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A Meta-analysis of the Efficacy of Gabapentin for Treating Alcohol Use Disorder

. Author manuscript; available in PMC: 2020 Sep 1.

Addiction. 2019 Jun 5;114(9):1547–1555. doi:

Studies of the efficacy of gabapentin for treating alcohol use disorder (AUD) have yielded mixed findings. The aims of our study were to estimate gabapentin’s effects on six alcohol-related outcomes, test potential moderators, examine publication bias, and evaluate the quality of the studies.

Meta-analysis of placebo-controlled randomized controlled trials (RCTs). Using PubMed and ClinicalTrials.gov, we selected RCTs of gabapentin’s effects on alcohol consumption or a biochemical correlate of it, excluding studies limited to other primary outcomes or that combined gabapentin with other medications. We assessed study quality and used a random effects model to analyze each outcome measure and the Egger regression test and funnel plots to assess publication bias.

We identified seven RCTs of gabapentin that met study criteria. The quality of the studies overall was good and there was no evidence of publication bias. Four to seven studies contributed to the analysis of the six outcome measures. For all outcome measures the effect estimates were in a direction that favored gabapentin over placebo. However, only for percent heavy drinking days was there good evidence of a benefit (g= −0.64, 95% CI= −1.22 to −0.06).

Although gabapentin appears to be more efficacious than placebo in treating AUD, the only measure on which the analysis clearly favors the active medication is percent heavy drinking days. Additional studies are needed to define more clearly the role of gabapentin in AUD treatment.

Keywords: Gabapentin, Meta-Analysis, Alcohol Use Disorder, Effect Size, Pharmacotherapy, Treatment Guidelines

The immediate-release formulation of gabapentin, 1-(aminomethyl) cyclohexaneacetic acid was approved by the Food and Drug Administration (FDA) in 1998 at a dosage of 900–1800 mg/day as an adjunctive treatment for partial onset seizures. In 2002, the drug was approved for the management of postherpetic neuralgia at a dosage of 300–900 mg/day. In 2011, an extended-release (ER) prodrug (gabapentin enacarbil) was approved to treat restless legs syndrome at a dosage of 600 mg/day and postherpetic neuralgia at a dosage of 600–1200 mg/day. The precise mechanisms of the therapeutic actions of gabapentin are unknown [1], though it appears to inhibit selectively voltage-gated calcium channels containing the alpha-2-delta-1 subunit, enhances voltage-gated potassium channels, and modulates GABA activity [2]. Although gabapentin is structurally related to gamma-aminobutyric acid (GABA), it has no effect on GABA binding, uptake, or degradation [1].

Chronic alcohol consumption, particularly at high levels, is associated with increased inhibitory neurotransmission, which upon abrupt discontinuation, and together with increased glutamatergic excitation, is manifested as alcohol withdrawal signs and symptoms [3]. Gabapentin, which has effects on both inhibitory and excitatory neurotransmission, was tested initially to treat alcohol withdrawal and subsequently to reduce drinking or promote abstinence in individuals with alcohol use disorder (AUD) [4]. In 2007, Furieri et al. [5] reported the first clinical trial that examined the efficacy of gabapentin for treating AUD. Since that time, additional studies have been published, but to date there has been no meta-analysis of the gabapentin for treating AUD, presumably because of the limited published literature on the topic. A review conducted in 2015 [4] identified five published gabapentin treatment trials for AUD, including three randomized, placebo-controlled trials (RCTs) (total N=231) that tested its effects on drinking outcomes and five other studies that examined the drug’s effects on alcohol withdrawal. In the past year, data became available from three additional RCTs (total N=482) [6–8]. The availability of data from these RCTs made it feasible to conduct this meta-analysis of the drug’s effects on multiple alcohol consumption outcomes.

Tolerability and safety are important additional considerations in evaluating a medication for any indication. An alcohol interaction study showed that gabapentin alone impaired balance and, although it did not significantly alter the subjective or performance effects of alcohol or alcohol craving, it dose-dependently increased alcohol-induced tachycardia [9]. A second interaction study monitored the effects of gabapentin in the natural environment during medication treatment and during a bar-laboratory study [10]. It showed that gabapentin’s subjective effects during natural drinking and its effects on subjective high and intoxication during the bar–laboratory drinking session were comparable to those of placebo. Of some concern, though, gabapentin treatment, particularly at high dosages (e.g., greater than 1800 mg/day) [10], is associated with a greater likelihood than placebo of a number of clinically important adverse effects [11]. Further, a systematic review showed that about 1% of the U.S. general population misused gabapentin for recreational purposes, self-medication, or intentional self-harm, either alone or in combination with other substances (including alcohol) [13]. The authors reported that individuals with a history of substance use, misuse, or dependence, particularly those who used opioids, were most likely to misuse the drug. In the face of a dramatic increase in the medical use of gabapentin in the United States in recent years (an increase of 25 million prescriptions to 64 million in the period 2012 to 2016, which made gabapentin the tenth most commonly prescribed medication [14]), clear evidence of its safety and efficacy in treating AUD is needed.

The aims of this meta-analysis were to evaluate the efficacy of gabapentin using all available placebo-controlled trials, including all alcohol-related outcomes; to test potential moderators of the observed effects; to examine evidence for publication bias; and to evaluate the quality of the studies included in the analyses.

Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [15], we conducted a search for relevant studies using PubMed (last search date November 28, 2018) and ClinicalTrials.gov (last search date October 29, 2018). No filters were applied to the searches for date, language, country, or publication type. Search terms in PubMed included: “gabapentin and alcohol dependence and treatment” (86 articles) and “gabapentin and clinical trials and alcohol use disorder” (37 articles). In ClinicalTrials.gov, gabapentin was used as the search term with the following conditions: “alcohol dependence” (11 studies), “alcoholism” (11 studies), “alcohol-related disorders” (10 studies), “alcohol use disorder” (4 studies), and “heavy drinking” (2 studies). We also searched the reference sections of relevant articles manually for previously unidentified studies, which provided one additional study. These efforts yielded a total of 162 articles.

We included RCTs that: (1) enrolled study subjects aged 18 or older with a diagnosis of DSM-IV alcohol dependence or DSM-5 AUD, irrespective of severity, (2) compared gabapentin to placebo rather than either an active comparator or no comparator, (3) included quantitative measures of alcohol consumption, and (4) focused on alcohol treatment. To ensure a clear focus on the efficacy of gabapentin for reducing drinking or sustaining abstinence, we excluded studies whose primary focus was treating alcohol withdrawal or insomnia; those that combined gabapentin with another medication for treating AUD; and experimental/laboratory paradigm studies, case reports, and review articles. We did not exclude studies based on the date or language of publication, the duration or setting of the intervention, gabapentin dosage or dosing schedule, or the number of participants assigned to each study arm.

Three authors (EH, PM, RF) independently screened the titles and abstracts of search hits to select studies of interest. Two authors (HK, RF) reviewed the full text of each potentially relevant study identified in the search to evaluate it for inclusion. Disagreements were resolved by discussion among the authors. The literature search identified a total of 162 records, from which duplicates were removed, leaving 98 records (see Figure 1). Screening the titles and abstracts of these records provided information that led to the exclusion of 87 articles, while full-text review of the remaining 11 articles led to the exclusion of 3 that also failed to meet study criteria, leaving 8 articles. One unpublished study [16] was excluded during data extraction because it provided insufficient data for analysis, leaving a total of seven studies for inclusion in the analyses.

Flow Diagram of Study Selection

We attempted to contact the authors of five published studies via email to request data on drinking outcomes that were not reported in their publications. We also requested data from investigators of three studies that were shown as having been completed on ClinicalTrials.gov, but whose results were not yet posted. If there was no response to the first request, authors were contacted a second time. Two authors of published studies provided additional data. Furieri et al. [5] provided data on the percent of participants that relapsed to heavy drinking and the percent that abstained from drinking. Choompookham et al. [6] provided data on the percent of participants that relapsed to heavy drinking, percent that abstained from drinking, mean percent heavy drinking days, mean percent abstinent days, and mean drinks per day. Authors of the remaining six studies (three published, three from ClinicalTrials.gov) indicated that they were unable to provide the requested data or did not respond to our requests.

We used the Cochrane Risk of Bias tool [17] to assess the quality of the studies included in the meta-analysis. The facets of study quality that we assessed with this tool were: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other sources of bias.

We used the risk ratio (RR) as the efficacy measure for binary outcomes (total abstinence and relapse to heavy drinking; the definition of the latter was based on that reported in each study). RRs were calculated using the sample sizes and the number (or percentage) of subjects who experienced the event of interest (abstinence or relapse to heavy drinking). We used Hedges’ g as the efficacy measure for continuous outcomes [percent heavy drinking days, percent days abstinent, mean drinks per day, and gamma-glutamyltranserase (GGT) concentration]. Hedges’ g is similar to Cohen’s d, but removes the small sample bias associated with the latter by using pooled weighted standard deviations [18], important here because five of the studies that we reviewed had one or more treatment arms with fewer than 50 subjects. Hedges’ g was calculated using the reported raw means and standard deviations or the value of the test statistic comparing groups with the associated degrees of freedom when descriptive statistics were not reported. Values of Hedges’ g are 0.2 = small effect; 0.5 = medium effect, and 0.8 = large effect.

Although one study that was included examined gabapentin’s effects on insomnia [17], it also examined effects on alcohol-related outcomes. For the outcome of mean alcohol consumption, two studies [5, 6] reported drinks per day and two studies [7, 19] reported drinks per week. We analyzed the two measures of alcohol consumption together, as they are a linear transformation of one another. Two studies [20, 21] had three treatment arms. Mason et al. [20] compared two gabapentin groups (900 mg/day and 1800 mg/day) with placebo. Trevisan et al. [21] compared gabapentin with valproic acid and placebo. We used only the gabapentin and placebo arms from that study in the meta-analysis. We used two approaches to handling these non-independent comparisons with placebo. First, we treated the two dosages as separate studies, with each dosage arm being compared to the placebo arm. Second, we included only the 1800-mg/day arm, which showed a larger effect than the 900-mg arm. Insofar as the results were largely similar for the two models, we present the model that included both gabapentin treatment arms and findings for the secondary analysis when the results differed between the two approaches. Whereas Falk et al. [7] used ER-gabapentin while the other RCTs included in the meta-analysis used the immediate-release formulation, we conducted a sensitivity analysis that omitted the data from Falk et al. [7]. Here too, insofar as the results were largely similar whether the Falk et al. study [7] was included or not, we present the findings from the analysis that included the study, supplemented by the findings that differed between the two approaches.

We used a random effects model for all measures and the Q statistic to test for heterogeneity among the study effect sizes. I² is reported as a measure of the proportion of variance attributed to study heterogeneity. Forest plots were generated to display the effect sizes from the individual studies with confidence intervals and the weighted aggregate effects. The weights shown in the figures and the aggregated effects are from the random effects model. The study weights reflect both the within-sample variance and the heterogeneity of the studies in the analysis.

We conducted a bivariate meta-regression for each outcome measure with gabapentin dosage (in 100-mg increments), trial duration (in weeks), and the percentage of patients who completed the treatment trial as potential moderators of the observed heterogeneity of effect sizes. We included these moderator variables because a dosage effect of gabapentin was shown by Mason et al. [20] in a study of AUD treatment, treatment duration was shown to predict outcomes in nicotine replacement therapy for smoking cessation [22], and the rate of study completion is lower in treatment trials of alcohol and drug use disorders than other psychiatric diagnoses [22]. Because of the small number of studies, we tested each moderator individually. The statistical test for moderator effects used the t-distribution with the degrees of freedom based on the number of studies. We used the Egger regression test [25] and funnel plots to assess publication bias. All analyses were conducted in SAS v9.4 (SAS Institute, Cary NC).

Table 1 presents the characteristics of the included studies. A total of seven RCTs and 32 effect measures were included in the analysis. Excluding the 900-mg arm from the study by Mason et al. [20] reduced the number of effect measures to 28. Two studies were conducted outside the United States and three studies were published in 2018. The sample sizes ranged from 21–338, with only three of the trials randomizing >100 participants. Males (52.4–100%) and European ancestry individuals (51.5–84.5%, excluding the study from Thailand) comprised the largest percentage of participants in the trials. The maximal dosage of gabapentin varied from 300–3600 mg/day and the trial duration varied from 3–26 weeks. The effective dosage of gabapentin enacarbil, a prodrug formulation, at a dosage of 1200 mg/day was treated as the equivalent of 1080 mg/day of the immediate-release formulation [7]. The percentage of participants who completed the trial varied from 33–80%. As shown in Supplementary Table 1, overall, the quality of the studies was good. Limited data were available to assess the quality of one study [8], as the study findings were drawn from the posting on Clinicaltrials.gov. The results of the Egger regression test [25] and the funnel plots (Supplementary Figures 1-6) revealed no evidence of publication bias (p-values for all outcomes > 0.5).

Risk of Bias in the Gabapentin Clinical Trials

As can be seen in Table 2 and Supplementary Figure 7, there was not good evidence that gabapentin contributed to the achievement of abstinence (RR = 1.33, 95% CI: 0.84–2.10, p = 0.23). The between-study heterogeneity for this measure was not significant (χ²₅ = 8.92, p = 0.11, I² = 0.44, 95% CI: 0.00–0.78).

Meta-Analysis Results

Similarly, for relapse to heavy drinking (Table 2 and Supplementary Figure 8), the overall weighted RR did not robustly favor gabapentin (RR = 0.80, 95% CI: 0.57–1.13, p = 0.21), and it was not substantially influenced by the removal of the Falk et al. study [7] from the model (RR = 0.71, 95% CI: 0.47–1.06, p = 0.09). Heterogeneity (χ²₅ = 14.25, p = 0.01), which accounted for 65% of the variance (I² = 0.65, 95% CI: 0.16–0.85) in the between-study effect size, was not moderated by any of the study characteristics (Table 3).

Meta-Regression Results

The overall weighted Hedges’ g for percent days abstinent also did not robustly favor gabapentin (g = 0.26, 95% CI: −0.16–0.69, p = 0.23) (Table 2 and Supplementary Figure 9), which was not significantly changed upon the removal of the Falk et al. study [7] (g = 0.41, 95% CI: −0.03–0.84, p = 0.07). Significant between-study heterogeneity of effect sizes (χ²₃ = 9.82, p = 0.020, I² = 0.69, 95% CI: 0.12–0.89) was not moderated by any of the potential moderators (Table 3).

The overall weighted Hedges’ g for percent days heavy drinking provided good evidence of a beneficial effect of gabapentin, with the difference being nearly 2/3 of a standard deviation (g = −0.64, 95% CI: −0.64– −0.06, p = 0.03) (Table 2 and Supplementary Figure 10). This is the only outcome measure that differed as a function of the inclusion of the 900-mg/day arm from the Mason et al. study [20]. When that study arm was excluded, the effect size was reduced to about one-half of a standard deviation and the overall weighted Hedges’ g did not robustly favor gabapentin over placebo (p = 0.09, g = −0.50, 95% CI: −1.10–0.09). For both models, there was significant between-study heterogeneity (χ²₆ = 80.40, p < 0.001, I² = 0.92, 95% CI: 0.87–0.96) that was not accounted for by any of the study characteristics (Table 3).

The overall weighted Hedges’ g for the number of drinks/day did not robustly favor gabapentin over placebo (g = −0.15, 95% CI: −0.64–0.35, p = 0.56) (Table 2 and Supplementary Figure 11). Although there was significant between-study heterogeneity (χ²₄ = 37.04, p < 0.001, I² = 0.89, 95% CI: 0.78–0.95), none of the study characteristics moderated the effect (Table 3).

Similarly, for GGT concentration, the overall Hedges’ g did not robustly favor gabapentin over placebo (g = −0.14, 95% CI: −0.46–0.18, p = 0.39) (Table 2 and Supplementary Figure 12), but there was no evidence for significant between-study heterogeneity (χ²₂ = 3.21, p = 0.20, I² = 0.38, 95% CI: 0.00–0.56) (Table 3).

With respect to the safety of gabapentin, there were no serious adverse events reported in the clinical trials for AUD reviewed here. A comparison of the frequency of adverse events associated with gabapentin treatment in these studies showed a 10% greater frequency of adverse events in the active treatment group than in the placebo group.

A practice guideline published recently by the American Psychiatric Association [26] recommends that the FDA-approved drugs disulfiram, naltrexone, and acamprosate be offered to patients with moderate-to-severe AUD. The guideline also recommends that gabapentin or topiramate be offered to patients who prefer one of these drugs or who are intolerant of or have not responded to the FDA-approved medications [26]. A meta-analysis of seven placebo-controlled RCTs of topiramate that examined four outcome measures: abstinence, heavy drinking, GGT concentration, and craving [27] showed significant effects of topiramate on all measures but craving, with the effect size (Hedges’ g) for both abstinence and heavy drinking exceeding 0.4. In contrast, the meta-analysis reported here showed an effect of gabapentin only on percent heavy drinking days, though the effect size for that measure exceeded 0.6, a medium effect size, with smaller, non-significant effect on abstinence, relapse to heavy drinking, percent abstinent days, the mean number of drinks/day, and GGT concentration.

We examined the impact of aspects of the two largest trials on the findings from the meta-analysis. Because the study by Mason et al. [20] was a three-arm trial that compared two dosages of gabapentin, we examined the effect of excluding the 900-mg arm on the percent heavy drinking days, which rendered the finding non-significant. Although this is consistent with a reduction in statistical power resulting from fewer drug-placebo comparisons, the finding also emphasizes the large influence that study had on the evidence of gabapentin’s efficacy on this outcome measure. Despite the high rate of early discontinuation by participants in that study (57% overall: 61% in the placebo group, 50% in the 900-mg group, and 60% in the 1800-mg group), the authors reported that data were available to categorize 60 of 65 dropouts on drinking and heavy drinking outcomes prior to dropout.

The largest study in the gabapentin meta-analysis [7] used an ER-prodrug (gabapentin enacarbil) that was designed to overcome the pharmacokinetic limitations of the immediate-release formulation (IR), i.e., the one tested in the other six studies. A pharmacokinetic comparison showed that the ER formulation resulted in a more sustained and less fluctuating daily exposure than the IR formulation. After dose normalization, gabapentin exposure with the ER formulation was ~1.4-fold that of the IR formulation [28]. Despite the more favorable pharmacokinetic profile of the ER formulation, it is possible that the lack of efficacy in the trial by Falk et al. [7] was due to the use of that formulation. A population pharmacokinetic analysis in the Falk et al. study [7] showed that greater exposure to gabapentin was associated with lower alcohol consumption. The ER formulation requires biotransformation, which can be reduced by alcohol, potentially leading to inadequate dosing, though this correlation could also have resulted from heavier drinking reducing gabapentin plasma concentrations. In any case, omitting the data from the Falk et al. study [7] from the model did not significantly affect the results, though it did modestly increase the effect size of gabapentin on two outcome measures: relapse to heavy drinking and percent days abstinent. Additional studies, particularly of the ER formulation of gabapentin, are needed to determine the impact of using that formulation on gabapentin’s efficacy in treating AUD.

There was evidence of significant heterogeneity of effect sizes for gabapentin on four of the six outcome measures, while the two non-significant outcomes showed moderate heterogeneity (I² ≈ 40%). This between-study variance may explain why there was a lack of association between the study weights and the sample sizes and necessitated the use of a random effects model. Using a fixed effects analysis, which weights studies more directly to their sample size, we found smaller effects for all outcomes. Although there remained a statistically reliable beneficial effect of gabapentin on heavy drinking days, it was reduced in size by about one-half relative to the random effects model.

The small number of placebo-controlled RCTs of gabapentin available for meta-analysis limited the statistical power, but is consistent with the limited literature generally on the use of medications for treating AUD. In fact, the only such medications for which at least 15 placebo-controlled RCTs have been published are the FDA-approved medications naltrexone and acamprosate. In addition, the sample size for most of the available studies was small. Because the goal of the meta-analysis was to examine the use of gabapentin as monotherapy for treating AUD, we excluded studies that primarily examined the effects of the medication on alcohol withdrawal [5] or combination therapy [e.g., ref. 29]. Another factor limiting statistical power is that, in all of the available studies, early discontinuation resulted in missing data. Although none of the studies showed differential drop-out between treatment arms, it is possible that differences in drinking behavior between arms could have affected the results. Individual patient-level data could be useful in evaluating the impact of this potential confounder, but were not available for analysis.

Despite these limitations, this is the first meta-analysis to examine the efficacy of gabapentin for treating AUD. It was made feasible by three recent RCTs of the medication, which included a total of 482 participants (238 treated with gabapentin and 244 treated with placebo). We evaluated six different alcohol treatment outcome measures with a minimum of four studies contributing to each. The process of identifying and evaluating studies for inclusion followed PRISMA guidelines [15]. In addition to testing the efficacy of gabapentin on multiple outcomes, we tested three potential moderators of heterogeneity of effect sizes: study completion rate, gabapentin dosage, and study duration.

In summary, we found that gabapentin was efficacious on only one of the six alcohol treatment outcome measures that we examined. Thus, recommendations that it be used in treating AUD should be qualified to emphasize its potential to reduce the frequency of heavy drinking. Although gabapentin has been associated with clinically significant adverse events [11] and frequent misuse [12], these features do not seem to be relevant in treating AUD. Additional RCTs of gabapentin for treating AUD could: 1) provide a clearer estimate of its risk-benefit ratio in treating AUD, 2) determine whether treatment with the medication should be limited to individuals with alcohol withdrawal signs or symptoms, who may be most responsive to the medication’s effects on drinking behavior, and 3) define the optimal dosage for treating AUD, given the wide range of dosages that have been tested to date.

Figures&Tables

Dr. Kranzler’s effort was supported by NIAAA grants R01 AA023192 and R01 AA021164 and his effort and that of Dr. Hartwell were supported by the Mental Illness Research, Education and Clinical Center of the Veterans Integrated Service Network 4, U.S. Department of Veterans Affairs. The authors thank Daniel Falk, Ph.D. and Raye Litten, Ph.D. for their review and helpful comments on the manuscript and the investigators who generously shared unpublished data with us.

Disclosure: Dr. Kranzler is a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was sponsored in the past three years by AbbVie, Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Ethypharm, Indivior, Lilly, Lundbeck, Otsuka, and Pfizer. Dr. Kranzler is named as an inventor on PCT patent application #15/878,640 entitled: “Genotype-guided dosing of opioid agonists,” filed January 24, 2018.

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