Dzevka Dragic

Sue-Ling Chang

Kaoutar Ennour-Idrissi

Francine Durocher

Gianluca Severi

Ph.D Caroline Diorio
email: caroline.diorio@crchudequebec.ulaval.ca.

Association between alcohol consumption and DNA methylation in blood: a systematic review of observational studies

Keywords: Alcohol consumption, DNA methylation, Differentially Methylated Positions (DMP), Blood, Epigenetic biomarkers, Systematic review

Word count: 117) Aim: We systematically reviewed and evaluated current literature on alcohol consumption and DNA methylation (DNAm) at the genome-wide and probe-wise level in blood of adults. Materials & methods: Five databases (PubMed, Embase, Web of Science, CINAHL, and PsycInfo) were searched until December 20th, 2020.

Studies assessing the effect of alcohol dependence on DNAm were not eligible. Results: Eleven cross-sectional studies were included with 88 to 9,643 participants. Overall, all studies had a risk of bias criteria unclear or unmet. Epigenome-wide association studies identified between 0 and 5,458 differentially methylated positions, and 15 were observed in at least four studies. Conclusion: Potential methylation markers for alcohol consumption have been identified, but further validation in large cohorts is needed.

Introduction

Alcohol consumption is a family, friendship, and social habit, identified as a risk factor for several diseases. In 2016, about 43% of the world's population aged 15 years and older consumed alcohol in the past twelve months, with an average of 32.8 grams of pure alcohol consumed per person per day [1]. Percentages were higher in the European Region (59.9%), the Region of the Americas (54.1%), and the Western Pacific Region (53.8%) [1]. By gender, 53.6% of the male world's population and 32.3% of the female population consumed alcohol (European Region: 69.2% for men and 51.4% for women; Region of the Americas: 66.8% and 41.9%, respectively) [1].

Alcohol consumption results in about three million deaths worldwide, accounting for 5.3% of all deaths, and 132.6 million disability-adjusted life years (DALYs), corresponding to 5.1% of the global burden of diseases and injuries [1]. Consequently, reducing the global level of alcohol consumption has been an increasing public health priority in recent years.

Alcohol consumption is a well-known modifiable risk factor associated with several chronic diseases, including cardiovascular diseases (CVD) and cancers [2]. CVD are the leading cause of mortality, with 17.9 million deaths in 2019 [3]. Cancers are the second leading cause, with almost 10 million deaths in 2020 [4]. An estimated 593,000 deaths (3.3% of all CVD deaths) and 13 million DALYs (3.2% of all CVD DALYs) caused by CVD; and 400,000 deaths (4.2% of all cancer deaths) and 10.3 million DALYs (4.2% of all cancer DALYs) caused by cancers were attributable to alcohol consumption [1]. Recently, higher alcohol consumption (100 g per week) was associated with an increased risk of all stroke subtypes (hazard ratio [HR]: 1.14, 95% confidence interval [CI]:

1.10-1.17), coronary disease excluding myocardial infarction (HR: 1.06, 95%CI: 1.00-1.11), heart failure (HR:

1.09, 95%CI: 1.03-1.15), and several less common CVD subtypes [5]. Regarding cancers, an increase of seven drinks of alcohol consumed per week among drinkers was associated with an increased risk of cancers of the upper aerodigestive tract (HR: 1.19, 95%CI: 1.10-1.29), mouth and pharynx (HR: 1.18, 95%CI: 1.08-1.29), esophagus (HR: 1.22, 95%CI: 1.04-1.43), colorectum (HR: 1.09, 95%CI: 1.04-1.15), colon (HR: 1.13, 95%CI: 1.06-1.20), liver (HR: 1.22, 95%CI: 1.04-1. [START_REF] Kennel | Impairment of Myocardial Glutamine Homeostasis Induced By Suppression of the Amino Acid Carrier SLC1A5 in Failing Myocardium[END_REF]) and breast (HR: 1.11, 95%CI: 1.02-1.21) [6]. Even though several studies have reported associations between alcohol consumption and these chronic diseases, the underlying mechanism is still not well-defined. Research on the relationship between chronic diseases and alcohol consumption at the epigenetic scale is a potential source of targets' discovery.

One of the most studied epigenetic mechanisms in humans is DNA methylation (DNAm). This potentially reversible mechanism controlling gene expression [7] occurs predominantly at cytosines within CG dinucleotides (CpG sites) in mammalian genomes. It is a tissue-and cell-specific mechanism mainly measured in blood since sample collection is less invasive than that for other tissues. Epigenome-wide association studies (EWAS) have explored the link between DNAm and various chronic diseases [8][9][10]. In addition to its heritable part, a number of studies have suggested that environmental and lifestyle factors, such as alcohol consumption, might impact DNAm [11]. A previous systematic review on the association between DNAm in blood, saliva, or brain tissue and alcohol use disorders (AUD) was conducted for articles published until April 2016. This review reported higher global methylation; hypermethylation of promoter regions of AVP, DNMT3B, HERP, HTR3A, OPRM1, and SNCA genes; hypomethylation of the GDAP1 promoter region; and DNAm changes at CpG sites across the epigenome in individuals with AUD [12]. While this review focused on DNAm changes related to alcohol dependence, there is a lack of information on DNAm changes associated with alcohol consumption. Thus, exploring the relationship between alcohol consumption and DNAm in blood may help understand the mechanism linking alcohol consumption and chronic diseases and might serve as a target for prevention and treatment research. We hypothesized that DNAm could be a potential mediator in these relationships and its measurement in blood could be used as a proxy for DNAm in another tissue. The aim of this systematic review was to evaluate the association between alcohol consumption and genome-wide DNAm in blood of adults.

Materials & methods

A systematic review was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [13].

Eligibility criteria

Studies were selected based on the eligibility criteria described below. We used the Population, Exposure, Comparator, and Outcomes (PECO) approach to define the eligibility criteria [14]. Only studies published in English, for which full texts were available, were included.

Population: Participants had to be adults aged 18 years old or older. If a study population was not only composed of adults, it was included if adult-specific data were available.

Exposure: Different levels or patterns of any alcohol consumption measured with any type of tools: self-report questionnaires or interviews to assess frequency/quantity/intensity of drinking such as food frequency questionnaire (FFQ) and any validated tools for alcohol consumption assessment.

Comparator: Comparator group was a non-drinking group (lifetime abstainers or former alcohol consumers) or a group with a lower level or pattern of alcohol consumption.

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Outcome: Genome-wide DNAm measured in blood with any probe-wise methods: bisulfite sequencing, microarray or bead array, and Methyl-Sensitive Cut Counting (MSCC) [15].

Study design: Cohort studies, case-control studies, and cross-sectional studies were considered.

Exclusion criteria included studies in animals, analyses of global DNAm, analyses of candidate genes or a preselected subset of CpG sites (e.g. epigenetic clock, replication studies of previous CpG sites identified), study sample only composed of individuals with AUD, studies assessing the association of alcohol withdrawal with DNAm and reports of unpublished manuscripts and conference abstracts. We excluded AUD cohorts to distinguish potential biomarkers of addiction/dependence from potential biomarkers of alcohol consumption.

In the literature, it has been observed that addiction susceptibility is associated with DNAm [16]. As Kaplan et al. defined it "Addiction is a chronically relapsing neuropsychiatric disease that occurs in some, but not all, individuals who use substances of abuse". Thus, among alcohol users only some are AUD cases. Consequently, it is important to distinguish DNAm changes that may contribute to an individual's susceptibility to AUD from those that may be a consequence of alcohol consumption [17]. For instance, at the genetic level, variants associated with AUD overlapped strongly with psychiatric disorders, while variants associated with alcohol consumption seem to be more associated with the metabolism [18].

Information sources

Five databases were searched: two biomedical databases, PubMed and Embase; a multidisciplinary database, Web of Science; and two health databases, CINAHL and Ovid PsycInfo, from their respective inceptions to December 20 th , 2020. The choice of databases to be consulted was made and discussed by DD and CD.

Search strategy

The search strategy was first designed in PubMed based on controlled vocabulary search terms and free-text vocabulary terms related to the key concepts "alcohol consumption" and "DNA methylation". It was then translated into Embase, Web of Science, CINAHL, and PsycInfo. Search strategies are reported in Supplementary Tables 1-5 for PubMed, Embase, Web of Science, CINAHL, and PsycInfo via Ovid, respectively. Search strategies were elaborated by DD with the help of CD. All references included in the systematic review on the association between DNAm and AUD were identified by our search strategy providing support to its exhaustiveness [12].

Selection process

The selection process was carried out independently by two reviewers (DD and CD), first by title and abstract, then full-text reading. The selection was made following a preliminary pilot selection of studies by title and abstract on 10% of the total unique records identified to ensure that the inclusion criteria were well defined and understood. Inconsistencies were discussed to reach a consensus at each step. Two authors were consulted whenever there was uncertainty about inclusion (KEI and SLC).

Data collection process

Data extraction was carried out by a single investigator (DD) using an exhaustive standardized form designed previously [17] and adapted for this review. When required, a second reviewer (CD) was consulted.

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Data items

Information was extracted from each included study : (1) study characteristics (first author, year of publication, country where the study was conducted, recruitment sites, and study design); (2) population characteristics (name of the study population source, number of participants analyzed, follow-up time, health status, mean age, sex, ethnicity and details on alcohol consumption); (3) exposure information (definition of alcohol consumption, method of assessment, variable type used in the statistical analysis and unit of measurement, period of exposure) and outcome information (method of assessment, measurement tool, variable type used in the statistical analysis, biological sample from which DNA was extracted, method for cell-type proportion assessment, preprocessing methods applied and number of CpG included in the analysis); and (4) the association between exposure and outcome (including association estimation methods, methylation metrics, covariables, significance threshold and multiple testing correction used, number of differentially methylated positions identified, number of associated-genes and pathways analyses). Corresponding or first authors of studies were contacted by email to obtain missing information when needed.

Study risk of bias assessment

The risk of bias in the included observational studies was assessed by one reviewer (DD) using the appropriate 8-item checklist for analytical cross-sectional studies from the Joanna Briggs Institute (JBI) [19]. The tool evaluated selection bias, confounding bias, information bias, and statistical analyses. Each checklist item was rated as yes, no, unclear, or not applicable. When required, a second reviewer (CD) was consulted.

Summary measures

The primary outcome measure was the mean methylation (beta-value or M-value) difference at each CpG site for an additional unit of alcohol consumed if the variable was continuous, or according to different levels of alcohol intake (with no or lower alcohol consumption as reference) if the variable was dichotomous or polytomous. We reported the number of differentially methylated regions (DMR) and the number of differentially methylated positions (DMP) with their number of related genes. Furthermore, we compiled DMP identified in the studies to report the most frequently identified sites.

Synthesis methods

We performed a qualitative synthesis using the results of descriptive analyses from extracted data to investigate the relationship between alcohol consumption and DNAm in blood. First, we described the selection process using the PRISMA 2020 flow diagram. Second, study characteristics, participants, exposure, outcome, statistical analyses, and reported results were summarized in a table. Third, we evaluated the methodological risk of bias using an appropriate tool according to the study design. Considering that high heterogeneity between studies was expected (exposure measurement methods, variable type and unit, methods for DNAm assessment, and preprocessing), quantitative synthesis was not considered appropriate. Finally, results from each study were recorded together (probe name, associated gene, chromosome, position on the genome, mean difference value, p-value, and adjusted p-value) to identify the number of occurrences for each methylation probe from the included studies.

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Results

Study selection

We found 11,366 records with our search strategy. After removing duplicates, 6,948 unique records were identified. Of these, we reviewed 27 full-text documents and included 11 studies [20][21][22][23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF][START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF]. The PRISMA 2020 flow diagram of the study search and selection process is depicted in Figure 1.

Study and participant characteristics

Study characteristics are presented in Table 1. All eleven included studies were cross-sectional [20][21][22][23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF][START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF]. They included between 88 [20] and 9,643 [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF] participants with a mean age ranging from 23 [20] to 64 years [22].

Two were composed of male participants only [21, 22], three were composed of female participants only [START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF], and the percentage of female participants varied from 27% [START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF] to 64% [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF] in the other studies. Most of the studies were conducted specifically in Caucasian populations (n=3) [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF] or, when ethnicity was not reported, in populations likely to be largely if not exclusively Caucasian (n=5 in Europe and n=1 in Australia) [20,23,[START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF][START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF]. Three studies included predominantly African ancestries participants (from >80% [21] to 100% [22,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF]), and one study had a mixed population composed of 47% of white participants, 27% of black participants, and 32% of participants of other ethnicities [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF]. Two studies treated alcohol consumption as a categorical variable, comparing regular vs. no regular alcohol consumption [START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF]. Six studies examined alcohol consumption as a continuous variable [22][23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF], and three studies explored both [20,21,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF]. Alcohol consumption was measured by validated and reliable tools in five studies [20][21][22][23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF], including one also using Liquid Chromatography coupled with tandem Mass Spectrometry (LC-MS/MS) [21]. Regarding probe-wise DNAm analyses, seven studies only analyzed DMP [20-22, 25, 26, 28, 30], one study only analyzed DMR [START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF], and three studies analyzed both [23, [START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF]. All studies assessed DNAm in blood using mostly microarray methods (Illumina 450K in seven studies [20,21,[23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF] and Illumina EPIC in three studies [22,[START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF]), while one study used next-generation sequencing combined with the use of the methyl-CpG-binding domain protein [START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF].

Risk of bias in studies

Figure 2 presents the risk of bias assessment for cross-sectional studies. Overall, there is clear evidence for confounding bias and unclear evidence for selection and information bias. Inclusion criteria were clearly defined in all studies [20][21][22][23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF][START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF], but details on study subjects and settings were unclear for five studies [20,23,[START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF][START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF].

Furthermore, the risk of selection bias was unclear in all studies because none mentioned if DNAm measurement was done blinded to participant characteristics [20][21][22][23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF][START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF]. All studies indicated the strategies used to deal with confounding factors [20][21][22][23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF][START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF]. Still, three studies did not use the minimal set of confounding factors (ethnicity, sex, age, smoking, and cell-type proportions) [START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF]. All studies used valid and reliable tools for DNAm measurement [20][21][22][23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF][START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF], but a potential information bias regarding the exposure was present for six studies using tools for measuring alcohol consumption with unclear validity and reliability [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF][START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF]. Finally, all studies used appropriate statistical analyses [20][21][22][23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF][START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF]. Eight studies had only one [21,22], two [20,23,[START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF], or three [START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF] out of eight criteria unclear. In contrast, three studies had bias in one criterion ("Were confounding factors identified?") [START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF] and potential bias in two [START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF] or three [START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF] other criteria (Supplementary Table 6).

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Results of individual studies

Among eleven studies assessing the association between alcohol consumption and genome-wide probe-wise methylation, six studies [20,21,[23][START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF]] conducted more than one analysis to test different alcohol consumption exposures [20,21,23,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF], different population samples [20,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF], or conducted both DMR and DMP analyses [23, [START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF]. Out of 20 DMP analyses, two analyses did not identify DMP associated with alcohol consumption with an FDR <0.05 [20,21] and eighteen analyses identified 2 [START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF] to 5,458 [START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF] DMP. DMP-related genes varied between 2 [START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF] and 842 [23], but two studies did not report [START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF] or partially reported their DMPrelated genes [START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF]. The majority of studies reported their list of identified DMP online, except one that provided the complete list of DMP by email [START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF], one that only listed results from its meta-analyses [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF], and one study that only reported the Q-Q plots for the analysis [START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF]. Supplementary Table 7 lists available information on all DMP identified. The four DMR analyses identified between 33 [START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF] and 221 [23] DMR associated with alcohol consumption. One study did not report the individual results for the DMR analysis [START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF]. Supplementary Table 8 lists available information on all DMR identified. Pathway analysis was only performed by one study [23] and identified 1,457 Gene Ontology (GO) terms and 42 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for alcohol consumed in the last week (DMP, n=1,318), and 686 GO terms and 16 KEGG pathways for lifetime alcohol consumption (DMP, n=392) (Supplementary Table 9). For alcohol consumed in the last week, the strongest association with GO terms was observed for "negative regulation of gene expression" and the three most significant KEGG pathways were "chronic myeloid leukemia", "ribosome", and "glycosaminoglycan biosynthesis". For lifetime alcohol consumption, the three most significant KEGG pathways were "regulation of actin cytoskeleton", "biosynthesis of amino acids", and "platelet activation".

Results of syntheses

In total, alcohol consumption was significantly associated with 6,873 unique DMP reported in nine studies, including 995 CpG sites identified in more than one study. Table 2 shows the DMP identified in at least four studies. The most common CpG site, cg06690548 located in the SLC7A11 gene was identified in six studies [21,23,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF]. The cg11376147 locus located in SLC43A1 gene was identified in five studies [21,23,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF].

Thirteen additional DMP were identified in four studies including cg00422488 in ANP32B [23, [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF], cg02578470 in MCM2 [23, [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF], cg02583484 in HNRNPA1 [23, [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF], cg02711608 in SLC1A5 [23, [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF], cg05512483 in SNORD30 [23, [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF], cg06644515 in SNORD47 [23, [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF], cg12825509 in TRA2B [23, [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF], cg13442969 in DYRK2 [21,23,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF], cg14476101 in PHGDH [23, [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF], cg18590502 in CCDC71 [21,23,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF], and loci not related to any known gene, namely cg01940273 [20,23,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF], cg17962756 [21,23,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF] and cg18917643 [23, [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF][START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF]. Higher alcohol consumption was associated with the hypomethylation of all these CpG sites. At the gene level, CDH23 DNAm was also identified in at least four studies [20,23,[START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF]. Among the DMR associated with alcohol consumption, three were identified by two studies: a region starting at position 139162808 containing the SLC7A11 gene [23,[START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF], a region starting at position 47287778 containing the SLC1A5 gene [23,[START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF], and a region starting at position 31650735 near the LY6G5C gene [23,[START_REF] Perrier | Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC study[END_REF].

Discussion

Article Body Template

The present systematic review of the association between alcohol consumption and epigenome-wide DNAm in blood of adults reported highly variable findings with some notable consistencies. Although alcohol consumption has been associated with many CpG sites, only a small proportion was identified by multiple studies. Still, we were able to highlight that higher alcohol consumption was consistently associated with the hypomethylation of 15 DNAm sites located in SLC7A11 (cg06690548), SLC43A1 (cg11376147), ANP32B (cg00422488), MCM2 (cg02578470), HNRNPA1 (cg02583484), SLC1A5 (cg02711608), TRA2B (cg12825509), DYRK2 (cg13442969), PHGDH (cg14476101), CCDC71 (cg18590502), SNORD30 (cg05512483), SNORD47 (cg06644515) genes and other loci locations (cg01940273, cg17962756, and cg18917643) in more than four studies. Three studies identified all 15 DMP [23, [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF][START_REF] Wilson | Alcohol and DNA Methylation: An Epigenome-Wide Association Study in Blood and Normal Breast Tissue[END_REF], one study identified 11 out of 15 DMP [START_REF] Mccartney | Epigenetic prediction of complex traits and death[END_REF], one study identified 5 out of 15 DMP [21], and two studies identified 1 out of 15 DMP [20,[START_REF] Langdon | Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer[END_REF]. We noticed that the 15 DNAm sites were not identified by all three ancestry-stratified meta-analyses performed by Liu et al. (Caucasian, African ancestries, and mixed populations) [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF]. When stratified by ancestry, 13 DNAm sites were identified in Caucasian participants, eight DNAm sites were identified in African ancestries participants, and four DNAm sites were identified in the mixed population [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF]. Comparing the meta-analysis in African ancestries participants [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF] with the other study involving >80% of African-ancestries participants [21], only one DNAm site (cg11376147 in SLC43A1) overlapped among the 15 sites identified in at least four studies. Thus, the DNAm sites are more likely to be representative of the Caucasian population, and further investigations is needed for other ethnicities.

Besides their large sample size, no criterion differed between the three studies that identified the 15 DMP and the other studies. Interestingly, one study with a relatively small sample size compared to the three mentioned above (n=580) and in which alcohol consumption was associated with 11 DMP was able to identify 5 out of 15 DMP. These results may be explained by the use of a reliable measure of alcohol consumption (phosphatidylethanol). Moreover, Dugué et al. (2019) also conducted a longitudinal analysis using DNAm sites associated with alcohol consumption in their cross-sectional analysis, and confirmed 12 out of the 15 DMP (cg06690548 in SLC7A11, cg02711608 in SLC1A5, cg14476101 in PHGDH, cg11376147 in SLC43A1, cg02583484 in HNRNPA1, cg13442969 in DYRK2, cg17962756, cg05512483 in SNORD30, cg06644515 in SNORD47, cg01940273, cg00422488 in ANP32B, cg02578470 in MCM2).

After the databases were searched for the literature review, the largest EWAS of alcohol consumption to date (n=8,161) was published in 2021 [START_REF] Lohoff | Epigenome-wide association study of alcohol consumption in N = 8161 individuals and relevance to alcohol use disorder pathophysiology: identification of the cystine/glutamate transporter SLC7A11 as a top target[END_REF]. In a predominantly European ancestry population, Lohoff et al. identified 2,504 DMP with a Bonferroni threshold associated with alcohol consumption in units of alcohol per week. Of the 15 DMP we found, thirteen (cg06690548, cg11376147, cg00422488, cg02578470, cg02583484, cg02711608, cg12825509, cg13442969, cg14476101, cg05512483, cg06644515, cg17962756, and cg18917643) overlapped with the EWAS with six of them (cg06690548, cg11376147, cg02711608, cg12825509, cg14476101, and cg06644515 ) being in the top 20 CpG associated with alcohol consumption.

In the literature, gene-related DMP identified by at least four studies (SLC7A11, SLC43A1, SLC1A5, PHGDH, ANP32B, MCM2, HNRNPA1, SNORD47, TRA2B, DYRK2, and CDH23) have been associated with alcohol consumption and diseases particularly cancers. The solute carrier (SLC) gene family are known to be transporter proteins that facilitate the transit of a molecule across all cellular membranes. They carry out critical physiological functions that serve to maintain cell homeostasis. The solute carrier family 7 member 11 gene, SLC7A11, encodes one of the components of an amino acid transporter specific to cystine:glutamate antiporter activity; and is associated with ferroptosis. The solute carrier family 43 member 1, SLC43A1, is a protein-coding gene linked to neutral amino acid transmembrane transporter activity and L-amino acid transmembrane transporter activity. The solute carrier family 1 member 5 gene, SLC1A5, encodes a neutral amino acid transporter linked to signaling receptor activity and amino acid transmembrane transporter activity and is associated with central carbon metabolism in cancer and protein digestion and absorption pathways. The dysfunction of SLC transporters may cause diseases when a particular type of molecule is not properly delivered to a target cell or is not removed from the cell for disposal or use elsewhere [START_REF] Lai | 1 -Membrane transporters and the diseases corresponding to functional defects[END_REF]. In the literature, methylation changes in SLC7A11 and SLC43A1 have been associated with alcohol consumption [START_REF] Lohoff | Epigenome-wide association study of alcohol consumption in N = 8161 individuals and relevance to alcohol use disorder pathophysiology: identification of the cystine/glutamate transporter SLC7A11 as a top target[END_REF][START_REF] Xu | Epigenome-Wide DNA Methylation Association Analysis Identified Novel Loci in Peripheral Cells for Alcohol Consumption Among European American Male Veterans[END_REF]. Moreover, the expression of SLC7A11, SLC43A1 and SLC1A5 have been associated with a variety of cancers (liver, laryngeal, breast, prostate, oral, head and neck, hepatocellular) [START_REF] He | Intra-Tumoral Expression of SLC7A11 Is Associated with Immune Microenvironment, Drug Resistance, and Prognosis in Cancers: A Pan-Cancer Analysis[END_REF][START_REF] Zhang | Overexpression of SLC7A11: a novel oncogene and an indicator of unfavorable prognosis for liver carcinoma[END_REF][START_REF] Ma | SLC7A11, a component of cysteine/glutamate transporter, is a novel biomarker for the diagnosis and prognosis in laryngeal squamous cell carcinoma[END_REF][START_REF] Timmerman | Glutamine sensitivity analysis identifies the xCT antiporter as a common triple-negative breast tumor therapeutic target[END_REF][START_REF] Rii | Functional analysis of LAT3 in prostate cancer: Its downstream target and relationship with androgen receptor[END_REF][START_REF] Wang | L-type amino acid transport and cancer: targeting the mTORC1 pathway to inhibit neoplasia[END_REF][START_REF] Luo | ASCT2 overexpression is associated with poor survival of OSCC patients and ASCT2 knockdown inhibited growth of glutamine-addicted OSCC cells[END_REF][START_REF] Zhang | ASCT2 (SLC1A5)-dependent glutamine uptake is involved in the progression of head and neck squamous cell carcinoma[END_REF][START_REF] Sun | GLUT1 and ASCT2 as Predictors for Prognosis of Hepatocellular Carcinoma[END_REF][START_REF] Bernhardt | Proteomic profiling of breast cancer metabolism identifies SHMT2 and ASCT2 as prognostic factors[END_REF], and the expression of SLC1A5 has been associated with failing myocardium [START_REF] Kennel | Impairment of Myocardial Glutamine Homeostasis Induced By Suppression of the Amino Acid Carrier SLC1A5 in Failing Myocardium[END_REF]. Furthermore, the methylation of SLC7A11, SLC43A1, SLC1A5, and PHGDH genes has also been associated with several risk factors for CVD [START_REF] Richard | DNA Methylation Analysis Identifies Loci for Blood Pressure Regulation[END_REF][START_REF] Kho | Epigenetic loci for blood pressure are associated with hypertensive target organ damage in older African Americans from the genetic epidemiology network of Arteriopathy (GENOA) study[END_REF][START_REF] Lee | Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions[END_REF][START_REF] Truong | Blood triglyceride levels are associated with DNA methylation at the serine metabolism gene PHGDH[END_REF][START_REF] Tin | Epigenome-wide association study of serum urate reveals insights into urate co-regulation and the SLC2A9 locus[END_REF][START_REF] Nano | Epigenome-Wide Association Study Identifies Methylation Sites Associated With Liver Enzymes and Hepatic Steatosis[END_REF]. Concerning PHGDH, its expression has been associated with a variety of cancers (bladder, thyroid, lung, breast) [START_REF] Yoshino | Characterization of PHGDH expression in bladder cancer: potential targeting therapy with gemcitabine/cisplatin and the contribution of promoter DNA hypomethylation[END_REF][START_REF] Jeon | High Phosphoglycerate Dehydrogenase Expression Induces Stemness and Aggressiveness in Thyroid Cancer[END_REF][START_REF] Zhang | PHGDH Defines a Metabolic Subtype in Lung Adenocarcinomas with Poor Prognosis[END_REF][START_REF] Chandrika | Prognostic significance of phosphoglycerate dehydrogenase in breast cancer[END_REF]. Indeed, PHGDH initiates serine biosynthetic pathway activity, a characteristic of metabolic adaptation in carcinogenesis [START_REF] Zhu | High Expression of PHGDH Predicts Poor Prognosis in Non-Small Cell Lung Cancer[END_REF]. The acidic nuclear phosphoprotein 32 family member B gene, ANP32B, is involved in RNA polymerase binding activity and histone binding activity [START_REF] Munemasa | Promoter region-specific histone incorporation by the novel histone chaperone ANP32B and DNA-binding factor KLF5[END_REF]. Its link with alcohol intake needs to be further studied but in vitro study has observed an association between its expression and hepatocellular carcinoma [START_REF] Ohno | Downregulation of ANP32B exerts anti-apoptotic effects in hepatocellular carcinoma[END_REF] and in vivo study suggests that members of this family (ANP32A) protect hepatocytes from injury caused by ethanol feeding [START_REF] Liu | Hepatopoietin Cn reduces ethanol-induced hepatoxicity via sphingosine kinase 1 and sphingosine 1-phosphate receptors[END_REF]. The minichromosome maintenance complex component 2 gene, MCM2, is implicated in the cell cycle and DNA replication pathways, and its expression has been associated with a variety of cancers (prostate, pancreatic, lung, breast) [START_REF] Hsu | MCM2-7 complex is a novel druggable target for neuroendocrine prostate cancer[END_REF][START_REF] Deng | MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer[END_REF][START_REF] Li | Prognostic significance of minichromosome maintenance mRNA expression in human lung adenocarcinoma[END_REF][START_REF] Reena | Minichromosome maintenance protein 2 is a reliable proliferative marker in breast carcinoma[END_REF][START_REF] Issac | MCM2, MCM4, and MCM6 in Breast Cancer: Clinical Utility in Diagnosis and Prognosis[END_REF]. The MCM complex formed by the MCM proteins is involved in unwinding DNA during replication. Disruptions in DNA replication pave the way for carcinogenesis. The heterogeneous nuclear ribonucleoprotein A1, HNRNPA1, is a protein-coding gene that regulates alternative splicing and amyotrophic lateral sclerosis. In the literature, methylation changes in HNRNPA1 have been associated with alcohol consumption [START_REF] Xu | Epigenome-Wide DNA Methylation Association Analysis Identified Novel Loci in Peripheral Cells for Alcohol Consumption Among European American Male Veterans[END_REF][START_REF] Endo | Establishment of the MethyLight Assay for Assessing Aging, Cigarette Smoking, and Alcohol Consumption[END_REF], and its expression has been associated with lung and breast cancers [START_REF] Ryu | HNRNP A1 Promotes Lung Cancer Cell Proliferation by Modulating VRK1 Translation[END_REF][START_REF] Otsuka | Regulatory role of resveratrol, a microRNA-controlling compound, in HNRNPA1 expression, which is associated with poor prognosis in breast cancer[END_REF], and congenital heart defects [START_REF] Yu | Mutations in Hnrnpa1 cause congenital heart defects[END_REF]. Interestingly, HNRNPA1 regulates TRA2B transcription [START_REF] Nishikawa | HnRNPA1 interacts with G-quadruplex in the TRA2B promoter and stimulates its transcription in human colon cancer cells[END_REF]. The transformer 2 beta homolog, TRA2B, is a nuclear proteincoding gene that plays a role in mRNA processing, splicing patterns, and gene expression. GO annotations linked to this gene include nucleic acid binding and nucleotide binding, and associated KEGG pathways are alcoholic liver disease and absorption. Methylation changes in TRA2B have been associated with alcohol consumption [START_REF] Lohoff | Epigenome-wide association study of alcohol consumption in N = 8161 individuals and relevance to alcohol use disorder pathophysiology: identification of the cystine/glutamate transporter SLC7A11 as a top target[END_REF], and its expression has been associated with a variety of cancers (ovarian, endometrial, prostate, colon) [START_REF] Nishikawa | HnRNPA1 interacts with G-quadruplex in the TRA2B promoter and stimulates its transcription in human colon cancer cells[END_REF][START_REF] Iborra | Alterations in expression pattern of splicing factors in epithelial ovarian cancer and its clinical impact[END_REF][START_REF] Ouyang | Expression levels of hnRNP G and hTra2-beta1 correlate with opposite outcomes in endometrial cancer biology[END_REF][START_REF] Diao | Prognostic value of transformer 2β expression in prostate cancer[END_REF]. The dual specificity tyrosine phosphorylation regulated kinase 2 gene, DYRK2, plays a role in the regulation of cellular growth and/or development due to its capability to autophosphorylate on tyrosines residues. DYRK2 gene expression has been associated with a variety of cancers (breast, colon, liver, ovary, brain, lung) [START_REF] Tandon | Emerging roles of DYRK2 in cancer[END_REF][START_REF] Moreno | The stress-responsive kinase DYRK2 activates heat shock factor 1 promoting resistance to proteotoxic stress[END_REF][START_REF] Enomoto | Downregulation of DYRK2 can be a predictor of recurrence in early stage breast cancer[END_REF], and it regulates p53 to induce apoptosis in response to DNA damage [START_REF] Taira | DYRK2 is targeted to the nucleus and controls p53 via Ser46 phosphorylation in the apoptotic response to DNA damage[END_REF]. The phosphoglycerate dehydrogenase gene, PHGDH, is involved in phosphoglycerate dehydrogenase activity and is associated with the following KEGG pathways: biosynthesis of amino acids, carbon metabolism, glycine serine and threonine metabolism, cysteine and methionine metabolism, methane metabolism, metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in diverse environments. The coiled-coil domain containing 71 gene, CCDC71, is predicted to play a role in cellular lipid metabolic process and positive regulation of fat cell differentiation. Experimental studies have alluded to possible involvement in chromatin organization suggesting a role in genomic stability [START_REF] Scott | ATRX proximal protein associations boast roles beyond histone deposition[END_REF]. The cadherin related 23 gene, CDH23, encodes a calcium dependent cell-cell adhesion glycoprotein and its expression has been associated with renal cell carcinoma and melanoma [START_REF] Yu | A novel highly frequent single-nucleotide polymorphism site of cadherin 23 in clear cell renal cell carcinoma with sarcomatoid differentiation based on whole exome sequencing[END_REF][START_REF] Campos | High-Throughput Sequencing Identifies 3 Novel Susceptibility Genes for[END_REF].

These findings support the possibility of using these sites as biomarkers for alcohol consumption or potential mediators of alcohol-associated chronic diseases.

Among the DMP identified in more than one study (n=995), 70 CpG sites were used by [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF] [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF] and Philibert et al. (2019) [79] to classify individuals according to heavy alcohol consumption. Those studies demonstrated a very good ability, to discriminate heavy drinkers versus non-drinkers using DNAm in blood [START_REF] Liu | A DNA methylation biomarker of alcohol consumption[END_REF]: Area Under the Curve [AUC] varying from 0.90 to 0.99 across five cohorts using the full model, including 144 CpG sites in addition to age, sex, and body mass index; Philibert et al. 2019: AUC=0.96 using the model of 4 CpG sites). In the review, among the 70 CpG sites identified in more than one study, one was identified in six studies, another was identified in five studies, three were identified in four studies, thirty-two were identified in three studies, and thirty-three were identified in two studies. Thus, it would be interesting to define a new biomarker set composed of the five CpG sites identified by at least four studies from our review (cg06690548, cg11376147, cg02583484, cg12825509, and cg13442969) and compare its ability to classify alcohol consumers.

The main limitation of the present review is the high heterogeneity between CpG sites identified by the studies that may be explained by the different methodologies used: difference in the alcohol consumption variable type used in the analysis, DNAm preprocessing methods (quality assessment, batch correction, background correction and normalization, exclusion of bad detection probes, probes located in single nucleotide polymorphisms or known for cross-hybridization), and covariables included in the model. Heterogeneity in findings may also have resulted from differences in sample sizes, confounding bias (not measured or not adjusted for confounders, confounding factors to consider at least: ethnicity, sex, age, smoking status, cell-type proportions), and information bias (recall bias and social desirability bias for alcohol consumption measurement). To compare and interpret the results despite this limitation, we focused on DNAm measured in blood because the methylation is different in specific tissues and cells, and we did not want to introduce an additional source of heterogeneity. Regarding alcohol consumption variable type, we are aware that the inclusion of both categorical and continuous exposures may not be ideal for the comparison of the results.

Nevertheless, among the eleven included studies, three studies explored both continuous and categorical variables of alcohol consumption and only two studies used it as a categorical variable (regular vs no regular alcohol consumption). The results of these two studies were not mixed with the other results because one study [START_REF] Flanagan | Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research[END_REF] that performed a DMP analysis only reported the Q-Q plots as results and the other study [START_REF] Clark | Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use[END_REF] conducted a DMR analysis and there was no overlap with other DMR analyses. Thus, results from the review reflect predominantly the use of alcohol consumption as a continuous variable. Furthermore, we could not conclude that all DMP are specific to alcohol consumption and not to alcohol dependence. Indeed, the study sample could contain alcohol-dependent subjects even though we tried to limit this situation by excluding studies with a sample composed only of individuals with AUD and studies assessing the association of alcohol withdrawal with DNAm. Another limitation is the lack of longitudinal analysis to distinguish whether alcohol consumption causes DNAm or is caused by DNAm and to study the reversibility of DNAm changes in response to alcohol consumption variation within a subject.

There are several strengths to this systematic review. First, we used the rigorous PRISMA 2020 statement to report the review, making the methodology transparent and results reproducible. Second, two reviewers Article Body Template independently screened and selected studies to be included in the review. Third, the search strategies in five databases that we developed were exhaustive. Fourth, we assessed the risk of bias for all included studies. Findings from this review suggest a body of research under development assessing the association between alcohol consumption and genome-wide methylation in search for biomarkers. There are a couple of essential questions worth further exploring, such as assessing the temporal relationship between alcohol consumption, DNAm, and alcohol-related diseases by conducting longitudinal analyses and stratifying for women and men.

Promising results from these analyses can then be validated using gene-candidate methods, such as PCR and sequencing. Validated DNAm sites could then be used in observational or clinical studies to classify individuals according to their alcohol consumption, thus, reducing the information bias linked to alcohol consumption measurement by self-reported questionnaire or interview (social desirability bias or recall bias). Then, the CpG sites associated with alcohol consumption could be used to improve disease risk prediction for each individual and evaluated as mediators of the effect of alcohol on disease risk, that could be targeted by preventive interventions.

Conclusion

This systematic review highlighted that alcohol consumption was consistently associated with different DNAm sites in at least four studies. These sites could be potential markers for alcohol consumption or potential mediators of alcohol-associated chronic diseases such as CVD and cancers that could also be targeted for early prevention of these diseases.

• Future Perspective:

Over the past decade, EWAS have been widely used to identify associations between epigenetic modifications and various biological traits. Unlike genetic variants, epigenetic modifications may be reversible and may be used to develop preventive interventions or treatment strategies. EWAS of alcohol consumption are beginning to provide a valuable knowledge base of DNAm sites that could be used as biomarkers. Future research in the field should focus on two main aspects. First, to undertake new EWAS to replicate and identify new CpG sites while addressing methodological challenges (ethnicities, confounding factors, DNAm preprocessing and analytical methods, and more importantly, alcohol consumption definition). Second, to assess the predicting role of the identified CpG sites in relation to alcohol-associated diseases and to evaluate their role as mediators of the effect of alcohol.

• Executive Summary: (Word count: 253).

• Alcohol consumption is a well-known modifiable risk factor associated with several chronic diseases, including cardiovascular diseases (3.3% of deaths attributable to alcohol consumption) and cancers (4.2% of deaths attributable to alcohol consumption). Yet, the underlying mechanism is still not well-defined.

• Exploring the relationship between alcohol consumption and DNA methylation (DNAm) in blood may help understand the mechanism between alcohol consumption and chronic diseases and might serve as a target for prevention and treatment research for these diseases.

• Of 6,948 records screened, 11 cross-sectional studies evaluated the association between alcohol consumption and DNAm at the genome-wide and probe-wise level in blood of adults.

• The epigenome-wide association studies identified between 0 and 5,458 differentially methylated positions (DMP), including 995 DMP identified in more than one study and 15 DMP identified in at least four studies.

• The 15 DMP, including loci located in SLC7A11 (cg06690548), SLC43A1 (cg11376147), ANP32B (cg00422488), MCM2 (cg02578470), HNRNPA1 (cg02583484), SLC1A5 (cg02711608), SNORD30 (cg05512483), SNORD47 (cg06644515), TRA2B (cg12825509), DYRK2 (cg13442969), PHGDH (cg14476101), CCDC71 (cg18590502) genes, and other loci locations (cg01940273, cg17962756, and cg18917643) were all hypomethylated with higher alcohol consumption.

• DMP identified in this review were highly heterogeneous between studies mainly due to differences in methodological approaches.

• The 15 DNAm sites highlighted in this systematic review could be potential markers for alcohol consumption or potential mediators of alcohol-associated chronic diseases that could also be targeted for early prevention.

• Future studies with longitudinal data and a more standardized methodology for DNAm preprocessing are needed to identify clinical epigenetic biomarkers for female and male adults. -Poor-quality probes -Cross-hybridizing probes -Probes with SNP 1. The continuous exposure variable was defined as the average grams of ethanol consumed per day. It was further categorized into four drinking categories. 'Nondrinkers' were subjects with no alcohol consumption (g per day = 0); 'light drinkers' were subjects who consumed 0-28/0-14 g per day for men/women; 'at risk-drinkers' were subjects who consumed 28-42/14-28 g per day for men/women; 'heavy drinkers' were subjects who consumed ⩾42/28 g per day for men/women 2. Self-administered questionnaires or structured interviews with a trained psychologist 3. i. Continuous (g/day); ii. Polytomous No limitation on humans was applied in the search strategy because the exclusion of animal studies in a non-biomedical database is riskier.

Reporting bias assessment

14 Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases). Not done

Certainty assessment

15 Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome. Not done

RESULTS

Study selection 16a

Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram.

162-164

16b Cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded. NA 19 For all outcomes, present, for each study: (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g. confidence/credible interval), ideally using structured tables or plots.

201-219

Results of syntheses 20a For each synthesis, briefly summarise the characteristics and risk of bias among contributing studies.

222-236

20b Present results of all statistical syntheses conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect.

OTHER INFORMATION

Registration and protocol 24a Provide registration information for the review, including register name and registration number, or state that the review was not registered. NA 24b Indicate where the review protocol can be accessed, or state that a protocol was not prepared. NA 24c Describe and explain any amendments to information provided at registration or in the protocol. NA Support 25 Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review.

See Title page

Competing interests 26 Declare any competing interests of review authors.

See Title page

Availability of data, code and other materials 27 Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all analyses; analytic code; any other materials used in the review.

416-438

From

OR

  Summary Points (Research articles & Company profiles only):N/A • Figure/Table legends

Figure 1 :Figure 2 :

 12 Figure 1: Study selection process tree (PRISMA 2020 flow diagram) File 2.1 Figure 2.jpeg: Figure 2: Assessment of risk of bias using the Joanna Briggs Institute tools for cross-sectional studies (proportion of each category over the total number of studies: n=11) File 2.1. Tables1-2.docx:

Figure 1 :Figure 2 :

 12 Figure 1: Study selection process tree (PRISMA 2020 flow diagram)

  included study and present its characteristics. 167-183 Risk of bias in studies 18 Present assessments of risk of bias for each included study. 186-198 Results of individual studies

NA20c

  Present results of all investigations of possible causes of heterogeneity among study results. NA 20d Present results of all sensitivity analyses conducted to assess the robustness of the synthesized results. NA Reporting biases 21 Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed. Not done Certainty of evidence 22 Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed. Not done DISCUSSION Discussion 23a Provide a general interpretation of the results in the context of other evidence. 239-332 23b Discuss any limitations of the evidence included in the review. 333-356 23c Discuss any limitations of the review processes used. 357-360 23d Discuss implications of the results for practice, policy, and future research. 361-371

Table 1 :

 1 Studies of alcohol exposure on blood-derived genome-wide DNA methylation

Table 2 :

 2 Alcohol consumption associated with specific DNAm sites identified in at least four studies Files 2.2. Supplementary-materials_20220422.docx and 2.3. Supplementary-tables.xlsx:

	Word file

  Alcohol Use Disorders Identification Test-Consumption; BMI: Body Mass Index; BMIQ: Beta-Mixture Quantile Normalization; DNA: Deoxyribonucleic Acid; DMP: Differentially Methylated Position; DMR: Differentially Methylated Region; EWAS: Epigenome-Wide Association Study; FDR: False Discovery Rate; FFQ: Food Frequency Questionnaire; GO: Gene Ontology; g/day: Gram per Day; HIV: Human Immunodeficiency Virus; HPV16E6: Human Papillomavirus 16 oncoprotein E6; IDOL: Identifying Optimal Libraries; KEGG: Kyoto Encyclopedia of Genes and Genomes; LC-MS/MS: Liquid Chromatography coupled with tandem Mass Spectrometry; L-DMR: Leukocyte Differentially Methylated Regions; n: sample size; ng/ml: Nanogram per Milliliter; NA: Not Applicable; No.: Number; NR: Not Reported; PC: Principal Component; PEth: Phosphatidylethanol; SD: Standard Deviation; SNP: Single-Nucleotide Polymorphism; SSAGA: Semi-Structured Assessment for the Genetics of

	4. Mainly through team; female friends	3. Healthy	8. 2819/2268; median:	representative of a normal	-Outliers, sex mismatches, non--Probes 100% methylated	2. NR 4. p <10 -4	excess signal at the
	primary care and from the registers of collaborating general practices via the Community Health Number and family nominated by a participant 5. Cross-sectional at T1) two-time points (T0 and		8 units per week [Q1,Q3: 2,15]	week 2. Self-report 3. Continuous (log[units per week + 1]) 4. Same time as blood draws	blood samples -Samples with ≥1% of CpGs having a detection p-value over 0.05 -Probes with a bead count <3 in reference beta >0.80 and 0% Batch effect correction located within the probe sequence unmethylated reference beta <0.20 -Probes with annotated SNPs	3. Age, sex, and ten genetic PC 4. NA	tail. 7. NR 8. NA
	5. Cross-sectional AUDIT-C:				≥6 samples and when ≥0.5% of 9. 355,633 samples had a detection p-value >0.05
					-Probes with missing values	1. Inverse variance-	5. NA
					-Probes on X and Y chromosomes	weighted random-effects model	6. i. 363; ii. 11
	1. Clark 2. 2015 3. Sweden 4. National population registers 5. Cross-sectional	1. NA 1. Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium plus 2. 2,423 Consortium (CHARGE+) 2. 619 3. Healthy	5. 55.6 ± 11.7 cohorts 31% across the 3 5. 42 to 73 across the cohorts 6. 1,560 (64) 7. 0; 100; 0 8. Heavy drinkers: 2-6. 287 (41.4) 7. NR 8. 580/39	1. Binary alcohol use 4. NR question which asked whether participants had ever considered themselves to have consumed alcohol 3. Dichotomous (Regular drinkers/Never regular drinkers) 2. Self-report regularly during their lifetime	-Probes that are non-CpG sites -Probes not present on the Illumina 450 k array 9. NR 5. Illumina 450K 5. MDB-seq (Next-generation 6. Continuous 7. Houseman et al. 8. Exclusion of: -Poor-quality probes and samples -Cross-hybridizing probes -Probes with SNP Quantile normalization 9. ~ 440,000 CpG sites into a single mean coverage estimates at adjacent by collapsing highly intercorrelated adaptively combined sample), the CpGs were set (67.3 million reads per 8. To reduce the size of the data 7. Accounted by PC adjustment sequencing combined with the use of the methyl-CpG-binding domain [MBD] protein) 6. Continuous	2. Beta values 3. Age, sex, BMI, technical covariates (lab, experiment date, plate, and row), and 1. Multiple regressions cell-type proportions 4. Bonferroni p <1 x 10 -7 4. FDR <0.1 Items 1 to 4 are identical for the three analyses conducted by Liu and al. 3 PC sample batch), and first DNA captured, and methylation-enriched material, quantity of 2. NR 3. Age, sex, technical covariates (quantity of genomic DNA starting	7. i. 257 ii. 10 8. NA 5. 33 8. NA 5. NA 6. i. 165; ii. 115 7. i. 105; ii. 82 6. NA 7. 21 8. NA
		3. Mixed			coverage estimate, resulting in
	1. Flanagan 1. McCartney 2. 2018 3. Scotland 2. 2015 3. UK 4. 3 principal sources: list of supporters of Breakthrough Breast Cancer (charity); consequence of publicity, women contacted the study	1. Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium plus (CHARGE+) 1. Breakthrough Consortium 2. 1,251 3. Mixed 1. Generation Scotland: The Scottish Family Health Study 2. 5,087 Generations Study (BGS) 2. 92 3. Healthy	5. 60 6. 645 (52) 7. 47; 21; 32 8. Heavy drinkers: 4% 5. At T0: 53 (35-77); 5. 48.5 ± 14.0 6. 3131 (61.5) 7. NR At T1: 59 (41-83) 6. 92 (100) 7. 100; 0; 0 8. At T0: 74/18; At T1: 74/7 (11 missings)	(light/at-risk/heavy drinkers versus non-drinkers) 4. Past 12 months 1. NR 1. Alcohol was assessed in units per week and was only considered in those who reported that their intake was 2. Self-administered questionnaire 3. Dichotomous (Regular drinkers/No regular drinkers) 4. NR	5,074,560 CpG "blocks" Exclusion of: -blocks with very low (<99% of noise) levels of coverage 9. 4,274,420 CpG "blocks" 5. Illumina 450K 5. Illumina EPIC 6. Continuous 7. NR 8. Exclusion of: 6. Continuous 7. Houseman et al. 8. Exclusion of: -Probes with detection p-value >0.05 -Samples with >1% of probes not detected -Probes not detected in >20% of samples	Items 1 to 4 are identical for the three analyses conducted by Liu and al. 1. Linear regressions 1. Predictive model: Penalized linear regression models (LASSO) 2. M values 3. Age and geographical location (defined by postcodes into regions of South, East Anglia, Midlands, North, Scotland, Wales) ± surrogate variables obtained by SVA	5. NA 6. i. 62 ii. 4 7. i. 46 ii. 3 8. NA 5. NA 5. NA 6. 450 7. NR 8. NA 6. NR -Examination of Q-Q plots for cross-sectional analyses of methylation levels and alcohol use at T0 and T1 showed little evidence for association with methylation levels, with some indication of an

Alcoholism; SVA: Surrogate Variable Analysis; SWAN: Subset-quantile Within Array Normalization; UK: United Kingdom; USA: United States of America; y: years; %: percentage.

Table 2 : Alcohol consumption associated with specific DNAm sites identified in at least four studies

 2

	DMP (n=1) in 6 studies	Gene	Direction
	cg06690548	SLC7A11	hypomethylated
	DMP (n=1) in 5 studies	Gene	Direction
	cg11376147	SLC43A1	hypomethylated
	DMP (n=13) in 4 studies Gene	Direction
	cg00422488	ANP32B	hypomethylated
	cg01940273	NA	hypomethylated
	cg02578470	MCM2	hypomethylated
	cg02583484	HNRNPA1	hypomethylated
	cg02711608	SLC1A5	hypomethylated
	cg05512483	SNORD30	hypomethylated
	cg06644515	SNORD47	hypomethylated
	cg12825509	TRA2B	hypomethylated
	cg13442969	DYRK2	hypomethylated
	cg14476101	PHGDH	hypomethylated
	cg17962756	NA	hypomethylated
	cg18590502	CCDC71	hypomethylated
	cg18917643	NA	hypomethylated

DMP: Differentially methylated positions; DNAm: DNA methylation; NA: not applicable

Table 2 :

 2 Search strategy in Embase ':ti,ab,kw OR 'epigenetic':ti,ab,kw OR 'epigenetics':ti,ab,kw OR 'epigenomic':ti,ab,kw OR 'epigenomics':ti,ab,kw OR 'methylome':ti,ab,kw OR 'methylomes':ti,ab,kw OR 'methylat*':ti,ab,kw OR 'hypomethylat*':ti,ab,kw OR 'hypermethylat*':ti,ab,kw OR 'EWAS':ti,ab,kw)

	Supplementary Database/	#	Concept	Search strategy
	Consultation date

('alcohol'/de OR 'drinking behavior'/de OR 'alcoholism'/de OR 'alcoholic beverage'/exp OR 'drinking behavior':ti,ab,kw OR 'alcoholism':ti,ab,kw OR 'alcohol':ti,ab,kw OR 'liquor':ti,ab,kw OR 'beverage*':ti,ab,kw OR 'beer':ti,ab,kw OR 'cider':ti,ab,kw OR 'wine':ti,ab,kw OR 'spirits':ti,ab,kw OR 'ethanol':ti,ab,kw OR 'absinthe':ti,ab,kw OR 'brandy':ti,ab,kw OR 'gin':ti,ab,kw OR 'vodka':ti,ab,kw OR 'rum':ti,ab,kw OR 'sake':ti,ab,kw OR 'whiskey':ti,ab,kw OR 'tequila':ti,ab,kw)

Table 4 :

 4 Search strategy in CINAHL

	Database/	#	Concept	Search strategy
	Consultation date

(MH "Alcohol-Related Disorders") OR (MH "Drinking Behavior") OR (MH "Alcohol Drinking") OR (MH "Alcoholic Beverages") OR (MH "Ethanol") OR (TI("drinking behavior" OR alcoholism OR alcohol OR liquor OR "beverage*" OR beer OR cider OR wine OR spirits OR ethanol OR absinthe OR brandy OR gin OR vodka OR rum OR sake OR whiskey OR tequila) OR AB("drinking behavior" OR alcoholism OR alcohol OR liquor OR "beverage*" OR beer OR cider OR wine OR spirits OR ethanol OR absinthe OR brandy OR gin OR vodka OR rum OR sake OR whiskey OR tequila)) 115157 No limitation on humans was applied in the search strategy because the exclusion of animal studies in a non-biomedical database is riskier.

Supplementary

Table 5 :

 5 Search strategy in PsycInfo Ovid OR epigenome.ti,ab,id OR epigenetic.ti,ab,id OR epigenetics.ti,ab,id OR epigenomic.ti,ab,id OR epigenomics.ti,ab,id OR methylome.ti,ab,id OR methylomes.ti,ab,id OR methylat*.ti,ab,id OR hypomethylat*.ti,ab,id OR hypermethylat*.ti,ab,id OR EWAS.ti,ab,id

	Database/	#	Concept	Search strategy
	Consultation date

OR "drinking behavior".ti,ab,id OR alcoholism.ti,ab,id OR alcohol.ti,ab,id OR liquor.ti,ab,id OR "beverage*".ti,ab,id OR beer.ti,ab,id OR cider.ti,ab,id OR wine.ti,ab,id OR spirits.ti,ab,id OR ethanol.ti,ab,id OR absinthe.ti,ab,id OR brandy.ti,ab,id OR gin.ti,ab,id OR vodka.ti,ab,id OR rum.ti,ab,id OR sake.ti,ab,id OR whiskey.ti,ab,id OR tequila.ti,ab,id 147823

:

  Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71

	Supplementary Tables
	Supplementary
	Reference studies' information
	Column legends
	Abreviations
		For more information, visit: http://www.prisma-statement.org/
	MF	Molecular function
	MZ	Monozygotic
	no	Number
	NR	Not reported
	PEth	Phosphatidylethanol
	W	White

Table 8 :

 8 All differentially methylated regions (DMR) associated with alcohol consumption identified from four studies included in the review (only data available online) Supplementary Table7: All differentially methylated probes (DMP) associated with alcohol consumption identified from nine studies included in the review

	Liu 2018	9643 EA	Cont	g/day	cg22510139 PRDM16	1	3058822	-0,00023917 1,29E-08	NR	N_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg21848084 PRDM16	1	3264381	0,000149616 5,49E-08	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg00546117 RERE	1	8445545	-0,00020768 7,33E-08	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg03617456 EXOSC10	1	11160205	-9,87E-05	2,68E-08	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg07180646 TMEM51	1	15481541	-0,0002294 4,13E-09	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg05129477 KIF17	1	21044945	-0,00023488 5,60E-08	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg02343604 ID3	1	23884703	-0,00015201 5,75E-08	NR	N_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg17164905 AHDC1	1	27931310	-0,00013746 1,17E-08	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg11980897	1	32237457	-0,00013619 9,37E-08	NR	N_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg24141382 CTPS	1	41446726	-0,00015032 6,43E-08	NR	S_Shore	NR
	Liu 2018	2423 AA	Cont	g/day	cg19534753 MYLK	3	123340306 -3,46E-05	3,62E-08	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg19554457 NUDT4	12	93774772	-0,00023447 1,28E-08	NR	S_Shelf	NR
	Liu 2018	9643 EA	Cont	g/day	cg19572487 RARA	17	38476024	-0,00024775 5,60E-10	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg05756780 IL6R	1	154380705 -0,00014366 3,98E-08	NR	S_Shelf	NR
	Liu 2018	9643 EA	Cont	g/day	cg22505006 ZBTB7B	1	154981829 -0,00016328 1,28E-08	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg09182138 BCAN	1	156612436 -0,00015431 3,60E-09	NR	Island	NR
	Liu 2018	9643 EA	Cont	g/day	cg19578183	2	128991530 -0,00024825 3,33E-09	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg00313914 NAV1	1	201618901 -0,0004466 9,89E-08	NR	Island	NR
	Liu 2018	9643 EA	Cont	g/day	cg12067421	1	203246482 -0,00014735 1,34E-09	NR	S_Shelf	NR
	Liu 2018	9643 EA	Cont	g/day	cg10456541	2	8721512	-0,00019049 2,69E-08	NR	N_Shelf	NR
	Liu 2018	9643 EA	Cont	g/day	cg14274621	2	8825107	-0,00013727 6,67E-09	NR	Island	NR
	Liu 2018	9643 EA	Cont	g/day	cg07502661	2	43398339	-0,00018711 2,60E-12	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg00883689 SPTBN1	2	54802904	-0,00028474 3,23E-12	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg19693031 TXNIP	1	145441552 -0,00043938 1,02E-11	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg06096336 PSMD1	2	231989800 -0,0002662 1,24E-10	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg24642820 NUP210	3	13462465	-0,00030238 9,53E-09	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg14473743 NUP210	3	13462496	-0,00026099 3,38E-09	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg18483487	3	45208900	-0,00020568 2,42E-08	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg27386529 SCAP	3	47517807	-0,00019881 2,61E-09	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg19731612 NSD1	5	176559334 -0,00062134 3,80E-08	NR	Island	NR
	Liu 2018	9643 EA	Cont	g/day	cg14035045 HEMK1	3	50606289	-0,00016938 1,85E-08	NR	N_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg12554573 PARP3	3	51976667	-0,00014151 1,79E-08	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg18328933 ABHD14B	3	52008538	-0,00012035 6,55E-10	NR	N_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg22631938 EIF4E3	3	71802420	-0,00023234 3,99E-11	NR	N_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg17185710 MBNL1	3	151984822 -0,00021238 8,10E-12	NR	N_Shore	NR
	Liu 2018	2423 AA	Cont	g/day	cg19825437	3	169383292 -6,04E-05	3,04E-11	NR	N_Shelf	NR
	Liu 2018	9643 EA	Cont	g/day	cg06657944 TBL1XR1	3	176915062 -7,46E-05	1,81E-08	NR	N_Shore	NR
	Liu 2018	2423 AA	Cont	g/day	cg19827923 GPR55	2	231790777 -3,50E-05	7,14E-08	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg13729116 LETM1	4	1859262	-0,00017824 6,70E-18	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg18101225 FGFBP2	4	15964913	0,000102643 8,45E-09	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg04487857 SLC7A11	4	139096540 0,000109109 2,65E-09	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg04006143 NDUFC1	4	140217312 -0,0002397 4,37E-08	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg02282631	5	42953543	-0,00032486 3,07E-08	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg08840551	5	56718349	-0,00017046 2,36E-08	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg27020362 MGC29506	5	138723394 0,000194607 6,26E-08	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg25518868 DIAPH1	5	140984057 -0,00012305 2,28E-12	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg01201215 ATOX1	5	151137430 -0,000156	6,76E-09	NR	N_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg15699524 FGF18	5	170845610 -0,0001532 4,13E-09	NR	N_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg03846076 STC2	5	172743837 0,000366331 8,58E-08	NR		NR
	Liu 2018	9643 EA	Cont	g/day	cg19939077 PPIF	10	81108060	-0,00022641 2,24E-11	NR	S_Shore	NR
	Liu 2018	9643 EA	Cont	g/day	cg01487468	6	14622793	-0,00014583 5,69E-08	NR		NR

Table 8 :

 8 All differentially methylated regions (DMR) associated with alcohol consumption identified from four studies included in the review (only data available online)

	Study	n	Ethnicity	Variable type Variable	Chr Block Start Block End	Probe: * Probe names included in the region * no. of CpGs	Value: * beta_min; beta_max for Perrier * T-value for Clark * meanbetafc for Dugue	p-value	q-value	Genes
	Clark 2015	619 NR	Cat	Regular user/Never regular user 20	58761092	58761152	NR	-6.10	1.93E-09	0.008
	Clark 2015	619 NR	Cat	Regular user/Never regular user 12	20088334	20088355	NR	-5.68	2.14E-08	0.046
	Clark 2015	619 NR	Cat	Regular user/Never regular user 15	102383775 102383775 NR	-5.54	4.54E-08	0.065	GPCRLTM7
	Clark 2015	619 NR	Cat	Regular user/Never regular user 18	592215	592241	NR	-5.44	7.68E-08	0.066	CETN1/CLUL1
	Clark 2015	619 NR	Cat	Regular user/Never regular user 6	11012186	11012186	NR	-5.44	7.72E-08	0.066	ELOVL2
	Clark 2015	619 NR	Cat	Regular user/Never regular user 8	26168818	26168887	NR	-5.39	1.02E-07	0.072	PPP2R2A
	Clark 2015	619 NR	Cat	Regular user/Never regular user 7	84887803	84887816	NR	-5.36	1.18E-07	0.072
	Clark 2015	619 NR	Cat	Regular user/Never regular user 4	140427487 140427494 NR	-5.33	1.40E-07	0.074	SETD7
	Clark 2015	619 NR	Cat	Regular user/Never regular user 12	10829931	10829931	NR	-5.31	1.56E-07	0.074	STYK1
	Clark 2015	619 NR	Cat	Regular user/Never regular user 17	37504336	37504433	NR	-5.27	1.92E-07	0.079	FBXL20
	Clark 2015	619 NR	Cat	Regular user/Never regular user 12	70152252	70152293	NR	-5.24	2.25E-07	0.079	RAB3IP
	Clark 2015	619 NR	Cat	Regular user/Never regular user 5	112271135 112271135 NR	-5.22	2.43E-07	0.079	REEP5
	Clark 2015	619 NR	Cat	Regular user/Never regular user 8	16136003	16136078	NR	-5.22	2.44E-07	0.079
	Clark 2015	619 NR	Cat	Regular user/Never regular user 6	36529872	36529954	NR	-5.21	2.59E-07	0.079	STK38
	Clark 2015	619 NR	Cat	Regular user/Never regular user 5	29305151	29305155	NR	-5.19	2.84E-07	0.081
	Clark 2015	619 NR	Cat	Regular user/Never regular user 7	123887980 123888000 NR	-5.15	3.54E-07	0.086
	Clark 2015	619 NR	Cat	Regular user/Never regular user 15	66622358	66622368	NR	-5.14	3.66E-07	0.086	DIS3L/TIPIN/SCARNA14
	Clark 2015	619 NR	Cat	Regular user/Never regular user 13	77687117	77687210	NR	-5.13	3.99E-07	0.086	MYCBP2
	Clark 2015	619 NR	Cat	Regular user/Never regular user 18	57638757	57638757	NR	-5.12	4.13E-07	0.086
	Clark 2015	619 NR	Cat	Regular user/Never regular user 12	74585726	74585731	NR	-5.11	4.27E-07	0.086	LOC100507377
	Clark 2015	619 NR	Cat	Regular user/Never regular user 5	37626839	37626839	NR	-5.11	4.39E-07	0.086	WDR70
	Clark 2015	619 NR	Cat	Regular user/Never regular user 6	14523209	14523289	NR	-5.10	4.60E-07	0.086
	Clark 2015	619 NR	Cat	Regular user/Never regular user 3	179634002 179634111 NR	-5.09	4.87E-07	0.086	PEX5L
	Clark 2015	619 NR	Cat	Regular user/Never regular user 8	113454886 113454986 NR	-5.07	5.29E-07	0.086	CSMD3
	Clark 2015	619 NR	Cat	Regular user/Never regular user 6	139500651 139500795 NR	-5.07	5.34E-07	0.086	HECA
	Clark 2015	619 NR	Cat	Regular user/Never regular user 1	66275102	66275102	NR	-5.06	5.47E-07	0.086	PDE4B
	Clark 2015	619 NR	Cat	Regular user/Never regular user 8	125900827 125900847 NR	-5.06	5.70E-07	0.086
	Clark 2015	619 NR	Cat	Regular user/Never regular user 1	108047119 108047119 NR	-5.05	5.77E-07	0.086
	Clark 2015	619 NR	Cat	Regular user/Never regular user 2	67680420	67680514	NR	-5.05	5.86E-07	0.086
	Clark 2015	619 NR	Cat	Regular user/Never regular user 3	108017604 108017633 NR	-5.02	6.81E-07	0.097	HHLA2
	Clark 2015	619 NR	Cat	Regular user/Never regular user 6	10951883	10951912	NR	-5.00	7.68E-07	0.099	SYCP2L
	Clark 2015	619 NR	Cat	Regular user/Never regular user 6	38371404	38371450	NR	-5.00	7.69E-07	0.099	BTBD9
	Clark 2015	619 NR	Cat	Regular user/Never regular user 5	127258073 127258108 NR	-4.99	7.71E-07	0.099
	Langdon 2020 409 NR	Cont	units per week	4	139162808 139163020 cg06690548, cg13903162	NR	1.45 × 10-10 NR	SLC7A11
	Dugue 2019	5606 NR	Cont	g/day in last week	13	36048892	36053408	23	-0,0002	2,13E-38	NR	MAB21L1
	Dugue 2019	5606 NR	Cont	g/day in last week	19	13120555	13123217	7	-0,0003	1,49E-36	NR	NFIX
	Dugue 2019	5606 NR	Cont	g/day in last week	19	47287778	47288263	7	-0,0001	6,81E-32	NR	SLC1A5
	Dugue 2019	5606 NR	Cont	g/day in last week	7	27169674	27171528	25	-0,0002	3,2E-31	NR	HOXA4
	Dugue 2019	5606 NR	Cont	g/day in last week	1	173834077 173836636 9	-0,0002	3,93E-30	NR	GAS5
	Dugue 2019	5606 NR	Cont	g/day in last week	11	62620843	62622234	9	-0,0002	6,05E-30	NR	SNHG1
	Dugue 2019	5606 NR	Cont	g/day in last week	19	47289410	47289611	3	-0,0002	7,05E-29	NR	SLC1A5
	Dugue 2019	5606 NR	Cont	g/day in last week	7	29605237	29606985	8	-0,0004	5,67E-27	NR	PRR15
	Dugue 2019	5606 NR	Cont	g/day in last week	15	45027253	45028595	6	-0,0002	2,22E-26	NR	TRIM69
	Dugue 2019	5606 NR	Cont	g/day in last week	3	51740201	51741473	8	-0,0003	3,25E-25	NR	GRM2
	Dugue 2019	5606 NR	Cont	g/day in last week	4	139162808 139163780 5	-0,0002	3,51E-23	NR	SLC7A11
	Dugue 2019	5606 NR	Cont	g/day in last week	7	30635214	30636176	6	-0,0003	1,05E-21	NR	GARS
	Dugue 2019	5606 NR	Cont	g/day in last week	2	16845412	16846569	3	-0,0003	2,36E-21	NR	FAM49A
	Dugue 2019	5606 NR	Cont	g/day in last week	8	42234243	42234975	8	-0,0002	3,87E-21	NR	DKK4
	Dugue 2019	5606 NR	Cont	g/day in last week	11	68606654	68608813	9	-0,0001	4,61E-20	NR	CPT1A
	Dugue 2019	5606 NR	Cont	g/day in last week	16	46918160	46920075	9	-0,0001	1,96E-19	NR	GPT2
	Dugue 2019	5606 NR	Cont	g/day in last week	22	39918625	39919237	3	-0,0002	2,34E-19	NR
	Dugue 2019	5606 NR	Cont	g/day in last week	7	55376098	55377050	3	-0,0003	6,26E-19	NR
	Dugue 2019	5606 NR	Cont	g/day in last week	17	25798202	25799447	9	-0,0001	1,08E-18	NR	KSR1

Table 9 :

 9 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways and GO (Gene Ontology) pathways associated with identified DMP in Dugue et al.

	Study	n	Ethnicity	Variable type Variable	Collection	Pathway entry	Pathway name	Ont	N	DE	P.DE	FDR
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa04810	Regulation of actin cytoskeleton	NA	207	10	0,0010	0,34
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa01230	Biosynthesis of amino acids	NA	70	5	0,0039	0,64
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa04611	Platelet activation	NA	123	6	0,010	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa00430	Taurine and hypotaurine metabolism	NA	11	2	0,011	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa03010	Ribosome	NA	131	6	0,014	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa03450	Non-homologous end-joining	NA	13	2	0,016	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa03030	DNA replication	NA	36	3	0,016	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa00533	Glycosaminoglycan biosynthesis -keratan sulfate	NA	14	2	0,018	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa04670	Leukocyte transendothelial migration	NA	110	5	0,025	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa01210	2-Oxocarboxylic acid metabolism	NA	17	2	0,026	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa00970	Aminoacyl-tRNA biosynthesis	NA	44	3	0,028	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa05203	Viral carcinogenesis	NA	198	7	0,029	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa01521	EGFR tyrosine kinase inhibitor resistance	NA	78	4	0,029	0,74
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa04010	MAPK signaling pathway	NA	294	9	0,032	0,76
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa00220	Arginine biosynthesis	NA	20	2	0,036	0,78
	Dugue 2019		NR	Cont	g/day overlifetime KEGG pathway	path:hsa04727	GABAergic synapse	NA	88	4	0,043	0,88
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa05220	Chronic myeloid leukemia	NA	76	13	0,00014	0,043
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa03010	Ribosome	NA	131	17	0,00046	0,043
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa00533	Glycosaminoglycan biosynthesis -keratan sulfate	NA	14	5	0,00052	0,043
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04218	Cellular senescence	NA	157	19	0,00053	0,043
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04010	MAPK signaling pathway	NA	294	29	0,00071	0,047
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04070	Phosphatidylinositol signaling system	NA	99	13	0,0019	0,076
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04371	Apelin signaling pathway	NA	136	16	0,0019	0,076
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04022	cGMP-PKG signaling pathway	NA	162	18	0,0020	0,076
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04210	Apoptosis	NA	137	16	0,0021	0,076
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04722	Neurotrophin signaling pathway	NA	119	14	0,0036	0,12
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa05167	Kaposi's sarcoma-associated herpesvirus infection	NA	176	18	0,0049	0,14
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04971	Gastric acid secretion	NA	75	10	0,0055	0,14
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa05212	Pancreatic cancer	NA	75	10	0,0055	0,14
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04810	Regulation of actin cytoskeleton	NA	207	20	0,0059	0,14
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa05223	Non-small cell lung cancer	NA	66	9	0,0071	0,16
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa05203	Viral carcinogenesis	NA	198	19	0,0077	0,16
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa05416	Viral myocarditis	NA	56	8	0,0083	0,16
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04920	Adipocytokine signaling pathway	NA	69	9	0,010	0,17
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04611	Platelet activation	NA	123	13	0,012	0,21
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa00562	Inositol phosphate metabolism	NA	73	9	0,014	0,22
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa05100	Bacterial invasion of epithelial cells	NA	74	9	0,015	0,23
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04724	Glutamatergic synapse	NA	114	12	0,016	0,24
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa01521	EGFR tyrosine kinase inhibitor resistance	NA	78	9	0,020	0,29
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04659	Th17 cell differentiation	NA	105	11	0,021	0,29
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04720	Long-term potentiation	NA	67	8	0,023	0,29
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa05166	HTLV-I infection	NA	252	21	0,023	0,29
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04931	Insulin resistance	NA	107	11	0,024	0,29
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04270	Vascular smooth muscle contraction	NA	121	12	0,024	0,29
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04612	Antigen processing and presentation	NA	69	8	0,027	0,31
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04014	Ras signaling pathway	NA	230	19	0,033	0,34
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa04062	Chemokine signaling pathway	NA	185	16	0,033	0,34
	Dugue 2019		NR	Cont	g/day in last week	KEGG pathway	path:hsa05330	Allograft rejection	NA	35	5	0,034	0,34

, and approved the manuscript. DD is responsible for the information provided. DD holds a Scholarship of Excellence from the University Laval Cancer Research Center and a doctoral funding from the University Paris-Saclay Doctoral School of Public Health EDSP. Authors have no potential conflicts of interest. ▪ Information pertaining to writing assistance: N/A.

data analysis with CD, KEI, and SL participations when required. DD drafted