The Use of Precision Epigenetic Methods for the Diagnosis and Care of Stable Coronary Heart Disease Reduces Healthcare Costs.

INTRODUCTION: The cost of secondary prevention of coronary heart disease (CHD) is continuing to increase, with a substantial portion of this acceleration being driven by the expense of confirmatory diagnostic testing. Conceivably, newly developed precision epigenetic technologies could drive down these costs. However, at the current time, their impact on overall expense for CHD care is poorly understood. We hypothesized that the use of a newly developed, highly sensitive, and specific epigenetic test, PrecisionCHD, could decrease the costs of secondary prevention. METHODS: To test this hypothesis, we constructed a budget impact analysis using a cost calculation model that examined the effects of substituting PrecisionCHD for conventional CHD diagnostic tests on the expenses of the initial evaluation and first year of care of stable CHD using a 1-year time horizon with no discounting. RESULTS: The model projected that for a commercial insurer with one million members, full adoption of PrecisionCHD as the primary method of initial CHD assessment would save approximately $113.6 million dollars in the initial year. CONCLUSION: These analyses support the use of precision epigenetic methods as part of the initial diagnosis and care of stable CHD and can meaningfully reduce cost. Real-world pilots to test the reliability of these analyses are indicated.

Validation of an Integrated Genetic-Epigenetic Test for the Assessment of Coronary Heart Disease.

BACKGROUND: Coronary heart disease (CHD) is the leading cause of death in the world. Unfortunately, many of the key diagnostic tools for CHD are insensitive, invasive, and costly; require significant specialized infrastructure investments; and do not provide information to guide postdiagnosis therapy. In prior work using data from the Framingham Heart Study, we provided in silico evidence that integrated genetic-epigenetic tools may provide a new avenue for assessing CHD. METHODS AND RESULTS: In this communication, we use an improved machine learning approach and data from 2 additional cohorts, totaling 449 cases and 2067 controls, to develop a better model for ascertaining symptomatic CHD. Using the DNA from the 2 new cohorts, we translate and validate the in silico findings into an artificial intelligence-guided, clinically implementable method that uses input from 6 methylation-sensitive digital polymerase chain reaction and 10 genotyping assays. Using this method, the overall average area under the curve, sensitivity, and specificity in the 3 test cohorts is 82%, 79%, and 76%, respectively. Analysis of targeted cytosine-phospho-guanine loci shows that they map to key risk pathways involved in atherosclerosis that suggest specific therapeutic approaches. CONCLUSIONS: We conclude that this scalable integrated genetic-epigenetic approach is useful for the diagnosis of symptomatic CHD, performs favorably as compared with many existing methods, and may provide personalized insight to CHD therapy. Furthermore, given the dynamic nature of DNA methylation and the ease of methylation-sensitive digital polymerase chain reaction methodologies, these findings may pave a pathway for precision epigenetic approaches for monitoring CHD treatment response.

The Reversion of the Epigenetic Signature of Coronary Heart Disease in Response to Smoking Cessation.

Coronary heart disease (CHD) is the leading cause of death worldwide. However, current diagnostic tools for CHD, such as coronary computed tomography angiography (CCTA), are poorly suited for monitoring treatment response. Recently, we have introduced an artificial-intelligence-guided integrated genetic-epigenetic test for CHD whose core consists of six assays that determine methylation in pathways known to moderate the pathogenesis of CHD. However, whether methylation at these six loci is sufficiently dynamic to guide CHD treatment response is unknown. To test that hypothesis, we examined the relationship of changes in these six loci to changes in cg05575921, a generally accepted marker of smoking intensity, using DNA from a cohort of 39 subjects undergoing a 90-day smoking cessation intervention and methylation-sensitive digital PCR (MSdPCR). We found that changes in epigenetic smoking intensity were significantly associated with reversion of the CHD-associated methylation signature at five of the six MSdPCR predictor sites: cg03725309, cg12586707, cg04988978, cg17901584, and cg21161138. We conclude that methylation-based approaches could be a scalable method for assessing the clinical effectiveness of CHD interventions, and that further studies to understand the responsiveness of these epigenetic measures to other forms of CHD treatment are in order.

External validation of integrated genetic-epigenetic biomarkers for predicting incident coronary heart disease.

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.

Lay abstract Current lipid-based methods for assessing risk for coronary heart disease (CHD) have limitations. Conceivably, incorporating epigenetic information into risk prediction algorithms may be beneficial, but underlying genetic variation obscures its effects on risk. In order to develop a better CHD risk assessment method, we used artificial intelligence to identify genome-wide genetic and epigenetic biomarkers from two independent datasets of subjects characterized for incident CHD. The resulting algorithm significantly outperformed the current assessment methods in independent test sets. We conclude that artificial intelligence-moderated genetic-epigenetic algorithms have considerable potential as clinical tools for assessing risk for CHD.

Cost-utility analysis of an integrated genetic/epigenetic test for assessing risk for coronary heart disease.

Aim: New epigenetically based methods for assessing risk for coronary heart disease may be more sensitive but are generally more costly than current methods. To understand their potential impact on healthcare spending, we conducted a cost-utility analysis. Methods: We compared costs using the new Epi + Gen CHD™ test with those of existing tests using a cohort Markov simulation model. Results: We found that use of the new test was associated with both better survival and highly competitive negative incremental cost-effectiveness ratios ranging from -$42,000 to -$8000 per quality-adjusted life year for models with and without a secondary test. Conclusion: The new integrated genetic/epigenetic test will save money and lives under most real-world scenarios. Similar advantages may be seen for other epigenetic tests.

A simple, rapid, interpretable, actionable and implementable digital PCR based mortality index.

Mortality assessments are conducted for both civil and commercial purposes. Recent advances in epigenetics have resulted in DNA methylation tools to assess risk and aid in this task. However, widely available array-based algorithms are not readily translatable into clinical tools and do not provide a good foundation for clinical recommendations. Further, recent work shows evidence of heritability and possible racial bias in these indices. Using a publicly available array data set, the Framingham Heart Study (FHS), we develop and test a five-locus mortality-risk algorithm using only previously validated methylation biomarkers that have been shown to be free of racial bias, and that provide specific assessments of smoking, alcohol consumption, diabetes and heart disease. We show that a model using age, sex and methylation measurements at these five loci outperforms the 513 probe Levine index and approximates the predictive power of the 1030 probe GrimAge index. We then show each of the five loci in our algorithm can be assessed using a more powerful, reference-free digital PCR approach, further demonstrating that it is readily clinically translatable. Finally, we show the loci do not reflect ethnically specific variation. We conclude that this algorithm is a simple, yet powerful tool for assessing mortality risk. We further suggest that the output from this or similarly derived algorithms using either array or digital PCR can be used to provide powerful feedback to patients, guide recommendations for additional medical assessments, and help monitor the effect of public health prevention interventions.

Array-Based Epigenetic Aging Indices May Be Racially Biased.

Epigenetic aging (EA) indices are frequently used as predictors of mortality and other important health outcomes. However, each of the commonly used array-based indices has significant heritable components which could tag ethnicity and potentially confound comparisons across racial and ethnic groups. To determine if this was possible, we examined the relationship of DNA methylation in cord blood from 203 newborns (112 African American (AA) and 91 White) at the 513 probes from the Levine PhenoAge Epigenetic Aging index to ethnicity. Then, we examined all sites significantly associated with race in the newborn sample to determine if they were also associated with an index of ethnic genetic heritage in a cohort of 505 AA adults. After Bonferroni correction, methylation at 50 CpG sites was significantly associated with ethnicity in the newborn cohort. The five most significant sites predicted ancestry with a receiver operator characteristic area under the curve of 0.97. Examination of the top 50 sites in the AA adult cohort showed that methylation status at 11 of those sites was also associated with percentage European ancestry. We conclude that the Levine PhenoAge Index is influenced by cryptic ethnic-specific genetic influences. This influence may extend to similarly constructed EA indices and bias cross-race comparisons.

AHRR methylation predicts smoking status and smoking intensity in both saliva and blood DNA.

Many existing DNA repositories do not have robust characterizations of smoking, while for many currently ongoing studies, the advent of vaping has rendered traditional cotinine-based methods of determining smoking status unreliable. Previously, we have shown that methylation status at cg05575921 in whole blood DNA can reliably predict cigarette consumption. However, whether methylation status in saliva can be used similarly has yet to be established. Herein, we use DNA from 418 biochemically confirmed smokers or nonsmokers to compare and contrast the utility of cg05575921 in classifying and quantifying cigarette smoking. Using whole blood DNA, a model incorporating age, gender, and methylation status had a receiver operating characteristic (ROC) area under the curve (AUC) for predicting smoking status of 0.995 with a nonlinear demethylation response to smoking. Using saliva DNA, the ROC AUC for predicting smoking was 0.971 with the plot of the relationship of DNA methylation to daily cigarette consumption being very similar to that seen for whole blood DNA. The addition of information from another methylation marker designed to correct for cellular heterogeneity improved the AUC for saliva DNA to 0.981. Finally, in 31 subjects who reported quitting smoking 10 or more years previously, cg05575921 methylation was nonsignificantly different from controls. We conclude that DNA methylation status at cg05575921 in DNA from whole blood or saliva predicts smoking status and daily cigarette consumption. We suggest these epigenetic assessments for objectively ascertaining smoking status will find utility in research, clinical, and civil applications.

A Four Marker Digital PCR Toolkit for Detecting Heavy Alcohol Consumption and the Effectiveness of Its Treatment.

Background.-Heavy alcohol consumption (HAC) is a shared concern of the forensic, medical and insurance underwriting communities. Unfortunately, there is a relative lack of clinically employable tools for detecting HAC and monitoring treatment response. Building on the results of 3 genome wide methylation studies, we have previously shown in a small group of samples that methylation sensitive digital PCR assays (MSdPCR) have the potential to accurately classify individuals with respect to HAC in a small set of individuals. Objective.-We now expand on those earlier findings using data and biomaterials from 143 participants with current HAC and 200 abstinent controls. Results.-We show that a set of 4 digital PCR assays that have a receiver operating characteristic (ROC) area under the curve (AUC) of 0.96 for detecting those with HAC. After a mean of 21 days of inpatient enforced abstinence, methylation status at one of these markers, cg04987734, began to revert to baseline values. Re-examination of methylation data from our smaller 2014 study with respect to this locus demonstrated a similarly significant reversion pattern at cg04987734 in association with treatment enforced abstinence. Conclusions.-We conclude that clinically implementable dPCR tools can sensitively detect the presence of HAC and that they show promise for monitoring alcohol treatment results. These dPCR tools could be useful to clinicians and researchers in monitoring those enrolled in substance use disorder treatment, employee wellness programs and insurance underwriting.

AHRR Methylation is a Significant Predictor of Mortality Risk in Framingham Heart Study.

Background.-The ability to predict mortality is useful to clinicians, policy makers and insurers. At the current time, prediction of future mortality is still an inexact process with some proposing that epigenetic assessments could play a role in improving prognostics. In past work, we and others have shown that DNA methylation status at cg05575921, a well-studied measure of smoking intensity, is also a predictor of mortality. However, the exact extent of that predictive capacity and its independence of other commonly measured mortality risk factors are unknown. Objective.-To determine the capacity of methylation to predict mortality. Method.-We analyzed the relationship of methylation at cg05575921 and cg04987734, a recently described quantitative marker of heavy alcohol consumption, to mortality in the Offspring Cohort of the Framingham Heart Study using proportional hazards survival analysis. Results.-In this group of participants (n = 2278) whose average age was 66 ± 9 years, we found that the inclusion of both cg05575921 and cg04987734 methylation to a base model consisting of age and sex only, or to a model containing 11 commonly used mortality risk factors, improved risk prediction. What is more, prediction accuracy for the base model plus methylation data was increased compared to the base model plus known predictors of mortality (CHD, COPD, or stroke). Conclusion.-Cg05575921, and to a smaller extent cg04987734, are strong predictors of mortality risk in older Americans and that incorporation of DNA methylation assessments to these and other loci may be useful to population scientists, actuaries and policymakers to better understand the relationship of environmental risk factors, such as smoking and drinking, to mortality.

A Direct Comparison of the Relationship of Epigenetic Aging and Epigenetic Substance Consumption Markers to Mortality in the Framingham Heart Study.

A number of studies have examined the relationship of indices of epigenetic aging (EA) to key health outcomes. Unfortunately, our understanding of the relationship of EA to mortality and substance use-related health variables is unclear. In order to clarify these interpretations, we analyzed the relationship of the Levine EA index (LEA), as well as established epigenetic indices of cigarette (cg05575921) and alcohol consumption (cg04987734), to all-cause mortality in the Framingham Heart Study Offspring Cohort (n = 2256) Cox proportional hazards regression. We found that cg05575921 and cg04987734 had an independent effect relative to LEA and vice versa, with the model including all the predictors having better performance than models with either LEA or cg05575921 and cg04987734 alone. After correction for multiple comparisons, 195 and 327, respectively, of the 513 markers in the LEA index, as well as the overall index itself, were significantly associated with cg05575921 and cg04987734 methylation status. We conclude that the epigenetic indices of substance use have an independent effect over and above LEA, and are slightly stronger predictors of mortality in head-to-head comparisons. We also conclude that the majority of the strength of association conveyed by the LEA is secondary to smoking and drinking behaviors, and that efforts to promote healthy aging should continue to focus on addressing substance use.

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

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.

Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study.

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.

Genetically contextual effects of smoking on genome wide DNA methylation.

Smoking is the leading cause of death in the United States. It exerts its effects by increasing susceptibility to a variety of complex disorders among those who smoke, and if pregnant, to their unborn children. In prior efforts to understand the epigenetic mechanisms through which this increased vulnerability is conveyed, a number of investigators have conducted genome wide methylation analyses. Unfortunately, secondary to methodological limitations, these studies were unable to examine methylation in gene regions with significant amounts of genetic variation. Using genome wide genetic and epigenetic data from the Framingham Heart Study, we re-examined the relationship of smoking status to genome wide methylation status. When only methylation status is considered, smoking was significantly associated with differential methylation in 310 genes that map to a variety of biological process and cellular differentiation pathways. However, when SNP effects on the magnitude of smoking associated methylation changes are also considered, cis and trans-interaction effects were noted at a total of 266 and 4353 genes with no marked enrichment for any biological pathways. Furthermore, the SNP variation participating in the significant interaction effects is enriched for loci previously associated with complex medical illnesses. The enlarged scope of the methylome shown to be affected by smoking may better explicate the mediational pathways linking smoking with a myriad of smoking related complex syndromes. Additionally, these results strongly suggest that combined epigenetic and genetic data analyses may be critical for a more complete understanding of the relationship between environmental variables, such as smoking, and pathophysiological outcomes.

MTHFR methylation moderates the impact of smoking on DNA methylation at AHRR for African American young adults.

Smoking has been shown to have a large, reliable, and rapid effect on demethylation of AHRR, particularly at cg05575921, suggesting that methylation may be used as an index of cigarette consumption. Because the availability of methyl donors may also influence the degree of demethylation in response to smoking, factors that affect the activity of methylene tetrahydrofolate reductase (MTHFR), a key regulator of methyl group availability, may be of interest. In the current investigation, we examined the extent to which individual differences in methylation of MTHFR moderated the association between smoking and demethylation at cg05575921 as well as at other loci on AHRR associated with a main effect of smoking. Using a discovery sample (AIM, N = 293), and a confirmatory sample (SHAPE, N = 368) of young adult African Americans, degree of methylation of loci in the first exon of MTHFR was associated with amplification of the association between smoking and AHRR demethylation at cg05575921. However, genetic variation at a commonly studied MTHFR variant, C677T, did not influence cg05575921 methylation. The significant interaction between MTHFR methylation and the smoking-induced response at cg05575921 suggests a role for individual differences in methyl cycle regulation in understanding the effects of cigarette consumption on genome wide DNA methylation.

A pilot investigation of the impact of smoking cessation on biological age.

BACKGROUND AND OBJECTIVES: Smoking is known to increase biological age. However, whether this process is reversible through smoking cessation is not known. In this pilot study, we attempt to determine whether smoking cessation reduces biological age. METHODS: We conducted regression analyses of methylation data from 22 subjects, as they entered and exited inpatient substance use treatment, to determine change in biological age, as indicated by the deviation of their methylomic age from chronological age across two time points. RESULTS: We found that, as compared to those subjects who did not stop smoking, subjects who significantly decreased their smoking consumption over a 1 month time period exhibited a marked reduction in methylomic age. CONCLUSION: The rapid and substantial reversal of accelerated aging associated with successful smoking cessation suggests that it can reverse well-known smoking effects on methylomic aging. This preliminary finding can be readily examined in other, larger data sets, and if replicated, this observation may provide smokers with yet another good reason to quit smoking. SCIENTIFIC SIGNIFICANCE: Successful smoking cessation makes patients appear biologically younger than they were at baseline, and to do so quite rapidly. In today's youth driven society, our observations may serve as a powerful impetus for some to quit smoking. (Am J Addict 2017;26:129-135).

Alcohol and tobacco consumption alter hypothalamic pituitary adrenal axis DNA methylation.

Alcohol and cigarette consumption have profound effects on genome wide DNA methylation and are common, often cryptic, comorbid features of many psychiatric disorders. This cryptic consumption is a possible impediment to understanding the biology of certain psychiatric disorders because if the effects of substance use are not taken into account, their presence may confound efforts to identify effects of other behavioral disorders. Since the hypothalamic pituitary adrenal (HPA) axis is known to be dysregulated in these disorders, we examined the potential for confounding effects of alcohol and cigarette consumption by examining their effects on peripheral DNA methylation at two key HPA axis genes, NR3C1 and FKBP5. We found that the influence of alcohol and smoke exposure is more prominent at the FKBP5 gene than the NR3C1 gene. Furthermore, in both genes, loci that were consistently significantly associated with smoking and alcohol consumption demethylated with increasing exposure. We conclude that epigenetic studies of complex disorders involving the HPA axis need to carefully control for the effects of substance use in order to minimize the possibility of type I and type II errors.

Methylomic Aging as a Window onto the Influence of Lifestyle: Tobacco and Alcohol Use Alter the Rate of Biological Aging.

OBJECTIVES: To examine the effect of the relationship between alcohol and cigarette consumption on biological aging using deoxyribonucleic acid methylation-based indices. DESIGN: Hierarchical linear regression modeling followed by fitting of higher-order effects. SETTING: Longitudinal studies of aging and the effect of psychosocial stress. PARTICIPANTS: Participants in two ethnically informative cohorts (n = 656 white, n = 180 black). MEASUREMENTS: Deviation of biological age from chronological age as a result of smoking and alcohol consumption. RESULTS: Greater cigarette consumption was associated with accelerated biological aging, with strong effects evident at even low levels of exposure. In contrast, alcohol consumption was associated with a mixed effect on biological aging and pronounced nonlinear effects. At low and heavy levels of alcohol consumption, there was accelerated biological aging, whereas at intermediate levels of consumption there was a relative decelerating effect. The decelerating effects of alcohol were particularly notable at loci for which methylation increased with age. CONCLUSION: These data support prior epidemiological studies indicating that moderate alcohol use is associated with healthy aging, but we urge caution in interpreting these results. Conversely, smoking has strong negative effects at all levels of consumption. These results also support the use of methylomic indices as a tool for assessing the impact of lifestyle on aging.

Current and Future Prospects for Epigenetic Biomarkers of Substance Use Disorders.

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.