Abstract 15920: A Whole Blood Gene-Expression Based Surrogate Test for Smoking Status
Background Smoking is the leading cause of preventable death, resulting in over 5 million deaths per year worldwide (~ 500,000 occurring in the United States) and has been shown to be detrimental to human health, increasing the risk of multiple diseases including coronary artery disease (CAD). We have identified a set of genes whose levels of expression in whole blood correlate with self-reported smoking status.
Methods Total whole blood RNA was isolated from 1074 patients enrolled in PREDICT, a clinical trial designed to identify markers associated with CAD from patients clinically referred for invasive angiography (NCT 00500617). Subjects were grouped into 4 self-reported smoking status categories: never smoked (n=476), former smoker (quit > 2 months; n=341), recent smoker (quit < 2 months; n=27), or current smoker (n=195). RT-qPCR was performed on 260 genes previously shown by microarray analysis to be correlated with smoking or smoking-related diseases such as CAD. A predictive model was built using stepwise forward logistic regression, with smoking status as the dependent variable and age, sex, and gene expression as the independent variables.
Results The final model contained five genes: CLDND1, LRRN3, MUC1, GOPC, LEF1. For predicting self-reported status, using 5000 iterations the model had a 10-fold cross validated mean AUC of 0.93. Using a cutoff probability of 0.5, the model had a fitted sensitivity of 0.78 and specificity of 0.95. The model was validated on 180 independent subjects, with an AUC of 0.82 (CI 0.69-0.94), sensitivity of 0.63 and specificity of 0.94. Serum from these subjects was used to assess levels of cotinine, a nicotine metabolite. Using the standard continine threshold of 10 ng/ml resulted in a AUC of 0.89 (CI 0.81-0.97), sensitivity of 0.81 and specificity 0.97.
Conclusion We have constructed a highly accurate whole blood gene expression test for the evaluation of smoking status. The test demonstrates that clinical and environmental factors contributing to cardiovascular disease risk can be assessed by gene expression surrogates.
- © 2011 by American Heart Association, Inc.