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

Abstract

BackgroundAlterations 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.ResultsMicroarray 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.ConclusionsWe 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 informationPREDICT, Personalized Risk Evaluation and Diagnosis in the Coronary Tree, http://www.clinicaltrials.gov, NCT00500617

Genetics paired with CT angiography in the setting of atherosclerosis

“…The PREDICT is a prospective, multicenter, observational study aimed to establish a diagnostic blood assay by real-time PCR to quantify the genes' expression of atherosclerotic CAD and stenosis measured by CTA [50,53]. Unlike other similar trails, in this study, Alexandra Lansky et al pooled high-throughput gene expression data to validate a previously developed novel gene expression evaluation system-GES [54] to predict the likelihood of obstructive CAD. Numerous validation studies include the association between GES and atherosclerotic plaque burden, as well as coronary luminal stenosis status (blockages or narrowing) also be performed in nearly the same period.…”

Markers of Carotid Atherosclerosis in Patients with Type 2 Diabetes Mellitus

“…Candidate gene approach is hypothesis-driven approach, whereas in GWAS approach appropriate subset of patients (i.e. patients with coronary artery disease, myocardial infarction, T2DM, bronchial asthma, Crohn disease and so forth) are genetically analyzed and distribution of genotypes/alleles compared with appropriate control subjects [4][5][6][7][8].…”

“…Genetic variability in the candidate genes implicated in atherosclerosis may alter their transcriptional activity and contribute to susceptibility to cardiovascular disease [3][4][5][6][7][8][9][10][11]. In the pathogenesis of atherosclerosis genetic, environmental, and epigenetic factors are involved [4][5].…”

“…Genetic and epigenetic factors may be studied with two main approaches, candidate gene approach and genome wide association study (GWAS) approach [4][5][6][7][8][9]11]. Candidate gene approach is hypothesis-driven approach, whereas in GWAS approach appropriate subset of patients (i.e.…”

Diagnosis of coronary heart diseases using gene expression profiling; stable coronary artery disease, cardiac ischemia with and without myocardial necrosis

“…A wide-scale gene expression profiling identified fifty six divergent genes for atherosclerotic and non-atherosclerotic human coronary arteries, wherein 49 were never associated with CAD before (Archacki et al 2003). Elashoff and co-authors identified a set of classifying genes which with the information of age and sex were strongly correlated to obstructive CAD in non-diabetic patients (Elashoff et al 2011). The divergent gene expressions were identified which discriminated ischemic and non-ischemic cardiomyopathies conditions among the end-stage patients (Kittleson et al 2004; Kittleson et al 2005).…”