Open AccessExternal validation of integrated genetic-epigenetic biomarkers for predicting incident coronary heart disease
Author(s) -
Meeshanthini V. Dogan,
Stacey Knight,
Timur Dogan,
Kirk U. Knowlton,
Robert A. Philibert
Publication year - 2021
Publication title -
epigenomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.265
H-Index - 60
eISSN - 1750-1911
pISSN - 1750-192X
DOI - 10.2217/epi-2021-0123
Subject(s) - framingham risk score , epigenetics , framingham heart study , risk assessment , generalizability theory , disease , genome wide association study , bioinformatics , medicine , biology , machine learning , computer science , genetics , statistics , single nucleotide polymorphism , mathematics , genotype , gene , computer security
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.