Coronary Artery Disease Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease Svati H. Shah, MD, MHS, a,b,f Jie-Lena Sun, MS, c,f Robert D. Stevens, PhD, d,f James R. Bain, PhD, d,f Michael J. Muehlbauer, PhD, d,f Karen S. Pieper, MS, c,f Carol Haynes, BS, b,f Elizabeth R. Hauser, PhD, b,f William E. Kraus, MD, a,f Christopher B. Granger, MD, a,c,f Christopher B. Newgard, PhD, d,f Robert M. Califf, MD, a,e,f and L. Kristin Newby, MD, MHS a,c,f Durham, NC Background Cardiovascular risk models remain incomplete. Small-molecule metabolites may reflect underlying disease and, as such, serve as novel biomarkers of cardiovascular risk. Methods We studied 2,023 consecutive patients undergoing cardiac catheterization. Mass spectrometry profiling of 69 metabolites and lipid assessments were performed in fasting plasma. Principal component analysis reduced metabolites to a smaller number of uncorrelated factors. Independent relationships between factors and time-to-clinical events were assessed using Cox modeling. Clinical and metabolomic models were compared using log-likelihood and reclassification analyses. Results At median follow-up of 3.1 years, there were 232 deaths and 294 death/myocardial infarction (MI) events. Five of 13 metabolite factors were independently associated with mortality: factor 1 (medium-chain acylcarnitines: hazard ratio [HR] 1.12 [95% CI, 1.04-1.21], P = .005), factor 2 (short-chain dicarboxylacylcarnitines: HR 1.17 [1.05-1.31], P = .005), factor 3 (long-chain dicarboxylacylcarnitines: HR 1.14 [1.05-1.25], P = .002); factor 6 (branched-chain amino acids: HR 0.86 [0.75-0.99], P = .03), and factor 12 (fatty acids: HR 1.19 [1.06-1.35], P = .004). Three factors independently predicted death/MI: factor 2 (HR 1.11 [1.01-1.23], P = .04), factor 3 (HR 1.13 [1.04-1.22], P = .005), and factor 12 (HR 1.18 [1.05- 1.32], P = .004). For mortality, 27% of intermediate-risk patients were correctly reclassified (net reclassification improvement 8.8%, integrated discrimination index 0.017); for death/MI model, 11% were correctly reclassified (net reclassification improvement 3.9%, integrated discrimination index 0.012). Conclusions Metabolic profiles predict cardiovascular events independently of standard predictors. (Am Heart J 2012;163:844-850.e1.) Despite advances in diagnosis and treatment, cardio- vascular disease is expected to remain the leading cause of death and disability in the United States and worldwide. 1,2 Effectively counseling patients about risks and directing advanced therapies to patients who are most likely to benefit from them will require advances in risk stratification. High-throughput molecular profiling techniques show potential for identifying biomarkers for better risk classification and for advancing our mechanis- tic understanding of the pathophysiology of cardiovas- cular disease. Metabolomics is the study of small-molecule metabolites that are by-products of cellular metabolism. As an emerging discipline for molecular profiling, metabolomics may increase understanding of human diseases and clinical risk because changes in metabolite levels provide a real-time estimate of disease state and reflect the integrated effects of genomic, transcriptomic, and proteomic variations. The primary goal of the Measurement to Understand the Reclassification of Disease of Cabarrus and Kannap- olis Cardiovascular Study (MURDOCK CV) is to assess the use of molecular profiles integrated with clinical data to form “clinomic” profiles for improved risk classification for clinical cardiovascular events. In this component of the MURDOCK CV study, we hypothesized that baseline metabolomic profiles would predict incident cardiovas- cular events in patients referred for evaluation of suspected coronary artery disease. From the a Division of Cardiovascular Medicine, Department of Medicine, Duke University Medical Center, Durham, NC, b Duke Center for Human Genetics, Duke University Medical Center, Durham, NC, c Duke Clinical Research Institute, Duke University Medical Center, Durham, NC, d Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, and e Duke Translational Medicine Institute, Duke University Medical Center, Durham, NC. f For the MURDOCK Horizon 1 Cardiovascular Disease Investigators. John K. French, MB, PhD, served as guest editor for this article. Submitted January 20, 2012; accepted February 8, 2012. Reprint requests: Svati H. Shah, MD, MHS, Duke University Medical Center, DUMC Box 3445, Durham, NC 27710. E-mail: svati.shah@duke.edu 0002-8703/$ - see front matter © 2012, Mosby, Inc. All rights reserved. doi:10.1016/j.ahj.2012.02.005