Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients.

Abstract

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.

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Citation

Published Version (Please cite this version)

10.1186/1755-8794-4-26

Publication Info

Elashoff, Michael R, James A Wingrove, Philip Beineke, Susan E Daniels, Whittemore G Tingley, Steven Rosenberg, Szilard Voros, William E Kraus, et al. (2011). Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients. BMC Med Genomics, 4. p. 26. 10.1186/1755-8794-4-26 Retrieved from https://hdl.handle.net/10161/13547.

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Scholars@Duke

Kraus

William Erle Kraus

Richard and Pat Johnson University Distinguished 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 practice as a preventive cardiologist with a focus on cardiometabolic risk and exercise physiology for older athletes.  My research space has both a basic wet laboratory component and a human integrative physiology one.

One focus of our work is an integrative physiologic examination of exercise effects in human subjects in clinical studies of exercise training in normal individuals, in individuals at risk of disease (such as pre-diabetes and metabolic syndrome; STRRIDE), and in individuals with disease (such as coronary heart disease, congestive heart failure and cancer).

A second focus of my research group is exploration of genetic determinates of disease risk in human subjects.  We conduct studies of early onset cardiovascular disease (GENECARD; CATHGEN), congestive heart failure (HF-ACTION), peripheral arterial disease (AMNESTI), and metabolic syndrome.  We are exploring analytic models of predicting disease risk using established and innovative statistical methodology.

A third focus of my group’s work is to understand the cellular signaling mechanisms underlying the normal adaptive responses of skeletal muscle to physiologic stimuli, such as occur in exercise conditioning, and to understand the abnormal maladaptive responses that occur in response to pathophysiologic stimuli, such as occur in congestive heart failure, aging and prolonged exposure to microgravity.

Recently we have begun to investigate interactions of genes and lifestyle interventions on cardiometabolic outcomes.  We have experience with clinical lifestyle intervention studies, particularly the contributions of genetic variants to interventions responses.  We call this Lifestyle Medicopharmacogenetics.

KEY WORDS:

exercise, skeletal muscle, energy metabolism, cell signaling, gene expression, cell stretch, heart failure, aging, spaceflight, human genetics, early onset cardiovascular disease, lifestyle medicine

Ginsburg

Geoffrey Steven Ginsburg

Adjunct Professor in the Department 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.


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