Background: Regular smoking is associated with a wide variety of syndromes with prominent inflammatory components such as cancer, obesity and type 2 diabetes. Heavy regular smoking is also associated with changes in the DNA methylation of peripheral mononuclear cells. However, in younger smokers, inflammatory epigenetic findings are largely absent which suggests the inflammatory response(s) to smoking may be dose dependent. To help understand whether peripheral mononuclear cells have a role in mediating these responses in older smokers with higher cumulative smoke exposure, we examined genome-wide DNA methylation in a group of well characterized adult African American subjects informative for smoking, as well as serum C-reactive protein (CRP) and interleukin-6 receptor (IL6R) levels. In addition, complementary bioinformatic analyses were conducted to delineate possible pathways affected by long-term smoking.Results: Genome-wide DNA methylation analysis with respect to smoking status yielded 910 significant loci after Benjamini-Hochberg correction. In particular, two loci from theAHRRgene (cg05575921 and cg23576855) and one locus from theGPR15gene (cg19859270) were identified as highly significantly differentially methylated between smokers and non-smokers. The bioinformatic analyses showed that long-term chronic smoking is associated with altered promoter DNA methylation of genes coding for proteins mapping to critical sub-networks moderating inflammation, immune function, and coagulation.Conclusions: We conclude that chronic regular smoking is associated with changes in peripheral mononuclear cell methylation signature which perturb inflammatory and immune function pathways and may contribute to increased vulnerability for complex illnesses with inflammatory components.

An improved method for detecting coronary heart disease (CHD) could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated classifier was capable of classifying symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.42, respectively, but with a specificity of 0.89 in the test set. Regression analyses of the methylation signatures illustrate our ability to map these signatures to known risk factors in CHD pathogenesis. These results demonstrate the capability of an integrated approach to effectively model symptomatic CHD status. These results also suggest that future studies of biomaterial collected from longitudinally informative cohorts that are specifically characterized for cardiac disease at follow-up could lead to the introduction of sensitive, readily employable integrated genetic-epigenetic algorithms for predicting onset of future symptomatic CHD.

Substance abuse has an enormous impact on economic and quality of life measures throughout the world. In more developed countries, overutilization of the most common forms of substances of abuse, alcohol and tobacco, is addressed primarily through prevention of substance use initiation and secondarily through the treatment of those with substance abuse or dependence. In general, these therapeutic approaches to substance abuse are deemed effective. However, there is a broad consensus that the development of additional tools to aid diagnosis, prioritize treatment selection and monitor treatment response could have substantial impact on the effectiveness of both substance use prevention and treatment. The recent demonstrations by a number of groups that substance use exposure is associated with robust changes in DNA methylation signatures of peripheral blood cells suggests the possibility that methylation assessments of blood or saliva could find broad clinical applications. In this article, we review recent progress in epigenetic approaches to substance use assessment with a particular emphasis on smoking (and alcohol) related applications. In addition, we highlight areas, such as the epigenetics of psychostimulant, opioid and cannabis abuse, which are markedly understudied and could benefit from intensified collaborative efforts to define epigenetic biomarkers of abuse and dependence.

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.