is a variable most epidemiological studies collect. Therefore, studies reporting gene–tobacco smoking interactions tend to be large and probably have adequate statistical power to examine single gene–single factor interactions. This is still far from ideal as no single study can yet fully address the complex interactions involving multiple genes and environmental factors. Genes currently under examination span a variety of metabolic pathways, including the obvious ones involved in drug metabolism and detoxification, those involved in lipid traits known to be modified by smoking (Gambier et al., 2006; Goldenberg et al., 2007; Cornelis et al., 2007b; Manfredi et al., 2007) as well as others involved in a variety of more remotely connected metabolic functions (Lee et al., 2006; Saijo et al., 2007; Jang et al., 2007; Stephens et al., 2008). In addition, the arrival of genome-wide association studies has allowed the identification of chromosomal regions of interest (North et al., 2007) for which no candidate genes have yet been identified. The increased size of recent studies and the creation of large consortia are allowing the examination of disease events and non-invasive biomarkers of disease (Lee et al., 2006; Goldenberg et al., 2007; Cornelis et al., 2007b; Manfredi et al., 2007; North et al., 2007; Stephens et al., 2008) (Table 1.1). As expected from the publication bias, most published reports identify significant gene–smoking interactions and most of the studies, but not all of them (Goldenberg et al., 2007; Cornelis et al., 2007b; Saijo et al., 2007), conclude that carriers of the minor alleles were more susceptible to the deleterious effects of tobacco smoking. While uncovering gene–smoking interactions may reveal interesting physiological mechanisms, the practical utility of this knowledge is limited as the relevant public health advice is that every smoker should quit, regardless of genotype.

Table 1.1

Gene–smoking interactions

Gene–Alcohol Interactions

As with gene–smoking interactions, lipid researchers have long been interested in interactions between genetic factors and alcohol consumption (Hannuksela et al., 1994). Like smoking, recent reports describing gene–alcohol interactions are based on observational studies with information about alcohol drinking derived from medical records or questionnaires involving thousands of subjects (Beulens et al., 2007; Jensen et al., 2007; Volcik et al., 2007; Tsujita et al., 2007) (Table 1.2). The genes selected include traditional lipid candidate genes (Tsujita et al., 2007; Jensen et al., 2007; Volcik et al., 2007) as well as those involved in alcohol metabolism (Beulens et al., 2007). For the most part, the modulating effect appears to be driven by HDL-C concentrations as reported in earlier work (Hannuksela et al., 1994). Moderate alcohol consumption is positively associated with health, but the potential benefits may not be universal. Knowledge of these studies could help identify subjects who benefit from moderate drinking, those who have no effect and those who experience