ABSTRACT
Cardiovascular diseases (CVD) remain leading causes of morbidity, mortality, and early disability, and are
exacerbated by obesity. It is well known that obesity stresses metabolic pathways, thereby accelerating CVD
risk. Yet, the specific biologic mechanisms remain poorly understood. Metabolites are biologically active small-
molecule intermediates and byproducts of metabolism that lie along pathways linking genetic susceptibility with
CVD and are responsive to obesity, related health behaviors, and CVD risk factors. Thus, metabolites can be
powerful disease biomarkers and therapeutic targets and may provide targetable “mechanistic bridges” linking
genome-wide association study (GWAS) findings with CVD risk factors and clinical disease. We hypothesize
that: (1) genetic susceptibility influences CVD risk along specific metabolic pathways; (2) that metabolites on
these pathways (i) affect and (ii) are affected by CVD risk factors to (3) increase clinical disease risk; and that
(4) obesity modifies a subset of metabolite effects. Yet, the majority of metabolomics studies to-date have been
largely cross-sectional or clinical efforts in older, European-ancestry populations, with inconsistent control of
confounders, including diet, and they have ignored plausible modifiers, including obesity. To address these major
research gaps, we will generate longitudinal untargeted and targeted metabolomics profiles in the biracial (47%
African American) CARDIA study (n=5,115; 18-30 years in 1985-86; n~3,270 in 2020-21). The CARDIA study is
uniquely suited to test the proposed study hypotheses, with 35 years of longitudinal data collected over the key
your adult lifecycle period when CVD risk accelerates in concert with increasing obesity. We will develop and
employ cutting-edge metabolomics and statistical methods to characterize known and unknown metabolite
signals. Longitudinal data, Mendelian randomization, and pathway-based modeling enable assessment of (i)
metabolic perturbations that influence CVD and (ii) CVD risk factors that influence metabolic perturbations, (iii)
overall and in the context of a growing obesity burden. We address the following specific aims: 1) identify
metabolites and major metabolic pathways that influence metabolic CVD risk factors (cholesterol, blood
pressure, and glycemic phenotypes); 2) identify metabolic CVD risk factors that influence metabolites and major
metabolic pathways; 3) leverage statistical innovations and existing `omics, phenotype, and covariate data for
causal inference, to evaluate mechanistic frameworks, and characterize novel metabolites; and 4) test
metabolites identified in the CARDIA study for evidence of association with CVD risk factors and clinical
endpoints (coronary heart disease, heart failure, and stroke) in the biracial Atherosclerosis Risk in Communities
(ARIC) study. We anticipate that the proposed project, prepared by a multi-disciplinary team with expertise in
CVD and metabolic epidemiology, nutritional biochemistry, metabolomics, bioinformatics, biostatistics, and
genetics, will inform disease mechanisms, with strong potential for identifying biomarkers of CVD risk. Together,
our innovations will help identify novel therapeutic and nutritional targets to reduce the global burden of CVD.