ZSCAN25 methylation predicts seizures and severe alcohol withdrawal syndrome.

Currently, clinicians use their judgement and indices such as the Prediction of Alcohol Withdrawal Syndrome Scale (PAWSS) to determine whether patients are admitted to hospitals for consideration of withdrawal syndrome (AWS). However, only a fraction of those admitted will experience severe AWS. Previously, we and others have shown that epigenetic indices, such as the Alcohol T-Score (ATS), can quantify recent alcohol consumption. However, whether these or other alcohol biomarkers, such as carbohydrate deficient transferrin (CDT), could identify those at risk for severe AWS is unknown. To determine this, we first conducted genome-wide DNA methylation analyses of subjects entering and exiting alcohol treatment to identify loci whose methylation quickly reverted as a function of abstinence. We then tested whether methylation at a rapidly reverting locus, cg07375256, or other existing metrics including PAWSS scores, CDT levels, or ATS, could predict outcome in 125 subjects admitted for consideration of AWS. We found that PAWSS did not significantly predict severe AWS nor seizures. However, methylation at cg07375256 (ZSCAN25) and CDT strongly predicted severe AWS with ATS (p < 0.007) and cg07375256 (p < 6 × 10-5) methylation also predicting AWS associated seizures. We conclude that epigenetic methods can predict those likely to experience severe AWS and that the use of these or similar Precision Epigenetic approaches could better guide AWS management.

Epigenetic and Proteomic Biomarkers of Elevated Alcohol Use Predict Epigenetic Aging and Cell-Type variation Better Than Self-Report.

Excessive alcohol consumption (EAC) has a generally accepted effect on morbidity and mortality, outcomes thought to be reflected in measures of epigenetic aging (EA). As the association of self-reported EAC with EA has not been consistent with these expectations, underscoring the need for readily employable non-self-report tools for accurately assessing and monitoring the contribution of EAC to accelerated EA, newly developed alcohol consumption DNA methylation indices, such as the Alcohol T Score (ATS) and Methyl DetectR (MDR), may be helpful. To test that hypothesis, we used these new indices along with the carbohydrate deficient transferrin (CDT), concurrent as well as past self-reports of EAC, and well-established measures of cigarette smoking to examine the relationship of EAC to both accelerated EA and immune cell counts in a cohort of 437 young Black American adults. We found that MDR, CDT, and ATS were intercorrelated, even after controlling for gender and cotinine effects. Correlations between EA and self-reported EAC were low or non-significant, replicating prior research, whereas correlations with non-self-report indices were significant and more substantial. Comparing non-self-report indices showed that the ATS predicted more than four times as much variance in EA, CDT4 cells and B-cells as for both the MDR and CDT, and better predicted indices of accelerated EA. We conclude that each of the non-self-report indices have differing predictive capacities with respect to key alcohol-related health outcomes, and that the ATS may be particularly useful for clinicians seeking to understand and prevent accelerated EA. The results also underscore the likelihood of substantial underestimates of problematic use when self-report is used and a reduction in correlations with EA and variance in cell-types.

Using Cg05575921 methylation to predict lung cancer risk: a potentially bias-free precision epigenetics approach.

The decision to engage in lung cancer screening (LCS) necessitates weighing benefits versus harms. Previously, clinicians in the United States have used the PLCOM2012 algorithm to guide LCS decision-making. However, that formula contains race and gender-based variables. Previously, using data from a European study, Bojesen and colleagues have suggested that cg05575921 methylation could guide decision-making. To test this hypothesis in a more diverse American population, we examined DNA and clinical data from 3081 subjects from the National Lung Screening Trial (NLST) study. Using survival analysis, we found a simple linear predictor consisting of age, pack-year consumption and cg05575921, to have the best predictive power among several alternatives (AUC = 0.66). Results showed that the highest quartile of risk was more than 2-fold more likely to develop lung cancer than those in the lowest quartile. Race, ethnicity, and gender had no effect on prediction with both cg05575921 and pack years contributing equally (both p < 0.003) to risk prediction. Current smokers had considerably lower methylation than former smokers (46% vs 67%; p < 0.001) with the average methylation of those who quit approaching 80% after 25 years of cessation. Finally, current male smokers had lower mean cg05575921 percentage than female smokers (46% vs 49%; p < 0.001). We conclude that cg05575921 (along with age and pack years) can be used to guide LCS decision-making, and additional studies might focus on how best to use methylation to inform decision-making.

Digital methylation assessments of alcohol and cigarette consumption account for common variance in accelerated epigenetic ageing.

Smoking and Heavy Alcohol Consumption (HAC) are established risk factors for myriad complex disorders of ageing. Yet many prior studies of Epigenetic Ageing (EA) have shown only modest effects of smoking and drinking on accelerated ageing. One potential reason for this conundrum might be the reliance of some prior EA studies on self-reported substance use, which may be unreliable in many samples. To test whether novel, non-self-reported indices would show a stronger association of smoking and HAC to EA, we used methylation sensitive digital PCR (MSdPCR) and data from 437 African American subjects from Wave 7 of the Family and Community Health Study Offspring Cohort to examine the effects of subjective and objective measures of smoking and HAC on 7 indices of EA. Because of limited overall correlations between the various EA indices, we examined patterns of association separately for each index. Consistent with expectations, MSdPCR assessments of smoking and HAC, but not self-reported alcohol consumption, were strongly correlated with accelerated EA. MSdPCR assessments of smoking and HAC accounted for 57% of GrimAge acceleration and the shared variance in GrimAge and DunedinPOAM accelerated EA. We conclude that MSdPCR assessments of smoking and HAC are valuable tools for understanding EA, represent directly targetable conditions for the prevention of premature ageing, and substantially improve upon self-reported assessment of smoking and HAC.

Additive and Interactive Genetically Contextual Effects of HbA1c on cg19693031 Methylation in Type 2 Diabetes.

Type 2 diabetes mellitus (T2D) has a complex genetic and environmental architecture that underlies its development and clinical presentation. Despite the identification of well over a hundred genetic variants and CpG sites that associate with T2D, a robust biosignature that could be used to prevent or forestall clinical disease has not been developed. Based on the premise that underlying genetic variation influences DNA methylation (DNAm) independently of or in combination with environmental exposures, we assessed the ability of local and distal gene x methylation (GxMeth) interactive effects to improve cg19693031 models for predicting T2D status in an African American cohort. Using genome-wide genetic data from 506 subjects, we identified a total of 1476 GxMeth terms associated with HbA1c values. The GxMeth SNPs map to biological pathways associated with the development and complications of T2D, with genetically contextual differences in methylation observed only in diabetic subjects for two GxMeth SNPs (rs2390998 AG vs. GG, p = 4.63 × 10-11, Δß = 13%, effect size = 0.16 [95% CI = 0.05, 0.32]; rs1074390 AA vs. GG, p = 3.93 × 10-4, Δß = 9%, effect size = 0.38 [95% CI = 0.12, 0.56]. Using a repeated stratified k-fold cross-validation approach, a series of balanced random forest classifiers with random under-sampling were built to evaluate the addition of GxMeth terms to cg19693031 models to discriminate between normoglycemic controls versus T2D subjects. The results were compared to those obtained from models incorporating only the covariates (age, sex and BMI) and the addition of cg19693031. We found a post-pruned classifier incorporating 10 GxMeth SNPs and cg19693031 adjusted for covariates predicted the T2D status, with the AUC, sensitivity, specificity and precision of the positive target class being 0.76, 0.81, 0.70 and 0.63, respectively. Comparatively, the AUC, sensitivity, specificity and precision using the covariates and cg19693031 were only 0.71, 0.74, 0.67 and 0.59, respectively. Collectively, we demonstrate correcting for genetic confounding of cg19693031 improves its ability to detect type 2 diabetes. We conclude that an integrated genetic-epigenetic approach could inform personalized medicine programming for more effective prevention and treatment of T2D.

DNA methylation differentiates smoking from vaping and non-combustible tobacco use.

Increasing use of non-combusted forms of nicotine such as e-cigarettes poses important public health questions regarding their specific risks relative to combusted tobacco products such as cigarettes. To fully delineate these risks, improved biomarkers that can distinguish between these forms of nicotine use are needed. Prior work has suggested that methylation status at cg05575921 may serve as a specific biomarker of combusted tobacco smoke exposure. We hypothesized combining this epigenetic biomarker with conventional metabolite assays could classify the type of nicotine product consumption. Therefore, we determined DNA methylation and serum cotinine values in samples from 112 smokers, 35 e-cigarette users, 19 smokeless tobacco users, and 269 controls, and performed mass spectroscopy analyses of urine samples from all nicotine users and 22 verified controls to determine urinary levels of putatively nicotine product-specific substances; propylene glycol, 2-cyanoethylmercapturic acid (CEMA), and anabasine. 1) Cigarette smoking was associated with a dose dependent demethylation of cg05575921 and increased urinary CEMA and anabasine levels, 2) e-cigarette use did not demethylate cg05575921, 3) smokeless tobacco use also did not demethylate cg05575921 but was positively associated with anabasine levels 4) CEMA and cg05575921 levels were highly correlated and 5) propylene glycol levels did not reliably distinguish use groups. Cg05575921 assessments distinguish exposure to tobacco smoke from smokeless sources of nicotine including e-cigarettes and smokeless tobacco, neither of which are associated with cg05575921 demethylation. A combination of methylomic and metabolite profiling may allow for accurate classification use status of a variety of nicotine containing products.

Epigenetic Analyses of Alcohol Consumption in Combustible and Non-Combustible Nicotine Product Users.

Alcohol and tobacco use are highly comorbid and exacerbate the associated morbidity and mortality of either substance alone. However, the relationship of alcohol consumption to the various forms of nicotine-containing products is not well understood. To improve this understanding, we examined the relationship of alcohol consumption to nicotine product use using self-report, cotinine, and two epigenetic biomarkers specific for smoking (cg05575921) and drinking (Alcohol T Scores (ATS)) in n = 424 subjects. Cigarette users had significantly higher ATS values than the other groups (p < 2.2 × 10-16). Using the objective biomarkers, the intensity of nicotine and alcohol consumption was correlated in both the cigarette and smokeless users (R = -0.66, p = 3.1 × 10-14; R2 = 0.61, p = 1.97 × 10-4). Building upon this idea, we used the objective nicotine biomarkers and age to build and test a Balanced Random Forest classification model for heavy alcohol consumption (ATS > 2.35). The model performed well with an AUC of 0.962, 89.3% sensitivity, and 85% specificity. We conclude that those who use non-combustible nicotine products drink significantly less than smokers, and cigarette and smokeless users drink more with heavier nicotine use. These findings further highlight the lack of informativeness of self-reported alcohol consumption and suggest given the public and private health burden of alcoholism, further research into whether using non-combustible nicotine products as a mode of treatment for dual users should be considered.

The relationship of smoking to cg05575921 methylation in blood and saliva DNA samples from several studies.

Numerous studies have shown that cg05575921 methylation decreases in response to smoking. However, secondary to methodological issues, the magnitude and dose dependency of that response is as of yet unclear. This lack of certainty is a barrier to the use of DNA methylation clinically to assess and monitor smoking status. To better define this relationship, we conducted a joint analysis of methylation sensitive PCR digital (MSdPCR) assessments of cg05575921 methylation in whole blood and/or saliva DNA to smoking using samples from 421 smokers and 423 biochemically confirmed non-smokers from 4 previously published studies. We found that cg05575921 methylation manifested a curvilinear dose dependent decrease in response to increasing cigarette consumption. In whole blood DNA, the Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) of cg05575921 methylation for predicting daily smoking status was 0.98. In saliva DNA, the gross AUC was 0.91 with correction for cellular heterogeneity improving the AUC to 0.94. Methylation status was significantly associated with the Fagerstrom Test for Nicotine Dependence score, but with significant sampling heterogeneity. We conclude that MSdPCR assessments of cg05575921 methylation are a potentially powerful, clinically implementable tool for the assessment and management of smoking.

Alcohol Use Intensity Decreases in Response to Successful Smoking Cessation Therapy.

Smokers frequently drink heavily. However, the effectiveness of smoking cessation therapy for those with comorbid alcohol abuse is unclear, and the content of smoking cessation programs often does not address comorbid alcohol consumption. In order to achieve a better understanding of the relationship between changes in rate of smoking to the change in intensity of alcohol consumption, and the necessity for alcohol-specific programming for dual users, we quantified cigarette and alcohol consumption in 39 subjects undergoing a 3-month contingency management smoking cessation program using recently developed DNA methylation tools. Intake alcohol consumption, as quantified by the Alcohol T Score (ATS), was highly correlated with cg05575921 smoking intensity (adjusted R2 = 0.49) with 19 of the 39 subjects having ATS scores indicative of Heavy Alcohol Consumption. After 90 days of smoking cessation therapy, ATS values decreased with the change in ATS score being highly correlated with change in cg05575921 smoking intensity (adjusted R2 = 0.60), regardless of whether or not the subject managed to completely quit smoking. We conclude that alcohol consumption significantly decreases in response to successful smoking cessation. Further studies to determine whether targeted therapy focused on comorbid alcohol use increases the success of smoking cessation in those with dual use should be explored.

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.

The Reversion of cg05575921 Methylation in Smoking Cessation: A Potential Tool for Incentivizing Healthy Aging.

Smoking is the largest preventable cause of mortality and the largest environmental driver of epigenetic aging. Contingency management-based strategies can be used to treat smoking but require objective methods of verifying quitting status. Prior studies have suggested that cg05575921 methylation reverts as a function of smoking cessation, but that it can be used to verify the success of smoking cessation has not been unequivocally demonstrated. To test whether methylation can be used to verify cessation, we determined monthly cg05575921 levels in a group of 67 self-reported smokers undergoing biochemically monitored contingency management-based smoking cessation therapy, as part of a lung imaging protocol. A total of 20 subjects in this protocol completed three months of cotinine verified smoking cessation. In these 20 quitters, the reversion of cg05575921 methylation was dependent on their initial smoking intensity, with methylation levels in the heaviest smokers reverting to an average of 0.12% per day over the 3-month treatment period. In addition, we found suggestive evidence that some individuals may have embellished their smoking history to gain entry to the study. Given the prominent effect of smoking on longevity, we conclude that DNA methylation may be a useful tool for guiding and incentivizing contingency management-based approaches for smoking cessation.

Refinement of cg05575921 demethylation response in nascent smoking.

The initiation of adolescent smoking is difficult to detect using carbon monoxide or cotinine assays. Previously, we and others have shown that the methylation of cg05575921 is an accurate predictor of adult smoking status. But the dose and time dependency of the demethylation response to smoking initiation in adolescents is not yet well understood. To this end, we conducted three consecutive annual in-person interviews and biological samplings of 448 high school students (wave 1 (W1)-wave 3 (W3)). At W1 (n = 448), 62 subjects reported using tobacco and 72 subjects reported using cannabis at least once in their life-time with 38 and 20 subjects having a positive cotinine and cannabinoid levels, respectively, at W1 intake. At W3 (n = 383), 67 subjects reported using tobacco and 60 subjects reported using cannabis at least once with 75 and 60 subjects having positive cotinine and cannabinoid levels, respectively, at W3. Subjects with undetectable cotinine levels at all three-time waves had stable levels of cg05575921 methylation throughout the study (88.7% at W1 and 88.8% at W3, n = 149), while subjects with positive cotinine levels at all 3 time points manifested a steady decrease in cg05575921 methylation (81.8% at W1 and 71.3% at the W3, n = 12). In those subjects with an affirmative smoking self-report at W3 (n = 17), the amount of demethylation at cg05575921 was correlated with time and intensity of smoking. We conclude that cg05575921 methylation is a sensitive, dose-dependent indicator of early stages of smoking, and may help to identify smokers in the early stages of smoking.

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.

Saliva DNA Methylation Detects Nascent Smoking in Adolescents.

Objectives: Early identification of smoking, essential for the successful implementation of interventions, arrests the escalation of smoking and smoking-associated risk behaviors in adolescents. However, because nascent smoking is typically episodic and infrequent, enzyme-linked immunoassay reagent-based approaches that detect cotinine, a key nicotine metabolite, are not effective in identifying adolescents in the earliest stages of smoking. Epigenetic methods may offer an alternative approach for detecting early-stage smokers. In prior work, we and others have shown that the methylation status of cg05575921 of whole-blood DNA accurately predicts smoking status in regularly smoking adults and is sensitive to nascent smoking. Yet, the blood draws necessary to obtain DNA for this method may be poorly accepted by adolescents. Saliva could be an alternative source of DNA. However, the ability of saliva DNA methylation status to predict smoking status among adolescents is unknown. Methods: To explore the possibility of using salivary DNA for screening purposes, we examined the DNA methylation status at cg05575921 in saliva DNA samples from 162 high school aged subjects for whom we also had paired serum cotinine values. Results: Overall, the reliability of self-report of nicotine/tobacco use in these adolescents was poor with 67% of all subjects whose serum levels of cotinine was ≥2 ng/mL (n = 75) denying any use of nicotine-containing products in the past 6 months. However, the correspondence of the two biological measures of smoking was high, with serum cotinine positivity being strongly correlated with cg05575921 methylation (p < 0.0001). Receiver operating characteristic (ROC) analyses showed that cg05575921 methylation status could be used to classify those with positive serum cotinine values (≥2 ng/mL) from those denying smoking and have undetectable levels of cotinine. Conclusions: We conclude that saliva DNA methylation assessments hold promise as a means of detecting nascent smoking.