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

机译：基于机器学习的基于血液的生物标记物用于预测弗雷明汉心脏研究中五年突发性冠心病的风险

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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.
机译：一种用于预测发生冠心病（CHD）风险的改进方法可以大大改善心血管健康。以前，我们已经证明遗传和表观遗传位点比常规危险因素更能预测冠心病的状态。本文中，我们研究了是否可以使用类似的机器学习方法来开发用于预测事件性冠心病的类似面板。培训和测试集分别由1180和524个人组成。数据挖掘技术被用来挖掘训练集中的预测性生物特征。在训练集上训练了一组由四个遗传和四个后生基因座组成的随机森林模型，然后在测试集上进行了评估。测试灵敏度和特异性分别为0.70和0.74。相比之下，Framingham风险评分和动脉粥样硬化性心血管疾病（ASCVD）风险估算器的测试灵敏度分别为0.20和0.38。值得注意的是，综合遗传表观遗传模型对男女的风险预测都更好，并且在三年风险预测窗口中也很好。我们描述了一种新型的基于DNA的精密医学工具，该工具能够捕获导致CHD风险的复杂遗传和环境关系，并映射到可用于指导风险修正工作的可行风险因素。

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