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(Circulation. 2004;110:3444-3451.)
© 2004 American Heart Association, Inc.

Heart Failure

Identification of a Gene Expression Profile That Differentiates Between Ischemic and Nonischemic Cardiomyopathy

Michelle M. Kittleson, MD; Shui Q. Ye, MD, PhD; Rafael A. Irizarry, PhD; Khalid M. Minhas, MD; Gina Edness, RN; John V. Conte, MD; Giovanni Parmigiani, PhD; Leslie W. Miller, MD; Yingjie Chen, MD, PhD; Jennifer L. Hall, PhD; Joe G.N. Garcia, MD; Joshua M. Hare, MD

From Departments of Medicine, Division of Cardiology (M.M.K., K.M.M., G.E., J.M.H.) and Pulmonary and Critical Care Medicine (S.Q.Y., J.G.N.G.), Department of Surgery, Cardiothoracic Division (J.V.C.), Johns Hopkins University School of Medicine and the HOPGENE Applied Genomics in Cardiopulmonary Disease (M.M.K., S.Q.Y., K.M.M., G.E., J.G.N.G., J.M.H.), Baltimore, Md; Department of Biostatistics (R.A.I., G.P.), Johns Hopkins University School of Public Health, Baltimore, Md; and Department of Medicine (L.W.M., Y.C., J.L.H.), University of Minnesota, Minneapolis, Minn.

Correspondence to Joshua M. Hare, MD, Ross 1059, 720 Rutland Ave, Baltimore, MD 21205. E-mail jhare{at}mail.jhmi.edu

Received June 1, 2004; revision received September 15, 2004; accepted September 29, 2004.

Background— Gene expression profiling refines diagnostic and prognostic assessment in oncology but has not yet been applied to myocardial diseases. We hypothesized that gene expression differentiates ischemic and nonischemic cardiomyopathy, demonstrating that gene expression profiling by clinical parameters is feasible in cardiology.

Methods and Results— Affymetrix U133A microarrays of 48 myocardial samples from Johns Hopkins Hospital (JHH) and the University of Minnesota (UM) obtained (1) at transplantation or left ventricular assist device (LVAD) placement (end-stage; n=25), (2) after LVAD support (post-LVAD; n=16), and (3) from newly diagnosed patients (biopsy; n=7) were analyzed with prediction analysis of microarrays. A training set was used to develop the profile and test sets to validate the accuracy of the profile. An etiology prediction profile developed in end-stage JHH samples was tested in independent samples from both JHH and UM with 100% sensitivity and 100% specificity in end-stage samples and 33% sensitivity and 100% specificity in both post-LVAD and biopsy samples. The overall sensitivity was 89% (95% CI 75% to 100%), and specificity was 89% (95% CI 60% to 100%) over 210 random partitions of end-stage samples into training and test sets. Age, gender, and hemodynamic differences did not affect the profile’s accuracy in stratified analyses. Select gene expression was confirmed with quantitative polymerase chain reaction.

Conclusions— Gene expression profiling accurately predicts cardiomyopathy etiology, is generalizable to samples from separate institutions, is specific to disease stage, and is unaffected by differences in clinical characteristics. This strongly supports ongoing efforts to incorporate expression profiling-based biomarkers in determining prognosis and response to therapy in heart failure.

Key Words: heart failure • genetics • cardiomyopathy

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