Role of overlapping genetic and environmental factors in the relationship between early adolescent conduct problems and substance use in young adulthood

Abstract

Aims: To determine (1) the prospective associations of conduct problems during early adolescence with tobacco, alcohol and cannabis use in young adulthood and (2) to what extent these associations are due to overlapping genetic versus environmental influences.

Design: A prospective twin study using biometric twin modelling.

Setting: Finland.

Participants: A total of 1847 Finnish twins (943 males and 904 females) were interviewed in early adolescence, 73% of whom (n = 1353, 640 males and 713 females) were retained in young adulthood.

Measurements: Symptom counts of conduct disorder (CD) criteria were obtained from a semi-structured clinical interview in early adolescence [age 14-15 years, mean = 14.2, standard deviation (SD) = 0.15]. Frequency of alcohol, tobacco and cannabis use was obtained from a semi-structured clinical interview in young adulthood (age 19.9-26.6 years, mean = 22.4, SD = 0.7).

Findings: We found modest to moderate phenotypical correlations (r = 0.16-0.35) between early adolescent CD symptoms and substance use in young adulthood. In males, the phenotypical correlations of CD symptoms with all three substance use variables are explained largely by overlapping genetic influences. In females, overlapping shared environmental influences predominantly explain the phenotypical correlation between CD symptoms and tobacco and cannabis use.

Conclusions: Conduct disorder symptoms in early adolescence appear to moderately predict substance use in early adulthood. In males, genetic influences seem to be most important in explaining the relationship between conduct disorder symptoms and substance use whereas in females, shared environmental influences seem to be most important.

Keywords: Adolescent twins; common environments; conduct problems; genetics; substance use; young adult twins.

Figure 1

Multivariate Cholesky Decomposition. The boxes represent the observed variables and the circles represent the latent variables that influence the observed variables. A, C, and E denote latent genetic, shared environmental and residual influences, respectively. A1 represents the genetic influences on CD symptoms, and the crosspaths from A1 to tobacco, alcohol, and cannabis use indicate the extent to which these genetic influences are shared with each substance use trait. A2, A3, and A4 represent genetic influences on tobacco, alcohol and cannabis use that are not shared with CD symptoms. Crosspaths from A2 to alcohol and cannabis use, and from A3 to cannabis use capture the covariation between these substance use traits. The same structure applies for the C and E factors. Dashed lines indicate non-significant pathways (p>0.05). Note that the pathways from E1 to CD symptoms, E2 to tobacco use, E3 to alcohol use, and E4 to cannabis use were not tested for significance; these pathways cannot be dropped from the model as measurement error is always present.

Figure 1

Multivariate Cholesky Decomposition. The boxes represent the observed variables and the circles represent the latent variables that influence the observed variables. A, C, and E denote latent genetic, shared environmental and residual influences, respectively. A1 represents the genetic influences on CD symptoms, and the crosspaths from A1 to tobacco, alcohol, and cannabis use indicate the extent to which these genetic influences are shared with each substance use trait. A2, A3, and A4 represent genetic influences on tobacco, alcohol and cannabis use that are not shared with CD symptoms. Crosspaths from A2 to alcohol and cannabis use, and from A3 to cannabis use capture the covariation between these substance use traits. The same structure applies for the C and E factors. Dashed lines indicate non-significant pathways (p>0.05). Note that the pathways from E1 to CD symptoms, E2 to tobacco use, E3 to alcohol use, and E4 to cannabis use were not tested for significance; these pathways cannot be dropped from the model as measurement error is always present.