The primary goal of this proposal is to identify and refine novel metabolic biomarkers that incrementally improve CVD risk prediction models and help predict the best obesity intervention to improve metabolic health, to begin to move towards a more personalized approach to obesity management for prevention of CVD. While obesity is a well-recognized risk factor for CVD-related morbidity and mortality, attributed to the increased prevalence of intermediate risk factors, there is heterogeneity in the prevalence of these risk factors in obese individuals.5 This disconnect leads to incomplete CVD risk prediction models and difficulty in identification of those obese individuals at greatest need for intensive therapeutic interventions for prevention of CVD events. Emerging molecular profiling technologies have begun to fill this gap. Building on a foundation of our previous work, we now propose here to use metabolomics profiling to develop biomarkers for identification of those obese individuals at greatest risk of CVD and, importantly, to build integrated clinico-metabolic models that would facilitate personalization of obesity interventions to prevent CVD.
Our Specific Aims are (1) to test the hypothesis that previously identified metabolic signatures are associated with CVD-related clinical measures in obese individuals and with obesity interventions; (2) to use unbiased metabolomics approaches to identify novel biomarkers associated with these biomarkers; and (3) to develop an integrated clinico-molecular model incorporating clinical variables and metabolic biomarkers.

Public Health Relevance

The prevalence of obesity continues to increase in the United States and globally, and is the major driver of associated diseases such as type 2 diabetes mellitus and heart disease. While obesity is a well-recognized risk factor for these diseases, not all obese individuals develop them, and in fact not all obese individuals develop earlier, related disorders such as high blood pressure, high cholesterol and pre-diabetes. This heterogeneity results in difficulty in predicting disease risk and thus identifying those obese individuals at greatest need for intervention. New technologic advances have enabled us to measure genes and metabolites in the blood that may help us better identify these individuals. Thus, we propose to measure genes and metabolites in stored blood from large human studies of obesity and cardiovascular disease to test whether we can improve risk prediction and use these markers to identify which interventions are best suited for a given individual.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL127009-03
Application #
9407064
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Srinivas, Pothur R
Project Start
2016-01-01
Project End
2019-12-31
Budget Start
2018-01-01
Budget End
2018-12-31
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Duke University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Dewey, Frederick E; Gusarova, Viktoria; Dunbar, Richard L et al. (2017) Genetic and Pharmacologic Inactivation of ANGPTL3 and Cardiovascular Disease. N Engl J Med 377:211-221