Metabolite profiling of blood from individuals

Technical advance

Metabolite profiling of blood from individuals undergoing planned myocardial infarction reveals early markers of myocardial injury Gregory D. Lewis,1,2,3,4 Ru Wei,4 Emerson Liu,1,2,3 Elaine Yang,4 Xu Shi,2 Maryann Martinovic,1 Laurie Farrell,1 Aarti Asnani,1,2,4 Marcoli Cyrille,2 Arvind Ramanathan,4 Oded Shaham,4,5 Gabriel Berriz,6 Patricia A. Lowry,1 Igor F. Palacios,1 Murat Taş an,6 Frederick P. Roth,6 Jiangyong Min,7 Christian Baumgartner,8 Hasmik Keshishian,4 Terri Addona,4 Vamsi K. Mootha,4,5 Anthony Rosenzweig,7 Steven A. Carr,4 Michael A. Fifer,1 Marc S. Sabatine,1,3,9 and Robert E. Gerszten1,2,3,4 1Cardiology

Division and 2Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA. W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston, Massachusetts, USA. 4Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. 5Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA. 6Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA. 7Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA. 8Research Group for Clinical Bioinformatics, University for Health Sciences, Medical Informatics and Technology (UMIT), Tirol, Austria. 9Cardiovascular Division, Brigham and Women’s Hospital, Boston, Massachusetts, USA. 3Donald

Emerging metabolomic tools have created the opportunity to establish metabolic signatures of myocardial injury. We applied a mass spectrometry–based metabolite profiling platform to 36 patients undergoing alcohol septal ablation treatment for hypertrophic obstructive cardiomyopathy, a human model of planned myocardial infarction (PMI). Serial blood samples were obtained before and at various intervals after PMI, with patients undergoing elective diagnostic coronary angiography and patients with spontaneous myocardial infarction (SMI) serving as negative and positive controls, respectively. We identified changes in circulating levels of metabolites participating in pyrimidine metabolism, the tricarboxylic acid cycle and its upstream contributors, and the pentose phosphate pathway. Alterations in levels of multiple metabolites were detected as early as 10 minutes after PMI in an initial derivation group and were validated in a second, independent group of PMI patients. A PMI-derived metabolic signature consisting of aconitic acid, hypoxanthine, trimethylamine N-oxide, and threonine differentiated patients with SMI from those undergoing diagnostic coronary angiography with high accuracy, and coronary sinus sampling distinguished cardiac-derived from peripheral metabolic changes. Our results identify a role for metabolic profiling in the early detection of myocardial injury and suggest that similar approaches may be used for detection or prediction of other disease states. Introduction Novel metabolomics technologies have enhanced the feasibility of acquiring high-throughput snapshots of a whole organism’s metabolic status. Metabolomics studies to date have focused principally on model organisms (1–4). However, the profiling of low–molecular weight biochemicals, including lipids, sugars, and amino acids, that serve as substrates and products in metabolic pathways is particularly relevant to human conditions such as myocardial ischemia (5). In addition to serving as disease biomarkers, circulating metabolites may themselves participate in previously unanticipated roles as regulatory signals with hormone-like functions. In this regard, a recent publication suggests that the tricarboxylic acid (TCA) cycle intermediate succinate, released from ischemic tissues, may activate a novel G protein–coupled receptor in the kidney to modulate blood pressure (6). Nonstandard abbreviations used: CK, creatine kinase; CK-MB, CK muscle and brain subunits; HOCM, hypertrophic obstructive cardiomyopathy; LC, liquid chromatography; MI, myocardial infarction; MS, mass spectrometry; PMI, planned MI; SMI, spontaneous MI; TCA, tricarboxylic acid; TMNO, trimethylamine N-oxide. Conflict of interest: While this manuscript was under review, the Massachusetts General Hospital filed for patents related to metabolic biomarkers of myocardial ischemia on behalf of R.E. Gerszten, M.A. Fifer, and M.S. Sabatine. Citation for this article: J. Clin. Invest. 118:3503–3512 (2008). doi:10.1172/JCI35111.

However, metabolomics techniques still suffer from signal-tonoise issues, and applications to humans have been limited by the profound degree of interindividual variability. Studies to identify novel disease-related pathways have been further restricted by the inherent unpredictability of the onset of pathological states. Human metabolomics studies have also been complicated by potentially confounding clinical variables such as diet or drug effects, particularly if NMR or mass spectrometry–based (MS-based) profiling techniques are used without unambiguously identifying metabolites associated with peaks of interest. Indeed, published findings of 1H-NMR spectral variation correlating with coronary artery disease severity (7) were later found to be confounded by cholesterol-lowering (i.e., HMG-CoA reductase inhibition) therapy (8). The analysis of samples from large patient cohorts, stratified by known risk factors or exposures, may minimize the impact of clinical confounding variables (8). However, the throughput of many technologies, particularly those that are MS based, currently precludes the analysis of large numbers of samples. To help circumvent these problems, we applied a targeted MS-based metabolomics platform, which provides high analyte specificity (9, 10), to serial blood samples obtained from carefully phenotyped patients undergoing alcohol septal ablation for hypertrophic obstructive cardiomyopathy (HOCM). This human

The Journal of Clinical Investigation http://www.jci.org Volume 118 Number 10 October 2008

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technical advance Table 1 Baseline clinical characteristics of study cohorts

PMI derivation (n = 20)

PMI validation (n = 16)

PMI control (n = 16)

SMI (n = 12)

SMI control (n = 9)

Age (yr) Male (%) White (%) Creatinine baseline (mg/dl) Peak troponin T (ng/ml) Peak CK (U/l) Peak CK-MB (ng/ml) Total cholesterol (mg/dl) Aspirin exposure (%) Beta-blocker exposure (%) Calcium channel blocker exposure (%) Statin exposure (%) Unfractionated heparin exposure (%)

63 ± 14 40 90 0.9 ± 0.2 5.0 ± 3.0 1,149 ± 509 187 ± 98 167 ± 26 100 90 40 50 100

60 ± 15 50 94 1.0 ± 0.2 4.0 ± 2.0 1,296 ± 1,328 217 ± 102 172 ± 48 100 70 30 44 100

63 ± 13 63 83 1.1 ± 0.3 0.01) between the same time points in the control cohort of patients undergoing diagnostic catheterization without MI, were selected as candidate early biomarkers for testing in the PMI validation cohort that consisted of 16 patients. Criteria for validation was P < 0.05 by Wilcoxon signedrank test with the direction of change concordant with that observed in the derivation cohort. The relationship between change in metabolites in the derivation and validation cohorts was assessed with a Spearman correlation coefficient. Data in all tables indicate median percent change from baseline and interquartile ranges. Further analysis was carried out in the subgroup of 13 PMI patients with matched coronary sinus and peripheral samples. Metabolites were considered to be enriched in the coronary sinus if they changed significantly in the coronary sinus at 10 or 60 minutes compared with baseline (P < 0.05, Wilcoxon signed-rank test) and changed to a greater extent in the coronary sinus than in the periphery (median change in coronary sinus at least 1.3-fold that in the periphery; P < 0.05, Wilcoxon signed-rank test). To evaluate whether metabolic changes observed in the PMI patients were generalizable to SMI, we selected all of those metabolites that displayed significant changes from baseline at 1, 2, and 4 hours in the derivation and validation PMI cohorts (P < 0.05 at all 3 time points). A Wilcoxon rank-sum test was used to examine levels of these individual metabolites in the patients presenting with SMI compared with control patients presenting to the cardiac catheterization suite with nonacute cardiovascular dis-


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technical advance ease. These metabolites were also compiled into a composite MS intensity unit score for SMI and control patients. To ensure equal weighting of each metabolite in this composite score, the intensity values of each metabolite were rescaled to have a common median intensity of 1.0 × 106 arbitrary units. The composite score was defined as the sum of metabolites that increased in PMI minus the sum of metabolites that decreased in PMI.

Acknowledgments The authors gratefully acknowledge support from the NIH (grants R01 HL072872 to R.E. Gerszten and M.S. Sabatine; U01HL083141 to R.E. Gerszten, M.S. Sabatine, and F.P. Roth; and R01DK081572 to R.E. Gerszten), the Donald W. Reynolds Foundation (to R.E. Gerszten and M.S. Sabatine), Fondation Leducq (to A. Rosenzweig and R.E. Gerszten), Heart Failure Society of America (to G.D. Lewis), an American Heart Association Fellow-to-Faculty Award (to G.D. Lewis), the Harvard/MIT Clinical Investigator Training Pro 1. Nicholson, J.K., and Wilson, I.D. 2003. Opinion: understanding ‘global’ systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov. 2:668–676. 2. Raamsdonk, L.M., et al. 2001. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19:45–50. 3. Allen, J., et al. 2003. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21:692–696. 4. An, J., et al. 2004. Hepatic expression of malonylCoA decarboxylase reverses muscle, liver and wholeanimal insulin resistance. Nat. Med. 10:268–274. 5. Beecher, C.W.W. 2003. Chap. 17 in The human metabolome. Kluwer Academic Publishers. Boston, Massachusetts, USA. 311–319. 6. He, W., et al. 2004. Citric acid cycle intermediates as ligands for orphan G-protein-coupled receptors. Nature. 429:188–193. 7. Brindle, J.T., et al. 2002. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat. Med. 8:1439–1444. 8. Kirschenlohr, H.L., et al. 2006. Proton NMR analysis of plasma is a weak predictor of coronary artery disease. Nat. Med. 12:705–710. 9. Lee, M.S., and Kerns, E.H. 1999. LC/MS applications in drug development. Mass. Spectrom. Rev. 18:187–279. 10. Sabatine, M.S., et al. 2005. Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation. 112:3868–3875. 11. Sigwart, U. 1995. Non-surgical myocardial reduction for hypertrophic obstructive cardiomyopathy. Lancet. 346:211–214. 12. Knight, C., et al. 1997. Nonsurgical septal reduction for hypertrophic obstructive cardiomyopathy: outcome in the first series of patients. Circulation. 95:2075–2081. 13. Lakkis, N.M., et al. 1998. Echocardiography-guided ethanol septal reduction for hypertrophic obstructive cardiomyopathy. Circulation. 98:1750–1755. 14. Lakkis, N.M., Nagueh, S.F., Dunn, J.K., Killip, D., and Spencer, W.H., 3rd. 2000. Nonsurgical septal reduction therapy for hypertrophic obstructive cardiomyopathy: one-year follow-up. J. Am. Coll. Cardiol. 36:852–855. 15. Yoerger, D.M., et al. 2006. Time course of pressure gradient response after first alcohol septal ablation for obstructive hypertrophic cardiomyopathy. Am. J. Cardiol. 97:1511–1514. 16. Zimmerman, J., et al. 1999. Diagnostic marker cooperative study for the diagnosis of myocardial infarction. Circulation. 99:1671–1677. 17. Ransohoff, D.F. 2005. Bias as a threat to the valid-

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gram (to G.D. Lewis), and the Broad Institute Scientific Planning and Allocation of Resources Committee to establish the metabolomics platform. A. Asnani was supported by a predoctoral award from the Sarnoff Cardiovascular Research Foundation. F.P. Roth was also supported in part by National Human Genome Research Institute, NIH, grants HG003224, HG0017115, NS054052, and HG004233 and by the Keck Foundation. Received for publication January 22, 2008, and accepted in revised form July 9, 2008. Address correspondence to: Robert E. Gerszten, Cardiology Division and Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Room 8307, 149 13th Street, Charlestown, Massachusetts 02129, USA. Phone: (617) 724-8322; Fax: (617) 726-1544; E-mail: [email protected].

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