Abstract 1261: Plasma Metabolomic Profiles Predict Future Cardiovascular Events
Background Circulating small molecule metabolites may reflect underlying disease states. As part of the MURDOCK CV study, we hypothesized that baseline metabolomic profiles predict future cardiovascular events in patients undergoing evaluation for CAD.
Methods Consecutive subjects (N=2002) undergoing cardiac catheterization at Duke University Medical Center formed the base population. Clinical and procedural data and longitudinal follow-up were collected through the Duke Databank for Cardiovascular Disease. Mass-spectrometry-based quantitative targeted profiling of 69 metabolites and routine lipid assessment was performed in fasting plasma samples. Principal components analysis (PCA) was used to reduce the large number of metabolites into a smaller number of uncorrelated factors. Multivariable Cox proportional hazards modeling was used to assess the independent relationships between metabolite factors and time to death and time to death or myocardial infarction (MI), adjusting for previously identified clinical predictors of these events.
Results Over a median follow-up of 3.1 years, there were 232 deaths and 294 death or MI events. Thirteen PCA-derived metabolite factors were identified. In the multivariable clinical model for death, 3 of these factors were independently associated with mortality: Factor 1 (medium chain acylcarnitines), HR 1.10 [95% CI 1.02–1.19], p=0.01; Factor 3 (long-chain dicarboxylacylcarnitines), HR 1.14 [1.05–1.24], p=0.003; and Factor 12 (nonesterified fatty acids), HR 1.14 [1.01–1.28], p=0.03. Four factors were independently associated with death or MI: Factor 1, HR 1.10 [1.01–1.19], p=0.02; Factor 2 (short chain dicarboxylacylcarnitines), HR 1.12 [1.01–1.24], p=0.02; Factor 3, HR 1.15 [1.06–1.24], p=0.001; Factor 12, HR 1.18 [1.06–1.31], p=0.003. Metabolites added to the discriminative capability of the clinical model for death (c-indices: clinical 0.812 vs. clinical + metabolites 0.822) and for death/MI (clinical 0.787 vs. clinical + metabolites 0.797).
Conclusions Metabolomic profiles predict future cardiovascular events independent from standard clinical predictors. These observations provide insights into novel mechanisms of risk.
This research has received full or partial funding support from the American Heart Association, National Center.