Introduction: The American Heart Association’s framework “ideal cardiovascular health” (CVH) focuses on modifiable risk factors to reduce the overall burden of cardiovascular disease (CVD). Metabolomics provide important pathobiological insights into risk factors and CVD development. Whether circulating metabolites relate to CVH status and mediate the relations of CVH score with atrial fibrillation (AF) and heart failure (HF) has not been explored. Hypothesis: We hypothesized: a) CVH score associates with metabolites; and b) metabolites mediate the relation of CVH score with incident AF and HF. Methods and Results: We analyzed 3056 adults (mean age 54 years; 54% women) in FHS Offspring Cohort (Exam 5); 2059 participants had metabolomics data. Each metabolite measurement was log transformed and standardized for data normalization. Using linear mixed models, CVH score was associated with 144 metabolites (false discovery rate, q-value < 0.05), adjusting for age, sex, estimated glomerular filtration rate (eGFR) and family structure. Of these 144 metabolites, 65 metabolites overlapped with key cardiometabolic components of the CVH score (body mass index, blood pressure, and fasting blood sugar). In mediation analyses (adjusting for age, sex, eGFR), no metabolites mediated the relation between CVH score and incident AF. However, 11 metabolites (C36:4 phosphatidylcholine, isocitrate, glycerol, ornithine, aminoadipate, 3-indolepropionic acid, cystathionine, asparagine, alpha-hydroxybutyrate, phosphorylated saccharides, and C52:5 triacylglycerol) partially mediated the association between CVH score and incident HF in multivariable-adjusted models. Conclusion: Multiple metabolites associate with the CVH score and many of the metabolites are specifically associated with the cardiometabolic components of the CVH score. Several metabolic signatures, particularly lipids and tricarboxylic acid cycle intermediates, partially mediated the relation between CVH score and incident HF. Our findings lend insight into the role of metabolites in the relations of modifiable cardiovascular risk factors with incident HF.
Objective: We evaluated whether intra-individual changes in blood metabolites in response to an oral glucose challenge were associated with incident CVD and mortality. Methods: An oral glucose tolerance test (OGTT; 75g glucose) was administered to a subsample of non-diabetic Framingham Heart Study participants (n=361). Metabolite profiling was performed on blood samples drawn before and 2 hours post OGTT. We compared pre- and post-OGTT log2(metabolite levels) using t-tests; for each metabolite with statistically significant changes from pre- to post-OGTT (Δmetabolites), we calculated log2(post/pre) and standardized values to mean=0, standard deviation=1. We constructed multivariable-adjusted Cox models relating each Δmetabolite with CVD and death. Models were adjusted for clinical risk factors of age, sex, baseline metabolite level, diabetes, systolic blood pressure, hypertension treatment, BMI, smoking, and total/HDL cholesterol. Results: Our sample included 42% women, with a mean age of 56±9 years, and body mass index of 30.2±5.3 kg/m 2 . The pre- to post-OGTT change (Δmetabolite) was statistically significant for 170 metabolites (at FDR ≤0.05). A total of 132 CVD events and 144 deaths occurred during follow up (mean 23±5 years for CVD, 26±2 years for death). In Cox models, four Δmetabolites were associated with incident CVD and six Δmetabolites were associated with death, at P<0.05 ( Table ). Notably, baseline metabolite levels were not associated with either outcome in models excluding Δmetabolites. Significant Δmetabolites included those with established roles in cardiometabolic disease (e.g., glutamate, α-ketoglutarate, N-methylmalonamic acid) and metabolites with less defined roles (e.g., glucoronate, lipid species). Conclusion: Intra-individual changes in circulating metabolites in response to an OGTT were associated with CVD and death beyond their resting measures. Dynamic changes in metabolite levels with an OGTT have potential relevance for understanding and predicting CVD risk.
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