Objective 1: Conduct and analyze dietary intervention studies to validate gene-diet interactions and identify the underlying mechanisms using omic technologies. Sub-Objective 1A: To characterize the response of cardiometabolic, epigenetics and other age-related biomarkers and the microbiome to diets differing in saturated fat and prebiotics content (animal-based diet versus plant-based diet) in individuals carrying CC and TT genotypes at the common APOA2 -265T>C (rs5082) SNP using a short-term crossover, randomized feeding study, and to elucidate the physiological mechanism(s) by which diet impinges on metabolic pathways through APOA2 genotypes. Sub-Objective 1B: To characterize the TCF7L2-by-diet interaction with respect to those type 2 diabetes (T2D) and cardiovascular disease (CVD) risk factors identified in observational studies for validation in the context of a short-term randomized controlled feeding study (low-fat diet versus Mediterranean diet), and to elucidate the molecular mechanisms responsible for these GxD interactions using epigenetics and metabolomics. Sub-Objective 1C: To develop polygenic risk scores (PRS) predicting the changes in and relationships between cardiovascular disease (CVD) risk factors and disease incidence in response to long-term (>=1 y) dietary interventions [Mediterranean diet (MedDiet) or Low-fat control diet]. Objective 2: Identify genomic, epigenomic, metabolomic, and microbiome-related biomarkers that sustain healthy aging, and define specific personalized dietary, physical activity, and other lifestyle factors associated with optimal health of older adults. Subobjective 2A: To identify genetic and dietary factors that modify CPT1A methylation and cardio-metabolic traits. Subobjective 2B: To identify interactions between the genome, epigenome and diet and lifestyle on lipid profiles that signify CMD risk.
Promoting healthy aging by tailoring nutritional guidance based on a person's genetic makeup is an emerging science that has great promise. The Nutrition and Genomics lab is a pioneer in this area and focuses its research on the role of precision nutrition and cardiometabolic diseases – the leading cause of death in the United States. Our approach harnesses the availability of tremendous computing power and huge datasets from existing cohorts to study the crosstalk between habitual diets and the genome to identify gene-by-diet interactions that sustain individual optimal health for older adults. This objective will be accomplished using Big Data analytics of omics data (i.e., genome-wide datasets on gene and protein expression, genetic variation, methylation, and metabolite levels). We also conduct short-term feeding studies in people preselected based on particular genotypes to validate gene-by-diet interactions revealed by previous observational studies and, using multi-omic data integration (i.e., genomics, epigenomics, microbiomics, and metabolomics) methods, identifying the mechanisms underlying such interactions. This research will generate new knowledge on how non-modifiable and modifiable factors interact to prevent cardiovascular diseases and type 2 diabetes. Further, it will contribute much-needed evidence and tools to define and implement personalized nutrition as a common practice for the benefit of all stakeholders.
Progress was made on the two objectives, both of which fall under National Program 107. The National Program in Human Nutrition is designed to improve the health of all Americans throughout the lifespan. Under Objective 1, our goal is to conduct and analyze dietary intervention studies to validate gene-diet interactions and identify the underlying mechanisms using omic technologies. In support of this objective, and in collaboration with investigators in the U.K. and other U.S. sites, we examined the genetic, metabolic, microbiome, and meal composition/context contributions to postprandial metabolic responses (in clinic and at home) in the PREDICT 1 Study. This study has enrolled 1,102 twins and unrelated healthy adults in the U.K. and U.S. Our analysis reveals large and consistent differences between individuals in blood triglyceride, glucose, and insulin responses to identical meals. Person-specific factors, including gut microbiome, have a greater influence than meal macronutrients; genetic variants have a modest impact on predictions. Modifiable factors such as meal timing were found to have large effects. As predictors of cardiometabolic disease risk, postprandial triglyceride and glucose were more accurate than traditional fasting clinical markers. Moreover, we have developed a machine-learning model that predicts both triglyceride and glycemic responses to food. These findings may be informative for developing personalized diet strategies. For Subobjective 2a, we have examined three distinct populations representing different global genetic ancestries and found that the total amount of carbohydrate and the ratio of total carbohydrate to total fat intake have a direct association with DNA methylation at CPT1A gene. In contrast, total fat intake intake has an inverse association with DNA methylation. In addition, we found supportive evidence that the higher DNA methylation found at the CPT1A gene induced by carbohydrate intake can lower the risk of metabolic conditions and diseases including high triglycerides, obesity, type 2 diabetes, hypertension, and metabolic syndrome. Conversely, the lower DNA methylation at the CPT1A, as promoted with greater fat intake, can raise the risk of these diseases. Our findings on CPT1A methylation suggest that there is a balance between carbohydrate and fat in the diet that can influence the regulation of gene activity, and that such communication between diet and genome has health consequences. In support of Subobjective 2b, we analyzed the regional assignment of human amylase (AMY1) gene, encoding a starch-digesting enzyme, with regard to dietary carbohydrate intake and type 2 diabetes, and the ABCG1 gene, whose protein transports cholesterol byproducts, for epigenetic changes relating to a certain drug class and risk of type 2 diabetes. In addition, we developed specialized software that compares chemical structures to identify natural food chemicals with high potential to mimic commonly used pharmacological compounds. Starchy foods are a major source of dietary carbohydrates and contribute significantly to the energy intake of many Americans. An important carbohydrate- and starch-digesting enzyme is AMY1 amylase in the saliva, which initiates digestion. The number of copies of the AMY1 gene varies greatly among humans. People with a low copy number of AMY1 (fewer than 6 copies) had increased risk of insulin resistance as they aged, illustrating that AMY1 copy number interact with age to affect the risk of type 2 diabetes. Together, these results imply that people with low AMY1 copy numbers might benefit from lower starch intake as they age, in order to reduce the risk of type 2 diabetes. A large number of adults are prescribed cholesterol-lowering medication, most frequently statins, for the prevention or treatment of cardiovascular diseases. An unexpected side effect of these drugs is an increased risk of type 2 diabetes for reasons that are not known. We have examined two cohorts and found a strong association between statin use and DNA methylation (an epigenetic mark) at the ATP Binding Cassette Subfamily G Member 1 (ABCG1) gene. The research showed that statin use is the cause of changes in ABCG1 methylation, and this leads to the observed increased risk of type 2 diabetes. Because there is a scarcity of information on how chemical compounds naturally found in different foods contribute to specific health effects, we built software that could match, when possible, these chemical compounds to pharmacological compounds for which such information is documented. This software uses a machine-learning algorithm to compare chemical structures and then uses the wealth of biological and health information on drugs to assign potential biological effects to chemical compounds naturally found in food. A test case of the software began with the target of common diabetes medications and identified several natural compounds with potentially similar beneficial effects. Using this software can guide researchers in designing specific experiments to test if a food compound and its food source actually function as predicted in alleviating, either wholly or partially, specific conditions of common age-related metabolic diseases.
1. People with specific genes are more likely to gain weight when consuming sugar-sweetened beverages. Consuming sugar-loaded drinks is associated with obesity and obesity-related diseases, but the biological mechanism that connects sugar-sweetened beverage intake to obesity is not completely understood. ARS researchers in Boston, Massachusetts, examined the relationship of biochemical compounds found in the blood of participants in the Boston Puerto Rican Health Study as it related to their intake of sugar sweetened beverages and body mass index (BMI). The scientists identified 28 compounds, many of them implicated in fatty liver, that linked sugar-sweetened beverage intake to obesity. These findings suggest that drinking sugar-sweetened beverages disrupts liver metabolism leading to an increased risk of obesity in persons with specific versions of genes. Reducing consumption of sugar-sweetened beverages would contribute to reducing the risk of obesity and fatty liver disease that currently affects millions of Americans.
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