Early cannabis use and DSM-IV nicotine dependence: A twin study

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Abstract

Background: Evidence suggests that cannabis users are at increased risk for cigarette smoking - if so, this may potentially be the single most alarming public health challenge posed by cannabis use. We examine whether cannabis use prior to age 17 years is associated with an increased likelihood of DSM-IV nicotine dependence and the extent to which genetic and environmental factors contribute to this association. Methods: A population-based cohort of 24-36-year-old Australian male and female twins (n = 6257, 286 and 229 discordant pairs) was used. The co-twin-control method, with twin pairs discordant for early cannabis use, was used to examine whether, after controlling for genetic and familial environmental background, there was evidence for an additional influence of early cannabis use on DSM-IV nicotine dependence. Bivariate genetic models were fitted to the full data set to quantify the genetic correlation between early cannabis use and nicotine dependence. Results: The early cannabis-using twin was about twice as likely to report nicotine dependence, when compared to their co-twin who had experimented with cigarettes but had never used cannabis. Even when analyses were restricted to cannabis users, earlier age cannabis use onset conferred greater risk (1.7) for nicotine dependence than did later onset. This association was governed largely by common genetic liability to early cannabis use and nicotine dependence, as demonstrated by genetic correlations of 0.41-0.52. Conclusions: Early-onset cannabis users are at increased risk for nicotine dependence, but this risk is attributable largely to common genetic vulnerability. There is no evidence for a causal relationship between cannabis use and nicotine dependence.

Original languageEnglish
Pages (from-to)1896-1904
Number of pages9
JournalAddiction
Volume103
Issue number11
DOIs
StatePublished - Nov 2008

Keywords

  • Cannabis
  • Discordant twins
  • Genetic
  • Mx
  • Nicotine dependence
  • Twin modeling

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