Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients.
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BACKGROUND: Alterations in gene expression in peripheral blood cells have been shown to be sensitive to the presence and extent of coronary artery disease (CAD). A non-invasive blood test that could reliably assess obstructive CAD likelihood would have diagnostic utility. RESULTS: Microarray analysis of RNA samples from a 195 patient Duke CATHGEN registry case:control cohort yielded 2,438 genes with significant CAD association (p < 0.05), and identified the clinical/demographic factors with the largest effects on gene expression as age, sex, and diabetic status. RT-PCR analysis of 88 CAD classifier genes confirmed that diabetic status was the largest clinical factor affecting CAD associated gene expression changes. A second microarray cohort analysis limited to non-diabetics from the multi-center PREDICT study (198 patients; 99 case: control pairs matched for age and sex) evaluated gene expression, clinical, and cell population predictors of CAD and yielded 5,935 CAD genes (p < 0.05) with an intersection of 655 genes with the CATHGEN results. Biological pathway (gene ontology and literature) and statistical analyses (hierarchical clustering and logistic regression) were used in combination to select 113 genes for RT-PCR analysis including CAD classifiers, cell-type specific markers, and normalization genes.RT-PCR analysis of these 113 genes in a PREDICT cohort of 640 non-diabetic subject samples was used for algorithm development. Gene expression correlations identified clusters of CAD classifier genes which were reduced to meta-genes using LASSO. The final classifier for assessment of obstructive CAD was derived by Ridge Regression and contained sex-specific age functions and 6 meta-gene terms, comprising 23 genes. This algorithm showed a cross-validated estimated AUC = 0.77 (95% CI 0.73-0.81) in ROC analysis. CONCLUSIONS: We have developed a whole blood classifier based on gene expression, age and sex for the assessment of obstructive CAD in non-diabetic patients from a combination of microarray and RT-PCR data derived from studies of patients clinically indicated for invasive angiography. CLINICAL TRIAL REGISTRATION INFORMATION: PREDICT, Personalized Risk Evaluation and Diagnosis in the Coronary Tree, http://www.clinicaltrials.gov, NCT00500617.
Coronary Artery Disease
Gene Expression Regulation
Reverse Transcriptase Polymerase Chain Reaction
Published Version (Please cite this version)10.1186/1755-8794-4-26
Publication InfoElashoff, MR; Wingrove, JA; Beineke, P; Daniels, SE; Tingley, WG; Rosenberg, S; ... Topol, E (2011). Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients. BMC Med Genomics, 4. pp. 26. 10.1186/1755-8794-4-26. Retrieved from https://hdl.handle.net/10161/13547.
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Professor of Medicine
Dr. Geoffrey S. Ginsburg's research interests are in the development of novel paradigms for developing and translating genomic information into medical practice and the integration of personalized medicine into health care.
Richard and Pat Johnson University Professor
My training, expertise and research interests range from human integrative physiology and genetics to animal exercise models to cell culture models of skeletal muscle adaptation to mechanical stretch. I am trained clinically as an internist and preventive cardiologist, with particular expertise in preventive cardiology and cardiac rehabilitation. My research training spans molecular biology and cell culture, molecular genetics, and integrative human exercise physiology and metabolism. I pr
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