1: Identify metabolomic-based biomarkers of dietary and exercise behavior in normal-weight and obese elderly individuals and the genetic variants associating with the baseline levels of these metabolites. 1.A. Identify metabolomic-based biomarkers of dietary behavior in normal-weight and obese aging individuals and the genetic variants associating with endogenous variability of these metabolites. 1.B. Identify metabolomic-based biomarkers of physical activity in normal-weight and obese aging individuals and the genetic variants associating with endogenous variability of these metabolites. 2: Determine the demographic, behavioral, metabolomic and genetic drivers of the excess obesity in elderly population(s) suffering health disparity. 3: Determine the relationships between aging-related changes in gene expression, endogenous and exogenous microRNAs, metabolic factors and chronotype in response to metabolic challenges such as unhealthy dietary habits, high-fat loads and physical inactivity. 4: Identify genomic, and epigenomic and metabolic markers that predict cardiovascular status and metabolic health during aging and define specific dietary, physical activity and other lifestyle factors that are most suitable to an individual’s genetic and epigenetic profile. 5: Use a multi-omics approach to identify multi-level genome/metagenome/diet interactions that modulate inflammation and aging pathways in normal-weight and obese individuals. 5.A. Determine which of a panel of aging and obesity-related phenotypes associate with genetic markers of obesity in an obese-non-obese comparison (or, in a manner dependent on obesity status) and which of those genetic associations are modulated by dietary factors and exercise. 5.B. Assess microRNA expression levels as correlating with the obese condition irrespective of genetics. 5.C. Collect metabolomics data to define individuals metabolically as obese or non-obese, irrespective of anthropometrics. 5.D. Perform gene network, systems biology analysis on those genes and genetic markers showing associations, either modified by diet or exercise or not, with the aging and obesity-related phenotypes.
Our research on the genetic basis of the responses to diet and their metabolic consequences has demonstrated that the onset and progression of age-related disorders depends on an individual’s metabolic flexibility. With respect to cardiometabolic diseases several factors act in concert and converge to challenge metabolic flexibility. These include an inadequate diet, insufficient physical activity, chronodisruption, decreased metabolic reserve, altered gut microbiome, and reduced immune system capacity. Our primary focus is to determine the specific elements from each of these factors that interact together and with common genetic variants to either promote or disrupt a program of metabolic flexibility in the context of aging, obesity and cardiovascular disease. Our approach aims to identify new metabolite-based markers, substantiate intake of certain foods, nutrients or dietary patterns, define the degree and mechanisms by which circadian control affects cardiometabolic diseases, to describe the roles of microRNAs in these diseases, and to do so in the context of populations suffering health disparities. This will be tested, using high throughput “omic” (i.e., genomics, epigenomics, metabolomics) techniques, both in ongoing studies of free-living populations from different ethnic groups and in intervention studies. We also propose to establish statistical methods whereby a genome-optimized diet is evaluated for its ability to lower plasma triglycerides. Lastly, available datasets will be used to construct gene-SNP-metabolite-diet-aging networks for the purpose of generating testable hypotheses relevant to delaying the onset and progression of cardiometabolic disorders. Outcomes of this research will generate new and better strategies for the prevention of age-related disorders and for slowing the aging process using nutritional and behavioral approaches.
Progress was made on all five objectives, all 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 4, one goal is to build a statistical tool that predicts future risk of cardiovascular and other metabolic diseases, and we successfully implemented a specialized statistical approach called Generalized Multifactor Dimensionality Reduction. Generalized Multifactor Dimensionality Reduction is a nonparametric and genetic model-free alternative to standard linear or logistic regression for detecting and characterizing nonlinear interactions among discrete genetic and environmental attributes. All these processes have been executed in a Graphics Processing Unit (GPU)-based mapping software, named QTXNetwork, using in-house high-speed computers to identify up to three-way interactions between genetic, dietary and lifestyle factors (~800,000 markers) that contribute in this application to the risk of type 2 diabetes. Prediction models based on genomic best linear unbiased prediction (gBLUP) and semi-supervised anomaly detection methods have been tested and validated with data from different populations. So far, the best validation for prediction of binary outcomes (like type 2 diabetes, have the disease or not) reached the area under the receiver operating characteristic curve of 0.811±0.014. The ROC-AUC (receiver operating characteristic curve-area under the curve) value is a measure of accuracy of the prediction of disease risk, and the value of 0.811 is rather good. Improvement of prediction models with other methods will be further tested. DNA methylation is an ideal biomarker for many applications, because it has both the stability for prognostic use and the plasticity to be altered by environmental variables, including diet. Investigations of relationships between DNA methylation and cardiovascular disease have been performed, but these cross-sectional analyses are susceptible to reverse causation, making causal inference difficult. Thus, under Objective 4, we performed an epigenome-wide association study for incident cardiovascular disease (CVD) in individuals from the Framingham Heart Study Offspring Cohort and Women’s Health Initiative studies. Methylation data were previously collected from whole blood samples using the Illumina Infinium HumanMethylation450k platform. Cox regression methods were used to identify relationships between relative methylation levels and incident CVD both at specific CpG sites and using a region-based approach. We identified 10 single CpG sites and 4 regions demonstrating suggestive associations with incident CVD. Mendelian randomization approaches provided preliminary evidence of a causal relationship between methylation within the APOB gene and CVD. Investigations of cell-type specific relationships using available epigenomic annotations highlighted the importance of monocytes in mediating the risk captured by methylation data in blood. Finally, relationships between these CpG sites and known traditional cardiovascular risk factors were examined to understand how they might interact to increase CVD risk. Based on these results, DNA methylation may provide a useful complement to existing cardiovascular risk prediction methods and provides insights into genomic processes that may mediate the effects of genetics and environmental factors on disease risk.
1. People with this gene are more likely to gain weight. The APOA2 gene, one of the most common proteins that moves fats through the body and plays an important role in the cardiovascular system, may also be associated with increased body mass index in people who have this variant. ARS and ARS-funded researchers in Boston, Massachusetts used comprehensive scientific approaches to examine genes at the molecular level as well as the genetic background of people involved in the Boston Puerto Rican Health Study, and the Framingham Heart Study. By closely examining the characteristics of people who reported eating high levels of red meat, poultry, cheese and butter, the researchers found that only people with the APOA2 variant gene were likely to gain weight while people without the variant maintained lower body mass index.
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