Alcoholism has a profound impact on millions of people throughout the world. However, the ability to determine if a patient needs treatment is hindered by reliance on self-reporting and the clinician’s capability to monitor the patient’s response to treatment is challenged by the lack of reliable biomarkers. Using a genome-wide approach, we have previously shown that chronic alcohol use is associated with methylation changes in DNA from human cell lines. In this pilot study, we now examine DNA methylation in peripheral mononuclear cell DNA gathered from subjects as they enter and leave short-term alcohol treatment. When compared with abstinent controls, subjects with heavy alcohol use show widespread changes in DNA methylation that have a tendency to reverse with abstinence. Pathway analysis demonstrates that these changes map to gene networks involved in apoptosis. There is no significant overlap of the alcohol signature with the methylation signature previously derived for smoking. We conclude that DNA methylation may have future clinical utility in assessing acute alcohol use status and monitoring treatment response.

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.

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