Blood-Based Biomarkers for Predicting the Risk for Five-Year Incident Coronary Heart Disease in the Framingham Heart Study Via Machine Learning

Author

Meeshanthini V. Dogan
Steven R.H. Beach
Ronald L. Simons
Amaury Lendasse, Missouri University of Science and TechnologyFollow
Brandan Penaluna
Robert A. Philibert

Abstract

An Improved Approach for Predicting the Risk for Incident Coronary Heart Disease (CHD) Could Lead to Substantial Improvements in Cardiovascular Health. Previously, We Have Shown that Genetic and Epigenetic Loci Could Predict CHD Status More Sensitively Than Conventional Risk Factors. Herein, We Examine Whether Similar Machine Learning Approaches Could Be Used to Develop a Similar Panel for Predicting Incident CHD. Training and Test Sets Consisted of 1180 and 524 Individuals, respectively. Data Mining Techniques Were Employed to Mine for Predictive Biosignatures in the Training Set. an Ensemble of Random Forest Models Consisting of Four Genetic and Four Epigenetic Loci Was Trained on the Training Set and Subsequently Evaluated on the Test Set. the Test Sensitivity and Specificity Were 0.70 and 0.74, Respectively. in Contrast, the Framingham Risk Score and Atherosclerotic Cardiovascular Disease (ASCVD) Risk Estimator Performed with Test Sensitivities of 0.20 and 0.38, Respectively. Notably, the Integrated Genetic-Epigenetic Model Predicted Risk Better for Both Genders and Very Well in the Three-Year Risk Prediction Window. We Describe a Novel DNA-Based Precision Medicine Tool Capable of Capturing the Complex Genetic and Environmental Relationships that Contribute to the Risk of CHD and Being Mapped to Actionable Risk Factors that May Be Leveraged to Guide Risk Modification Efforts.

Recommended Citation

M. V. Dogan et al., "Blood-Based Biomarkers for Predicting the Risk for Five-Year Incident Coronary Heart Disease in the Framingham Heart Study Via Machine Learning," Genes, vol. 9, no. 12, article no. 641, MDPI, Dec 2018.

The definitive version is available at https://doi.org/10.3390/genes9120641

Department(s)

Engineering Management and Systems Engineering

Publication Status

Open Access

Comments

National Institutes of Health, Grant R01DA037648

Keywords and Phrases

Biomarkers; Coronary heart disease; Epigenetics; Genetics; Machine learning; Risk factors; Risk prediction

International Standard Serial Number (ISSN)

2073-4425

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2024 The Authors, All rights reserved.

Creative Commons Licensing

This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Dec 2018