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A Next-Generation Artificial Intelligence-Based Integrated Genetic-Epigenetic Prediction of 5-Year Risk for Coronary Heart Disease

A Next-Generation Artificial Intelligence-Based Integrated Genetic-Epigenetic Prediction of 5-Year Risk for Coronary Heart Disease

Author(s): Meesha Dogan, Cardio Diagnostics, Coralville IA; Ron Simons, Steve Beach, University of Georgia, Athens GA; Amaury Lendasse, University of Houston, Houston TX; Rob Philibert, Cardio Diagnostics, Coralville IA.

Presented: American Heart Association Scientific Sessions 2018

Coronary heart disease (CHD) is a leading cause of death in the US. CHD associated morbidity and mortality can be reduced if those at risk can be identified well in advance, to put in place prevention interventions prior to an acute coronary event. Limitations of current approaches call for additional efforts to improve risk predictions. We recently developed a Precision Medicine tool capable of predicting 5-year risk, that can also guide personalized prevention interventions and monitor changes in risk over time. It was developed using an application of AI, machine learning, in the Framingham Heart Study to mine integrated genetic-epigenetic biosignatures that capture the complex interplay between our genome and the environment in conferring risk for CHD. The training and test sets consisted of 1180 (19/695 females and 23/485 males developed symptomatic CHD within 5-years) and 524 (8/293 females and 12/231 males developed symptomatic CHD within 5-years) individuals, respectively, of Northern European ancestry. Non-linear data mining and modeling techniques were employed on the training set to identify prediction signatures from genome-wide SNP and DNA methylation (DNAm) data. We identified 14 DNAm and 10 SNPs from the training set (training bootstrap average sensitivity and specificity of 0.61 and 0.71, respectively) that predicted the 5-year risk in the test set with a sensitivity and specificity of 0.60 and 0.69, respectively. Performance was compared to that of the Framingham Risk Score (FRS) and the ASCVD Risk Estimator. The sensitivity and specificity in the test set were 0.05 and 0.99 for FRS, and 0.38 and 0.85 for ASCVD. Even with a small number of incident cases, our tool performed with superior sensitivity, which is vital because, while a false positive would result in additional testing, a false negative can be detrimental. Unlike genetics, because DNAm is dynamic, it can be mapped to actionable risk factors to guide personalized interventions and over time, re-assess risk and continuously monitor heart health. We demonstrate translational feasibility of this tool via next-gen digital PCR assays. Additional efforts are ongoing to optimize, validate and extend our tool to further improve performance in larger, more ethnically diverse cohorts.

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