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. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: Psychiatry Res. 2024 Jan 27;333:115757. doi: 10.1016/j.psychres.2024.115757

Genome-wide DNA methylation analysis of cannabis use disorder in a veteran cohort enriched for posttraumatic stress disorder

Melanie E Garrett a, Michelle F Dennis b,c,d, Kyle J Bourassa b,c,e, VA Mid-Atlantic MIRECC Workgroup c, Michael A Hauser a, Nathan A Kimbrel b,c,d, Jean C Beckham b,c,d, Allison E Ashley-Koch a,*
PMCID: PMC10922626  NIHMSID: NIHMS1963212  PMID: 38309009

Abstract

Cannabis use has been increasing over the past decade, not only in the general US population, but particularly among military veterans. With this rise in use has come a concomitant increase in cannabis use disorder (CUD) among veterans. Here, we performed an epigenome-wide association study for lifetime CUD in an Iraq/Afghanistan era veteran cohort enriched for posttraumatic stress disorder (PTSD) comprising 2,310 total subjects (1,109 non-Hispanic black and 1,201 non-Hispanic white). We also investigated CUD interactions with current PTSD status and examined potential indirect effects of DNA methylation (DNAm) on the relationship between CUD and psychiatric diagnoses. Four CpGs were associated with lifetime CUD, even after controlling for the effects of current smoking (AHRR cg05575921, LINC00299 cg23079012, VWA7 cg22112841, and FAM70A cg08760398). Importantly, cg05575921, a CpG strongly linked to smoking, remained associated with lifetime CUD even when restricting the analysis to veterans who reported never smoking cigarettes. Moreover, CUD interacted with current PTSD to affect cg05575921 and cg23079012 such that those with both CUD and PTSD displayed significantly lower DNAm compared to the other groups. Finally, we provide preliminary evidence that AHRR cg05575921 helps explain the association between CUD and any psychiatric diagnoses, specifically mood disorders.

Keywords: Epigenetics, Cannabis use disorder, PTSD, EWAS, AHRR, mediation

1. Introduction

Over the past 20 years, rates of cannabis use have been increasing among adults in the United States (US). According to the National Survey on Drug Use and Health (NSDUH)(Administration, 2021), 34.5% of adults age 18–25 reported past year marijuana use in 2020 compared to 29.8% in 2002. More striking, 16.3% of adults above age 25 reported past year marijuana use in 2020 compared to 7.0% in 2002. Likewise, the prevalence of cannabis use disorder (CUD) continues to increase not only among the general US population(Hasin, 2018) but has more than doubled among US military veterans in recent years(Bonn-Miller, Harris and Trafton, 2012; Davis et al., 2018; Hill et al., 2021a). Veterans diagnosed with insomnia, anxiety disorders, major depressive disorder (MDD), and posttraumatic stress disorder (PTSD), are particularly inclined to use cannabis as a coping mechanism(Boden et al., 2013; Bonn-Miller et al., 2007; Metrik et al., 2016). In 2020, the rate of lifetime CUD was 9.2% among veterans, 12.1% among veterans with PTSD, and 8.9%–13.1% in veterans with other psychiatric and substance use disorders(Hill et al., 2021a). Among veterans who report nonmedical cannabis use, the rate of lifetime CUD was even higher (17.4%) and was associated with several psychiatric and substance use disorders(Browne et al., 2022). This increase is of great concern as CUD has been associated with suicidal behaviors in veterans(Adkisson et al., 2019; Grove et al., 2023; Hill et al., 2021b; Shamabadi et al., 2023), including those from our own cohort(Kimbrel et al., 2017).

Despite the perceived alleviation of trauma-related symptoms, there is a lack of evidence to support the use of therapeutic cannabis for mental health disorders(Black et al., 2019; Solmi et al., 2023). In fact, CUD is associated not only with negative health outcomes such as pulmonary conditions(Aldington et al., 2007; Hancox et al., 2010) and myocardial infarction(Lindsay et al., 2005), but also with lower educational and occupational attainment(Compton et al., 2014; Lynskey and Hall, 2000), as well as a higher likelihood of other substance use disorders (SUDs), anxiety and mood disorders, and PTSD(Gentes et al., 2016; Gunn et al., 2020; Hasin et al., 2016; Livingston et al., 2022; Metrik et al., 2022). Indeed, PTSD is the most highly comorbid psychiatric disorder among veterans with CUD(Bonn-Miller, Harris and Trafton, 2012; Cougle et al., 2011). A recent study found the prevalence of PTSD among Iraq/Afghanistan era veterans with a CUD diagnosis was 72.3%(Bryan et al., 2021). This comorbidity is particularly concerning as PTSD symptom severity has been linked not only to cannabis use, but to severity of cannabis withdrawal and emotion-related cravings(Boden et al., 2013) as well as a slower decline in cannabis use during early cessation attempts(Bonn-Miller et al., 2015).

Understanding the impact of CUD on psychiatric and health outcomes in veterans is of utmost importance. Moreover, the ability to identify testable biomarkers for CUD could greatly improve veteran health management, for both those with PTSD and those without. DNA methylation (DNAm) is an epigenetic modification involving the transfer of a methyl group at the C5 position of a cytosine nucleotide to form 5-methylcytosine and can be measured across the genome at CpG (cytosine-phosphate-guanine) sites. DNAm can be influenced by a variety of factors including genetics and developmental processes, disease and health conditions, and environmental exposures including stress and external toxins. As such, researchers have explored epigenome-wide studies (EWAS) for cannabis and its derivatives in animal models(Murphy et al., 2018; Wanner et al., 2020, 2021; Watson et al., 2015) as well as in human sperm cells(Murphy et al., 2018; Schrott et al., 2021) and whole blood(Clark et al., 2021; Markunas et al., 2021; Nannini et al., 2023; Osborne et al., 2020), but to our knowledge, no EWAS for CUD has been performed in a veteran cohort. The high prevalence of PTSD, CUD, and suicide among veterans, and the complex relationships between these disorders, highlights the importance of investigating DNAm and CUD in this at-risk population.

Here, we examined the association between lifetime CUD and genome-wide DNAm using participants from the Veterans Affairs (VA) Mid-Atlantic Mental Illness Research, Education and Clinical Center (MIRECC) Study of Post-Deployment Mental Health, an Iraq/Afghanistan-era veteran cohort enriched for PTSD. In addition to differential analysis, and owing to the remarkably high co-occurrence of PTSD and CUD that has been reported previously(Bryan et al., 2021), we investigated potential moderating effects of current PTSD status on the relationship between CUD and CUD-associated DNAm probes. Finally, because of the strong correlations between CUD and other psychiatric disorders, we examined potential indirect effects of DNAm on the relationship between CUD and CUD-associated psychiatric disorders, including suicidal thoughts and behaviors.

2. Methods

2.1. Study participants

Participants were selected from the multi-site VA MIRECC study of Iraq/Afghanistan-era veterans (n=2,310), which has been described previously(Calhoun et al., 2010). Study protocols were approved by local institutional review boards or ethics committees, and all participants provided written informed consent prior to enrollment. The current analysis was comprised of 1,109 non-Hispanic black (NHB) and 1,201 non-Hispanic white (NHW) subjects who provided a blood sample and completed the necessary self-report questionnaires and interviews.

2.2. Phenotypic measures and endpoints

The Structured Clinical Interview for DSM-IV Disorders (SCID)(First, 1994) was used to diagnose all psychiatric disorders including CUD and other substance use disorders, posttraumatic stress disorder (PTSD), other anxiety disorders, major depressive disorder (MDD) and other mood disorders, psychotic disorders, and eating disorders. Smoking status was determined via self-report, where participants indicated they were either current smokers, ex-smokers, or never smokers. Years of education was determined by self-report during study ascertainment. Suicidal behaviors in this cohort have been described previously(Kimbrel et al., 2018; Kimbrel et al., 2023). Briefly, lifetime history of suicide attempts was defined as one or more suicide attempts as reported on item #20 of the Beck Scale for Suicide Ideation (BSI)(Beck, 1991), whereas suicidal ideation was defined as any prior history of suicidal thoughts, plans, or attempts, as reported on items #1–20 on the BSI, item #9 on the Beck Depression Inventory-II(Beck, 1996), or item #15 from the Symptom Checklist-90 (SCL-90)(Derogatis, 1994).

2.3. DNA methylation

DNAm was assessed using either the Infinium HumanMethylation450 (450k) Beadchip or the Infinium MethylationEPIC (EPIC) Beadchip (Illumina, San Diego, CA), as described previously(Kimbrel et al., 2023). NHB and NHW samples were run separately, but each batch was randomized by sex and PTSD status. This resulted in four analysis groups: NHW 450k (n=176), NHW EPIC (n=1025), NHB 450k (n=274), and NHB EPIC (n=835). Sample exclusions were made using the following quality control (QC) metrics: average fluorescence signal intensity <2000 arbitrary units or <50% of the mean intensity of all samples, >10% of probes were not detectable (detection p >0.001), evidence of a sex mismatch, or if the sample was classified as an outlier from principal component analysis (PCA). Probes were excluded if they were not detected (detection p > 0.001) in >10% of samples or if they hybridized to multiple locations in the genome(Chen et al., 2013; Pidsley et al., 2016). 423,945 DNAm probes that were present in all samples and passed QC were retained. Cell type composition was estimated using the method described by Houseman et al.(Houseman et al., 2012) using publicly available reference datasets(Reinius et al., 2012; Salas et al., 2018). Raw beta values were normalized using the dasen approach(Pidsley et al., 2013) and adjustments for both batch and chip were performed using ComBat(Johnson, Li and Rabinovic, 2007). M-values were calculated from the resulting normalized and adjusted beta values for statistical analysis.

2.4. Statistical analysis

Subject characteristics summarized by race/ethnicity and relationships between CUD and smoking status, PTSD and other psychiatric diagnoses, and years of education were assessed using R. Chi-square tests and logistic regression were used to test for differences between either race/ethnicity or CUD and categorical variables, while linear regression was used to test for differences between either race/ethnicity or CUD and continuous variables.

Association analysis between each DNAm probe and CUD was performed in each analysis group separately using MOMENT in the OSCA toolkit(Zhang et al., 2019), controlling for experimental batch and current smoking status. MOMENT employs linear mixed models with random effect components comprised of all other distal DNAm probes to account for unobserved confounders, such as cell type composition, in a reference-free manner. Results from the four analysis groups were meta-analyzed using metal(Willer, Li and Abecasis, 2010) and false discovery rate (FDR) q-values were generated from meta-analysis p-values using the R package qvalue(Storey JD, 2023). Post-hoc analysis of cumulative cannabis use disorder (never CUD, n=1957; ex-CUD, n=215, current CUD, n=34) was performed for the CpG sites found to be associated with lifetime CUD using linear models in R glm, controlling for age, sex, race, methylation chip (450k or EPIC), cell type estimates, and current smoking status. To further investigate the effect of current smoking on DNAm and CUD, we performed a sensitivity analysis in only subjects who reported never smoking cigarettes. Interactions between PTSD and CUD affecting CUD-associated CpGs were tested using linear regression in R glm, controlling for age, sex, race, methylation chip (450k or EPIC), cell type estimates, and current smoking status.

Statistical mediation analysis was performed using the R package mediation(Tingley D., 2014) to determine if CUD-associated CpGs exert indirect effects on the relationship between CUD and several CUD-associated phenotypes including lifetime PTSD, lifetime mood disorders, lifetime substance use disorders (excluding cannabis), and the presence of any lifetime psychiatric disorder included in the SCID, as well as suicidal ideation and suicide attempts. Fifteen subjects were removed from the analysis of lifetime mood disorders, substance use disorders, and presence of any psychiatric disorder because they had substance-induced diagnoses, which could have been due to CUD. Beta values for tested CpG sites were residualized for methylation chip, cell type estimates, and current smoking status prior to mediation analysis. Age, sex, and race were included as covariates. Average causal mediation effects (ACME), or indirect effects, and average direct effects (ADE) were computed and nonparametric bootstrapping with 10,000 simulations was used for variance estimation. Due to significant correlation among outcomes, a Bonferroni correction was applied to the ACME p-values within each phenotype, rather than across all phenotypes, to adjust for multiple testing (4 CpGs; pbon=0.0125).

3. Results

3.1. Study participants

Participant characteristics are shown in Table 1. NHB participants were older (39 years vs. 36 years, p<0.0001), had more female representation (29.22% vs. 13.91%, p<0.0001), and reported lower educational attainment (14.22 vs. 14.43 years, p=0.0088) compared to NHW participants. The prevalence of lifetime CUD was not different between NHB and NHW participants (11.30% vs. 11.26%, p=0.973). A higher proportion of NHW participants reported current smoking (28.68% vs. 21.38%, p<0.0001), while a higher proportion of NHB participants reported never smoking (59.51% vs. 44.90%, p<0.0001). The two ancestry groups did not differ by proportion of current PTSD (p=0.3205), lifetime mood disorders (p=0.6393), or lifetime psychotic disorders (p=0.9583). However, more NHW participants were diagnosed with lifetime substance disorders (48.83% vs. 38.94%, p<0.0001) and any lifetime DSM-IV diagnosis (73.94% vs. 67.52%, p=0.0009) compared to NHB participants. Finally, NHW participants reported higher rates of suicidal ideation (27.96% vs. 23.14%, p=0.0106) and suicide attempts (10.37% vs. 5.46%, p=0.0002) compared to NHB participants.

Table 1.

Participant characteristics by race

NHB (n=1109) NHW (n=1201) p-value
Mean age (SD) 38.80 (9.82) 36.05 (10.14) <0.0001
Female (%) 324 (29.22%) 167 (13.91%) <0.0001
lifetime CUD (%) 119 (11.3%) 130 (11.26%) 0.973
current CUD (%) 20 (1.9%) 14 (1.21%) 0.1902
lifetime PTSD (%) 543 (49.86%) 604 (50.76%) 0.6698
current PTSD (%) 367 (33.09%) 421 (35.05%) 0.3205
years of education (SD) 14.22 (1.86) 14.43 (2.04) 0.0088
current smokers (%) 236 (21.38%) 343 (28.68%) <0.0001
never smokers (%) 657 (59.51%) 537 (44.90%) <0.0001
lifetime mood disorders (%) 521 (49.48%) 583 (50.48%) 0.6393
lifetime psychotic disorders (%) 8 (0.76%) 9 (0.78%) 0.9583
lifetime substance disorders (%)a 410 (38.94%) 564 (48.83%) <0.0001
any lifetime psychiatric diagnosis (%) 711 (67.52%) 854 (73.94%) 0.0009
suicidal ideation (%) 240 (23.14%) 312 (27.96%) 0.0106
suicide attempts (%) 46 (5.46%) 93 (10.37%) 0.0002
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a

excludes cannabis use disorder

3.2. Demographic and clinical associations with CUD

Lifetime CUD was associated with many demographic and clinical characteristics (Table 2). Those with CUD were younger (34 vs. 38 years, p<0.0001), had more male representation (88.76% vs. 77.13%, p<0.0001), and reported lower educational attainment (13.92 vs. 14.40 years, p=0.0002) compared to those without CUD. Those with CUD reported a higher proportion of current smoking (48.19% vs. 21.73%, p<0.0001) and a lower proportion of never smoking (20.88% vs. 56.24%, p<0.0001) compared to those without CUD. Neither current nor lifetime PTSD were associated with lifetime CUD (p=0.6516 and p=0.4463, respectively). All lifetime psychiatric diagnosis assessed were higher among those with CUD compared to those without CUD: lifetime mood disorders (58.63% vs. 48.90%, p=0.0038), lifetime psychotic disorders (2.81% vs. 0.51%, p<0.0001), lifetime substance disorders (80.72% vs. 39.46%, p<0.0001), and any lifetime DSM-IV diagnosis (93.57% vs. 67.99%, p<0.0001). Finally, those with CUD reported higher rates of suicidal ideation (34.50% vs. 23.44%, p=0.0003) and suicide attempts (15.25% vs. 6.81%, p<0.0001) compared to those without CUD.

Table 2.

Participant characteristics by CUD

CUD=N (n=1959) CUD=Y (n=249) p-value
Mean age (SD) 37.85 (10.09) 34.10 (9.43) <0.0001
Female (%) 448 (22.87%) 28 (11.24%) <0.0001
current PTSD (%) 633 (32.31)% 84 (33.73%) 0.6516
lifetime PTSD (%) 949 (48.44%) 127 (51%) 0.4463
years of education (SD) 14.40 (1.99) 13.92 (1.66) 0.0002
current smokers (%) 425 (21.73%) 120 (48.19%) <0.0001
never smokers (%) 1100 (56.24%) 52 (20.88%) <0.0001
lifetime mood disorders (%) 958 (48.90%) 146 (58.63%) 0.0038
lifetime psychotic disorders (%) 10 (0.51%) 7 (2.81%) <0.0001
lifetime substance disorders (%)a 773 (39.46%) 201 (80.72%) <0.0001
any lifetime psychiatric diagnosis (%) 1332 (67.99%) 233 (93.57%) <0.0001
suicidal ideation (%) 427 (23.44%) 79 (34.50%) 0.0003
suicide attempts (%) 102 (6.81%) 27 (15.25%) <0.0001
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a

excludes cannabis use disorder

3.3. Differential DNAm results

Differential meta-analysis of lifetime CUD identified four CpGs that surpassed correction for multiple testing, even after controlling for the effects of current smoking status (Figure 1, Table S1). The most significant CpG was cg05575921 in AHRR (p=1.44×10−9, q=5.57×10−4), a well-established marker for smoking. The other three significant CpGs were cg23079012 in LINC00299 (p=4.72×10−8, q=9.33×10−3), cg22112841 in VWA7 (p=1.88×10−7, q=0.0199), and cg08760398 in FAM70A (p=2.01×10−7, q=0.0199). Those with CUD displayed hypomethylation at cg05575921, cg23079012, and cg08760398, and hypermethylation at cg22112841. Post-hoc analysis of these four CpG sites also showed association with cumulative CUD, with the same direction of effects (cg05575921 p=2.76×10−16; cg23079012 p=5.95×10−12; cg22112841 p=2.47×10−5; cg08760398 p=1.35×10−3). To further address the impact of cigarette smoking on these associations, a sensitivity analysis was performed in participants who reported never smoking in their lifetime. Due to the distribution of samples on each methylation chip by CUD and smoking status, we were unable to perform this analysis in the NHW participants. Among the NHB never-smokers, two CpG sites surpassed correction for multiple testing: cg24405700 in B4GALNT3 (p=1.16×10−7, q=0.024), followed closely by AHRR cg05575921 (p=1.21×10−7, q=0.024). The three other CpG sites associated with CUD in the full dataset were all nominally associated with CUD in NHB never-smokers (cg08760398, p=0.0007; cg22112841, p=0.0073; and cg23079012, p=0.0186, respectively).

Figure 1. Manhattan plot depicting results for differential methylation analysis of lifetime CUD.

Figure 1.

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FDR significant CpGs are labeled (q<0.05).

3.4. Interactions between current PTSD and CUD affecting DNAm

In an effort to better understand the role current PTSD may play in these associations, interaction analyses were performed for the four CUD-associated CpG sites. We observed a significant interaction between PTSD and CUD affecting AHRR cg05575921 such that those with both PTSD and CUD displayed significantly lower methylation compared to other subjects (p=0.0345, Figure 2a). PTSD and CUD also interacted to affect cg23079012 but the pattern of association was different: mean DNAm by PTSD was not different among those with CUD, however those with PTSD and no CUD diagnosis displayed higher DNAm at cg23079012 compared to other subjects (p=0.005; Figure 2b). PTSD and CUD did not significantly interact to affect either cg22112841 or cg08760398 (p>0.05).

Figure 2. Average percent DNAm for pairwise PTSD and CUD comparisons.

Figure 2.

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A) AHRR cg05575921 and B) LINC00299 cg23079012. Boxes depict least squares means and error bars indicate the 95% confidence interval of the least squares mean. Means sharing a letter in each plot are not significantly different (Tukey-adjusted comparisons).

3.5. Statistical mediation analysis

Finally, we evaluated whether CUD-associated CpGs helped explain the association between CUD and PTSD, mood disorders, other substance use disorders, and any lifetime DSM-IV diagnosis, as well as suicidal ideation and suicide attempts (Table 3). As noted in Table 2, PTSD was not associated with CUD in this cohort, therefore statistical mediation was not considered. We observed partial mediation of the association between CUD and lifetime mood disorders (ACME p=0.0084, proportion mediated=12.31%) as well as lifetime DSM-IV diagnoses (ACME p=0.001, proportion mediated=3.92%) by AHRR cg05575921, which surpassed correction for multiple testing. Additionally, we detected a nominally significant indirect effect of AHRR cg05575921 on the association between CUD and other substance use disorders (ACME p=0.03, proportion mediated=2.52%). We did not detect indirect effects of AHRR cg05575921 affecting the relationship between CUD and suicidal behaviors, and no other CUD-associated CpGs (cg23079012, cg22112841, or cg08760398) displayed evidence for mediation (ACME p>0.05) with any of the tested phenotypes.

Table 3.

Statistical mediation effect estimates.

Outcome variable: lifetime mood disorders (n=2190)
AHRR cg05575921 cg23079012 VWA7 cg22112841 FAM70A cg08760398
Effect Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI
Total effect 0.101** [0.034, 0.17] 0.101** [0.035, 0.17] 0.101** [0.035, 0.17] 0.101** [0.035, 0.17]
ADE 0.088** [0.021, 0.15] 0.097** [0.031, 0.16] 0.097** [0.03, 0.16] 0.102** [0.036, 0.17]
ACME 0.012** [0.003, 0.02] 0.004 [−0.005, 0.01] 0.004 [−0.002, 0.01] −0.002 [−0.005, 0]
Outcome variable: lifetime substance use disorders (n=2190)
AHRR cg05575921 cg23079012 VWA7 cg22112841 FAM70A cg08760398
Effect Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI
Total effect 0.385**** [0.327, 0.44] 0.385**** [0.326, 0.44] 0.385**** [0.327, 0.44] 0.385**** [0.327, 0.44]
ADE 0.375**** [0.317, 0.43] 0.388**** [0.329, 0.44] 0.381**** [0.322, 0.44] 0.385**** [0.327, 0.44]
ACME 0.01* [0.001, 0.02] −0.003 [−0.013, 0] 0.004 [−0.001, 0.01] −0.0004 [−0.003, 0]
Outcome variable: any lifetime psychiatric diagnosis (n=2190)
AHRR cg05575921 cg23079012 VWA7 cg22112841 FAM70A cg08760398
Effect Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI
Total effect 0.256**** [0.217, 0.29] 0.256**** [0.217, 0.29] 0.255**** [0.216, 0.29] 0.255**** [0.217, 0.29]
ADE 0.246**** [0.206, 0.28] 0.252**** [0.212, 0.29] 0.253**** [0.212, 0.29] 0.256**** [0.217, 0.29]
ACME 0.01*** [0.004, 0.02] 0.003 [−0.002, 0.01] 0.003 [−0.001, 0.01] −0.001 [−0.003, 0]
Outcome variable: suicidal ideation (n=2048)
AHRR cg05575921 cg23079012 VWA7 cg22112841 FAM70A cg08760398
Effect Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI
Total effect 0.105** [0.042, 0.17] 0.105** [0.042, 0.17] 0.105** [0.041, 0.17] 0.105** [0.041, 0.17]
ADE 0.096** [0.033, 0.16] 0.103** [0.041, 0.17] 0.105** [0.041, 0.17] 0.105** [0.04, 0.17]
ACME 0.008† [−0.001, 0.02] 0.001 [−0.007, 0.01] −0.0004 [−0.006, 0.01] −0.0004 [−0.007, 0.01]
Outcome variable: suicide attempts (n=1671)
AHRR cg05575921 cg23079012 VWA7 cg22112841 FAM70A cg08760398
Effect Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI
Total effect 0.087*** [0.033, 0.14] 0.088** [0.034, 0.14] 0.088*** [0.034, 0.14] 0.086*** [0.034, 0.14]
ADE 0.083** [0.029, 0.14] 0.089** [0.036, 0.15] 0.087*** [0.032, 0.14] 0.892*** [0.037, 0.15]
ACME 0.005 [−0.005, 0.01] −0.001 [−0.01, 0.01] 0.001 [−0.004, 0.01] −0.003† [−0.008, 0]
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Cannabis use disorder (CUD) is the independent variable, CUD-associated CpGs are mediators, and psychiatric diagnoses and suicidal behaviors are the dependent variables. Asterisks denote the strength of association:

*

p<0.05

**

p<0.01

***

p<0.001

****

p<0.0001.

4. Discussion

Utilizing a large cohort of military veterans, we performed an EWAS for CUD and identified four CpG sites that surpass correction for multiple testing. The methylation site most strongly associated with CUD was cg05575921 in aryl hydrocarbon receptor repressor (AHRR), which encodes a protein involved in pro-inflammatory response and xenobiotic metabolism. In addition to being a well-established biomarker for smoking(Dogan et al., 2014; Joehanes et al., 2016; Joubert et al., 2012; Monick et al., 2012; Philibert et al., 2013; Shenker et al., 2013; Zeilinger et al., 2013), cg05575921 has previously been associated with recent and cumulative marijuana use, and with heavy cannabis use among tobacco smokers(Nannini et al., 2023; Osborne et al., 2020), though notably cannabis-only users did not evidence significant hypomethylation. The association we observed here persisted even when controlling for smoking status, suggesting that the association between cg05575921 and CUD exists independent of the association with smoking. In fact, cg05575921 was among the most strongly CUD-associated DNAm sites in a subset of subjects who reported never smoking in their lifetime. We also observed differential DNAm for CUD at cg23079012 in LINC00299, a non-coding RNA on chromosome 2, that has previously been associated with current tobacco smoking, with continued smoking in a two time-point study, and with metabolic traits(Petersen et al., 2014; Siemelink et al., 2018; Wilson et al., 2017; Zaghlool et al., 2018). Like cg05575921, the association we observed between cg23079012 and CUD was independent of smoking. Finally, cg22112841 in von Willibrand factor A domain containing 7 (VWA7) showed differential methylation for CUD. This CpG has been previously associated with aging in a longitudinal study(Wang et al., 2018), which is notable as lifetime marijuana use has been shown to predict accelerated epigenetic aging even when accounting for cigarette smoking(Allen et al., 2022). Allen and colleagues(Allen et al., 2022) provided evidence that this effect is fully mediated by AHRR cg05575921, suggesting that the accelerated epigenetic aging they observed may be due to hydrocarbon inhalation among marijuana smokers. More research is necessary to understand the complex relationships between CUD, smoking, and changes in DNAm.

Through larger consortia efforts, we have also shown that AHRR cg05595721 is associated with PTSD among both civilians and veterans, an association which is independent of smoking(Logue et al., 2020; Smith et al., 2020). Because we observed hypomethylation of cg05575921 among those with CUD, we posited that having both PTSD and CUD may magnify changes in DNAm. Indeed, those with both CUD and PTSD had significantly lower methylation at cg05575912 compared to those without CUD as well as those with CUD who did not have PTSD, demonstrating an even larger hypomethylation effect when individuals have both PTSD and CUD. Although AHRR was originally recognized for its role in mediating toxicity, it is becoming increasingly appreciated for its role in immune and inflammatory response, pathways that have also been implicated in PTSD(Katrinli et al., 2022; Passos et al., 2015). Moreover, higher C-reactive protein levels, a marker for low-grade inflammation, have been associated not only with PTSD, but also with hypomethylation at cg05575921(Ligthart et al., 2016; Miller et al., 2018). Because the combination of CUD and PTSD appears to exacerbate disruption of immune and inflammatory response, particular attention should be devoted to intervention in this subgroup. Fortunately, studies have shown recovery of DNA methylation, specifically at cg05575921, following smoking cessation(Philibert et al., 2016; Skov-Jeppesen et al., 2023; Wilson et al., 2017); more research is needed to determine if the same effect occurs among those who stop using cannabis.

Aside from changes in DNAm, CUD was associated not only with lower educational attainment and smoking in this cohort, but also with lifetime mood disorders (including major depressive disorder and bipolar disorders), psychotic disorders (including schizophrenia and delusional disorder), and other substance abuse disorders (including alcohol, sedatives, and stimulants), replicating previous work(Hasin et al., 2016; Hjorthoj, Posselt and Nordentoft, 2021; Jefsen et al., 2023; Lucatch et al., 2018; Solmi et al., 2023). Strikingly, 93.57% of those with CUD received at least one lifetime DSM-IV diagnosis, making clear the extensive interplay between CUD and psychiatric disorders in veteran populations. These associations may also impact the higher rates of suicidal ideation and suicide attempts we and others have observed among veterans with CUD(Adkisson et al., 2019; Grove et al., 2023; Hill et al., 2021b; Kimbrel et al., 2017). In light of these associations, we sought to determine whether CUD-associated CpG sites exert indirect effects on the associations observed here between CUD and psychiatric disorders. In doing so, we demonstrated that AHRR cg05575921 mediated 12.31% of the total effect of CUD on lifetime mood disorders and mediated 3.92% of the total effect of CUD on the presence of any lifetime DSM-IV diagnosis. This CpG site has been shown to mediate effects of smoking on lung cancer(Fasanelli et al., 2015), bladder cancer(Jordahl et al., 2019), atherosclerosis(Reynolds et al., 2015), and low offspring birthweight(Xu et al., 2021), but to our knowledge, our finding is the first to show potential indirect effects of DNAm on the association between CUD and psychiatric diagnoses. While the indirect effect sizes we observed are not as large as some that have been reported for cancers, the total effect of CUD on these psychiatric diagnoses is large (Table 3), therefore smaller percent mediation still reflects sizable impacts. Though not statistically significant, it’s worth noting that we observed a trending indirect effect of cg05575921 on the association between CUD and suicidal ideation (p=0.078). Larger sample sizes may be necessary to determine if this trend reflects a true association.

Several factors limit the conclusions that can be drawn from this study. First, mode of cannabis ingestion was not recorded, therefore we cannot determine if the effects we observe are specific to cannabis use and its derivatives or are due to more general effects of smoke inhalation. While we demonstrated that the effects of CUD on DNAm are independent of tobacco smoke, future studies should investigate other occupational exposures, including exposure to burn pits, to perhaps explain why we still observe significant DNAm changes among non-smokers. Second, due to sample size considerations, we opted to use lifetime CUD as our outcome of interest in this study. However, we did observe similar effects with cumulative CUD for the four CpG sites most strongly associated with lifetime CUD, demonstrating sustained effects at these methylation sites. Third, it is not known at which ages the criteria for CUD were met, and may reflect use before, during, or following military service. Future work examining the time course of CUD and DNAm would be helpful to better understand the impact of cannabis use on changes to DNAm and psychiatric outcomes, including refinement of the statistical mediation models presented here. It’s also important to note that lifetime CUD was not associated with current PTSD in this cohort. However, current CUD was associated with current PTSD: among veterans with current CUD, 58.8% also had current PTSD while 41.2% did not have current PTSD (p=0.0009). This effect was even more striking among the NHW veterans: 71.4% of those with current CUD also had current PTSD. Larger sample sizes of veterans with current CUD will be needed to replicate these associations.

In summary, CUD is becoming increasingly common among military veterans and is associated with a plethora of negative psychological outcomes, including suicidal thoughts and behaviors. In a cohort of military veterans, we identified differential DNA methylation among those with CUD at four CpG sites, including AHRR cg05575921. Notably, these associations were independent of tobacco smoking status. Veterans with both CUD and PTSD display even lower DNA methylation at cg05575921, resulting in greater perturbation of AHR signaling and immune response. Finally, we provide evidence that DNAm at cg05575921 may partially mediate the effect of CUD on lifetime mood disorders in veterans. Future studies should investigate whether evidence-based cannabis cessation treatment might alter DNAm associated with CUD and improve mental health among veterans.

Supplementary Material

supplement

Table S1. Full results for differential DNAm analysis of CUD. Column labeled “Direction” lists the direction of effect by analysis group in the following order: NHW 450k, NHB 450k, NHW EPIC, and NHB EPIC.

Acknowledgements

We sincerely thank all of the veterans who volunteered for this study. This research was supported by Award #IK2CX002694 to Dr. Bourassa from the Clinical Science Research and Development (CSR&D) Service of VA ORD, Award #IK2CX000525 to Dr. Kimbrel from the CSR&D Service of VA ORD, Award #I01BX002577 to Dr. Beckham from the Biomedical Laboratory Research and Development (BLR&D) Service, and a Senior Research Career Scientist Award (#lK6BX003777) to Dr. Beckham from CSR&D. The authors also received support from the VA Mid-Atlantic Mental Illness Research, Education and Clinical Center (MIRECC), the Mental Health and Research Services of the Durham VA Healthcare System, the Durham VA Geriatrics Research, Education, and Clinical Center (GRECC), and the Department of Psychiatry and Behavioral Sciences at the Duke University School of Medicine. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the VA or the United States government.

The VA Mid-Atlantic MIRECC Workgroup includes Pallavi Aurora, PhD, Patrick S. Calhoun, PhD, Eric Dedert, PhD, Eric B. Elbogen, PhD, Tate F. Halverson, PhD, Robin A. Hurley, MD, Jason D. Kilts, PhD, Angela Kirby, MS, Anna T. Magnante, PsyD, Sarah L. Martindale, Ph.D, Brandy S. Martinez, PhD, Christine E. Marx, MD, MS, Scott D. McDonald, PhD, Scott D. Moore, MD, PhD, Victoria O’Connor, PhD, Rajendra A. Morey, MD, MS, Jennifer C. Naylor, PhD, Jared Rowland, PhD, Robert D. Shura, PsyD, Cindy Swinkels, PhD, & Elizabeth E. Van Voorhees, PhD, and H. Ryan Wagner, PhD.

Footnotes

Conflicts of interest

All authors report no conflicts of interest.

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Supplementary Materials

supplement

Table S1. Full results for differential DNAm analysis of CUD. Column labeled “Direction” lists the direction of effect by analysis group in the following order: NHW 450k, NHB 450k, NHW EPIC, and NHB EPIC.

NIHMS1963212-supplement-supplement.xlsx (17.5MB, xlsx)

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