Peripheral blood gene expression signatures which reflect smoking and aspirin exposure are associated with cardiovascular events.

BACKGROUND: Cardiovascular disease and its sequelae are major causes of global mortality, and better methods are needed to identify patients at risk for future cardiovascular events. Gene expression analysis can inform on the molecular underpinnings of risk factors for cardiovascular events. Smoking and aspirin have known opposing effects on platelet reactivity and MACE, however their effects on each other and on MACE are not well described. METHODS: We measured peripheral blood gene expression levels of ITGA2B, which is upregulated by aspirin and correlates with platelet reactivity on aspirin, and a 5 gene validated smoking gene expression score (sGES) where higher expression correlates with smoking status, in participants from the previously reported PREDICT trial (NCT 00500617). The primary outcome was a composite of death, myocardial infarction, and stroke/TIA (MACE). We tested whether selected genes were associated with MACE risk using logistic regression. RESULTS: Gene expression levels were determined in 1581 subjects (50.5% female, mean age 60.66 +/-11.46, 18% self-reported smokers); 3.5% of subjects experienced MACE over 12 months follow-up. Elevated sGES and ITGA2B expression were each associated with MACE (odds ratios [OR] =1.16 [95% CI 1.10-1.31] and 1.42 [95% CI 1.00-1.97], respectively; p < 0.05). ITGA2B expression was inversely associated with self-reported smoking status and the sGES (p < 0.001). A logistic regression model combining sGES and ITGA2B showed better performance (AIC = 474.9) in classifying MACE subjects than either alone (AIC = 479.1, 478.2 respectively). CONCLUSION: Gene expression levels associated with smoking and aspirin are independently predictive of an increased risk of cardiovascular events.

Rheumatoid arthritis complicates noninvasive whole blood gene expression testing for coronary artery disease.

BACKGROUND: Our objective was to evaluate an age- and sex-specific gene expression score (ASGES) previously validated to detect obstructive coronary artery disease (CAD) in patients with rheumatoid arthritis (RA). METHODS: We evaluated 20 pairs of nondiabetic coronary patients with and without RA, selected by matching on age, sex, race, body mass index, tobacco use, and number of diseased coronary vessels. Peripheral blood gene expression levels of 23 CAD-associated genes were measured, and a previously validated CAD risk score including age, sex, and gene expression levels (Corus CAD) was computed. Linear regression was used to determine effects of both CAD and RA on the ASGES. RESULTS: Among patients with RA, the ASGES was not associated with CAD. The ASGES was elevated in patients with RA (P<.04) when compared with matched controls. The presence of RA was associated with significantly altered expression for 6 of the 23 genes (P<.05 for all, not adjusted for multiple comparisons): S100 calcium binding protein A12, interleukin-18 receptor accessory protein, caspase 5, S100 calcium binding protein A8, aquaporin 9, and cluster of differentiation 79b. CONCLUSIONS: Across a range of coronary artery disease severity, RA was associated with altered expression of CAD-associated genes. Notably, 2 of these genes, S100 calcium binding protein A8 and A12, are associated with neutrophil activation and are under investigation as therapeutic targets for both RA and CAD. These findings highlight common pathogenic mechanisms for RA and CAD and validate the prior exclusion of RA patients from ASGES-based evaluation of CAD likelihood.

An age- and sex-specific gene expression score is associated with revascularization and coronary artery disease: Insights from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial.

BACKGROUND: Identifying predictors of coronary artery disease (CAD)-related procedures and events remains a priority. METHODS: We measured an age- and sex-specific gene expression score (ASGES) previously validated to detect obstructive CAD (oCAD) in symptomatic nondiabetic patients in the PROMISE trial. The outcomes were oCAD (≥70% stenosis in ≥1 vessel or ≥50% left main stenosis on CT angiography [CTA]) and a composite endpoint of death, myocardial infarction, revascularization, or unstable angina. RESULTS: The ASGES was determined in 2370 nondiabetic participants (47.5% male, median age 59.5 years, median follow-up 25 months), including 1137 with CTA data. An ASGES >15 was associated with oCAD (odds ratio 2.5 [95% CI 1.6-3.8], P<.001) and the composite endpoint (hazard ratio [HR] 2.6 [95% CI 1.8-3.9], P<.001) in unadjusted analyses. After adjustment for Framingham risk, an ASGES >15 remained associated with the composite endpoint (P=.02); the only component that was associated was revascularization (adjusted HR 2.69 [95% CI 1.52-4.79], P<.001). Compared to noninvasive testing, the ASGES improved prediction for the composite (increase in c-statistic=0.036; continuous net reclassification index=43.2%). Patients with an ASGES ≤15 had a composite endpoint rate no different from those with negative noninvasive test results (3.2% vs. 2.6%, P=.29). CONCLUSIONS: A blood-based genomic test for detecting oCAD significantly predicts near-term revascularization procedures, but not non-revascularization events. Larger studies will be needed to clarify the risk with non-revascularization events.

Developing Peripheral Blood Gene Expression-Based Diagnostic Tests for Coronary Artery Disease: a Review.

The past 20 years has witnessed the development of technologies designed to measure changes in the expressed human genome, including the levels of RNA transcripts, proteins, and metabolites. Gene expression profiling, or the measurement of RNA transcripts, allows investigators to obtain a snapshot of a subject's current physiological state and may be used to assess disease likelihood. In this review, we provide an overview of recent work using peripheral blood gene expression to assess coronary artery disease (CAD) and discuss the best approaches for developing and validating tests utilizing such gene expression signatures.

Evaluation of a solid matrix for collection and ambient storage of RNA from whole blood.

BACKGROUND: Whole blood gene expression-based molecular diagnostic tests are becoming increasingly available. Conventional tube-based methods for obtaining RNA from whole blood can be limited by phlebotomy, volume requirements, and RNA stability during transport and storage. A dried blood spot matrix for collecting high-quality RNA, called RNA Stabilizing Matrix (RSM), was evaluated against PAXgene® blood collection tubes. METHODS: Whole blood was collected from 25 individuals and subjected to 3 sample storage conditions: 18 hours at either room temperature (baseline arm) or 37°C, and 6 days at room temperature. RNA was extracted and assessed for integrity by Agilent Bioanalyzer, and gene expression was compared by RT-qPCR across 23 mRNAs comprising a clinical test for obstructive coronary artery disease. RESULTS: RSM produced RNA of relatively high integrity across the various tested conditions (mean RIN ± 95% CI: baseline arm, 6.92 ± 0.24; 37°C arm, 5.98 ± 0.48; 6-day arm, 6.72 ± 0.23). PAXgene samples showed comparable RNA integrity in both baseline and 37°C arms (8.42 ± 0.17; 7.92 ± 0.1 respectively) however significant degradation was observed in the 6-day arm (3.19 ± 1.32). Gene expression scores on RSM were highly correlated between the baseline and 37°C and 6-day study arms (median r = 0.96, 0.95 respectively), as was the correlation to PAXgene tubes (median r = 0.95, p < 0.001). CONCLUSION: RNA obtained from RSM shows little degradation and comparable RT-qPCR performance to PAXgene RNA for the 23 genes analyzed. Further development of this technology may provide a convenient method for collecting, shipping, and storing RNA for gene expression assays.

A peripheral blood gene expression score is associated with atherosclerotic Plaque Burden and Stenosis by cardiovascular CT-angiography: results from the PREDICT and COMPASS studies.

OBJECTIVE: We previously validated a gene expression score (GES) based on age, sex and peripheral blood cell expression levels of 23 genes measured by quantitative real-time PCR (qRT-PCR) for diagnosis of obstructive coronary artery disease (CAD) (≥ 50% luminal diameter stenosis). In this study we sought to determine the association between the GES and coronary arterial Plaque Burden and Stenosis by CT-angiography. METHODS: A total of 610 patients (mean age: 57 ± 11; 50% male) from the PREDICT and COMPASS studies from 59 centers were analyzed. Coronary artery calcium (CAC) scoring, CT angiography (CTA)-based plaque and stenosis and GES measurements were performed. CAC was expressed as Agatston score and CTA evaluated for stenosis severity: 0. None; 1. Minimal, 2. Mild, 3. Moderate, 4. Severe and 5. Occluded. Correlation analysis, one-way analysis of variance (ANOVA) and receiver operating characteristics (ROC) analyses were performed. RESULTS: GES was significantly associated with plaque burden by CAC (r = 0.50; p < 0.001) and CTA (segment involvement score index: r = 0.37, p < 0.001); a low score (≤ 15) had sensitivity of 0.71 and a high score (≥ 28) a specificity of 0.97 for the prediction of zero vs. non-zero CAC. Increasing GES was associated with a greater degree of categorical stenosis by ANOVA (p < 0.001); GES significantly correlated with maximum luminal stenosis (r = 0.41; p < 0.01) and segment stenosis score index (r = 0.38; p < 0.01). A low score had sensitivity of 0.90 and a high score a specificity of 0.87 for ≥ 70% stenosis. CONCLUSIONS: A previously validated GES is significantly associated with Plaque Burden and Stenosis by CT. CLINICAL TRIAL REGISTRATION: (PREDICT [NCT00500617] and COMPASS [NCT01117506]), www.clinicaltrials.gov.

A whole blood gene expression-based signature for smoking status.

BACKGROUND: Smoking is the leading cause of preventable death worldwide and has been shown to increase the risk of multiple diseases including coronary artery disease (CAD). We sought to identify genes whose levels of expression in whole blood correlate with self-reported smoking status. METHODS: Microarrays were used to identify gene expression changes in whole blood which correlated with self-reported smoking status; a set of significant genes from the microarray analysis were validated by qRT-PCR in an independent set of subjects. Stepwise forward logistic regression was performed using the qRT-PCR data to create a predictive model whose performance was validated in an independent set of subjects and compared to cotinine, a nicotine metabolite. RESULTS: Microarray analysis of whole blood RNA from 209 PREDICT subjects (41 current smokers, 4 quit ≤ 2 months, 64 quit > 2 months, 100 never smoked; NCT00500617) identified 4214 genes significantly correlated with self-reported smoking status. qRT-PCR was performed on 1,071 PREDICT subjects across 256 microarray genes significantly correlated with smoking or CAD. A five gene (CLDND1, LRRN3, MUC1, GOPC, LEF1) predictive model, derived from the qRT-PCR data using stepwise forward logistic regression, had a cross-validated mean AUC of 0.93 (sensitivity=0.78; specificity=0.95), and was validated using 180 independent PREDICT subjects (AUC=0.82, CI 0.69-0.94; sensitivity=0.63; specificity=0.94). Plasma from the 180 validation subjects was used to assess levels of cotinine; a model using a threshold of 10 ng/ml cotinine resulted in an AUC of 0.89 (CI 0.81-0.97; sensitivity=0.81; specificity=0.97; kappa with expression model = 0.53). CONCLUSION: We have constructed and validated a whole blood gene expression score for the evaluation of smoking status, demonstrating that clinical and environmental factors contributing to cardiovascular disease risk can be assessed by gene expression.

Identification of factors contributing to variability in a blood-based gene expression test.

BACKGROUND: Corus CAD is a clinically validated test based on age, sex, and expression levels of 23 genes in whole blood that provides a score (1-40 points) proportional to the likelihood of obstructive coronary disease. Clinical laboratory process variability was examined using whole blood controls across a 24 month period: Intra-batch variability was assessed using sample replicates; inter-batch variability examined as a function of laboratory personnel, equipment, and reagent lots. METHODS/RESULTS: To assess intra-batch variability, five batches of 132 whole blood controls were processed; inter-batch variability was estimated using 895 whole blood control samples. ANOVA was used to examine inter-batch variability at 4 process steps: RNA extraction, cDNA synthesis, cDNA addition to assay plates, and qRT-PCR. Operator, machine, and reagent lots were assessed as variables for all stages if possible, for a total of 11 variables. Intra- and inter-batch variations were estimated to be 0.092 and 0.059 Cp units respectively (SD); total laboratory variation was estimated to be 0.11 Cp units (SD). In a regression model including all 11 laboratory variables, assay plate lot and cDNA kit lot contributed the most to variability (p = 0.045; 0.009 respectively). Overall, reagent lots for RNA extraction, cDNA synthesis, and qRT-PCR contributed the most to inter-batch variance (52.3%), followed by operators and machines (18.9% and 9.2% respectively), leaving 19.6% of the variance unexplained. CONCLUSION: Intra-batch variability inherent to the PCR process contributed the most to the overall variability in the study while reagent lot showed the largest contribution to inter-batch variability.

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

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.

Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients.

BACKGROUND: Diagnosing obstructive coronary artery disease (CAD) in at-risk patients can be challenging and typically requires both noninvasive imaging methods and coronary angiography, the gold standard. Previous studies have suggested that peripheral blood gene expression can indicate the presence of CAD. OBJECTIVE: To validate a previously developed 23-gene, expression-based classification test for diagnosis of obstructive CAD in nondiabetic patients. DESIGN: Multicenter prospective trial with blood samples obtained before coronary angiography. (ClinicalTrials.gov registration number: NCT00500617) SETTING: 39 centers in the United States. PATIENTS: An independent validation cohort of 526 nondiabetic patients with a clinical indication for coronary angiography. MEASUREMENTS: Receiver-operating characteristic (ROC) analysis of classifier score measured by real-time polymerase chain reaction, additivity to clinical factors, and reclassification of patient disease likelihood versus disease status defined by quantitative coronary angiography. Obstructive CAD was defined as 50% or greater stenosis in 1 or more major coronary arteries by quantitative coronary angiography. RESULTS: The area under the ROC curve (AUC) was 0.70 ± 0.02 (P < 0.001); the test added to clinical variables (Diamond-Forrester method) (AUC, 0.72 with the test vs. 0.66 without; P = 0.003) and added somewhat to an expanded clinical model (AUC, 0.745 with the test vs. 0.732 without; P = 0.089). The test improved net reclassification over both the Diamond-Forrester method and the expanded clinical model (P < 0.001). At a score threshold that corresponded to a 20% likelihood of obstructive CAD (14.75), the sensitivity and specificity were 85% and 43% (yielding a negative predictive value of 83% and a positive predictive value of 46%), with 33% of patient scores below this threshold. LIMITATION: Patients with chronic inflammatory disorders, elevated levels of leukocytes or cardiac protein markers, or diabetes were excluded. CONCLUSION: A noninvasive whole-blood test based on gene expression and demographic characteristics may be useful for assessing obstructive CAD in nondiabetic patients without known CAD. PRIMARY FUNDING SOURCE: CardioDx.

Lack of association between adrenergic receptor genotypes and survival in heart failure patients treated with carvedilol or metoprolol.

OBJECTIVES: This study investigated the role of adrenergic receptor genetics on transplant-free survival in heart failure (HF). BACKGROUND: Discordant results exist for genetic associations between adrenergic receptor alleles and end points of beta-blocker response in HF patients. METHODS: We identified 637 patients enrolled in 2 U.S. cardiovascular genetic registries with HF and left ventricular systolic dysfunction who were discharged on beta-blocker, angiotensin-converting enzyme inhibitor (ACEI) or angiotensin II receptor blocker (ARB), and diuretic medications. End points were determined through the national Social Security Death Master File and transplant records. We genotyped 5 polymorphisms in 3 genes: ADRB1 (S49G, R389G), ADRB2 (G16R, Q27E), and ADRA2C (Del322-325) using 5' nuclease assays and performed a multivariable clinical-genetic analysis. RESULTS: A total of 190 events (29.8%) occurred over a median follow-up of 1,070 days. Multivariable analysis showed a significant effect of 4 clinical factors on survival: age (p = 0.006), gender (p = 0.005), ejection fraction (p = 0.0002), and hemoglobin (p = 0.00010). There was no significant effect of the polymorphisms or haplotypes analyzed on survival. CONCLUSIONS: Genotypes and haplotypes of ADRB1, ADRB2, and ADRA2C did not significantly affect survival in metoprolol-treated or carvedilol-treated HF patients in this study. These results complement the findings of 2 similarly designed previous studies, but do not replicate an association of ADRB2 haplotypes and survival. All 3 studies differ from a survival benefit reported for bucindolol-treated homozygous ADRB1 R389 individuals. This may be attributable to a drug-specific interaction between genotype and outcome with bucindolol that does not seem to occur with metoprolol or carvedilol.

Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis.

BACKGROUND: The molecular pathophysiology of coronary artery disease (CAD) includes cytokine release and a localized inflammatory response within the vessel wall. The extent to which CAD and its severity is reflected by gene expression in circulating cells is unknown. METHODS AND RESULTS: From an initial coronary catheterization cohort we identified 41 patients, comprising 27 cases with angiographically significant CAD and 14 controls without coronary stenosis. Whole-genome microarray analysis performed on peripheral-blood mononuclear cells yielded 526 genes with >1.3-fold differential expression (P<0.05) between cases and controls. Real-time polymerase chain reaction on 106 genes (the 50 most significant microarray genes and 56 additional literature genes) in an independent subset of 95 patients (63 cases, 32 controls) from the same cohort yielded 14 genes (P<0.05) that independently discriminated CAD state in a multivariable analysis that included clinical and demographic factors. From an independent second catheterization cohort, 215 patients were selected for real-time polymerase chain reaction-based replication. A case:control subset of 107 patients (86 cases, 21 controls) replicated 11 of the 14 multivariably significant genes from the first cohort. An analysis of the 14 genes in the entire set of 215 patients demonstrated that gene expression was proportional to maximal coronary artery stenosis (P<0.001 by ANOVA). CONCLUSIONS: Gene expression in peripheral-blood cells reflects the presence and extent of CAD in patients undergoing angiography.