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Published in final edited form as: Drug Alcohol Depend. 2022 Dec 24;243:109753. doi: 10.1016/j.drugalcdep.2022.109753

Genetic Predisposition to Major Depressive Disorder Differentially Impacts Alcohol Consumption and High-Risk Drinking Situations in Men and Women with Alcohol Use Disorder

Victor M Karpyak a,e,*, Brandon J Coombes b,*, Jennifer R Geske b, Vanessa M Pazdernik b, Terry Schneekloth c, Bhanu Prakash Kolla a, Tyler Oesterle a, Larissa L Loukianova a, Michelle K Skime a, Ada Man-Choi Ho a, Quyen Ngo d, Cedric Skillon d, Ming-Fen Ho e, Richard Weinshilboum e, Joanna M Biernacka a,b
PMCID: PMC9869363  NIHMSID: NIHMS1865691  PMID: 36608483

Abstract

Lifetime history of major depressive disorder (MDD) has a sex-specific association with pretreatment alcohol consumption in patients with alcohol dependence. Here, we investigated the association of genetic load for MDD estimated using a polygenic risk score (PRS) with pretreatment alcohol consumption assessed with Timeline Follow Back in a sample of 287 men and 156 women meeting DSM-IV-TR criteria for alcohol dependence. Preferred drinking situations were assessed using the Inventory of Drug Taking Situations (IDTS). Linear models were used to test for association of normalized alcohol consumption measures with the MDD-PRS, adjusting for ancestry, age, sex, and number of days sober at baseline. We fit models both with and without adjustment for MDD history and alcohol-use-related PRSs as covariates. Higher MDD-PRS was associated with lower 90-day total alcohol consumption in men (β = −0.16, p = 0.0012) but not in women (β = 0.11, p = 0.18). The association of MDD-PRS with IDTS measures was also sex-specific: higher MDD-PRS was associated with higher propensity to drink in temptation-related situations in women, while the opposite (negative association)was found in men. MDD-PRS was not associated with lifetime MDD history in our sample, and adjustment for lifetime MDD and alcohol-related PRSs did not impact the results. Our results suggest that genetic load for MDD impacts pretreatment alcohol consumption in a sex-specific manner, which is similar to, but independent from, the effect of history of MDD. The clinical implications of these findings and contributing biological and psychological factors should be investigated in future studies.

Keywords: alcohol use disorder, alcohol consumption, depression, polygenic risk scores, sex differences

1. Introduction

Unipolar depressive disorders and alcohol use disorders (AUDs) are among the most prevalent mental, neurological and substance-use disorders, constituting 13% of the global burden of disease, surpassing cardiovascular disease and cancer (Collins et al., 2011). Depressive disorders and AUDs are also highly comorbid (Grant et al., 2004; Hasin and Grant, 2002; Hasin et al., 2007; Hesselbrock, 1991; Miller et al., 1996) and negatively impact treatment outcomes for both conditions (Compton et al., 2007; Conner et al., 2009; Driessen et al., 2001; Grant et al., 2004; Hesselbrock, 1991; Hesselbrock et al., 1985).

The mechanisms explaining the complex and likely multidirectional relationships between depression, alcohol consumption, craving, and other AUD-related phenotypes are not well understood. One explanation is that emotionally loaded situations progressively intensify the craving for alcohol in subjects receiving in-patient rehabilitation treatment, with a corresponding shift from drinking in positive to drinking in negative emotional situations (Victorio-Estrada and Mucha, 1997). The impact of mood on alcohol use is also believed to be sex-specific, with men more often reporting drinking in pleasant emotional situations (Connors et al., 1998; Dunne et al., 1993; Lemke et al., 2008; Yankelevitz et al., 2012; Zywiak et al., 2006) and women more often reporting drinking to suppress negative emotions (Choi and DiNitto, 2011; Lau-Barraco et al., 2009; Rubonis et al., 1994). The impact of negative mood on alcohol intake is even more pronounced in patients with AUD, with negative mood potentially playing a mediator role in sex-specific differences in alcohol consumption and contributing to return to use risk (Abulseoud et al., 2013; Lau-Barraco et al., 2009).

The importance of emotionally loaded high-risk situations has been recognized in the context of motivation for drinking and the development of personalized recommendations for return to use prevention (Marlatt, 1996). Early empirical research identified eight categories of self-reported return to use triggers, including unpleasant emotions, physical discomfort, pleasant emotions, testing personal control, urges and temptations to use, conflict with others, social pressure to use, and pleasant times with others (Marlatt, 1996). Subsequent analyses revealed that those risk categories could be further grouped into three types of risky situations (broadly defined as negative, positive, or temptation-related), which can be measured by the Inventory of Drug (and alcohol) Taking Situations (IDTS) (Turner et al., 1997). This approach is also consistent with a three-pathway contextual model of craving: defining the desire for rewarding properties of alcohol as positive/reward craving, and the desire for drinking to reduce tension and/or negative emotions as negative/relief craving, versus obsessive thoughts about drinking (i.e. temptation craving) and attributing those pathways to dysregulation in dopamine/opioid, glutamate/gamma-aminobutyric acid (GABA), and serotonin neurotransmission systems, respectively (Verheul et al., 1999).

However, recent findings suggest a more complex relationship between mood and AUD-related phenotypes, with sex-specific associations. Specifically, we found that state-dependent situational antecedents to alcohol use measured by the IDTS (i.e., negative or positive emotions and temptation to drink) were strongly associated with increased alcohol consumption in alcohol-dependent men and women. On the contrary, a lifetime history of MDD was associated with lower alcohol consumption in alcohol-dependent men, with no effect in alcohol-dependent women (Karpyak et al., 2019). A similar negative correlation was reported in the context of genetics (i.e. negative genetic correlation between MDD and alcohol consumption) in a large population-based study (Sanchez-Roige et al., 2019), while other studies have observed significant positive genetic correlations between AUD and MDD (Foo et al., 2018; Kranzler et al., 2019; Walters et al., 2018; Zhou et al., 2020). Here, we investigate whether genetic load for MDD (determined by polygenic risk score, PRS) demonstrates sex-specific associations with AUD-related phenotypes, including alcohol consumption and propensity to drink in high-risk situations. We also investigated the impact of adjusting for history of MDD and genetic load for AUD and alcohol consumption on the associations between alcohol consumption with MDD PRS.

2. Methods

2.1. Study Design

This study utilized clinical and genetic data from the Center for Individualized Treatment of Addiction (CITA) study of genetic markers associated with acamprosate treatment outcomes (Karpyak et al., 2014). All participants signed informed consent, and the Institutional Review Boards of Mayo Clinic Rochester and Mayo Clinic Health System approved the study.

2.2. Study Participants

A detailed description of the study sites, selection of participants, and enrollment procedures is presented elsewhere (Karpyak et al., 2016; Karpyak et al., 2014). Briefly, men and women 18–80 years old with a primary diagnosis of current alcohol dependence participating in treatment programs affiliated with Mayo Clinic and Mayo Clinic Health System sites in Minnesota and Wisconsin were invited to enroll. Subjects with unstable medical and/or psychiatric conditions, including renal or hepatic impairment, psychotic disorder, active suicidal ideation, as well as those unable to speak English or provide informed consent, subjects with a history of an allergic reaction to acamprosate, taking disulfiram, pregnant or lactating women or women planning to become pregnant during the subsequent year were excluded (Karpyak et al., 2014).

2.3. Assessments

Detailed information about study assessments is provided elsewhere (Karpyak et al., 2016; Karpyak et al., 2014). Presence of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria for alcohol dependence and MDD were determined using a the Psychiatric Research Interview for Substance and Mood Disorders (PRISM) (Hasin et al., 1996).

Alcohol use history during the 90 days preceding study enrollment was collected using the Timeline Follow Back (TLFB) (Sobell and Sobell, 1992). For statistical analyses, we quantified the following measures of alcohol use: the total number of drinks during 90 days prior to enrollment, the number of drinking days out of 90 days prior to enrollment, the average number of drinks per drinking day, and the maximum number of drinks consumed on a drinking day during the 90 days prior to enrollment.

The propensity to drink in positive or negative emotional situations or temptation-related circumstances over the past year was determined using the Inventory of Drug Taking Situations, IDTS (Annis et al., 1997). As recommended (Turner et al., 1997), the IDTS negative situation factor score was constructed by averaging raw scores of the IDTS subscales of “physical discomfort”, “unpleasant emotions”, and “conflict with others” and was used as a measure of the tendency to drink in negative emotional circumstances (and potentially reflective of negative craving). Similarly, the IDTS positive situation factor score was constructed by averaging the raw subscale scores of “pleasant emotions” and “pleasant times with others” and was used as a measure of the tendency to drink in positive emotional states. The IDTS temptation situation factor score was constructed by averaging the raw subscale scores of “social pressure”, “testing personal limits”, and “urges and temptations” and were used as measures of the tendency to drink when facing tempting situations.

2.4. Genotyping and Quality Control

Genotyping of the CITA samples and quality control (QC) and imputation of the genetic data have been described previously (Biernacka et al., 2021). Briefly, samples were genotyped with a combination of Illumina® HumanCore (Illumina, San Diego, CA, USA) and the Infinium® OmniExpressExome-8 BeadChips (Illumina, San Diego, CA, USA). Data from the two arrays were combined and processed to exclude samples with low call rate, extreme heterozygosity, disagreement between reported sex and genetically determined sex, or relatedness (randomly removing one individual from each pair with kinship coefficient > 0.2). SNPs with a call rate <99% or showing significant deviation from Hardy Weinberg Equilibrium (p < 1E-06) were removed. Genetic data from 436 participants with >70% European ancestry based on STRUCTURE (Pritchard et al., 2000) analysis and with call rates >.995, were retained after QC. Imputation was performed using the Michigan Imputation Server with the HRC reference panel (version HRC.r1-1.GRCh37.wgs.mac5.sites), and all variants with imputation dosage R2 ≥ 0.8 and minor allele frequency (MAF) ≥ 0.01 were used in genetic analyses.

2.5. Calculation of Polygenic Risk Scores

The PRS for MDD was calculated using summary statistics from the largest published genome-wide association study (GWAS) of MDD assessed with standardized diagnostic instruments, performed by the Psychiatric Genomic Consortium (PGC) (Wray et al., 2018); this study was selected for its precise and uniformly defined MDD phenotype. We also calculated PRSs for AUD and alcohol consumption based on GWASs in the Million Veterans Project (Kranzler et al., 2019) and the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN) meta-analysis with UK Biobank (Liu et al., 2019), respectively. PRSice2 (Choi and O’Reilly, 2019) was used to construct PRSs in the CITA sample using a pruning (--clump-kb 250 and –clump-r2 0.1) and thresholding (pT = 5×10−8, 10−7, 10−6, 10−5, 10−4, 0.001, 0.01, 0.1, 0.05, 0.1, 0.2, or 1) approach. To reduce multiple testing, we then used a PRS-PCA approach (Coombes et al., 2020) to combine the PRSs built under different p-value thresholds into one PRS. Each PRS was standardized to have mean zero and standard deviation one.

2.6. Data Analyses

Data from 434 study participants with clinical and genotyping data passing QC were analyzed. Due to a small amount of missing data for particular variables, some analyses included fewer subjects, as indicated in Table 1.

Table 1:

Demographic and Clinical Characteristics of the Study Sample

Characteristic N All Subjects
N=418		 Males
N=266		 Females
N=152		 p-valuea
N (%) or Mean (SD)
Demographics
Age (years) 418 41.9 (11.9) 42.1 (12.0) 41.4 (11.8) 0.518
Emotional state assessment
IDTS negative score 413 57.0 (22.0) 55.3 (21.6) 60.1 (22.5) 0.0315
IDTS positive score 413 56.3 (24.0) 57.5 (22.9) 54.3 (25.7) 0.192
IDTS temptation score 413 49.8 (23.5) 49.6 (22.7) 50.3 (24.8) 0.785
Baseline Alcohol consumption measures
Total drinks in 90 days 418 562.7 (493.5) 646.1 (511.0) 416.8 (424.9) 4E-06
Average drinks per drinking day 418 12.4 (7.86) 14.0 (8.19) 9.67 (6.42) 5E-08
Drinking days per 90 days 418 45.1 (24.8) 46.7 (25.1) 42.2 (24.0) 0.076
Maximum drinks per drinking day 418 18.1 (12.0) 19.6 (11.5) 15.4 (12.4) 0.00041
Days sober 418 −24.7 (16.4) −25.1 (16.0) −24.0 (17.2) 0.53
Comorbid Depression
Lifetime major depression disorderb 418 101 (24.2%) 53 (34.9%) 48 (18.1%) 0.00018
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a

p-value for comparison between male and female groups. p-values reflecting statistically significant differences (<0.05/9 tests = 0.006) bold.

b

Assessed by Psychiatric Research Interview for Substance and Mood Disorders (PRISM).

Consumption measures were normalized using a rank-based inverse normal transformation. Because consumption measures are highly correlated (Supplementary Figure 1), the baseline total drinks consumed was used as the primary outcome. Subsequently, we explored how associations with total drinks may have been driven by specific drinking measures – i.e., maximum or average number of drinks per drinking day (quantity) as well as the number of drinking days (frequency). The association of MDD-PRS with consumption measures was evaluated using linear regression models in the full sample and sex-stratified. We also evaluated the association of the MDD-PRS with IDTS positive, negative, and temptation factor scores using a similar linear regression approach. Furthermore, to test for sex differences in the associations between PRS and consumption and IDTS scores, an MDD-PRS by sex interaction was evaluated in the linear models. To aid in interpretation of results, we tested for association between MDD-PRS and MDD history in our sample (overall and in men and women separately) using logistic regression. Finally, we investigated whether the MDD-PRS associations with consumption outcomes are altered (overall, and in men and women separately) after adjusting for MDD history (found to be associated with alcohol consumption in our previous work (Karpyak et al., 2019)) and the PRSs for AUD and alcohol consumption. All of the described analyses were adjusted for the first genetic principal component, age, and sex. We additionally adjusted all analyses of consumption measures for the normalized days sober at baseline to account for differences in consumption in the 90 days before study enrollment due to variation in the amount of time that a patient abstained from alcohol prior to enrollment. To account for multiple testing in our primary analysis, we use a Bonferroni correction (p < 0.05/8 outcomes = 0.00625) to interpret our findings. All analyses were conducted using R version 4.0.3 software.

3. Results

The demographic and clinical characteristics of the sample are presented in Table 1. A summary of the overall and sex-specific associations between MDD-PRS and the consumption measures is presented in Figure 1. The primary outcome analysis showed that higher MDD-PRS is associated with lower total number of drinks (β = −0.09 [95% CI: −0.18, −0.01] per SD increase in the PRS; p =0.030). However, sex appeared to moderate this relationship (interaction p-value = 0.007), and stratified analyses revealed that higher MDD-PRS is associated with a significantly lower total drinks in men (β = −0.16 [95% CI: −0.26, −0.07]; p = 0.0012), but no evidence of association with total drinks was observed in women (β = 0.11 [95% CI: −0.05, 0.26]; p = 0.18). The association pattern was similar for all secondary consumption measures (Figure 1; Supplementary Table 1).

Figure 1:

Figure 1:

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Effect estimates (and 95% confidence intervals) of major depressive disorder (MDD) polygenic risk score (PRS) per SD increase in PRS for each normalized consumption measure in all alcohol-dependent (AD) patients, female AD patients, and male AD patients (colors denoted in the legend). The p-values for tests of the MDD-PRS×sex interaction effects for all the outcomes are shown in the x-axis labels.

We also observed statistically significant sex differences in the association of MDD-PRS with IDTS measures (Supplementary Table 1), with sex-stratified analyses suggesting that in men, higher MDD-PRS is associated with lower propensity to drink in temptation-related situations (β = −0.13 [95% CI: −0.24, −0.02]; p = 0.02; interaction p = 0.0053). In contrast, in women, higher MDD-PRS scores were associated with higher scores on all three IDTS subscales, although none of the subscore associations were statistically significant for women. Unexpectedly, MDD-PRS was not associated with MDD history in the combined sample nor in the sex-stratified subgroups (Figure 1; Supplementary Table 1).

Finally, we investigated whether the above associations persisted after adjusting for MDD history and alcohol-related PRSs (Supplementary Table 2). Because MDD-PRS was not associated with MDD history and had very low correlation with the alcohol-related PRSs (Supplementary Figure 1), the results of the association between the MDD-PRS and consumption or propensity to drink in different situations did not significantly change (Supplementary Table 2). Furthermore, the alcohol-related PRSs did not predict baseline alcohol consumption in the full treatment-seeking AUD sample nor in the sex-stratified samples.

4. Discussion

Our study indicates that genetic load for MDD (measured by PRS) is associated with pretreatment alcohol consumption in patients with alcohol dependence. A similar association was reported previously in a large community sample with most subjects not meeting the criteria for alcohol dependence, which found a negative genetic correlation between MDD and consumption derived from the Alcohol Use Disorders Identification Test (AUDIT) (Sanchez-Roige et al., 2019). However, our study is the first to show the role of genetic load for MDD in this inverse relationship in a sample of patients with AUD, and to demonstrate that this association is driven by men. This sex-specific association parallels previously reported associations between alcohol consumption and lifetime history of MDD in patients with AUD (Karpyak et al., 2019).

This is also the first study to investigate the association of genetic load for MDD with a propensity to drink in positive/negative emotional and temptation situations. Similar to previously reported findings with MDD history (Karpyak et al., 2019), the association of the MDD-PRS with propensity to drink in different situations (positive/negative emotional and temptation as measured by IDTS scores) was also sex-specific. Namely, the inverse association was observed only in alcohol dependent men, while an opposite trend, suggesting that higher MDD-PRS score may be associated with higher propensity to drink in all risky situations, was observed in alcohol-dependent women.

Our findings indicate potential sex-specific effects of MDD-PRS on alcohol consumption and on IDTS scores (i.e., alcohol use in emotionally loaded or temptation-related situations), which are similar to the sex-specific effects of MDD history that we previously observed in the same group of subjects (Karpyak et al., 2019). However, it is important to note that the similarity of these results is not due to a high correlation between MDD-PRS and MDD history. In fact, in our sample, MDD-PRS was not associated with MDD history. This implies the possibility that both the genetic component of MDD captured by MDD-PRS and the clinical phenotype of MDD itself (which has both genetic and non-genetic contributions) have a sex-specific moderation effect on relationships between alcohol consumption and the tendency to consume alcohol in negative emotional situations. The lack of association between MDD-PRS and MDD history in our sample of patients with AUD was unexpected. With the sample size of 434 in our PRS analysis, we had a statistical power of 54–83% to detect the association of the PRS with a lifetime diagnosis of MDD at a 5% significance level, assuming that the MDD-PRS would explain 1–2% of the variation in MDD liability, as was observed for this PRS in held-out samples from the Psychiatric Genomics Consortium (Wray et al., 2018). The fact that we observed no evidence of association of the MDD-PRS with MDD in our sample suggests the possibility that the MDD phenotype observed in patients with AUD has different genetic underpinnings and possibly different neurobiological mechanisms than MDD in people without AUD. Larger studies of depression in patients with and without AUD will be needed to evaluate this possibility.

The observed sex-specific associations of MDD-PRS with alcohol consumption and IDTS scores remained unchanged after adjustment for genetic load for AUD and alcohol consumption (measured by respective PRS scores, which correlated moderately with each other). This finding suggests that genetic factors captured by the MDD-PRS impact alcohol consumption independently from genetic factors captured by the AUD and alcohol consumption PRSs. Moreover, the alcohol-related PRSs did not predict baseline alcohol consumption in our sample of treatment-seeking patients with alcohol dependence. The alcohol-related PRSs were derived from GWASs either comparing cases to controls or using alcohol consumption in a veteran population (Kranzler et al., 2019; Liu et al., 2019), which differ from a treatment-seeking population that may have unique genetic factors influencing alcohol consumption. Future studies exploring the nature of these genetic factors and mechanisms by which they impact alcohol consumption may facilitate targeted and potentially sex-specific therapeutic interventions.

These findings underscore the importance of differentiation between trait-dependent phenotypes, such as AUD and MDD, versus state-dependent phenotypes, such as alcohol consumption and positive or negative mood states. While the relationships between alcohol consumption and mood states are reasonably well-understood (e.g., drinking to relieve negative or enhance positive emotions), the relationships between AUD and MDD are more complex and require further investigation (e.g., the similar impact of MDD history and MDD-PRS on alcohol consumption along with the lack of association between those in our sample).

These findings may help resolve somewhat conflicting observations regarding the effects of serotonergic treatment of subjects with AUD (Kranzler et al., 2006; Pettinati et al., 2000). In one relatively small study, treatment with the antidepressant sertraline reduced drinking in AUD patients without lifetime depression but showed no benefits over placebo in those with a lifetime diagnosis of MDD (Pettinati et al., 2001). Our findings suggest that it may be informative to perform sex-stratified analyses of AUD treatment outcomes related to selective serotonin reuptake inhibitors, ideally in a larger study powered to assess treatment effect differences in men versus women. If such a difference is confirmed, it may provide critically important guidance for personalized selection among treatment options.

The results of this study need to be considered in the context of the following limitations. First, the study sample was relatively small, limiting the possibility of discovering associations with small effects. This is especially relevant to findings in the subsample of women. Studies in larger samples, potentially enriched for women representation, will be necessary to confirm and expand our results. Second, the MDD-PRS used in our study was not based on sex-specific GWAS results, and it is possible that more precise findings may be elicited with the use of sex-specific MDD-PRS built in future studies. Third, the association findings reported here were acquired in a sample of European ancestry. Therefore, the possible generalizability and relevance of those findings for other ancestral groups need to be further investigated. Well-powered studies using properly collected clinical and biological data from a diverse sample of AUD patients will be necessary to fully comprehend the complex relationships between AUD and MDD and the development of targeted treatment strategies.

5. Conclusion

In conclusion, our findings demonstrate that genetic load for MDD captured by PRS impacts pretreatment alcohol consumption and propensity to drink in positive/negative emotional and temptation situations in men and women with AUD in a sex-specific manner, and this impact is similar to, but independent of, the impact of the history of MDD. The clinical implications of the reported findings as well as the biological and psychological factors contributing to these differences, need to be investigated in future studies.

Supplementary Material

1
2

Highlights.

History of major depressive disorder has sex-specific association with alcohol use

Genetic load for MDD was inversely associated with alcohol use in men but not women

Genetic load for MDD inversely correlated with temptation-driven use in women and men

MDD history and genetic load for MDD were associated with alcohol use independently

Clinical implications of these findings and their mechanisms should be investigated

Role of Funding Source

This work was funded by the National Institute of Health-NIAAA (U01AA027487 to Victor M. Karpyak and R21AA025214 to Joanna Biernacka). The data used for this manuscript was collected under previous work supported by the National Institute of Health-NIAAA (1P20AA017830-01 to David Mrazek). These funding sources had no role in the design, execution, analysis and interpretation of data or submission of results from this project.

Conflict of Interest

Victor M. Karpyak: No conflict declared

Brandon J Coombes: No conflict declared

Jennifer R. Geske: No conflict declared

Vanessa M. Pazdernik: No conflict declared

Terry Schneekloth: No conflict declared

Bhanu Prakash Kolla: No conflict declared

Tyler Oesterle: No conflict declared

Larissa L Loukianova: No conflict declared

Michelle K Skime: No conflict declared

Ada Man-Choi Ho: No conflict declared

Quyen Ngo: No conflict declared

Cedric Skillon: No conflict declared

Ming-Fen Ho: NIH grant K01AA28050

Richard Weinshilboum: OneOme, LLC; NIH grants (GM28157, R01AA27486)

Joanna M Biernacka: No conflict declared

Conflict of interest Disclosures

Co-authors disclosed relationships with funding sources, which may be perceived as potential conflicts of interest.

Footnotes

Trial Registration: ClinicalTrials.gov, Identifier: NCT00662571, http://clinicaltrials.gov

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