A Rapid and Highly Sensitive Artificial Intelligence-Based DNA Test for 3-Year Risk for Incident Coronary Heart Disease

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Author(s): Meesha Dogan, Cardio Diagnostics, Coralville IA; Stacey Knight, Intermountain Healthcare, Salt Lake City, UT; Amaury Lendasse, University of Houston, Houston TX; Kirk Knowlton, Intermountain Healthcare, Salt Lake City, UT; Rob Philibert, Cardio Diagnostics, Coralville IA.

Presented: American Heart Association Scientific Sessions 2019

Coronary heart disease (CHD) associated morbidity and mortality are largely preventable. Primary prevention of CHD includes the estimation of risk using a risk estimator as a basis to recommend treatments to patients. Commonly utilized risk estimators such as the Framingham Risk Score (FRS) and the Pooled Cohort Equation (PCE) have several limitations. As an alternative, we have developed and externally validated a novel and simpler DNA-based integrated genetic-epigenetic 3-year risk estimator for incident CHD that is more sensitive for both men and women. This tool, which couples digital PCR-based DNA testing from blood or saliva with artificial intelligence can be performed in less than 12 hours. It was developed using DNA methylation (DNAm) and SNP data from the Framingham Heart Study (FHS) Offspring cohort (n=1172 in training set and n=512 in test set) and externally validated in an Intermountain (IM) cohort (n=80 in validation set and n=79 in test set). Data mining, feature selection, model development and model tuning were performed on the FHS training set and validated on the IM validation set. The final prediction model (ensemble of SVM, Random Forest and Logistic Regression), which consisted of 6 loci (3 DNAm and 3 SNPs) was tested on the FHS and IM test sets. The FRS and PCE risk calculators were implemented on all FHS and IM cohort data. Prediction results stratified by gender are shown below. The superior sensitivity of our tool in males and especially females will ensure that more individuals at risk for incident CHD are identified well in advance to allow better clinical decision-making on primary prevention strategies. Also, because DNAm signatures are dynamic and map to actionable risk factors, they may be leveraged to personalize interventions and monitor risk. A clinically implementable version of this tool has been developed as part of its translation into a Laboratory Developed Test, and is being extended to include more ethnically diverse cohorts.

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