The structure and subtypes of gambling activities: Genetic, psychiatric and behavioral etiologies of gambling frequency

• Type of Material: Article
• Authors: Spencer B Huggett, Emory University; Evan A Winiger, University of Colorado Boulder; Rohan Palmer, Emory University; John K Hewitt, University of Colorado Boulder; Robin P Corley, University of Colorado Boulder; Michael C Stallings, University of Colorado Boulder
• Language: English
• Date: 2021-02-01
• Publisher: Elsevier Ltd.
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: Published by Elsevier Ltd.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.addbeh.2020.106662
• Title of Journal or Parent Work: Addictive Behaviors
• Volume: 113
• Start Page: 106662
• End Page: 106662
• Grant/Funding Information: We would like to thank the following funding sources for supporting this work DA011015, DA03580, DA17637 and DA042103. The first three funding sources supported data collection, whereas the last grant provided funds to help interpretation and analysis of the data.
• Keywords: Adult; Male; Discordant twin; Humans; Problem gambling; Gambling frequency; Skill gambling; Siblings; Causality; Twin study; Female; Alcoholism; Twins; Young Adult; Gambling; Factor mixture analysis

Abstract

The multitude of gambling activities has given rise to heterogeneous ways of analyzing these behaviors and may partially underlie the lack of replication in gambling research. The current study used complementary analyses to investigate the structure, typology and etiology of gambling behaviors in a discovery sample of 2,116 twins (54.86% female; Mage = 24.90) and a replication sample of 619 siblings (30.37% female; Mage = 28.00). Our approach was twofold. First, we used confirmatory factor analyses to investigate the structure across the frequency of eight gambling activities. Second, we used factor mixture models to identify gambling frequency subtypes. We assessed associations with gambling frequency as well as conducted genetically informed analyses to estimate the role of genetic and environmental influences. Across samples, a two-factor model fit the data best, with a Common Gambling factor influencing all activities and a separate factor for Skill Gambling. Our study identified four gambling frequency subtypes, which resembled the typology from the Pathways Model. We found distinct demographic, psychiatric, behavioral and genetic risk profiles for the different gambling factors and subtypes with robust associations observed for male sex, risk-taking, sensation seeking, alcohol dependence and problem gambling. Controlling for shared genetic and environmental influences (via co-twin control modeling), we found that sensation seeking directly increased Common Gambling frequency. In sum, we illustrated the utility of multi-dimensional statistical techniques for disentangling the structure and typology from complex multivariate gambling data.