DELTA OBESITY PREVENTION RESEARCH PROGRAM
Location: Delta Obesity Prevention Research Unit
Title: Personalizing nutrigenomics research through community based participatory research and omics technologies
| McCabe Sellers, Beverly |
| Lovera, Dalia |
| Nuss, Henry |
| Wise, Carolyn - NCTR |
| Ning, Baitang - NCTR |
| Teitel, Candee - NCTR |
| Shelby Clark, Beatrice - DELTA NIRI |
| Toennessen, Terri - NCTR |
| Green, Bridgett - NCTR |
| Bogle, Margaret |
| Kaput, Jim - NCTR |
Submitted to: OMICS: A Journal of Integrative Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 16, 2008
Publication Date: December 2, 2008
Citation: Mccabe Sellers, B.J., Lovera, D., Nuss, H.J., Wise, C., Ning, B., Teitel, C., Shelby Clark, B., Toennessen, T., Green, B., Bogle, M.L., Kaput, J. Personalizing nutrigenomics research through community based participatory research and omics technologies. 2008. Journal of Integrative Biology.12(4):263-272.
Interpretive Summary: Personal and public health information are often obtained from studies of large population groups. Risk factors for nutrients, toxins, genetic variation, and more recently, nutrient-–gene interactions are statistical estimates of the percentage reduction in disease in the population if the risk were to be avoided or the gene variant were not present. Since individuals differ in genetic make-up, lifestyle, and dietary patterns than those individuals in the study population, these risk factors are valuable guidelines, but may not apply to individuals. Intervention studies are likewise limited by small sample sizes, short time frames to assess physiological changes, and variable experimental designs, which often precludes comparative or consensus analyses. While significant advances have been made in developing the technologies for analyzing genes (genomics), RNA (transcriptomics), proteins (proteomics), metabolites (metabolomics), and statistical algorithms to find patterns among the datasets, a fundamental challenge for nutrigenomics will be to develop metabolic group and, eventually, individualized risk factors. To reach the goal of personalizing medicine and nutrition, new experimental strategies are needed for human study designs. A promising approach for more complete analyses of the interaction of genetic make-ups and environment relies on community based participatory research (CBPR) methodologies. CBPRs central focus is developing a partnership among researchers and individuals in a community that allows for more in-depth lifestyle analyses, but also translational research that improves the health of individuals and communities.
Advances in experimental technologies for analyzing genomes, proteins, metabolites, and transcripts are laying the foundation for developing recommendations for personalized nutrition and optimizing medical treatments for each individual. However, current experimental strategies rely on studies that yield the average response of individuals in a population. These data are reported as the attributable fraction (AF)--the proportional reduction in average disease risk over a specified time interval that would be achieved by eliminating the exposure of interest from the population while other factors remain unchanged. Although many reports explicitly report the data as the AF specific for that population, the data are often used by the commercial enterprises and the public as an individual risk factor. Since individuals may differ genetically, physiologically, and nutritionally from the population averages, the AF can only be considered an estimate of the risk for an individual. Intervention studies also yield information for medical treatments or recommendations for nutritional intakes. A recent example showed an association of three single nucleotide polymorphisms (SNPs) in IL1A and IL1B with response to a botanical that lowered c-reactive protein (CRP) levels. While nutrigenomic and nutritional intervention studies provide preliminary information about optimum diets, the small number of individuals in many of the studies and their undetermined genetic ancestry precludes using the information to predict responses in other individuals. Epistatic (gene-–gene) interactions have been shown to alter the influence of individual SNPs on measured phenotypes. These specific examples illustrate the need for developing new approaches to study the interaction of genetic make-up and environmental factors. We have previously described challenges to the study of gene-–nutrient interactions that include the genetic diversity of human populations, complexity of diets and cultures, the intricacies of physiological processes that depend on gene-–environment interactions, and the need for new experimental designs not based on population studies. These challenges are reviewed in the context of an emerging strategy for human population studies, the use of community based participatory research methods that may provide the path for developing recommendations for improving personal and public health.