https://orcid.org/0000-0001-8944-6145
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Abstract
Aim: The Framingham Risk Score (FRS) and atherosclerotic cardiovascular disease (ASCVD) Pooled Cohort Equation (PCE) for predicting risk for incident coronary heart disease (CHD) work poorly. To improve risk stratification for CHD, we developed a novel integrated genetic-epigenetic tool. Materials&methods: Using machine learning techniques and datasets from the Framingham Heart Study (FHS) and Intermountain Healthcare (IM), we developed and validated an integrated genetic-epigenetic model for predicting 3-year incident CHD. Results: Our approach was more sensitive than FRS and PCE and had high generalizability across cohorts. It performed with sensitivity/specificity of 79/75% in the FHS test set and 75/72% in the IM set. The sensitivity/specificity was 15/93% in FHS and 31/89% in IM for FRS, and sensitivity/specificity was 41/74% in FHS and 69/55% in IM for PCE. Conclusion: The use of our tool in a clinical setting could better identify patients at high risk for a heart attack.
Lay abstract
Current lipid-based methods for assessing risk for coronary heart disease (CHD) have limitations. Conceivably, incorporating epigenetic information into risk prediction algorithms may be beneficial, but underlying genetic variation obscures its effects on risk. In order to develop a better CHD risk assessment method, we used artificial intelligence to identify genome-wide genetic and epigenetic biomarkers from two independent datasets of subjects characterized for incident CHD. The resulting algorithm significantly outperformed the current assessment methods in independent test sets. We conclude that artificial intelligence-moderated genetic-epigenetic algorithms have considerable potential as clinical tools for assessing risk for CHD.
Financial & competing interests disclosure
This work was supported by National Institute of Health grants R01DA037648 (Philibert) and R44DA041014 (Philibert), and Cardio Diagnostics, Inc. On behalf of MV Dogan and R Philibert, the University of Iowa has filed intellectual property claims related to the integrated genetic-epigenetic technology described in this communication (US Patent application 62,455,416: Compositions and Methods for Detecting Predisposition to Cardiovascular Disease). On behalf of MV Dogan, R Philibert and TK Dogan, Cardio Diagnostics, Inc. has filed intellectual property claims related to the integrated genetic-epigenetic technology described in this communication (US Patent application 63,074,878: Methods and Compositions for Predicting Coronary Heart Disease). They are potential royalty recipients on these intellectual right claims. MV Dogan is the chief executive officer and stockholder of Cardio Diagnostics, Inc. R Philibert is the chief medical officer and stockholder of Cardio Diagnostics, Inc. TK Dogan is an employee and stockholder of Cardio Diagnostics, Inc. (www.cardiodiagnosticsinc.com). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The National Institutes of Health had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.
Ethical conduct of research
The procedures and protocols used for the analysis of the Framingham Heart Study data were approved by the University of Iowa Institutional Review Board (IRB# 201503802). The procedures and protocols used for the analyses of the Intermountain Healthcare materials were approved by the Intermountain Healthcare Institutional Review Board (IRB# 1024811). Informed consent was obtained from all participants involved in this study.