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.
This is the final report for the project 8050-51000-098-00D. 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 was to build a statistical tool that predicts future risk of cardiovascular and other metabolic diseases, and the Genomics lab successfully implemented machine learning methods to predict the extension and vascular distribution of subclinical atherosclerosis assessed by imaging techniques using all lifestyle and clinical features collected on middle-aged, asymptomatic individuals. Machine learning methods make use of a large number of interrelated variables with highly complex structures. This makes them optimal for building predictors for subclinical atherosclerosis and other age-related metabolic traits and diseases based on large sets of traditional and novel interacting factors. The lab applied four different machine learning algorithms for classification (Naive Bayes, NB; Distributed Random Forest, DRF; Gradient Boosting Machines, GBM; and Elastic Net, EN) and one more for regression (Deep Learning, DL) to predict the extension of subclinical atherosclerosis. The Elastic Net model improved in up to 5% the classification of subclinical atherosclerosis over traditional risk-scores. The Genomics lab’s machine learning model was very precise in the prediction of the current extension of subclinical atherosclerosis for individuals at high risk and was able to predict the progression of the disease for individuals without subclinical atherosclerosis at baseline. Moreover, unbiased analysis of the data identified different sets of variables associated with the presence of plaque and for coronary calcification. In conclusion, machine learning data-driven methods were able to identify personalized profiles of lifestyle and clinical features predictive of the current and future extension of subclinical atherosclerosis, outperforming traditional risk scores. The lab is implementing similar approaches for the prediction of type 2 diabetes mellitus. Epigenetics, a system of chemical marks that links genetics to environmental factors such as diet, may be important in increasing prediction and understanding of cardiovascular disease risk. Under Objectives 4 and 5, researchers in the lab examined DNA methylation (a type of epigenetic mark) using Epigenome-wide association studies (using a set of hundreds of thousands of sites across the genome) to see whether it was predictive of future cardiovascular events. For this purpose, the lab applied a series of novel statistical procedures, such as weighted gene correlation network analysis and the Comb-p algorithm to find methylation modules and regions associated with incident cardiovascular disease in the Women’s Health Initiative dataset. The Genomics lab discovered two modules (groups of genes) whose activation correlated with cardiovascular disease risk and replicated across cohorts (i.e., the Framingham Heart Study Offspring Cohort). One of these modules was enriched for development-related processes and overlapped strongly with epigenetic aging sites. For the other, the lab showed preliminary evidence for monocyte-specific effects and statistical links to cumulative exposure to traditional cardiovascular risk factors. Additionally, the Genomics lab found three regions (associated with the genes SLC9A1, SLC1A5, and TNRC6C) whose methylation associates with cardiovascular disease risk. Furthermore, the lab found statistical associations between these epigenetic modules and both current and past exposure to cardiovascular risk factors such as body weight and cholesterol levels. Overall, the results suggest that DNA methylation can act as a biomarker of cardiovascular disease, generate hypotheses for future research, and more concretely show that epigenetics can provide a “molecular readout” of past risk factor exposures.
1. Sugar-sweetened beverages affect the body. Sugar-sweetened beverages are drinks with added sugar, and consuming them has been associated with increased risk of obesity. However, the specific metabolic basis for weight gain and the individual responses to sugar-sweetened beverages are not known. ARS and ARS-funded researchers in Boston, Massachusetts, examined the metabolism and the genetic background of people involved in the Boston Puerto Rican Health Study, a population with a high prevalence of obesity and consumption of sugar-sweetened beverages. By carefully examining the people who reported high and low consumption of sugar-sweetened beverages, the researchers observed that sugar-sweetened beverages increased obesity risk by stimulating the production of certain fats in the body, known as glycerophospholipids. These findings could lead to more specific dietary recommendations for older adults, who are disproportionately burdened by obesity.
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