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United States Department of Agriculture

Agricultural Research Service


Location: Boston, Massachusetts

Title: Integrating nutrigenomics data to identify cardiometabolic gene-environment interactions

item Parnell, Laurence
item Lee, Yu-chi
item Dashti, Hassan
item Ma, Yiyi
item Pham, Lucia
item Sloan, Sarah
item Smith, Caren
item Richardson, Kris
item Lai, Chao Qiang
item Ordovas, Jose

Submitted to: Meeting Abstract
Publication Type: Proceedings
Publication Acceptance Date: 11/21/2013
Publication Date: 11/18/2013
Citation: Parnell, L.D., Lee, Y., Dashti, H., Ma, Y., Pham, L., Sloan, S., Smith, C.E., Richardson, K., Lai, C., Ordovas, J.M. 2013. Integrating nutrigenomics data to identify cardiometabolic gene-environment interactions. Meeting Abstract. p.1 Abstract #5.

Interpretive Summary:

Technical Abstract: Nutrition is a key factor in health and in many age-related diseases. This is particularly the case for cardiometabolic diseases such as cardiovascular disease, type 2 diabetes and hypertension, and is often precluded by obesity, glucose impairment and metabolic syndrome. Our research objectives are rather analogous to those of LINCS in that we seek to identify the connections between dietary patterns, food items and metabolites; genetic and epigenetic variation and its effects on gene expression; and disease phenotypes that often manifest from dysfunction within specific cell types. Importantly, we are actively identifying links between these three thematic datasets in order to describe gene-environment (GxE) interactions. Key findings include 1) HDL-cholesterol is highly sensitive to dietary and other environmental factors, much more so than other common blood lipid measures; 2) physical activity is the most common modulator of several cardiometabolic phenotypes; and 3) the GxE contribution to trait variance identifies many genes, via SNVs/SNPs, not previously assigned roles in that particular trait. Because a single standard or recommended diet will not have the same health effects and to the same degree in all persons, it is necessary to integrate data from metabolites derived from food with gene expression values in specific cells and tissues under the guidance of genetic and epigenetic variation both common and rare. These data will be important in the preparation of personalized nutrition guidance.

Last Modified: 10/19/2017
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