Abstract
An improved method for detecting coronary heart disease (CHD) could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated classifier was capable of classifying symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.42, respectively, but with a specificity of 0.89 in the test set. Regression analyses of the methylation signatures illustrate our ability to map these signatures to known risk factors in CHD pathogenesis. These results demonstrate the capability of an integrated approach to effectively model symptomatic CHD status. These results also suggest that future studies of biomaterial collected from longitudinally informative cohorts that are specifically characterized for cardiac disease at follow-up could lead to the introduction of sensitive, readily employable integrated genetic-epigenetic algorithms for predicting onset of future symptomatic CHD.
Highlights
Heart Disease is the leading cause of death in United States.[1]
A better understanding of the relationship of epigenetic changes to the pathogenesis of cardiovascular diseases is essential for the development of improved diagnostics and therapeutics
In our analysis to uncover epigenetic signatures associated with Coronary heart disease (CHD), cg26910465 from the ADAL gene was the most significantly differentially methylated with respect to CHD status
Summary
Heart Disease is the leading cause of death in United States.[1] Coronary heart disease (CHD) is the most common type of heart disease. Methods to identify those at risk for sudden death. Dogan are officers and stockholders of Behavioral Diagnostics LLC and Cardio Diagnostics LLC, respectively. Behavioral Diagnostics LLC owns a substantial portion of Cardio Diagnostics LLC. Behavioral Diagnostics LLC provided support in the form of salaries for authors RAP and MVD, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.