Dataset Information

Posttreatment effects of topiramate treatment for heavy drinking.

ABSTRACT:

Background

We examined whether the effects of topiramate and a single nucleotide polymorphism (rs2832407) in GRIK1, which encodes a kainate receptor subunit, persisted following a 12-week, placebo-controlled trial in 138 heavy drinkers with a treatment goal of reduced drinking. During treatment, topiramate 200 mg/d significantly reduced heavy drinking days and increased the frequency of abstinent days (Am J Psychiatry, 2014, 171:445). In the European-American (EA) subsample (n = 122), rs2832407 moderated the treatment effect on heavy drinking.

Methods

Patients were re-interviewed 3 and 6 months after the end of treatment. During treatment, we obtained 92.4% of drinking data, with 89.1 and 85.5% complete data at the 3- and 6-month follow-up visits, respectively. We examined 4 outcomes over time in the overall sample and the EA subsample: percent heavy drinking days (PHDD), percent days abstinent (PDA), serum γ-glutamyl transpeptidase (GGTP) concentration, and a measure of alcohol-related problems.

Results

In the full sample, the lower PHDD and higher PDA seen with topiramate treatment were no longer significant during follow-up. Nonetheless, the topiramate-treated patients had lower alcohol-related problem scores during treatment and both follow-up periods. Further, in the EA subsample, the greater reduction in PHDD seen with topiramate treatment in rs2832407*C-allele homozygotes persisted throughout follow-up, with no significant effects in A-allele carriers. A reduction in GGTP concentration was consistent with the reduction in heavy drinking, but did not reach statistical significance.

Conclusions

There are persistent therapeutic effects of topiramate in heavy drinkers, principally in rs2832407*C-allele homozygotes.

SUBMITTER: Kranzler HR

PROVIDER: S-EPMC4293099 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Publications

Posttreatment effects of topiramate treatment for heavy drinking.

Kranzler Henry R HR Wetherill Reagan R Feinn Richard R Pond Timothy T Gelernter Joel J Covault Jonathan J

Alcoholism, clinical and experimental research 20141201 12

<h4>Background</h4>We examined whether the effects of topiramate and a single nucleotide polymorphism (rs2832407) in GRIK1, which encodes a kainate receptor subunit, persisted following a 12-week, placebo-controlled trial in 138 heavy drinkers with a treatment goal of reduced drinking. During treatment, topiramate 200 mg/d significantly reduced heavy drinking days and increased the frequency of abstinent days (Am J Psychiatry, 2014, 171:445). In the European-American (EA) subsample (n = 122), rs ...[more]

PMID: 25581656

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Project description:AimsTheory-driven, exploratory study to: (i) identify a reward drinking phenotype in young adults; (ii) evaluate this phenotype as a predictor of naltrexone response; and (iii) examine mechanisms of naltrexone in reward drinkers.DesignSecondary analysis of a randomized controlled trial.SettingUSA.ParticipantsA total of 128 young adult (ages 18-25) heavy drinkers.InterventionsNaltrexone versus placebo.MeasurementsDaily surveys assessed affect, urge, drinking, and context. The Drinking Motives Questionnaire was used to identify phenotypes based on reward (enhancement motives) and relief (coping motives) drinking.FindingsWe identified three profiles: "Low reward/Low relief" (14.1%; low enhancement/low coping motives); "Reward drinkers" (62.2%; high enhancement/low coping motives); and "High reward/High relief" (22.7%; high enhancement/high coping motives). Among reward drinkers (versus low profile), naltrexone significantly reduced percent days drinking to intoxication (blood alcohol concentration [BAC] ≥0.08) (PDI) (d = 0.56; 95% CI [0.17, 0.96]) and percent high intensity drinking days (PHID) (8/10 drinks for women/men) (d = 0.32; 95% CI [0.01, 0.68]). Among the high reward/high relief profile drinkers (versus low profile), naltrexone reduced PHID (d = 0.69; 95% CI [0.02, 1.50]). Using profile-informed cutoffs and observed scores (for clinical applicability): (i) among cutoff-derived reward drinkers, we found a medium-to-large (d = 0.66; 95% CI [0.24, 1.16]) and small effect (d = 0.28; 95% CI [0.04, 0.72]) of naltrexone in reducing PDI and PHID, respectively; and (ii) among the cutoff-derived high reward/high relief subgroup, we found a medium-to-large effect (d = 0.63; 95% CI [0.05, 1.1]) of naltrexone in reducing PHID. Among reward drinkers (not other profiles), naltrexone reduced drinking on days a drinking event occurred by weakening the within-day association between positive affect and urges (P < 0.05).ConclusionsNaltrexone has pronounced effects in reducing risky drinking among young adult reward drinkers (high reward/low relief) by reducing urges on days when individuals have higher positive affect and are exposed to a drinking event. Naltrexone also appears to reduce risky drinking among young adult high reward/high relief drinkers, but not via the same mechanism.

Project description:BackgroundAccurate clinical prediction supports the effective treatment of alcohol use disorder (AUD) and other psychiatric disorders. Traditional statistical techniques have identified patient characteristics associated with treatment outcomes. However, less work has focused on systematically leveraging these associations to create optimal predictive models. The current study demonstrates how machine learning can be used to predict clinical outcomes in people completing outpatient AUD treatment.MethodWe used data from the COMBINE multisite clinical trial (n = 1383) to develop and test predictive models. We identified three priority prediction targets, including (1) heavy drinking during the first month of treatment, (2) heavy drinking during the last month of treatment, and (3) heavy drinking between weekly/bi-weekly sessions. Models were generated using the random forest algorithm. We used "leave sites out" partitioning to externally validate the models in trial sites that were not included in the model training. Stratified model development was used to test for sex differences in the relative importance of predictive features.ResultsModels predicting heavy alcohol use during the first and last months of treatment showed internal cross-validation area under the curve (AUC) scores ranging from 0.67 to 0.74. AUC was comparable in the external validation using data from held-out sites (AUC range = 0.69 to 0.72). The model predicting between-session heavy drinking showed strong classification accuracy in internal cross-validation (AUC = 0.89) and external test samples (AUC range = 0.80 to 0.87). Stratified analyses showed substantial sex differences in optimal feature sets.ConclusionMachine learning techniques can predict alcohol treatment outcomes using routinely collected clinical data. This technique has the potential to greatly improve clinical prediction accuracy without requiring expensive or invasive assessment methods. More research is needed to understand how best to deploy these models.

Dataset Information

Posttreatment effects of topiramate treatment for heavy drinking.

Background

Methods

Results

Conclusions

Publications

Posttreatment effects of topiramate treatment for heavy drinking.

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