**3. Results**

*3.1. Network Structure with AMPD Personality Facets and AUD Criteria, and Invariance According to Gender*

The estimated network is shown in Figure 1. The network consisted of 250 edges (out of a possible 630) that showed a partial correlation value different from zero. The weights of the edges ranged from −0.009 (Restricted Affectivity-Attention Seeking) to 0.431 (Depressivity-Anhedonia). The graphical analysis reveals an optimal 3-community solution (modularity index = 0.45), which organizes the nodes according to a structure that differentiates the facets most strongly linked to the internalizing spectrum (Anhedonia, Anxiousness, Depressivity, Distractibility, Eccentricity, Emotional Lability, Hostility, Intimacy Avoidance, Perceptual Dysregulation, Perseveration, Restricted Affectivity, Rigid Perfectionism, Separation Insecurity, Submissiveness, Suspiciousness, Unusual Beliefs and Experiences, and Withdrawal), to the externalizing spectrum (Attention Seeking, Callousness, Deceitfulness, Grandiosity, Impulsivity, Irresponsibility, Manipulativeness, and Risk Taking), and the AUD diagnostic criteria (Quit/Control, Time Spent, Activities Given Up, Tolerance, Withdrawal, Larger/Longer, Physical/Psychological Problems, Neglect Roles, Hazardous Use, Social/Interpersonal Problems, and Craving).

The standardized scores of the centrality indices, bridge-centrality indices, and the predictability of the nodes are displayed in Table 3. The nodes with the highest strength values were Anxiousness (1.94), Anhedonia (1.67), Perseveration (1.51), and Emotional lability (1.08). The highest EI1 values were for Perseveration (1.51), Anhedonia (1.48), Anxiousness (1.43), and Physical/ Psychological Problems (1.23). The highest EI2 values corresponded to Perseveration (1.57), Anhedonia (1.50), Eccentricity (1.24), and Anx-

iousness (1.20). The CS-coefficient (cor = 0.7) was 0.75 for Strength and 0.75 for EI (see Supplementary Figures S2 and S3, respectively), indicating strong stability and interpretability of the estimates. The nodes with the highest bridge strength, bridge EI1, and bridge EI2 values were Irresponsibility (bridge strength = 2.77; bridge EI1 = 2.91; bridge EI2 = 3.01), Impulsivity (2.49, 2.85, 2.84), Callousness (2.21, 1.34, 1.25), and Risk Taking (1.43, 1.47, 1.82). The predictability of each node is shown in Figure 1 and Table 3. The predictability values (R2) ranged from 0.62 (Anhedonia) to 0.22 (Separation Insecurity), with an average value of 0.42. The symptoms with the least explained variance (i.e., the most independent) were Separation Insecurity (R<sup>2</sup> = 0.22), Quit/Control ( R<sup>2</sup> = 0.24), Intimacy Avoidance (R<sup>2</sup> = 0.24), Larger/Longer (R<sup>2</sup> = 0.27), and Restricted Affectivity (R<sup>2</sup> = 0.27). In contrast, the symptoms with the greatest explained variance were Anhedonia ( R<sup>2</sup> = 0.62), Depressivity (R<sup>2</sup> = 0.60), Anxiousness (R<sup>2</sup> = 0.57), and Perseveration ( R<sup>2</sup> = 0.56). Table 3 also shows the PC and PR values. The highest PC values correspond to Hostility (PC = 0.53), Impulsivity (PC = 0.50), and Rigid Perfectionism (PC = 0.38), these being the nodes with edges that are distributed more equally among the communities. According to the PR values, the nodes with the strongest and most numerous edges were Irresponsibility (PR = 1), Perseveration (PR = 0.97), and Perceptual Dysregulation (PR = 0.90).

Concerning the invariance analysis, Table 4 shows the descriptive statistics (means and standard deviations) for the AMPD facets and AUD criteria in the total sample (*n* = 985), the male subsample (*n* = 495), and the female subsample (*n* = 490). It is observed that the mean difference between males and females has a small or null effect size for all AUD facets and criteria except for Emotional Lability, which yielded a medium effect size (*d* = 0.62).

**Figure 1.** Empirical network model (network structure estimated from a walktrap modularity analysis) for the complete sample (*n* = 985). Note. Each node represents a symptom. The edges represent the relationships (partial correlations) between the symptoms. Positive relationships are represented in green, and negative relationships in red. The thickness of the edge reflects the strength of the association, so that the most strongly related symptoms are connected by thicker edges. The blue pie chart surrounding each node represents the predictability of each node (a higher proportion of blue indicates greater predictability). The membership of the nodes to the different communities is represented by different colors: the symptoms of Community 1 are shown in blue, the symptoms of Community 2 are shown in salmon, and the symptoms of Community 3 are shown in green. The arrangemen<sup>t</sup> of the nodes was established based on the Fruchterman-Reingold algorithm.


Restricted

Separation

Unusual Bel. and

Irresponsibility 0.63 0.69 0.73 2.77 2.91 3.01 0.42 0.37 1 Manipulativeness 0.39 0.25 0.22 0.02 −0.27 −0.11 0.45 0.04 0.40 Perceptual Dysreg. 0.61 0.84 0.95 0.60 0.88 0.83 0.50 0.09 0.90 Perseveration 1.51 1.51 1.57 0.34 0.49 0.47 0.56 0.15 0.97 Affectivity −0.49 −1.75 −1.72 0.29 0.43 0.15 0.27 0.18 0.35 Rigid Perfectionism −0.75 −0.68 −0.70 0.96 0.75 0.60 0.33 0.38 0.48 Risk Taking −0.17 −0.02 0.02 1.43 1.47 1.82 0.35 0.23 0.67 Insecurity −1.58 −2.15 −2.15 −0.53 −0.57 −0.72 0.22 0.05 0.20 Submissiveness −1.84 −1.28 −1.28 −0.42 −0.28 −0.32 0.29 0.11 0.17 Suspiciousness 0.34 0.61 0.77 −0.40 −0.17 −0.15 0.50 0.04 0.85 Exp. −0.02 0.31 0.38 −0.05 0.21 0.30 0.48 0.05 0.48 Withdrawal 0.27 0.20 0.06 −0.56 −0.82 −0.74 0.46 0.03 0.39


Note. R2 = Predictability; PC = Participation Coefficient; PR = Participation Ratio.


**Table 4.** Means, standard deviations, and Cohen's d of scores on the PID-5-SF subscales and alcoholuse disorder criteria.

The above values are congruen<sup>t</sup> with the invariance test conducted for men (*n* = 495) and women (*n* = 490), detecting no significant differences in terms of network structure (i.e., differences in the edge connections of the two networks, M = 0.22; *p* = 0.838) or global strength (i.e., difference in the sum of the absolute weights between the two networks, S = 0.34; *p =* 0.494). The overall strength of the network estimated for the male sample was 17.18, and for the female sample this was 16.84.

#### *3.2. "Bridging Nodes" between AUD Criteria and Personality Facets*

Figure 2 shows (in yellow) the facets that act as "bridge nodes" between the different network structures (i.e., the nodes more connected to nodes of other communities with which they are directly related), according to the highest values of bridge strength shown in Table 3. Through the edges, it is observed that most of the relationships between the AUD criteria and the personality facets occur through these "bridge nodes". Furthermore, it appears that most of the facets acting as "bridge nodes" correspond to the "antagonism" and "disinhibition" domains of the AMPD. Only the bridge node corresponding to the "hostility" facet falls within the "negative affectivity" domain of the AMPD.

**Figure 2.** Empirical network model (network structure estimated from a walktrap modularity analysis) and bridge nodes for complete sample (*n* = 985). Note. Each node represents a symptom. The edges represent the relationships (partial correlations) between the symptoms. Positive relationships are represented in green. and negative relationships in red. The thickness of the edge reflects the strength of the association, so the most strongly related symptoms are connected by thicker edges. The blue pie chart surrounding each node represents the predictability of each node (a higher proportion of blue indicates greater predictability). The membership of the nodes to the different communities is represented by different colors: bridge symptoms are shown in yellow; the symptoms of Community 1 are shown in blue; the symptoms of Community 2 are shown in salmon; and the symptoms of Community 3 are shown in green. The arrangemen<sup>t</sup> of the nodes was established based on the Fruchterman-Reingold algorithm.

A detailed analysis of the relationships between the AUD criteria and the bridging nodes is displayed in Table 5, which shows the partial and Pearson correlations between the facets that constitute "bridging nodes" and the AUD diagnostic criteria. The bridge node "risk-taking" is the one that presents the most relationships with the AUD diagnostic criteria, followed by the facets of "irresponsibility" and "callousness." Concerning the AUD diagnostic criteria, it can be observed that the diagnostic criterion "withdrawal" shows the most associations with the bridging nodes, together with "neglect roles" and "craving".


**Table 5.** Partial and zero-order correlations between network symptoms on estimation sample.

Partial correlations are shown on the lower diagonal and zero-order correlations on the upper diagonal. The dashes represent correlation values = 0. The bold type reflects the relationships between the bridging nodes and the AUD criteria.

## **4. Discussion**

The present study aimed to deepen our knowledge of the existing comorbidity between PDs and AUD. For this purpose, this study aimed to complement the existing evidence [10–12] with new evidence obtained through network analysis and use of the DSM-5 AMPD trait model. To our knowledge, no previous studies have used network analysis to examine the relationship between the DSM-5 AMPD traits and AUD criteria.

Analysis of the centrality indices of the AUD criteria has shown relatively low values, indicating their low capacity to influence the personality facets. This finding is also evident in the visualization of the network, in which the AMPD facets and the AUD criteria are organized into three independent communities—albeit empirically related and theoretically grounded. The alcohol diagnostic criteria maintain close relationships, leading them to be organized into an independent community, while the AMPD facets are organized into two interrelated communities, linked to the internalizing and externalizing spectrum. This organization of the AMPD facets is consistent with studies that have applied a hierarchical analysis of personality [42–44], and the independent organization of the AUD is congruen<sup>t</sup> with the Hierarchical Taxonomy of Psychopathology (HiTOP) model [45] and previous network analysis studies [46–48]. Despite the relative independence of the three communities, the AUD diagnostic criteria show relationships with a set of personality facets that act as a bridge. These are mostly associated with the externalizing spectrum, highlighting the relationships with Callousness, Irresponsibility, and Risk Taking.

Our observation of these bridging facets is congruen<sup>t</sup> with the relationships observed in previous studies analyzing FFM traits in alcohol consumers. The specialized literature supports an association between alcohol consumption and the FFM traits of conscientiousness and agreeableness [10,12], which are aligned with the disinhibition and antagonism domains of the AMPD [49]. This study has shown that these relationships could largely be due to the connection between the AUD criteria and the bridging facets within the disinhibition (risk taking, irresponsibility, impulsivity, and rigid perfectionism) and antagonism (callousness, grandiosity, and hostility) domains. Similarly, it is worth noting the connection between the AUD criterion "withdrawal" and the facet "hostility" framed in the negative affectivity domain of the AMPD (aligned with the neuroticism trait included in the FFM). This finding is congruen<sup>t</sup> with the results of the meta-analysis conducted by Hakulinen et al. [12], which found that the domain "neuroticism" is associated primarily with people who engage in heavy alcohol consumption. It is also compatible with studies suggesting that people with AUD have biases toward recognizing and attributing hostile expressions and behaviors [50,51]. Thus, one hypothesis to explain this relationship could be that the occurrence of "withdrawal" exacerbates the "hostility" trait. Therefore, this association is observed mainly in heavy alcohol users and is weaker in those who consume alcohol in moderation.

On the other hand—and concerning PDs—some studies show that it is borderline and antisocial PDs that most frequently co-occur with AUD. According to the DSM-5 AMPD [52], antisocial PD is evaluated based on the presence of the facets "Callousness", "Irresponsibility", and "Risk Taking", among others, while Borderline PD includes the facets of "Risk Taking" and "Hostility" for its diagnosis. Thus, the bridging facets identified in this study are some of those required for the assessment of the two PDs mentioned above. Therefore, it is likely that the high comorbidity of AUD with these disorders is caused by the relationships between the diagnostic criteria of AUD and these bridging facets. This finding along with the results reported by other authors [53,54], could also help to explain why patients with antisocial and borderline PD have higher rates of relapse and treatment dropout. These authors found that patients who prematurely terminate treatment score higher on the facets of "Hostility", "Callousness", and "Risk-taking" than those who complete treatment. Thus, these bridging facets that are relevant to antisocial and borderline PD diagnoses, are also associated with premature patient dropout.

We consider the findings of this study to be useful for advancing our knowledge of the comorbidity of AUD with PDs. Nonetheless, it is also worth considering some aspects of the study sample that could have affected the results. As already indicated, the sample of patients shows the highest prevalence of emotional disorders (depressive and anxiety disorders). This may have maximized the relationships between the facets associated with the "negative affectivity" domain, which resulted in higher values of the centrality indices observed for these facets. However, aside from the centrality indices, the network structure is congruen<sup>t</sup> with the hierarchical analysis reported in previous studies [42–44], and so the impact of this sample composition is more likely to be limited to the understanding of comorbidity between AUDs and PDs.

We would also like to point out that the partial correlations between the "bridging nodes" and the AUD criteria are low. It is possible that these weak relationships were found because this study did not include a specific sample of patients with AUDs, with the sample of the general population being more representative. Consequently, greater variability in the personality facets and AUD criteria are observed compared to the case in which AUD patients are specifically selected, which could negatively affect the values of the partial correlations. Despite this, the results have revealed the existence of "bridging nodes" congruen<sup>t</sup> with evidence from previous meta-analysis [10–12]. Therefore, we consider that the results of this study provide novel insights that could help to improve our understanding of comorbidity between these disorders.

Finally, we consider it necessary to limit the generalizability of the relationships observed in this study to the cultural context in which it was carried out. In this sense, it should be noted that cultural studies analyzing the relationship between personality traits and alcohol consumption have shown differences in the relationships established between agreeableness, antisocial behavior, and alcohol-related consequences when comparing different countries [55]. Moreover, it should be considered that social norms about what constitutes, for example, "irresponsibility" or "risk taking" in the case of personality trait assessment, or "social/interpersonal problems" or "hazardous use" in the case of AUD assessment, may differ across cultures and even within a culture [56]. Therefore, we consider that the observed interrelationships be contextualized within the framework of Spanish culture as well as in other countries with equivalent social norms and legislation. Future cross-cultural studies should provide evidence on the stability of these relationships in other countries and cultures.
