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Clinical Trial
. 2014 Jan;19(1):50-4.
doi: 10.1038/mp.2012.155. Epub 2012 Nov 6.

Smoking quit success genotype score predicts quit success and distinct patterns of developmental involvement with common addictive substances

Affiliations
Clinical Trial

Smoking quit success genotype score predicts quit success and distinct patterns of developmental involvement with common addictive substances

G R Uhl et al. Mol Psychiatry. 2014 Jan.

Abstract

Genotype scores that predict relevant clinical outcomes may detect other disease features and help direct prevention efforts. We report data that validate a previously established v1.0 smoking cessation quit success genotype score and describe striking differences in the score in individuals who display differing developmental trajectories of use of common addictive substances. In a cessation study, v1.0 genotype scores predicted ability to quit with P=0.00056 and area under receiver-operating characteristic curve 0.66. About 43% vs 13% quit in the upper vs lower genotype score terciles. Latent class growth analyses of a developmentally assessed sample identified three latent classes based on substance use. Higher v1.0 scores were associated with (a) higher probabilities of participant membership in a latent class that displayed low use of common addictive substances during adolescence (P=0.0004) and (b) lower probabilities of membership in a class that reported escalating use (P=0.001). These results indicate that: (a) we have identified genetic predictors of smoking cessation success, (b) genetic influences on quit success overlap with those that influence the rate at which addictive substance use is taken up during adolescence and (c) individuals at genetic risk for both escalating use of addictive substances and poor abilities to quit may provide especially urgent focus for prevention efforts.

Trial registration: ClinicalTrials.gov NCT00894166.

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Conflict of interest statement

CONFLICT OF INTEREST

Drs. Rose and Uhl are listed as inventors for a patent application filed by Duke University based on genomic markers that distinguish successful quitters from unsuccessful quitters in data from other clinical trials.

Figures

Figure 1
Figure 1
v1.0 scores for nonquitters (NQ) and successful quitters (Q) in this clinical trial. Scores could range from 0 to 1000. Quitters reported continuous abstinence, confirmed by monitoring of CO in exhaled breath, for at least 11 weeks after the targeted quit date for this trial. *** P = 0.0005, t test. SEMs are 2.5 and 4.5, respectively.
Figure 2
Figure 2
Receiver operating characteristic curve fitted to data for v1.0 scores ability to predict continuous abstinence (11 weeks) in the smoking cessation clinical trial described herein. Blue line indicates the area under the fitted curve. Grey lines indicate 95% confidence intervals for this estimate. Area under the curve: 0.657 (http://www.rad.jhmi.edu/jeng/javaradroc/JROCFITi.html).
Figure 3
Figure 3
Trajectories of involvement with common abused substances for classes of prevention study subjects as derived using latent class growth analysis implemented in Mplus. Members of class 1 (green; 80.8% of subjects) used few substances during the followup period. Members of class 2 (blue; 8.8%) stably used a number of substances during the followup period. Members of class 3 (red; 10.6%) escalated use of substances during the followup period. X axis: age. Y axis: aggregate score for past year frequency of use of tobacco, alcohol and cannabis derived from self report data from followup interviews.
Figure 4
Figure 4
Cartoon suggesting one mechanism by which quit success genetics might influence trajectories of uptake of substance use, dependence and quitting over time. If initial bouts of use were terminated by processes shared with those involved in quitting after an extended course of substance use and dependence, current results might be explained. (Please note that the current results are also compatible with other explanatory models.)

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