Abstract
The propensity to engage in risky behaviors including excessive alcohol consumption may impose increased medical, emotional, and psychosocial burdens. Personality and behavioral traits of individuals may contribute in part to the involvement in risky behaviors, and therefore the classification of one’s traits may help identify those who are at risk for future onset of the addictive disorder and related behavioral issues such as alcohol misuse. Personality and behavioral characteristics including impulsivity, anger, reward sensitivity, and avoidance were assessed in a large sample of healthy young adults (n = 475). Participants also underwent diffusion tensor imaging for the analysis of structural brain networks. A data-driven clustering using personality and behavioral traits of the participants identified four subtypes. As compared with individuals clustered into the neutral type, individuals with a high level of impulsivity (A subtype) and those with high levels of reward sensitivity, impulsivity, anger, and avoidance (B subtype) showed significant associations with problem drinking. In contrast, individuals with high levels of impulsivity, anger, and avoidance but not reward sensitivity (C subtype) showed a pattern of social drinking that was similar to those of the neutral subtype. Furthermore, logistic regression analysis with ridge estimators was applied to demonstrate the neurobiological relevance for the identified subtypes according to distinct patterns of structural brain connectivity within the addiction circuitry [neutral vs. A subtype, the area under the receiver operator characteristic curve (AUC) = 0.74, 95% CI = 0.67–0.81; neutral vs. B subtype, AUC = 0.74, 95% CI = 0.66–0.82; neutral vs. C subtype, AUC = 0.77, 95% CI = 0.70–0.84]. The current findings enable the characterization of individuals according to subtypes based on personality and behavioral traits that are also corroborated by neuroimaging data and may provide a platform to better predict individual risks for addictive disorders.
Highlights
Addictive behaviors in everyday life may be frequently associated with negative consequences on health and psychological well-being (Deleuze et al, 2015)
The present study identified neurobiologically distinct subtypes in young adults without any prior history of addictive disorders and examined whether patterns of alcohol misuse may differ according to the subtypes
The current sample of young adults has been classified into four subtypes, each of which demonstrated unique behavioral tendency and differential propensity to alcohol misuse
Summary
Addictive behaviors in everyday life may be frequently associated with negative consequences on health and psychological well-being (Deleuze et al, 2015). Growing literature suggest the importance of personality traits in addiction, such as the role of personality in the vulnerability or resilience towards substance use disorders (Belcher et al, 2014) and addictive behaviors (Barkin et al, 2002), as well as the identification of specific traits that have been demonstrated as prevalent in substance users (Terracciano et al, 2008) Traits such as impulsivity (de Wit, 2009; Ersche et al, 2012a,b) and reward sensitivity (Ersche et al, 2013) have been previously reported in association with substance use. An investigational approach that can reliably identify at-risk individuals may be beneficial in the assessment of individual prognosis for addictive disorders, and necessary in building personalized preventive and intervention strategies
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.