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. 2025 May 22;20(5):e0322783. doi: 10.1371/journal.pone.0322783

Epigenetic assessments of alcohol consumption predict mortality in smokers at risk for lung cancer in the prostate, lung, colorectal and ovarian cancer screening trial

Robert Philibert 1,2,*, Steven RH Beach 3, James A Mills 1, Kelsey Dawes 2, Richard M Hoffman 4, Jessica C Sieren 5, Ellyse M Froehlich 1, Kaitlyn M deBlois 1, Jeffrey D Long 1,6
Editor: Chunyu Liu7
PMCID: PMC12097613  PMID: 40402951

Abstract

DNA methylation at cg05575921, an established biomarker for smoking predicts risk for lung cancer (LC). Although heavy alcohol consumption (HAC) frequently accompanies smoking, the relationship of HAC to overall mortality in those at risk for LC is not well known. Determining the contribution of HAC to mortality in those who smoke is important because HAC is also a major driver of mortality and is potentially treatable. To help answer this question, we examined the relationship of epigenetic biomarkers of smoking (cg05575921) and chronic heavy alcohol consumption (Alcohol T Score, ATS) in a cohort of 92 LC cases and 402 age, sex, ethnicity and smoking history matched controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial to all-cause mortality using proportional hazards survival analysis. We found that ATS values significantly predicted risk for all-cause mortality in those smokers who developed (p < 0.03) and did not develop lung cancer (p < 0.0001). When mortality data were analyzed using median splits, those who did and did not incur lung cancer with ATS values <3.6 lived 5.6 years and 3.2 years more, respectfully, than those with ATS values >3.6. Interestingly, in this group of 494 smokers or former smokers, after adjusting for the occurrence of lung cancer, cg05575921 methylation did not predict mortality. In summary, we found that excessive alcohol consumption is a significant risk factor for all-cause mortality in those at risk for LC and suggest that lung cancer screening efforts to address problem drinking could increase survival.

Introduction

Lung cancer (LC) is the leading cause of cancer death in the United States [1]. Epidemiological studies have suggested that smoking accounts for 90% of lung cancer mortality [2,3]. This effect of smoking is dose-dependent, and studies have shown that cg05575921 methylation, an established biomarker of smoking, predicts likelihood of LC [4,5].

Those who smoke heavily often also drink alcohol heavily [6,7]. However, the impact of excessive alcohol consumption in those who smoke is not well described. In part, this lack of understanding is secondary to the difficultly of separating the deleterious effects of smoking from that of drinking.

Further advances in epigenetics may help resolve these issues. Using the same approach used to identify the cg05575921 methylation site in 2012, we developed a metric of chronic HAC, defined as consuming ≥ 6 drinks per day for 8 or more weeks, called the Alcohol T Score (ATS) [8]. The ATS uses methylation sensitive digital polymerase chain reaction (MSdPCR) to quantify methylation at four CpG sites that are sensitive to alcohol but unaffected by smoking [9]. In abstinent individuals, ATS is zero-centered metric with a standard deviation of 2.2 that non-linearly increases as a function of increasing chronic alcohol consumption [10]. The performance of the ATS has been examined in ten studies (for review see [10]), including three direct comparisons against carbohydrate deficient transferrin (CDT), an established biomarker of heavy drinking [11]. In these three studies, the ATS outperformed the CDT in predicting chronic HAC, alcohol withdrawal and alcohol related immune cell changes [9,12,13].

In at least one disease associated with HAC, coronary heart disease [14], the ATS predicts mortality. Building on work showing that the CpG sites surveyed in the ATS predict mortality in large population cohorts, we examined the relationship of ATS values to survival in subjects admitted for acute coronary syndrome. We found the ATS strongly predicted survival, even more so than age or degree of coronary artery obstruction.

Using self-report data of smoking and drinking, epidemiological studies have shown that smoking is also associated with HAC [6,7]. This is consistent with our epigenetic studies which show strong correlations between cg05575921 methylation and ATS values. However, whether ATS values predict survival independently of the effects of smoking in those with a history of smoking is not well characterized. Because the most recent recommendations by the United States Preventative Services Task Force has recommended reducing the lower limit for low dose cancer risk screening from 30 pack years to 20 pack years of smoking and there has been an increased emphasis by the Surgeon General on the need for alcohol prevention and treatment, the possibility that lung cancer risk screening visits could be expanded to include thorough assessments of alcohol consumption could represent an opportunity for clinicians to further reduce substance use related morbidity and mortality.

In this pilot study, we examine the relationship of smoking and drinking intensities to survival using these two epigenetic metrics and the bioresources of lung cancer cases and matched controls from the Prostate Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial [15].

Methods

Study design

The design and methods of the PLCO trial have been previously reported [15]. In brief, the PLCO Cancer Screening Trial included approximately 148,000 individuals enrolled between 1993 and 2006 who were randomized to either an intervention or usual care arm at 10 screening centers in the United States, who were then followed for up to 13 years with respect to key clinical outcomes.

All subjects in the study provided written informed consent. The overall project was approved by the institutional review board at the National Cancer Institute and at each of the ten recruitment sites. The de-identified clinical data for the current study were provided by Etiology and Early Marker Studies (EEMS) coordinating center of the Cancer Data Access System. The DNA specimens for this study were obtained from National Cancer Institute’s Cancer Genomic Research Laboratory (Frederick, MD).

Ethics statement

As this study used only de-identified data and samples provided by the repository, it was exempt from human subjects review.

Study sample

The DNA samples and data for the 494 subjects, included in this study are a subset of a larger study of 4910 subjects with whom the goal is to construct an algorithm incorporating cg05575921 methylation to predict lung cancer. The strategy employed by EEMS to select the overall sample cohort matched each case to 3 controls with respect to gender, race, age at randomization, smoking history and year of randomization by the EEMS coordinating center staff. The 494 subjects included in this study represent every smoker or former smoker from the first six DNA plates provided by Cancer Genomic Research Laboratory and -for whom ATS values were also determined as part of routine quality control measures.

Cg05575921 determinations

500 ng samples of DNA from each subject were bisulfite converted using a Qiagen (Germany) Epitect kits. Then for cg05575921 methylation, a 3 µl aliquot of each sample was pre-amped, diluted 1:3000, and then PCR amplified using fluorescent, dual labeled primer probe sets specific for the locus (Behavioral Diagnostics, Coralville, IA), in combination with digital PCR reagents and a QuantStudio Absolute Q Digital PCR System from ThermoFisher (Hercules, CA). Then, methylation values ((C/C + T) ratios) were determined using the proprietary software.

ATS determinations

ATS values for each subject were determined using fluorescent, dual labeled primer probe sets from Behavioral Diagnostics specific for the four loci included in the ATS, cg02583484, cg04987734, cg09935388 and cg04583842, and droplet digital PCR machinery and reagents from Bio-Rad (Carlsbad, CA) as previously described [9,10]. For each locus, a 3 µl sample of bisulfite converted DNA was pre-amped, diluted 1:1500, and then amplified. Then methylation for each sample was determined using a QX-200 droplet reader and proprietary Bio-Rad software. Finally, the Z-scores for each CpG site was produced by subtracting the previously established mean of methylation at the locus in abstinent controls, then dividing the result by the standard deviation of the controls at that locus, then summing the four scores to form the ATS.

Data analysis

Initial analysis of data was conducted using JMP Version 17 (SAS Institute, Cary, SC). Group comparisons for both normally and non-normally distributed continuous variables were conducted using t-tests, and Wilcoxon rank sums, respectively. The event of interest was all-cause mortality, and the time metric was days on study (study entry was time 0). Kaplan-Meier curves were created for controls and LC cases using the median split of ATS values to illustrate the impact of alcohol consumption on survival for each group. Survival was more formally assessed using Cox proportional hazards models, with separate models fit for controls (N = 402) and LC cases (N = 92). To examine the interrelationship of smoking and drinking intensity, models included cg05575921 methylation and ATS values, adjusting for mortality risk factors age and sex. Another measure of smoking intensity, pack years, was also considered in the model. Plausibility of the proportional hazards assumption was assessed using graphical displays and appeared to be reasonable.

Results

Table 1 gives the key demographic characteristics for the 494 subjects (all were smokers or former smokers, controls did not have lung cancer, whereas cases did). Subjects were largely White and in their early 60s. As would be expected by the matching paradigm instituted by EEMS, there were minimal differences between the cases and controls with respect to age, sex, and race. However, the number of cases placed on the first six plates supplied by CGR (n = 92) was lower than would be expected in a 3:1 matching paradigm.

Table 1. Key Demographic and Clinical Characteristics of the Subjects.

Controls (No Cancer) Cases (Lung Cancer)
Male Female Male Female
N = 255 N = 147 N = 58 N = 34
Age (years) mean (SD) 62.4 ± 4.8 63.3 ± 5.6 62.6 ± 4.7 62.4 ± 4.9
Race:
 White, non-Hispanic 223 135 48 31
 Black, non-Hispanic 11 8 6 2
 Hispanic 5 2 1 –
 Asian 14 – 3 1
 Pacific Islander 1 1 – –
 American Indian 1 1 – –
Ethnicity:
 Hispanic 6 2 1 –
 No 240 144 55 34
 Missing 9 1 2 –
Pack Years Smoked: mean (SD) 43.6 ± 32.7 34.3 ± 25.6 54.1 ± 28.7 48.7 ± 42.1
Current Smoker: N (%)
 Yes 100 (39%) 57 (39%) 25 (43%) 16 (47%)
 No 155 (61%) 90 (61%) 33 (57%) 18 (53%)
Methylation
 ATS (unitless) 3.9 ± 3.3* 3.2 ± 3.1 4.3 ± 3.8 4.7 ± 3.1
 Cg05575921%: mean (SD) 61.6 ± 21.3 64.5 ± 19.5 54.2 ± 20.6 58.3 ± 20.5
Mortality during follow up: N (%)
 Overall1 23 (48%)* 56 (38%) 48 (83%) 27 (79%)
 Lung cancer -- -- 35 (60%) 18 (53%)
Survival (days) 6172 ± 1927* 6581 ± 1808 5914 ± 1875 5327 ± 2154
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SD = standard deviation.

*Different than paired female value at p < 0.05.

LC cases were not more likely than controls to be current smokers (45% vs 39%, Chi-square p < 0.34). Male (54 ± 28 vs 44 ± 33, p < 0.03) and female (49 ± 42 vs 34 ± 26, p < 0.02) LC cases had greater pack year histories than their sex matched counterparts.

Cg05575921 are non-normally distributed in both the case and control subjects (See Fig 1) with the modal peak occurring between 80–82% for controls but at 72–74% for case subjects. Male case subjects (54.2 ± 20.6 vs 61.6 ± 21.3, Wilcoxon p < 0.006) had lower cg05575921 methylation values (i.e., greater smoking intensity) than their sex matched controls. Female case subjects had non-significantly lower cg05575921 levels than their matched controls (58.3 ± 20.5 vs 64.5 ± 19.5, Wilcoxon p < 0.07).

Fig 1. The distribution of cg05575921 values in controls (n = 402) and LC case (n = 92) subjects.

Fig 1

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ATS values were slightly right skewed in both case and control subjects (see Fig 2). Female case subjects (4.7 ± 3.1 vs 3.2 ± 3.1, p < 0.02, Wilcoxon), but not male case subjects (4.3 ± 3.8 vs 3.9 ± 3.3, NS) had higher ATS values (i.e., greater alcohol intake) than their matched controls.

Fig 2. The distribution of ATS values in values in controls (n = 402) and LC case (n = 92) subjects.

Fig 2

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The red arrows indicate the cutoff of 3.5 for HAC as shown in Miller et al., 2019.

Fig 3 illustrates the relationship between ATS, which increases as a function of drinking intensity, and cg05575921, which decreases as a function of smoking intensity, values. Overall, the values were strongly negatively correlated, and a linear fit explaining nearly half of the variance (Bivariate Fit, Adjusted R2 = 0.45, p < 0.0001).

Fig 3. The relationship of cg05575921 to ATS values in all 494 subjects (Adjusted R2 = 0.45, p < 0.0001).

Fig 3

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The subjects were followed for a median of 20.7 years (interquartile range; 15.0–23.3 years). LC cases were much more likely to die (81.5% vs. 44.5%, p < 0.0001, Chi-Square) than control subjects during the follow up period (Table 1). Death certificates listed LC as the primary cause of death for 35 male LC cases and 18 female LC cases.

Table 2 presents the parameter estimates for the multivariable Cox models. Age was significantly positively associated with mortality risk and males had higher risk than females. In addition, ATS values were significantly positively associated with increased mortality risk for controls (p < 0.0001; HR = 1.14 for a 1-unit increase in ATS) and for LC cases (p < 0.03; HR = 1.10 for a 1-unit increase in ATS). Cg05575921 methylation was not significantly associated with mortality risk for either cases or controls in this analysis. In separate modeling not presented, models stratified by sex yielded similar parameter estimates for ATS and cg05575921 and the addition of pack year smoking history was not associated with mortality risk for either cases or controls in this analysis. Similarly, because the interaction term between the ATS and cg05575921 was not significant, it also was not included in the final model.

Table 2. Parameter Estimates for Proportional Hazards Modeling of Survival.

Controls (No Lung Cancer, n = 402) Cases (Lung Cancer, n = 92)
Variable Parameter Estimate Standard Error p-value Parameter Estimate Standard Error p-value
Age 0.12 0.016 <0.0001 0.050 0.025 <0.05
Sex (M) 0.41 0.16 <0.02 0.29 0.25 <0.25
Cg05575921 -0.0037 0.0050 <0.46 -0.011 0.0080 <0.17
ATS 0.13 0.032 <0.0001 0.099 0.043 <0.03
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Significant findings are bolded. M = male.

Fig 4 graphically illustrates the effects of alcohol consumption on survival in the two groups using median splits with respect to ATS values. Controls whose ATS was < 3.6 had an estimated median survival of 9226 days while those with ATS ≥ 3.6 had a median survival of 8067 days (a 1159-day or 3.2 year difference). The median survival for those LC cases with ATS < 3.6 was 7389 days while those with ATS ≥ 3.6 had a median survival of 5362 days (a 2027-day or 5.6 year difference).

Fig 4. Kaplan-Meier Curves by median split of the ATS values and LC status.

Fig 4

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The four groups are: 1) LC cases with ATS < 3.6, turquoise, 2) LC cases with ATS > 3.6, purple, 3) Controls (no cancer) with ATS < 3.6, red, and 4) Controls with ATS > 3.6, green. The number of risk and cumulative number of events for each group are given below.

Discussion

Using objective measures of substance use we showed that average alcohol consumption at the time of study intake significantly predicted of the overall mortality risk of subjects who were current or former smokers whether or not they were subsequently diagnosed with lung cancer. The fact that HAC makes a substantial impact on mortality in those who smoke is not surprising. Alcohol is thought to be the third leading cause of death in the United States by the Centers for Disease Control (CDC). Critically, these estimates of the effect of alcohol are based largely on self-report of alcohol use. However, both clinical and epidemiological self-reports of alcohol consumption can be unreliable [12,16,17]. In fact, as Nelson and colleagues have pointed out, over a 15-year period of time, the amount of alcohol sold in the United States according to the National Institute of Alcohol Abuse and Alcoholism surpassed the amount reported in CDC’s Behavioral Risk Factor Surveillance System by at least a factor of three [17]. The parameter estimates for the ATS listed in Table 2 suggest that for equal smoking intensity, the 62-year-old male who is abstinent from alcohol and does not develop lung cancer will live about 4 years longer than the average 62-year-old male with an ATS of 3.9 who does not develop lung cancer.

DNA methylation studies alone do not provide the in-depth information that is needed to understand how the excessive alcohol consumption causes mortality. The ATS was designed to predict heavy chronic alcohol consumption (6 or more drinks per day for 8 or more weeks) [9]. Binge drinking and other forms of alcohol intake of lesser duration, which may confer somewhat different health risks than chronic heavy consumption, are not perfectly correlated with the ATS. For example, in a study using a marker for recent (past 3 weeks) heavy alcohol consumption, referred to a ZSCAN25, we found only a 0.56 correlation between ZSCAN25 and the ATS values for 125 subjects admitted to the hospital for possible alcohol withdrawal with the ZSCAN marker better predicting the consequences of sustained recent drinking than the ATS in that study [12]. Critically, in our studies using both the ATS and cg05575921 assessments, the ATS has been highly correlated with smoking [8,9,12,13,18–22]. Therefore, we believe that our findings our with respect to mortality reflect the entirety of lifestyle risk biology, including risks from poor diets and lack of exercise, that track with the heavily tobacco use and chronic alcohol consumption [23].

Unfortunately, defining the exact relationship of smoking and drinking to the entire risk biology associated with their epigenetic signal will be difficult. Not only do these behaviors sort cross-sectionally, but when one risk behavior, such as smoking remits, the other risk factors such as excessive drinking or poor dietary choices tend to remit as well [19,24]. Consequently, deriving an exact quantitative understanding of the mortality associated each of these individual clinical risk factors that is captured by the cg05575921 and the ATS will be difficult.

Conversely, understanding how the drinking and smoking related biology conveys the excess mortality at the molecular level is achievablebut will require integrating the epigenetic findings with more traditional molecular, cellular, and serological assessments of human samples. Certainly, the molecular and cellular effects of smoking are well delineated [25]. But how they interact, if at all, with the effects of alcohol, is not as well understood. By incorporating a potentially powerful continuous objective measure like the ATS into existing analyses of those who currently smoke and drink, it may be possible to define potential cellular targets more precisely for preventive interventions.

After controlling for LC status, we did not find that cg05575921 predicted mortality. Clearly, cg05575921 predicts the occurrence of LC and in part, that is why these analyses are controlled for by lung cancer status [4,5]. However, it may well be as we have shown in those admitted for ACS [22] or in the FHS [26], that the effects of alcohol measured through this method on non-LC outcomes are greater than those that can be shown using cg05575921 with respect to CHD, and that with greater sample size, the effects assessed by cg05575921 on these outcomes, such as CHD, will become significant as well. Still, given the plethora of conditions for which both alcohol and smoking are risk factors, the use of this quantitative epigenetic approach may help more exactly parse the amount of risk for each of these disorders that can more directly be attributed to smoking as opposed to that associated with drinking.

Limitations of the study include the relatively small size of the cohort and the absence of other objective markers of alcohol consumption. The relatively small sample size in this study precludes determining the shape of the alcohol consumption curve with respect to survival.

Our findings, which need to be replicated in larger and more diverse populations, do have potential implications for lung cancer screening efforts. According to the United States Preventative Services Task Force guidelines, approximately 18 million smokers are potentially eligible for low dose computerized tomography [27]. If our findings are replicated, they suggest that these patients should be rigorously screened for excessive alcohol consumption, and when indicated, referred for alcohol treatment. Because combined alcohol and tobacco cessation treatment may be more effective than smoking cessation alone, this may improve patient outcomes [28]. Unfortunately, our data are silent as to whether incorporating smoking and drinking biomarker testing into lung cancer screening risk calculators will improve overall patient outcomes. We note that when combined with contingency management approaches, the use of biomarker testing can lead to marked improvements in abstinence rates for both smoking and drinking [29,30]. However, some patients have concerns about even verbal screening for alcohol consumption [25], suggesting that the use of this type of technology needs to be discussed by the medical community.

Acknowledgments

This work is dedicated to the memory of Dr. Meg Gerrard of the University of Connecticut whose work to prevent the effects of alcohol and smoking on risk for cancer have had a profound and lasting impact on the investigative team and the field in general.

Abbreviations

ACS

acute coronary syndrome

ATS

alcohol T score

CDC

centers for disease control

CDT

carbohydrate deficient transferrin

CHD

coronary heart disease

HAC

heavy alcohol consumption

LC

lung cancer

MSdPCR

methylation sensitive digital polymerase chain reaction

PLCO

Prostate, Lung, Colorectal and Ovarian screening trail.

Data Availability

We have uploaded the minimal dataset to the Open Science Foundation. The link is https://osf.io/4fhj8/.

Funding Statement

National Institutes of Health 1R44CA285136.

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Decision Letter 0

Chunyu Liu

3 Feb 2025

PONE-D-25-03475Epigenetics Assessments of Alcohol Consumption Predict Mortality in Smokers at Risk for Lung Cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening TrialPLOS ONE

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Academic Editor

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Reviewers' comments:

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Reviewer #1: Yes

Reviewer #2: No

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Thank you for the opportunity to read this interesting and well written manuscript that explored the association of epigenetic biomarkers of heavy alcohol drinking and smoking, with all-cause mortality. Findings indicated that Alcohol T Score was significantly related to all-cause mortality in smokers who developed and did not develop lung cancer. DNA methylation at cg05575921 did not have significant association with mortality. The author conclude that heavy alcohol consumption is a significant risk factor of all-cause mortality in smokers. The present study has several strengths including advanced modeling, long follow-up time for death, using participants from case-control study. Despite these strengths, there are several areas that limit my enthusiasm for the study. Below describe the major and minor weaknesses:

Key Weaknesses

1. In the results, you mentioned estimated survival split with AST median, but in the methods, you did not clearly state this analysis. Please add more details for it in the method.

2. Alcohol consumption had different burden on women and men due to their difference on body size, muscle mass, body fat and hormone level. Can you add sex-stratified analysis to further investigate the effect of heavy alcohol drinking on all-cause mortality?

3. Figure3 shows that ATS has a strong correlation with cg05575921. Please provide a more detailed explanation of how the adjusted R-squared value and p-value were calculated. Additionally, clarify the method used to generate the red line in the figure. Furthermore, Figure 3 appears to depict a negative relationship between ATS and cg05575921. Please explicitly state the direction of correlation in the text.

4. Can you add a test for interaction between ATS and cg05575921 in the cox model?

Additional Minor Weaknesses

1. In the introduction, line 70-72 “In abstinent individuals, ATS is zero-centered metric with a standard deviation of 2.2 that non-linearly increases as a function of increasing chronic alcohol consumption.” needs more clarification. Does this mean that, among non-drinkers, ATS has a mean of zero and a standard deviation of 2.2? Additionally, does the statement imply that if non-drinkers start drinking, then the relationship between alcohol consumption and ATS becomes non-linear?

2. In the Methods, the format of each small part is not consistent. Can you add subtitle for each part to improve consistency and readability? Suggested subtitles include Study sample, DNA methylation, AST value, Outcome, statistical analysis.

3. In the results, line 151 “LC cases were not more likely than controls to be current smokers (45% vs 39%, NS). ” What does “NS” mean? What was the p value to support your statement “LC cases were not more likely than controls to be current smokers”?

4. In the results, line 167-168 “median of 20.7 years (interquartile range; 5460-8521 days).”, can you convert the unit of days into years for consistency and clarity?

5. The sentence in the discussion line 205-207 “In a study using a marker for recent (past 3 weeks) heavy alcohol consumption, referred to a ZSCAN25, we found a 0.56 correlation” requires clarification. Could you specify which two variables the 0.56 correlation refers to? Additionally, explain how this correlation supports the claim that "binge drinking is not perfectly correlated with the ATS."

6. In the discussion, line 208-211 “Therefore, we believe that our findings with respect to mortality reflect the entirety of lifestyle risk biology, including risks from poor diets and lack of exercise, that track with the heavily tobacco use and chronic alcohol consumption. ” could benefit from further clarification. Specifically, how do the findings support the conclusion that risks from poor diet and lack of exercise are reflected? Are there specific references? Expanding on this would strengthen the argument.

7. In the discussion, line 222-224 “effects of alcohol measured through this method are greater than those that can be shown using cg05575921, and that with greater sample size, the effects assessed by cg05575921 will become significant.” What specific outcome is being affected by alcohol or cg05575921? Providing this information will help the reader understand the context and implications of these effects more precisely.

Reviewer #2: The authors investigated the associations among cg05575921 (a DNA methylation marker for smoking prediction), the alcohol methylation T score (ATS), and all-cause mortality in lung cancer patients and controls. The results indicate that the ATS score is associated with longer survival times. While the manuscript addresses an important topic, however clarification of the methods and presentation of results are needed to enhance its impact and clarity. Specific questions and comments regarding the manuscript are as follows:

1. The study focuses on two epigenetic markers for smoking and alcohol consumption and investigates their associations with mortality. Although methylation markers reflect a person’s cumulative lifestyle exposure, it would be more informative to compare these markers with actual lifestyle variables (e.g., smoking pack-years and alcohol consumption levels). While I understand that one of the major motivations of the study is the known unreliability of self-reported data in epidemiologic studies, including these variables and comparing the results from the epigenetic signatures could strengthen the findings.

2. Abstract: In lines 45–47, the phrase “in this small group” needs clarification.

3. Also, why did the authors control the occurrence of lung cancer in the analysis? Did the model for cg05575921 include both lung cancer patients and controls? If so, this approach appears inconsistent with that used for the ATS; please clarify.

4. The dataset comprises lung cancer patients and cancer-free individuals, with significant associations observed in both groups. However, the manuscript refers to “at risk for LC” and lung cancer screening efforts. Could the authors provide more contextual information on this point? If space in the abstract is limited, consider including additional details in the Methods section.

5. What is the rationale for providing sex-stratified descriptive statistics? It may be helpful to include P-values for comparisons of demographic and clinical characteristics in Table 1. In lines 156–159, the authors note differences in methylation values by sex. If such differences exist, why were the main association analyses not stratified by sex?

6. Line 110: the abbreviation “EEMS” is used. Please provide the full term when the abbreviation first appears.

7. Methods: The association was investigated using Cox proportional hazards models; however, the details of the models are not well specified. For instance, did the regression model include cg05575921 and ATS simultaneously? Clarification of the modeling strategy is needed.

8. Covariates: Was information on cancer stage and grade available for lung cancer patients? If so, these variables should be included in the analyses. Additionally, please explain why race/ethnicity was excluded from the model construction.

9. Figures: The point estimates mentioned in the text are not displayed in Figures 3 and 4. I am also uncertain whether Figures 1-3 provide sufficient additional information to warrant their inclusion in the main text. Reorganizing the presentation of the findings (both tables and figures) in the Results section would help readers follow the key messages more clearly.

10. Lines 187–189: the authors draw a conclusion that I find difficult to support based on the results presented. Specifically, the analyses were not conducted exclusively among smokers, and referring to “baseline alcohol consumption” is misleading when it is measured via a proxy marker (i.e., the methylation signature). Please clarify how these conclusions were derived from the results.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2025 May 22;20(5):e0322783. doi: 10.1371/journal.pone.0322783.r003

Author response to Decision Letter 1

10 Feb 2025

Editors of PLOS One

February 10, 2025

Please find the attached revised manuscript entitled “Epigenetic Assessments of Heavy Alcohol Consumption Predict Mortality in Smokers at Risk for Lung Cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial” as a research article. The original submission received two reviews and eight Editorial comments, several of which require a response. Please see the response to the Editorial comments, then Reviewer comments.

Editorial Comments:

Comment: 1. Please ensure that your manuscript meets PLOS ONE's style requirements,…

Response: We will do our best. We are now using the PLOS EndNote library.

Comment: 2… the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

Response: We seemingly cannot get access to the Financial Disclosure section to edit it. Would you please put in “This work was conducted using funding from the National Institutes of Health grant 1R44CA285136 award to R.P. and K.D. The website for the National Institutes of Health is https://www.nih.gov/. The sponsors did not play any role in the study design, data collection, analysis, decision to publish or preparation of the manuscript”? Thank you!

Comment: 3… Please state what role the funders took in the study.

Response: As usual for NIH funded R Series studies, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Please accept this as our statement. As per the above, we cannot get access to the portion of the website that would allow us to edit that state.

Comment: 4… Please remove any funding-related text from the manuscript…

Response: We have done this per the editor’s request

Comment: 5…Competing interests section….. Please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials…

Response: Our amended Competing Interests section should state “Dr. Philibert is the Chief Executive Officer of Behavioral Diagnostics. The use of cg05575921 to assess smoking status is covered by existing and pending patents including US Patents 8,637,652 and 9,273,358, while the use of DNA methylation to assess alcohol and predict is covered by existing patents and pending patent claims including European Union Patent 3149206. On behalf of Drs. Philibert, Behavioral Diagnostics and the University of Iowa have a filed an intellectual property claim on the use of DNA methylation to predict AWS and related phenomena. This does not alter our adherence to PLOS ONE policies on sharing data and materials.” Thank you for changing this for us.

Comment: 6. We note that you indicated that there are restrictions to data sharing for this study…

Response: As per our data sharing agreement with the National Cancer Institute, their data may not be shared with third parties. Per our funding agreement, the data are being deposited with the the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial repository. To gain access to that or any other PLCO data, you must submit a project proposal as described at https://cdas.cancer.gov/plco/. We have made that clear in the data availability statement.

Comment: 7. Please note that your Data Availability Statement is currently missing the repository name…

Response: We now have included the CDAS website information.

Comment: 8. Please amend either the title on the online submission form…

Response: Done as requested. A “s” was inadvertently added to the title on the website. It was removed.

Reviewer One

Comment: In the results, you mentioned estimated survival split with AST median, but in the methods, you did not clearly state this analysis. Please add more details for it in the method.

Response: This clarification was made as requested by the Reviewer.

Comment: … Can you add sex-stratified analysis to further investigate the effect of heavy alcohol drinking on all-cause mortality?

Response: These models were run and mentioned in the Results per the Reviewer’s request. The results of the stratified models indicate that the associations of ATS and cg05575921 to mortality do not appear to be impacted by sex. That is, the parameter estimates are similar in models that adjust for sex versus models that stratify by sex.

Comment: Please provide a more detailed explanation of how the adjusted R-squared value and p-value were calculated…clarify the method….and explicitly state the direction of the correlation in the text.

Response: Done per the Reviewer’s request.

Comment: Can you add a test for interaction between ATS and cg05575921 in the cox model?

Response: We did, but the effect was not significant, so it was dropped from the model. We have noted this in the text. But as the Reviewer well knows, the failure to demonstrate the significance is simply a matter of power. We have requested funds to conduct the ATS on all 5000 subjects in our collection. If we get that funding, I am almost certain that there will be a significant interaction effect.

Minor Comments

Comment: 1. In the introduction, line 70-72 “In abstinent individuals, ……..

Response: Yes! The Reviewer understands this exactly. Epigenetic responses are inherently non-linear and non-normally distributed. This is why our more advanced commercial models all use AI (which handles the non-linearity better) and why the use of reference-free approaches, such as MSdPCR, for assessing DNA methylation is necessary for the most accurate descriptions of epigenetic responses to environmental variables. We only normalize the “zero point” for the ATS to make it more interpretable. I just wrote a rather lengthy review of the ATS and ZSCAN25 markers (that was recently published in Epigenomics) that went through the pros and cons of this approach, as well as other related issues, in detail. But I doubt that many will read it.

Comment: 2…the format of each small part is not consistent. Can you add subtitle for each part to improve consistency and readability?

Response: Done per the Reviewer’s request.

Comment: 3…. What does “NS” mean? What was the p value to support your statement…

Response: Done per the Reviewer’s request.

Comment: 4… can you convert the unit of days into years for consistency and clarity?

Response: Done per the Reviewer’s request.

Comment: 5….. line 205-207… Could you specify which two variables the 0.56 correlation refers to? Additionally, explain how this correlation supports the claim that "binge drinking is not perfectly correlated with the ATS."

Response: Done per the Reviewer’s request. Specifically, we expanded the text here to make it more clear what we trying to convey. The ATS is good at picking up sustained alcohol intake, but its dynamic response is slow. Fortunately, there are other markers that respond more quickly. But the ATS does a lousy job at spotting self-reported binge drinking in our experience.

Comment: 6. In the discussion, line 208-211….. Specifically, how do the findings support the conclusion that risks from poor diet and lack of exercise are reflected? Are there specific references? Expanding on this would strengthen the argument.

Response: Done per the Reviewer’s request. Unsurprisingly, so many of these factors have significant collinearity. As a result, it is difficult to assign precise causality.

Comment: What specific outcome is being affected by alcohol or cg05575921?

Response: Good point. We have now added text to further explicate our views on this. As the Reviewer knows, the ATS and cg05575921 are simply good, yet imperfect, biomarkers for the behaviors that drive changes in their values. We have made that point clearer.

Reviewer Two

Comment: 1…. Although methylation markers reflect a person’s cumulative lifestyle exposure, it would be more informative to compare these markers with actual lifestyle variables (e.g., smoking pack-years and alcohol consumption levels). While I understand that one of the major motivations of the study is the known unreliability of self-reported data in epidemiologic studies, including these variables and comparing the results from the epigenetic signatures could strengthen the findings.

Response: The Reviewer has a good point that we are in the process of addressing. We do not yet have the self-report data on alcohol and the self-report data (e.g., pack years and current) on smoking does not add to prediction in this small data set. It simply is a matter of power and we do not wish to others to conclude that we are denigrating the value of the packyear measure because our sample is too small to show effects. Pack years matter-but not for this construct with this number of subjects.

We are just finishing our cg05575921 assessments on all 5000 subjects in our population and plan to address the issue of smoking self-report in the greater context of lung cancer risk prediction. I think that the Reviewer will find that article refreshing. But to be clear, in our extensive experience using these markers in a variety of settings, we find that the value of smoking self-report can be highly variable. In the PLCO data set, the degree of reliability with respect to smoking is considerably higher than many-including the FHS. Unfortunately, since the alcohol assay is much more expensive and time consuming to conduct, we will not have ATS data to accompany that paper. But we have submitted a U01 grant to get those funds as per below.

As far as the alcohol self-report. Well, to be blunt, the reliability of the alcohol self-report data in most public data sets is poor and in actual clinical samples, it is very poor (we cover this in several of our cited articles). We are eagerly awaiting the DHQ data that has the alcohol data on the PLCO population from NCI and have submitted a grant proposal to examine its reliability in the PLCO population. We thank the Reviewer for their patience in the matter. But this issue should be dealt with large numbers of subjects in publicly available datasets for all to peruse. And that is what we plan to do once we get funding for the alcohol assays-which are much more expensive to conduct.

Comment: 2. Abstract: In lines 45–47, the phrase “in this small group” needs clarification.

Response: Done per the Reviewer’s request.

Comment: 3. Also, why did the authors control the occurrence of lung cancer in the analysis?

Response: The analyses were conducted separately because of the large effects of Lung CA on mortality and the fact that the sample is a matched case control study (3:1) based on Lung CA status and additionally matched for smoking, ethnicity and sex. Each model included cg05575921 and ATS because each of the values is known to separately predict mortal risk in and above Lung CA status.

Comment: 4. … Could the authors provide more contextual information on this point? If space in the abstract is limited, consider including additional details in the Methods section.

Response: I see that by the below comments that the Reviewer did not understand that all of these subjects were smokers and former smokers. This is our fault for not making this clearer that all of these subjects were smokers or former smokers and that our overarching purpose is create a rationale for the addition of thorough alcohol screening to all lung cancer risk screening visits. We have added repeated mentions that these subjects are smokers or former smokers as well as test to the end of the introduction making the study rationale clearer.

Comment: 5. What is the rationale for providing sex-stratified descriptive statistics?

Response: Smoking and drinking behaviors differ by gender. So, we standardly present them in our publications. But to address the Reviewer’s comment, we now have noted those characteristics in which male and female subjects significantly differ. In brief, the ATS, mortality and survival were different in the male non-LC controls than in the female non-LC controls.

Comment: 6. Line 110: the abbreviation “EEMS” is used. Please provide the full term when the abbreviation first appears.

Response: Done per the Reviewer’s request.

Comment: Methods: The association was investigated using Cox proportional hazards models; however, the details of the models are not well specified… Clarification of the modeling strategy is needed.

Response: This clarification was made as requested by the Reviewer.

Comment: Covariates: Was information on cancer stage and grade available for lung cancer patients? If so, these variables should be included in the analyses. Additionally, please explain why race/ethnicity was excluded from the model construction.

Response: Yes, those data are available. But none of the patients had lung cancer at sampling, so one cannot put them into the prediction model. Furthermore, even if one did, it is difficult to know what “stage’ one would include. Over the course of ~13 years of observation, 92 of them developed lung cancer, that progressed from Stage 1 (which could have already been present in many cases unknowingly) and for the majority of the subjects with lung cancer, to stage 4 before they died or their data was censored. So, it is not possible to put stage into the initial model.

Race and ethnicity were not included in the model their presence was not significant. But that is not remarkable given the limited number of subjects. We have added this point to the results.

Comment: 9. Figures: The point estimates mentioned in the text are not displayed in Figures 3 and 4. I am also uncertain whether Figures 1-3 provide sufficient additional information to warrant their inclusion in the main text. Reorganizing the presentation of the findings (both tables and figures) in the Results section would help readers follow the key messages more clearly.

Response: If the Reviewer would be more explicit as to how it should be reorganized, we would be happy to oblige. However, Reviewer One did not find the presentation of results confusing and we have used this method of presenting results quite literally dozens of times previously. But are open to explicit suggestions.

Comment: 10. Lines 187–189: the authors draw a conclusion that I find difficult to support based on the results presented. Specifically, the analyses were not conducted exclusively among smokers, and referring to “baseline alcohol consumption” is misleading when it is measured via a proxy marker (i.e., the methylation signature). Please clarify how these conclusions were derived from the results.

Response: The Reviewer is in error. All the subject data analyzed in this paper were from smokers or former smokers. The non-smokers in the PLCO sample were all excluded. We apologize for not making that clearer and have made this more clear at several points of the manuscript. Furthermore, all markers, including those of self-report, are proxy measures, when examining complex human behaviors such as a smoking and drinking. But we see how “baseline alcohol consumption” could be misconstrued and have changed it to “average alcohol consumption at the time of study intake significantly predicted of the overall mortality risk of subjects who were current or former smokers…”.

Thank you for your consideration of this revised manuscript. I look forward to seeing the response from the Reviewers. We stand ready to make additional changes if necessary and desirable.

Sincerely yours,

Robert A. Philibert M.D., Ph.D.

Professor of Psychiatry and Biomedical Engineering

Member, Neuroscience and Genetics Programs

Decision Letter 1

Chunyu Liu

28 Mar 2025

Epigenetic Assessments of Alcohol Consumption Predict Mortality in Smokers at Risk for Lung Cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial

PONE-D-25-03475R1

Dear Dr. Philibert,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Chunyu Liu, PhD

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I did not have any additional comments. All my previous questions were solved. And the manuscript is well written.

Reviewer #2: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: No

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Acceptance letter

Chunyu Liu

PONE-D-25-03475R1

PLOS ONE

Dear Dr. Philibert,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Chunyu Liu

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Availability Statement

    We have uploaded the minimal dataset to the Open Science Foundation. The link is https://osf.io/4fhj8/.

    Articles from PLOS One are provided here courtesy of PLOS

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