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Cannabis and depression: a twin model approach to co-morbidity
Abstract   Peer reviewed

Cannabis and depression: a twin model approach to co-morbidity

M Smolkina, Katherine Morley, F Rijsdijk, A Agrawal, J Bergin, E Nelson, Dixie J Statham, N Martin and M Lynskey
Behavior Genetics, Vol.46(6), p.807
Behavior Genetics Association Annual Meeting, 46th (Brisbane, Australia, 20-Jun-2016–23-Jun-2016)
2016
DOI: https://doi.org/10.1007/s10519-016-9812-4
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https://doi.org/10.1007/s10519-016-9812-4View
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Abstract

Psychology Neurosciences
Various forms of cannabis involvement, including Cannabis Use Disorder (CUD: abuse and/or dependence), co-occur with Major Depressive Disorder (MDD) more frequently than would be expected by chance. However, studies to date have not produced a clear picture of the mechanisms driving this co-morbidity. Genetically informative studies can add valuable insight to this problem, as they allow one to evaluate various competing models of co-morbidity. This study uses data from the Australian Twin Registry to compare thirteen co-morbidity twin models initially proposed by Neale and Kendler (1995). The best fitting twin model can help to identify the mechanisms underlying this co-morbidity in the general population. The current data set is comprised of 3824 monozygotic and dizygotic twins, andtheir non-twin siblings. The majority of twins (75 % males, 65 % females) report lifetime cannabis use, with some meeting criteria for CUD (23 % males, 11 % females). Criteria for MDD are met in 16 % of males and 28 % of females. Adjusted for age and sex, MDD and CUD co-occur significantly (OR = 2.76; CI: 2.23-3.40). Of the 13 different co-morbidity models, two fit equally well: ''CUD causes MDD'' (v2 (11) = 15.50, p = .161, AIC = -6.50) and ''Random Multiformity of CUD'' (v2 (11) = 15.46, p = .162, AIC = -6.54). Both fit substantially better than the ''Correlated Liabilities'' model (v2 (9) = 15.21, p = .085, AIC = -2.79). In order to reach clearer conclusions, replications on different twin samples would be useful. However, the ''Random Multiformity of CUD'' model fits best within the context of the literature. It suggests that CUD-MDD co-morbidity arises when secondary depressive symptoms caused by CUD risk factors are mislabeled as MDD. Interestingly, this model is not widely used.

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