Psychopathology of addiction: Can the SCL90-based five-dimensional structure differentiate Heroin Use Disorder from a non-substance-related addictive disorder such as Gambling Disorder?
© The Author(s) 2018
Received: 22 September 2017
Accepted: 3 January 2018
Published: 16 January 2018
In the Gambling Disorder (GD), there is no exogenous drug administration that acts as the central core of the traditional meaning of addiction. A specific psychopathology of Substance Use Disorders has been proposed recently. In a sample of Heroin Use Disorder (HUD) patients entering opioid agonist treatment, it became possible to identify a group of 5 mutually exclusive psychiatric dimensions: Worthlessness-Being trapped (W-BT), Somatic Symptoms (SS), Sensitivity-Psychoticism (SP), Panic Anxiety (PA) and Violence-Suicide (VS). The specificity of these dimensions was suggested by the absence of their correlations with treatment choice, active substance use, psychiatric comorbidity and the principal substance of abuse and by the opportunity, through their use, of fully discriminating HUD from Major Depression patients and, partially, from obese non-psychiatric patients. To further support this specificity in the present study, we tested the feasibility of discriminating HUD patients from those affected by a non-substance-related addictive behaviour, such as GD. In this way, we also investigated the psychopathological peculiarities of GD patients.
We compared the severity and frequency of each of the five aspects found by us, in 972 (83.5% males; mean age 30.12 ± 6.6) HUD and 110 (50% males; average age 30.12 ± 6.6) GD patients at univariate (T test; Chi square) and multivariate (discriminant analysis and logistic regression) level.
HUD patients showed higher general psychopathology indexes than GD patients. The severity of all five psychopathological dimensions was significantly greater in HUD patients. Discriminant analysis revealed that SS and VS severity were able to discriminate between HUD (higher severity) and GD patients (lower severity), whereas PA and SP could not. W-BT severity was negatively correlated with SS and VS; GD patients were distinguished by low scores for SS and VS low scores associated with high ones for W-BT. Psychopathological subtypes characterized by SS and VS symptomatology were better represented in HUD patients, whereas PA symptomatology was more frequent in GD individuals. No differences were observed regarding the W-BT and SP dimensions. At multivariate level, the one prominent characteristic of HUD patients was the presence of SS (OR = 5.43) as a prominent qualification for psychopathological status.
Apart from the lower severity of all psychopathological dimensions, only the lower frequency of SS typology seems to be the prominent factor in GD patients. The SCL90-defined structure of opioid addiction seems to be useful even in non-substance-related addictive disorders, as in the case of GD patients, further supporting the possible existence of a psychopathology specific to addiction.
In the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), what had previously been referred to as Pathological Gambling (PG) become Gambling Disorder (GD) and was moved from “Impulse Control Disorder” to the chapter entitled “Substance-Related and Addictive Disorders” . In this way, DSM-5 became the first diagnostic system to recognize a behavioural addiction. In GD, there is no exogenous drug administration that acts as the central core of the traditional meaning of addiction, but people compulsively and dysfunctionally engage in behaviours that can be conceptualized within an addiction framework as different expressions of the same underlying syndrome. In a way parallel to addiction to substances, distinctive elements can be found regarding clinical expression (e.g. craving, tolerance, withdrawal symptoms), comorbidity, neurobiological profile, heritability, and treatment . Further similarities are found between GD and substance use disorders (SUD) in the life course, treatment outcome, diagnostic criteria (though there are differences, too) , even if scepticism remains strong .
Core elements of addiction include: (1) craving, (2) impaired control over behavioural engagement; and (3) continued behavioural engagement despite adverse consequences. Symptoms of PG and GD in DSM-5 have substantial overlap with the same five symptoms appearing in both diagnostic criteria sets (preoccupation, loss of control, psychosocial consequences in various contexts, tolerance, withdrawal) . Individuals with GD experience a euphoric state similar to a drug-induced ‘‘high’’ , symptoms of withdrawal (including restlessness, headaches, and irritability)  at levels comparable with those of individuals with alcohol use disorder . 91% of 222 GD patients reported “cravings” which were not related to the use of other drugs . Individuals with GD demonstrate changes in heart rate responses to gambling activities  and report increasing levels of gaming or bet size over time , phenomena resembling tolerance. This latter effect was linked with the aim of increasing chances of winning rather than improving or maintaining excitement levels .
In the development and maintenance of addictive behaviour, the central role of the prefrontal cortex and mesolimbic reward system is highlighted by all the existing theories: reward deficiency hypotheses , incentive-sensitization theory , impaired response inhibition and salience attribution . Neuroimaging research on SUD has demonstrated changes in fronto-striatal circuits at the brain’s structural and functional levels [12, 13]. The literature supports relatively diminished activation of the prefrontal cortex and subcortical regions (particularly the ventral striatum) [14, 15].
Various cognitive domain alterations (cognitive control, decision-making, reward/loss and “near-miss” processing, delay and probabilistic discounting, reversal learning, alternation learning, and risk-taking) have all been found in people suffering from GD. Nevertheless, the severity of these alterations was at a lower level compared with individuals who had SUD . Indeed, GD patients often show high scores on measures of impulsiveness and low ones on self-control . Concerning impulsivity, in a neurocognitive assessment, individuals with GD and SUD have shown high levels on self-reported measures [18–20]. The first group also did worst on the Iowa Gambling Task, a paradigm that assesses risk-reward decision-making .
The Hypothalamic–pituitary–adrenal axis is disrupted by chronic exposure to substances; the same effects are seen during engagement in gambling .
Studies on the serotoninergic system corroborated similarity between GD and SUD. The levels of platelet monoamine oxidase (MAO), a peripheral marker of serotonin functions, fall in subjects with GD. Similar findings have been observed in individuals with SUD. Indeed, low levels of the serotonin metabolite 5-hydroxy-indole-acetic acid have been found in the cerebrospinal fluids of people with GD and those with alcoholism .
GD, as diagnosed with DSM-5 criteria, shows a prevalence of about 20.4% in SUD patients . High rates of co-occurrence between SUDs and GD are present in both directions : in GD samples, 50% have a lifetime SUD . Prevalence rates are higher in treatment-seeking samples . Individuals with comorbid GD and alcohol tend to have more severe problems . Indeed, a history of having comorbid SUD often hampers efforts to achieve gambling abstinence. A current alcohol use disorder raises the risk of relapse after gambling treatment, and those with no lifetime history of SUD, compared with those who do have lifetime SUD, are 2.6 times more likely to achieve a 3-month period of gambling abstinence .
Despite all these similarities, the specific psychopathology of SUD and GD patients does not appear to have been studied, unless the investigation is related to the presence of a psychiatric comorbidity.
96% of individuals with lifetime GD also meet the criteria for at least one other lifetime psychiatric disorder . The literature estimates comorbidity for those with axis I disorders at 74.8% current and 75.5% lifetime. Among the former group, the highest rates are those referring to current mood disorders (23.1%) with a high incidence of depression [33, 34], alcohol use disorders (AUD) (21.2%), anxiety disorders (17.6%) and substance (non-alcohol) use disorders (7.0%) . Among the latter the highest lifetime rates for comorbidity are those recorded in cases of: mood disorders (49–56%), and anxiety disorders (41–60%), while rates for AUD (73%) and DUD (38%)  were shown to be particularly prevalent. Personality disorders are more common among GD patients , and the prevalence of multiple comorbid disorders was found to be at a high level as well.
A specific psychopathology of Substance Use Disorders (SUD) has been proposed recently, by applying an exploratory factor analysis to the Symptom Checklist-90 (SCL90), in a sample of HUD patients entering agonist opioid treatment. In this way, it became possible to identify a group of 5 mutually exclusive psychopathological/psychiatric dimensions: Worthlessness-Being trapped (W-BT), Somatic Symptoms (SS), Sensitivity-Psychoticism (S-P), Panic Anxiety (PA) and Violence-Suicide (V-S) . Subsequent studies proved that these dimensions are not substantially influenced by a series of confounding factors. They are, in fact, independent of active heroin use, lifetime psychiatric problems, kind of treatment received or particular drug of abuse (heroin, cocaine or alcohol) [38–41]. As investigated by applying the SCL90 in the studies just mentioned, the five psychopathological dimensions so identified were also correlated with the outcome of a variety of agonist opioid treatments and residential treatments. S-P HUD patients showed better retention in treatment if treated with methadone maintenance, and V-S ones if treated with buprenorphine. V-S HUD patients showed the worst results during a residential treatment [42, 43]. Besides, these five psychopathological dimensions have demonstrated their capability to discriminate HUD patients from other psychiatric patients, specifically those affected by Major Depression  and by obesity .
Aims: In the present study, we tested the feasibility of discriminating HUD, by applying the 5-dimensional psychopathology, from a non-substance-related addictive behaviour, such as GD. To do that, we have compared HUD with GD patients. Because of the similarities discussed above between SUD and GD, we expected to find no substantial differentiation even in the domain of psychopathology. The secondary aim was to investigate psychopathological peculiarities of GD patients.
Study design and population
Information on patients included in the present study comes from two different datasets. Data regarding HUD patients came from a database including anonymous individual information originally collected for clinical or other research purposes (‘Pisa Addiction Dataset’) at the Dual Diagnosis Unit, Santa Chiara University Hospital in Pisa, Italy. Data regarding GD individuals were extracted from a clinical dataset of patients treated at the Drug Addiction Unit in Castelfranco Veneto, Treviso, Italy. We did not use specific criteria for including patients in these databases other than ‘wish to be treated’ and ‘wanting to participate’ in future survey. The patient could decide independently to accept or decline to be inserted in the database. Acceptance, or a decline, did not affect in any way the care the patient received. The patient could withdraw his/her consent at any time without giving any explanation.
To fulfil the aims of the present analysis, only baseline data were used, implementing a cross-sectional approach. Moreover, specific inclusion criteria were applied: at least 18 years old, with a diagnosis of Heroin dependence, HUD or Pathological Gambling/GD based on DSM-IV/DSM-5 diagnostic criteria, and information available from the SCL-90 questionnaire. Patients undergoing a pharmacological, psychiatric or psychological treatment at baseline were excluded.
The information obtained was analysed after implementing a retrospective, naturalistic, cross-sectional comparative design, with a single evaluation of the patients, and with the purpose of estimating the magnitude of differences, on the severity and typology of psychopathological symptoms, between HUD and GD patients.
All the patients recruited for research aims had signed an informed consent document; patients evaluated for clinical purposes gave their informed consent to the anonymous use of their clinical data for independent studies. Apart from the research protocol, the patients’ assessment was entirely non-interventional, so the choice of the subsequent treatment was made following the conventional criteria that are routinely adopted in clinical practice.
The experimental procedures were approved by the pertinent ethics committees in accordance with internationally accepted criteria for ethical research.
Reflecting inclusion/exclusion criteria, the sample consisted of 1082 patients. Of all these patients, 885 (81.8%) were males, and 197 (18.2%) were females. Mean age was 31.99 ± 9.2 (range 16–74). 972 patients were diagnosed as HUD patients. Of these, 812 (83.5%) were males and 160 (16.5%) females. Mean age was 30.12 ± 6.6 (range 16–59). 110 patients were diagnosed as GD individuals. 53 (50.0%) were males. Mean age was 48.54 ± 12.5 (range 23–74).
Symptom Checklist-90 (SCL90)
Developed by Derogatis and colleagues , the SCL90 comprises 90 items, on a 5-point scale of distress. It is a self-report clinical rating scale for evaluating current symptomatology of psychiatric outpatients. In the case of substance use disorders, the 90 items reflect five primary symptom dimensions underlying the vast majority of symptomatology that is observed in this class of patients. The primary symptom dimensions are Worthlessness-Being trapped (W-BT), Somatic Symptoms (SS), Sensitivity-Psychoticism (S-P), Panic Anxiety (PA), and Violence-Suicide (V-S) .
These five dimensions have been empirically established and primarily validated on a sample involving over 2500 substance use disorder patients [40, 41, 47]. On the basis of the highest z scores obtained on the five SCL90 dimensions, subjects can be assigned to one of five mutually exclusive groups.
The three global scores usually calculated from the SCL90 items are the sum of all elements (Total SCL90); the PST (number of items rated positively), and the PSDI (positive symptom distress index). This latter is calculated by dividing the sum of all elements by the PST.
Psychiatric diagnostic evaluation. Structured clinical interview for DSM-IV Axis I disorders, Clinician Version (SCID-I)
This instrument  helps clinicians to make standardized, reliable, and accurate diagnoses, and avoid the common problem of ‘premature closure’ (first focusing on one diagnostic possibility). Specially adapted from the research standard for Axis I structured clinical interviewing for use in clinical settings, the SCID-I covers the DSM-IV diagnoses most commonly seen by clinicians. It includes the diagnostic criteria for these disorders with corresponding interview questions. The SCID-I comprises six self-contained modules: mood episodes; psychotic symptoms; psychotic disorders; mood disorders; substance use disorders; and anxiety, adjustment, and other disorders.
In this study, HUD and GD individuals were compared with demographic and psychopathological dimensions using the Chi-squared test, with Bonferroni’s correction, for categorical variables, and Student’s t test for continuous variables.
Differences in the severity of each psychopathological dimension were analysed through the T test, at the univariate level, and, using discriminant analysis, at the multivariate level. Differences in typology were assessed by applying the Chi-squared test, at the univariate level, and by using a logistic forward stepwise regression analysis, at the multivariate level, to take into account possible confounding factors. We considered, as criterion, the presence of an HUD diagnosis, and membership of one of the five psychopathological groups, along with various between-group demographic data and degrees of symptomatological severity, as predictors. Statistical routines of SPSS (v20.0) were used.
Sociodemographic characteristics and psychiatric-SUD comorbidity
GD patients were significantly (T = − 24.72; p ≤ 0.001) older (48.54 ± 12.5 years) than HUD ones (30.12 ± 6.5 years). Males were significantly (χ2 = 19.57; p < 0.001) better represented in HUD patients (83.5%) than in GD ones (66.4%). Consequently, females were better represented in GD patients (33.6%) than in HUD ones (16.5%).
GD patients more frequently (χ2 = 52.68; p < 0.001) reached a higher ≥ 8-year educational level than HUD ones (39.1% vs 12.7%, respectively). 44.8% of HUD and 15.5% of GD patients were unemployed (χ2 = 34.87; p < 0.001). No differences were found regarding marital status (χ2 = 2.07 p = 0.173).
13 GD patients showed SUD comorbidity. More specifically, 4 (3.6%) subjects were diagnosed with alcohol use disorder; 3 (2.7%) with stimulant use disorder; 8 (7.3%) with cannabinoid use disorder. None were affected by opioid use disorder, or hallucinogen use disorder or inhalant use disorder. Tobacco use was not considered. Interestingly, GD with and without SUD showed no significant differences when they were compared, in a multivariate way (discriminant analysis), regarding the severity of the five psychopathological dimensions (Wilks’s Lambda = 0.98; df (5); p = 0.119). Similarly, no differences emerged when comparing the psychopathological typology (χ2 = 3.01; df(4); p = 0.556).
29 (26.4%) GD subjects were diagnosed as dual disorder (DD) patients. GD with and without DD showed significant difference at multivariate level (Wilks’s Lambda = 0.87; df (5); p = 0.014). DD-GD patients showed high worthlessness-being trapped and Sensitivity-Psychoticism symptomatology combined with low Violence-Suicide symptoms. No differences emerged when comparing the psychopathological typology (χ2 = 2.40; df(4); p = 0.662).
Psychopathological typology in HUD and GD patients
SS was the most represented psychopathological typology in HUD patients. They were characterized by distress arising from perceptions of bodily dysfunction. Patients at treatment entry are characterized by soreness in their muscles, heavy feelings in their arms or legs, having hot and cold spells, nausea or upset stomach, trouble falling sleep, feeling weak in parts of their body, with pain in lower back, feeling low in energy or slowed down. Their sleep is restless or disturbed, and they wake up early in the morning. They report numbness or tingling in parts of their body, a lump in their throat, trembling, heart pounding or racing, trouble getting their breath, poor appetite, pains in heart or chest, feelings of being easily annoyed and irritated. In contrast, GD patients were mostly characterized at treatment entry by a set of symptoms and experiences usually associated clinically with high manifest anxiety. GD patients reported the feeling of being afraid in open spaces or on the streets; they were: afraid to go out of their house alone, afraid to travel on buses, subways, or on trains, afraid their will faint in public. They experienced spells of terror or panic, faintness or dizziness, feelings of fearful. They feel suddenly scared for no reason and had to avoid certain things, places, or activities because they frighten them.
V-S is the least frequent typology of GD patients. This dimension is characterized by three categories of hostile behaviour: thoughts, feelings, and actions, and it comprises thoughts of death and suicidal ideation.
W-BT was the dimension least frequent in HUD but the second best represented in GD patients. This dimension reflects a broad range of the concomitants of the clinical depressive symptoms maximally represented by feelings of worthlessness and of being trapped or caught. At treatment entry, GD patients felt lonely, blue, hopeless about the future, with no interest in things, lonely even when with other people, inferior to others, blocked in getting things done, tense or keyed up, nervous when left alone. They presented trouble in concentrating and had the idea that something was wrong with their mind. They showed loss of sexual interest or pleasure, thinking their mind was going blank, blaming themselves for things. They reported unwanted thoughts, words, or ideas that did not leave their mind. This dimension does not include being troubled by recurrent thoughts of death or suicidal ideation.
Differences between HUD and GD patients
Psychopathological severity in HUD and GD patients
N = 972
N = 110
M ± sd
M ± sd
Total SCL-90 severity
89.85 ± 57.3
52.77 ± 39.2
Global Severity Index
1.00 ± 00.6
0.59 ± 00.4
PST, number of
48.79 ± 18.5
31.43 ± 17.4
1.73 ± 00.5
1.54 ± 00.4
1.22 ± 0.8
0.83 ± 0.7
1.29 ± 0.8
0.64 ± 0.5
0.83 ± 0.6
0.51 ± 0.4
0.46 ± 0.6
0.21 ± 0.3
0.98 ± 0.7
0.46 ± 0.4
Psychopathological typology in HUD patients and PG patients
N = 972
N = 110
Prominent psychopathological characteristics of HUD patients who had been checked for their age, gender, educational level, working activity and severity of symptomatology (only significant results are reported in the present table)
SCL90 total score
The present study aimed to test whether the specific psychopathology already found in HUD patients could be likewise detected in GD patients. In general, we found that psychopathological symptoms were more severe in HUD patients than in GD ones; more specifically, HUD patients were distinguished by more severe ‘Somatic Symptoms’ and ‘Violence-Suicide’ symptomatology associated with less severe ‘Worthlessness-Being Trapped’ symptoms. ‘Somatic Symptoms’ is the most discriminant psychopathological typology, as it alone is sufficient to differentiate HUD from GD patients at treatment entry.
In one of the first studies on the psychopathological symptoms of gambling disorder, SCL90 items had been compared with PG, psychiatric outpatients and healthy controls, showing significantly lower Obsessive–Compulsive, Interpersonal Sensitivity, Anxiety, and Phobic Anxiety factor scores, but comparable scores on four scales: Somatization, Hostility, Paranoid Ideation, and Psychoticism. Consistently with clinical observations and empirical data reported in the literature, PG patients characteristically showed both cognitive and somatic expressions of clinical depression, including feelings of hopelessness, lack of motivation, suicidal thoughts, and loneliness. The depressive dimension was the only clinical scale to significantly exceed the psychiatric outpatient population sample . GD patients with concurrent SUD showed higher scores on all subscales of the SCL90 than GD patients without concomitant SUD . Three symptomatological clusters were observed by Gonzalez-Ibanez: the highest scores were found on scales of Obsessive–Compulsive, Interpersonal Sensitivity, Depression, and Paranoid Ideation . When dividing GD patients according to age into three groups (17–26 years; 27–43 years and ≥ 44 years), the two older groups showed higher scores, while the youngest stayed in the standard range . Lastly, by comparing single subgroups according to the patients’ age at the onset of GD in each, a higher age at onset of gambling problems proved to be associated with more severe psychopathological symptoms (Depression, Paranoid Ideation and Psychoticism). Conversely, in a study giving results in disagreement with ours, a lower age of onset was related to the greater severity of PG . Cross-sectional and quantitative studies, therefore, showed that high scores for depression were a typical feature of GD patients. In our study, the importance of the depressive dimension (Worthlessness-Being Trapped), in distinguishing GD patients, seems to be confirmed. Further, the depressive symptomatology shows a very similar frequency to that of HUD patients.
The ‘Worthlessness-Being-Trapped’ dimension is the primary factor found in HUD patients . It brings together depressive, obsessive–compulsive and psychotic symptoms, and is dominated by the feeling of uselessness and of being trapped in a corner. This dimension is strictly correlated with the age of patients. Subjects typified by ‘Worthlessness-Being Trapped’ features are, in general, older and, in many cases, have a long history of addictive behaviour . This dimension was unable to significantly differentiate between the pattern of features typifying HUD patients on their entry into Agonist Opioid (AO) or Therapeutic Community (TC) treatment . There was a close similarity, in frequencies, between HUD patients entering TC treatment after detoxification and other patients entering without it . The same thing happened in HUD patients with and without lifetime psychiatric problems , and patients using, as their primary substance of use, heroin, cocaine or alcohol . It is probable that this dimension has the status of the most significant psychopathological trait-state of SUD patients. Its special role is further confirmed in the separate case of a non-pharmacological addictive behaviour such as GD. The similarities to be found on neurobiological, genetic and clinical grounds between SUD and GD seem to be confirmed once again at the psychopathological level. Interestingly, the highest scores in this dimension are able to differentiate GD from HUD patients when they are associated with a low degree of severity of the ‘Somatic Symptoms’ and ‘Violence-Suicide’ dimensions. In other words, HUD patients are distinguished by a more severe withdrawal syndrome and aggressiveness, and GD patients by a higher degree of severity of the ‘Worthlessness-Being Trapped’ dimension.
It is, however, possible that the conspicuous presence of aggressiveness in HUD patients was biased by the strong prevalence of male sex in those patients. Male sex is, in fact, already known to correlate with a higher incidence of aggressiveness. Moreover, the multivariate analysis limits the importance of this dimension in discriminating HUD from GD patients.
The ‘Somatic Symptoms’ dimension is distinguished by some physical and anxious elements, which are usually a feature of opiate withdrawal. For this reason, this aspect is more prominent in HUD patients at the start of treatment with AO , in non-detoxified HUD patients at TC treatment entry , and again, in HUD patients, when they are compared with patients who have cocaine use disorder and/or alcohol use disorder . Nevertheless, the ‘Somatic Symptoms’ dimension was unable to significantly differentiate between HUD patients with and without lifetime psychiatric problems . At this point, it may be stated that this dimension, in general, is more closely related to HUD than to SUD. So the one available logical conclusion seems to be that ‘Somatic Symptoms’ is the only psychopathological aspect capable of differentiating HUD from GD patients.
In summary, in our study, at treatment entry psychopathology was more severe in HUD patients, but its typology was very similar, in any case. This fact is not surprising when considering that HUD and GD often coexist. GD among HUD patients, during methadone maintenance treatment, seems to be a frequent diagnosis (range 7–29%) among the different studies [54–60]. An even higher percentage (52.7%) has been found in patients receiving methadone maintenance treatment; one important finding was that a majority of patients had gambled during the previous 2 years .
In our GD sample, we observed the presence of psychiatric and SUD comorbidity, but the severity and typology of psychopathology both proved to be independent of SUD. The severity of symptomatology was linked to the GD, in this study, and, separately, in HUD patients in previous studies, but typology of psychopathology proved to be independent of psychiatric comorbidity in this and in previous studies [38, 39].
The obvious limitations of this study arise from the fact that this is a retrospective analysis carried out on a selected cohort of patients, rather than a study specifically designed to elucidate this issue. Besides, no data were collected about the prevalence of possible Axis II disorders in this sample, and the considerable differences in sample size and composition may have had an adverse impact on the power of the statistical tests that were applied. The strength of the study lies, however, in the care taken in carefully investigating psychiatric comorbidity and SUD in patients with GD, while avoiding the issue that the similarities observed depend on the areas of overlap between the characteristics of the two samples.
Using the SCL90-defined structure of opioid addiction, we expected to find no substantial differentiation between HUD and GD patients. The possibility of differentiating between groups, using only the SS typology, suggests the applicability of these 5 dimensions even to a non-substance-related addictive disorders, in particular to GD patients, bringing further support to the hypothesis that there is a psychopathology that is specific to addiction.
AGIM and IM conceived and designed the study, DG, AF, VC, GB, participated in the acquisition of data, IM and AGIM analysed data, GP participated with other authors in the interpretation of the data. AGIM drafted the manuscript and all authors revised it critically for important intellectual content. All authors gave final approval of the version to be published. All authors read and approved the final manuscript.
The authors acknowledge the decision of AU-CNS to offer open access fees.
The authors have no financial competing interests in relation to the publication of this manuscript. In addition, they have no political, personal, religious, ideological, academic or intellectual competing interests.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Consent for publication
Ethics approval and consent to participate
Information on patients included in the present study comes from two different datasets. Data regarding HUD patients came from a database including anonymous individual information originally collected for clinical or other research purposes (‘Pisa Addiction Dataset’) at the Dual Diagnosis Unit, Santa Chiara University Hospital in Pisa, Italy. Data regarding GD individuals were extracted from a pluriannual dataset of patients treated at the Drug Addiction Unit in Castelfranco Veneto, Treviso, Italy. All the patients recruited for research aims had signed an informed consent document; patients evaluated for clinical purposes gave their informed consent to the anonymous use of their clinical data for independent studies. Apart from the research protocol, the patients’ assessment was entirely non-interventional, so the choice of the subsequent treatment was made following the conventional criteria that are routinely adopted in clinical practice. The experimental procedures were approved by the pertinent ethics committees in accordance with internationally accepted criteria for ethical research. The information obtained was analysed after implementing a retrospective, naturalistic, cross-sectional comparative design, with a single evaluation of the patients.
Authors state that this study was financed with internal funds. No sponsor played a role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.
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- Rash CJ, Petry NM. Gambling Disorder in the DSM-5: opportunities to improve diagnosis and treatment especially in substance use and homeless populations. Curr Addict Rep. 2016;3(3):249–53.View ArticlePubMedPubMed CentralGoogle Scholar
- Yau YH, Potenza MN. Gambling Disorder and other behavioral addictions: recognition and treatment. Harv Rev Psychiatry. 2015;23(2):134–46.View ArticlePubMedPubMed CentralGoogle Scholar
- Weinstock J, Rash CJ. Clinical and research implications of Gambling Disorder in DSM-5. Curr Addict Rep. 2014;1(3):159–65.View ArticlePubMedPubMed CentralGoogle Scholar
- Schuckit MA. Editor’s corner: DSM-5–ready or not, here it comes. J Stud Alcohol Drugs. 2013;74(5):661–3.View ArticlePubMedGoogle Scholar
- Kyngdon A, Dickerson M. An experimental study of the effect of prior alcohol consumption on a simulated gambling activity. Addiction. 1999;94(5):697–707.View ArticlePubMedGoogle Scholar
- Nower L, Blaszczynski A. Recovery in pathological gambling: an imprecise concept. Subst Use Misuse. 2008;43(12–13):1844–64.View ArticlePubMedGoogle Scholar
- de Castro V, Fong T, Rosenthal RJ, Tavares H. A comparison of craving and emotional states between pathological gamblers and alcoholics. Addict Behav. 2007;32(8):1555–64.View ArticlePubMedGoogle Scholar
- Griffiths M. Tolerance in gambling: an objective measure using the psychophysiological analysis of male fruit machine gamblers. Addict Behav. 1993;18(3):365–72.View ArticlePubMedGoogle Scholar
- Comings DE, Blum K. Reward deficiency syndrome: genetic aspects of behavioral disorders. Prog Brain Res. 2000;126:325–41.View ArticlePubMedGoogle Scholar
- Robinson TE, Berridge KC. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev. 1993;18(3):247–91.View ArticlePubMedGoogle Scholar
- Volkow ND, Fowler JS, Wang GJ. The addicted human brain: insights from imaging studies. J Clin Invest. 2003;111(10):1444–51.View ArticlePubMedPubMed CentralGoogle Scholar
- Goldstein RZ, Volkow ND. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat Rev Neurosci. 2011;12(11):652–69.View ArticlePubMedPubMed CentralGoogle Scholar
- Hommer DW, Bjork JM, Gilman JM. Imaging brain response to reward in addictive disorders. Ann N Y Acad Sci. 2011;1216:50–61.View ArticlePubMedGoogle Scholar
- Reuter J, Raedler T, Rose M, Hand I, Glascher J, Buchel C. Pathological gambling is linked to reduced activation of the mesolimbic reward system. Nat Neurosci. 2005;8(2):147–8.View ArticlePubMedGoogle Scholar
- Grant JE, Potenza MN. Impulse control disorders: clinical characteristics and pharmacological management. Ann Clin Psychiatry. 2004;16(1):27–34.View ArticlePubMedGoogle Scholar
- Lawrence AJ, Luty J, Bogdan NA, Sahakian BJ, Clark L. Impulsivity and response inhibition in alcohol dependence and problem gambling. Psychopharmacology. 2009;207(1):163–72.View ArticlePubMedPubMed CentralGoogle Scholar
- Leeman RF, Potenza MN. Similarities and differences between pathological gambling and substance use disorders: a focus on impulsivity and compulsivity. Psychopharmacology. 2012;219(2):469–90.View ArticlePubMedGoogle Scholar
- Blaszczynski A, Steel Z, McConaghy N. Impulsivity in pathological gambling: the antisocial impulsivist. Addiction. 1997;92(1):75–87.View ArticlePubMedGoogle Scholar
- Petry NM. Substance abuse, pathological gambling, and impulsiveness. Drug Alcohol Depend. 2001;63(1):29–38.View ArticlePubMedGoogle Scholar
- Potenza MN, Steinberg MA, Skudlarski P, Fulbright RK, Lacadie CM, Wilber MK, Rounsaville BJ, Gore JC, Wexler BE. Gambling urges in pathological gambling: a functional magnetic resonance imaging study. Arch Gen Psychiatry. 2003;60(8):828–36.View ArticlePubMedGoogle Scholar
- Cavedini P, Riboldi G, Keller R, D’Annucci A, Bellodi L. Frontal lobe dysfunction in pathological gambling patients. Biol Psychiatry. 2002;51(4):334–41.View ArticlePubMedGoogle Scholar
- Zhou Y, Proudnikov D, Yuferov V, Kreek MJ. Drug-induced and genetic alterations in stress-responsive systems: implications for specific addictive diseases. Brain Res. 2010;1314:235–52.View ArticlePubMedGoogle Scholar
- el-Guebaly N, Mudry T, Zohar J, Tavares H, Potenza MN. Compulsive features in behavioural addictions: the case of pathological gambling. Addiction. 2012;107(10):1726–34.View ArticlePubMedGoogle Scholar
- Black DW, Moyer T, Schlosser S. Quality of life and family history in pathological gambling. J Nerv Ment Dis. 2003;191(2):124–6.PubMedGoogle Scholar
- Lobo DS, Kennedy JL. Genetic aspects of pathological gambling: a complex disorder with shared genetic vulnerabilities. Addiction. 2009;104(9):1454–65.View ArticlePubMedGoogle Scholar
- Rennert L, Denis C, Peer K, Lynch KG, Gelernter J, Kranzler HR. DSM-5 gambling disorder: prevalence and characteristics in a substance use disorder sample. Exp Clin Psychopharmacol. 2014;22(1):50–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Potenza MN. Should addictive disorders include non-substance-related conditions? Addiction. 2006;101(Suppl 1):142–51.View ArticlePubMedGoogle Scholar
- Lorains FK, Cowlishaw S, Thomas SA. Prevalence of comorbid disorders in problem and pathological gambling: systematic review and meta-analysis of population surveys. Addiction. 2011;106(3):490–8.View ArticlePubMedGoogle Scholar
- Cowlishaw S, Merkouris S, Chapman A, Radermacher H. Pathological and problem gambling in substance use treatment: a systematic review and meta-analysis. J Subst Abuse Treat. 2014;46(2):98–105.View ArticlePubMedGoogle Scholar
- Rash CJ, Weinstock J, Petry NM. Drinking patterns of pathological gamblers before, during, and after gambling treatment. Psychol Addict Behav. 2011;25(4):664–74.View ArticlePubMedPubMed CentralGoogle Scholar
- Hodgins DC, el-Guebaly N. The influence of substance dependence and mood disorders on outcome from pathological gambling: 5-year follow-up. J Gambl Stud. 2010;26(1):117–27.View ArticlePubMedGoogle Scholar
- Kessler RC, Hwang I, LaBrie R, Petukhova M, Sampson NA, Winters KC, Shaffer HJ. DSM-IV pathological gambling in the National Comorbidity Survey Replication. Psychol Med. 2008;38(9):1351–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Bergh C, Kuhlhorn E. Social, psychological and physical consequences of pathological gambling in Sweden. J Gambl Stud. 1994;10(3):275–85.View ArticlePubMedGoogle Scholar
- McCormick RA, Russo AM, Ramirez LF, Taber JI. Affective disorders among pathological gamblers seeking treatment. Am J Psychiatry. 1984;141(2):215–8.View ArticlePubMedGoogle Scholar
- Dowling NA, Cowlishaw S, Jackson AC, Merkouris SS, Francis KL, Christensen DR. Prevalence of psychiatric co-morbidity in treatment-seeking problem gamblers: a systematic review and meta-analysis. Aust N Z J Psychiatry. 2015;49(6):519–39.View ArticlePubMedPubMed CentralGoogle Scholar
- Petry NM, Stinson FS, Grant BF. Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. J Clin Psychiatry. 2005;66(5):564–74.View ArticlePubMedGoogle Scholar
- Maremmani I, Pani PP, Pacini M, Bizzarri JV, Trogu E, Maremmani AGI, Perugi G, Gerra G, Dell’Osso L. Subtyping Patients with Heroin Addiction at treatment entry: factors derived from the SCL-90. Ann Gen Psychiatry. 2010;9(1):15.View ArticlePubMedPubMed CentralGoogle Scholar
- Pani PP, Maremmani AGI, Trogu E, Vigna-Taglianti F, Mathis F, Diecidue R, Kirchmayer U, Amato L, Ghibaudi J, Camposeragna A, et al. Psychopathology of addiction: May a SCL-90 based five dimensions structure be applied irrespectively of the involved drug? Ann Gen Psychiatry. 2016;15:13.View ArticlePubMedPubMed CentralGoogle Scholar
- Pani PP, Maremmani AGI, Trogu E, Vigna-Taglianti F, Mathis F, Diecidue R, Kirchmayer U, Amato L, Davoli M, Ghibaudi J, et al. Psychic structure of opioid addiction: impact of lifetime psychiatric problems on SCL-90-based psychopathologic dimensions in heroin-dependent patients. Addict Disord Treat. 2016;15(1):6–16.View ArticleGoogle Scholar
- Pani PP, Maremmani AGI, Trogu E, Vigna-Taglianti F, Mathis F, Diecidue R, Kirchmayer U, Amato L, Davoli M, Ghibaudi J, et al. Psychopathological symptoms in detoxified and non-detoxified heroin-dependent patients entering residential treatment. Heroin Addict Relat Clin Probl. 2015;17(2–3):17–24.Google Scholar
- Pani PP, Trogu E, Vigna-Taglianti F, Mathis F, Diecidue R, Kirchmayer U, Amato L, Davoli M, Ghibaudi J, Camposeragna A, et al. Psychopathological symptoms of patients with heroin addiction entering opioid agonist or therapeutic community treatment. Ann Gen Psychiatry. 2014;13(1):35.View ArticlePubMedPubMed CentralGoogle Scholar
- Maremmani I, Pacini M, Lubrano S, Perugi G, Tagliamonte A, Pani PP, Gerra G, Shinderman M. Long-term outcomes of treatment-resistant heroin addicts with and without DSM-IV axis I psychiatric comorbidity (dual diagnosis). Eur Addict Res. 2008;14(3):134–42.View ArticlePubMedGoogle Scholar
- Maremmani AGI, Pani PP, Trogu E, Vigna-Taglianti F, Mathis F, Diecidue R, Kirchmayer U, Amato L, Ghibaudi J, Composeragna A, et al. The impact of psychopathological subtypes on retention rate of patients with substance use disorders entering residential therapeutic community treatment. Ann Gen Psychiatry. 2016;15(1):29.View ArticlePubMedPubMed CentralGoogle Scholar
- Maremmani AGI, Cerniglia L, Cimino S, Bacciardi S, Rovai L, Rugani F, Massimetti E, Gazzarrini D, Pallucchini A, Pani PP, et al. Towards a specific psychopathology of heroin addiction. Comparison between Heroin Use Disorder and Major Depression patients. Heroin Addict Relat Clin Probl. 2015;17(6):9–16.Google Scholar
- Maremmani AGI, Cerniglia L, Cimino S, Bacciardi S, Rovai L, Pallucchini A, Spera V, Perugi G, Maremmani I. Further evidence of a specific psychopathology of addiction. Differentiation from other psychiatric psychopathological dimensions (such as obesity). Int J Environ Res Public Health. 2017;14(8):943.View ArticlePubMed CentralGoogle Scholar
- Derogatis LR, Lipman RS, Rickels K. The Hopkins Symptom Checklist (HSCL)—A self report symptom inventory. Behav Sci. 1974;19:1–16.View ArticlePubMedGoogle Scholar
- Maremmani AGI, Rovai L, Maremmani I. Heroin addicts’ psychopathological subtypes. Correlations with the natural history of illness. Heroin Addict Relat Clin Probl. 2012;14(1):11–22.Google Scholar
- First MB, Spitzer RL, Gibbon M, Williams JBW. Structured clinical interview for DSM-IV Axis I disorders (SCID-I), Clinician Version. Arlington: American Psychiatric Publishing, Inc; 1997.Google Scholar
- Blaszczynski AP, McConaghy N. SCL-90 assessed psychopathology in pathological gamblers. Psychol Rep. 1988;62(2):547–52.View ArticlePubMedGoogle Scholar
- Jimenez-Murcia S, Del Pino-Gutierrez A, Fernandez-Aranda F, Granero R, Hakansson A, Tarrega S, Valdeperez A, Aymami N, Gomez-Pena M, Moragas L, et al. Treatment outcome in male Gambling Disorder patients associated with alcohol use. Front Psychol. 2016;7:465.View ArticlePubMedPubMed CentralGoogle Scholar
- Gonzalez-Ibanez A, Aymami MN, Jimenez S, Domenech JM, Granero R, Lourido-Ferreira MR. Assessment of pathological gamblers who use slot machines. Psychol Rep. 2003;93(3 Pt 1):707–16.View ArticlePubMedGoogle Scholar
- Gonzalez-Ibanez A, Mora M, Gutierrez-Maldonado J, Ariza A, Lourido-Ferreira MR. Pathological gambling and age: differences in personality, psychopathology, and response to treatment variables. Addict Behav. 2005;30(2):383–8.View ArticlePubMedGoogle Scholar
- Jimenez-Murcia S, Alvarez-Moya EM, Stinchfield R, Fernandez-Aranda F, Granero R, Aymami N, Gomez-Pena M, Jaurrieta N, Bove F, Menchon JM. Age of onset in pathological gambling: clinical, therapeutic and personality correlates. J Gambl Stud. 2010;26(2):235–48.View ArticlePubMedGoogle Scholar
- Bonnaire C, Kovess-Masfety V, Guignard R, Richard JB, du Roscoat E, Beck F. Gambling type, substance abuse, health and psychosocial correlates of male and female problem gamblers in a nationally representative French Sample. J Gambl Stud 2017;33(2):343–69.View ArticlePubMedGoogle Scholar
- Castren S, Salonen AH, Alho H, Lahti T, Simojoki K. Past-year gambling behaviour among patients receiving opioid substitution treatment. Subst Abuse Treat Prev Policy. 2015;10:4.View ArticlePubMedPubMed CentralGoogle Scholar
- Peles E, Schreiber S, Adelson M. Pathological gambling and obsessive compulsive disorder among methadone maintenance treatment patients. J Addict Dis. 2009;28(3):199–207.View ArticlePubMedGoogle Scholar
- Peles E, Schreiber S, Linzy S, Adelson M. Pathological gambling in methadone maintenance clinics where gambling is legal versus illegal. Am J Orthopsychiatry. 2010;80(3):311–6.View ArticlePubMedGoogle Scholar
- Spunt B. Pathological gambling and substance misuse. Subst Use Misuse. 2002;37(8–10):1299–304.View ArticlePubMedGoogle Scholar
- Toneatto T, Brennan J. Pathological gambling in treatment-seeking substance abusers. Addict Behav. 2002;27(3):465–9.View ArticlePubMedGoogle Scholar
- Weinstock J, Blanco C, Petry NM. Health correlates of pathological gambling in a methadone maintenance clinic. Exp Clin Psychopharmacol. 2006;14(1):87–93.View ArticlePubMedGoogle Scholar