**1. Introduction**

Alcohol is the most widely consumed drug worldwide [1], ye<sup>t</sup> only a fraction of alcohol consumers develop an alcohol-use disorder (AUD). Various factors have been suggested to increase the likelihood of developing AUDs, including genetic [2], social [3], neuropsychological [4], and psychopathological and personality traits [5].

Focusing on the latter, population-based studies show that comorbidity between AUD and PDs exceeds 40% [6], with the highest prevalence rates detected with antisocial, histrionic, and borderline personality disorders (PDs). Studies conducted with patient samples show that the prevalence rates of comorbid disorders are higher than those observed in the general population, noting that greater severity of AUD is associated with a higher

**Citation:** De la Rosa-Cáceres, A.; Narvaez-Camargo, M.; Blanc-Molina, A.; Romero-Pérez, N.; Dacosta-Sánchez, D.; González-Ponce, B.M.; Parrado-González, A.; Torres-Rosado, L.; Mancheño-Velasco, C.; Lozano-Rojas, Ó.M. Bridge Nodes between Personality Traits and Alcohol-Use Disorder Criteria: The Relevance of Externalizing Traits of Risk Taking, Callousness, and Irresponsibility. *J. Clin. Med.* **2022**, *11*, 3468. https://doi.org/10.3390/ jcm11123468

Academic Editors: Serge Brand and Nuri B. Farber

Received: 22 April 2022 Accepted: 14 June 2022 Published: 16 June 2022

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probability of presenting these disorders [7]. A review conducted by Guy et al. [8] showed that in patients diagnosed with antisocial PD, lifetime AUD reached 76.7%, while among patients diagnosed with borderline PD the prevalence of AUD was 52.2%. In patients with AUD, studies have reported mixed prevalence rates. However, Trull et al. [9] estimate that in patients diagnosed with AUD, the general prevalence of PD exceeds 45%. Likewise, these authors point out that in AUD patients, Cluster B PD is more prevalent than clusters A and C.

The above evidence indicates that several personality traits are likely to be shared among individuals with AUD and PD. In this regard, numerous studies have been conducted using the Five-Factor Model (FFM) to determine which traits are characteristic of heavy alcohol users and those with AUD. For instance, a meta-analysis conducted by Malouff et al. [10] showed that low conscientiousness, low agreeableness, and high neuroticism were associated with alcohol consumption. A subsequent meta-analysis by Kotov et al. [11] also found that low conscientiousness and high neuroticism traits were more frequently found in people with AUD. However, no association was found with the agreeableness trait. Moreover, the meta-analysis by Hakulinen et al. [12] also agrees with previous studies by showing that lower agreeableness and conscientiousness and higher neuroticism are associated with heavy alcohol consumption. In addition, these authors also found that higher extraversion is associated with heavy alcohol consumption in a specific way; that is, the traits associated with the transition from moderate to heavy alcohol consumption were lower conscientiousness and higher extraversion. The latter trait, aligned with the detachment domain of the Alternative Model for Personality Disorders (AMPD), was found by Moraleda et al. [13] to be a distinctive trait of patients with AUD compared to those with other substance-use disorders. However, the described relationships between personality traits and AUD should be contextualized in those countries where alcohol consumption is widely accepted in the culture. Factors associated with disapproval of alcohol consumption, differences in family and interpersonal values, or attitudinal aspects that differ across cultures may mediate the relationships between personality traits and alcohol consumption [14].

Despite previous evidence suggesting that people with high alcohol consumption exhibit certain personality traits, the specific relationships between these traits and AUD are largely unknown. In this regard, network analysis could help to delve deeper into the relationships between personality traits and AUD. Although this type of technique has its origins in sociological studies, in the last decade it has been applied to the study of mental disorders [15]. Network analysis constitutes a set of techniques that allow the reciprocal relationships between symptoms or diagnostic criteria to be depicted in graphical form. Each symptom or diagnostic criterion is represented by a node, allowing for analysis of the interrelationships between these nodes [16,17]. Those nodes that are more densely related form substructures or clusters [18], which can be distinguished from other possible clusters. In addition, this technique allows us to determine which criteria or symptoms exert a greater influence on the others [19] and identify those nodes that are most strongly related to nodes of other distinct substructures. These nodes are considered useful for explaining comorbidity between various disorders, as shown by previous studies that have used network analysis to depict associations between AUD and internalizing traits [20].

Thus, the present study aimed to (1) examine the relationships between personality facets and AUD criteria according to their organization into different substructures in the network, as well as to test for invariance according to gender by comparing the structure, global strength, and edges between the networks of men and women; and (2) identify the bridge nodes between the different substructures identified, which could help to explain the comorbidity between PD and AUD.
