Decision Making Impairment: A Shared Vulnerability in Obesity, Gambling Disorder and Substance Use Disorders?

RESEARCH ARTICLE Decision Making Impairment:A Shared Vulnerability in Obesity,Gambling Disorder and Substance Use Disorders? Nuria Mallorquı ´-Bague ´ 1,2‡ , Ana B. Fagundo 1,2‡ , Susana Jimenez-Murcia 1,2,3 , Rafael de la Torre 2,4 , Rosa M. Baños 2,5 , Cristina Botella 2,6 , Felipe F. Casanueva 2,7 , Ana B. Crujeiras 2,7 , Jose C. Ferna ´ndez-Garcı ´a 2,8 , Jose M. Ferna ´ndez-Real 2,9 , Gema Fru ¨hbeck 2,10 , Roser Granero 2,11 , Amaia Rodrı ´guez 2,10 , Iris Tolosa-Sola 1 , Francisco J. Ortega 2,9 , Francisco J. Tinahones 2,8 , Eva Alvarez-Moya 1 , Cristian Ochoa 1 , Jose M. Mencho ´n 1,3,12 , Fernando Ferna ´ndez-Aranda 1,2,3 * 1Department of Psychiatry, University Hospital of Bellvitge-IDIBELL, Barcelona, Spain, 2CIBER Fisiopatologı ´a de la Obesidad y Nutricio ´n (CIBERobn), Instituto Salud Carlos III, Madrid, Spain, 3Department of Clinical Sciences, School of Medicine, University of Barcelona, Barcelona, Spain, 4Integrated Pharmacology and Systems Neurosciences Research Group, Neuroscience Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain, Pompeu Fabra University (CEXS-UPF), Barcelona, Spain, 5Department of Psychological, Personality, Evaluation and Treatment of the University of Valencia, Valencia, Spain, 6Department of Basic Psychology, Clinic and Psychobiology of the University Jaume I, Castello ´, Spain, 7Endocrine Division, Complejo Hospitalario U. de Santiago, Santiago de Compostela University, Santiago de Compostela, Spain, 8Department of Endocrinology and Nutrition, Hospital Clı ´nico Universitario Virgen de Victoria, Ma ´laga, Spain, 9Department of Diabetes, Endocrinology and Nutrition, Institut d’Investigacio ´Biomèdica de Girona (IdlBGi) Hospital Dr Josep Trueta, Girona, Spain, 10 Department of Endocrinology and Nutrition, Clı ´nica Universidad de Navarra, University of Navarra, Pamplona, Spain, 11 Departament de Psicobiologia i Metodologia, Universitat Autònoma de Barcelona, Barcelona, Spain, 12 CIBER Salud Mental (CIBERsam), Instituto Salud Carlos III, Madrid, Spain ‡ These authors share first authorship. *[email protected] Abstract Introduction Addictions are associated with decision making impairments. The present study explores decision making in Substance use disorder (SUD), Gambling disorder (GD) and Obesity (OB) when assessed by Iowa Gambling Task (IGT) and compares them with healthy controls (HC). Methods For the aims of this study, 591 participants (194 HC, 178 GD, 113 OB, 106 SUD) were assessed according to DSM criteria, completed a sociodemographic interview and conducted the IGT. Results SUD, GD and OB present impaired decision making when compared to the HC in the overall task and task learning, however no differences are found for the overall performance in PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 1 / 11 a11111 OPEN ACCESS Citation: Mallorquı ´-Bague ´N, Fagundo AB, Jimenez-Murcia S, de la Torre R, Baños RM, Botella C, et al. (2016) Decision Making Impairment: A Shared Vulnerability in Obesity, Gambling Disorder and Substance Use Disorders? PLoS ONE 11(9): e0163901. doi:10.1371/journal. pone.0163901 Editor: Aviv M. Weinstein, Ariel University, ISRAEL Received: March 21, 2016 Accepted: September 18, 2016 Published: September 30, 2016 Copyright: ©2016 Mallorquı ´-Bague ´et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper. Funding: This manuscript was supported by grants from Instituto Salud Carlos III (ISCIII; FIS PI14/ 00290 and CIBERobn) and co-funded by Fondos Europeos de Desarrollo Regional (FEDER) funds - a way to build Europe, and AGAUR (2014 SGR 1672). This manuscript was co-funded by Ministerio de Economı ´a y Competitividad (PSI2015-68701-R). CIBERobn and CIBERSAM are both initiatives of ISCIII. Jose C. Ferna ´ndez-Garcı ´a is recipient of a research contract from Servicio the IGT among the clinical groups. Results also reveal some specific learning across the task patterns within the clinical groups: OB maintains negative scores until the third set where learning starts but with a less extend to HC, SUD presents an early learning followed by a progressive although slow improvement and GD presents more random choices with no learning. Conclusions Decision making impairments are present in the studied clinical samples and they display individual differences in the task learning. Results can help understanding the underlying mechanisms of OB and addiction behaviors as well as improve current clinical treatments. Introduction Evidence based neurocognitivemodels of addictionpropose that addiction-related behaviors are the result of an imbalanceof three neuralsystems: an impulsive neural system that promotes habitual and salient behaviors, interoceptive processes that are involved in uncertain risk and reward, and a reflectiveneural system for inhibitorycontrol and decision-making[1] [2]. Decision-makingentails the cognitiveprocess of makinga choice after reflectionon the consequencesof that choice, and it is a key component of addictiondevelopment and maintenance in both substance use disorders (SUD) and behavioral addictionssuch as gambling disorder (GD). The assessment of decision-makingis usually conducted through the Iowa Gambling Task (IGT), whichsimulates real life decisionmaking strategiesby factoring uncertainty, reward and punishment. The IGT is a relatively complex task as it is difficultto deconstruct into different cognitiveconstructs,however it measures decision-makingwith a high ecologicalvalidity[3]in a widerange of clinical and non-clinicalgroups[4].Specifically, impairments in this task have been demonstrated in patients with ventromedialprefrontal cortex lesions (VMPC;[5])and in different psychopathologicalconditions including addictions and eating disorders[6–8].Distinctively, while clinical individualswith decisionmaking impairment fail to learnthe contingenciesand prefer the choicesthat lead to higherlong term losses,healthy individualspresent a gradual learningacross the IGT [4][5]. According to the Somatic Marker Hypothesis, individualswho performpoorlyon the IGT have weaker physiological cues to guiderisky choices and present what is referred to as “myopia for the future” [9]. Complementary, obesity (OB) is an increasingworldwide problem that shares similar patterns to addictions[6].Individuals with obesity frequently decideto overeat despite being aware of its negative long-term health consequencesand they usually put extra but unsuccessful efforts into controlling their eating behaviour. Neuropsychological studies support the hypothesis of an alteration on inhibitorycontrol, emotion regulation and the executivefunction circuit in which one of the core cognitivetraits appears to be decisionmaking[10].Accordingly, recent data shows that individualswith obesity are characterizedby the tendency to engage in decisionsthat support a positive short-termreword even if it results in long term negative outcomes[7]. Furthermore when assessed by the IGT, individuals with obesity present significantdecisionmaking impairments in the overall task performanceas well as in learning across the task [7,11,12]. LikewiseGD and SUD individualspresent a similar decisionmaking pattern, beingthe overall task performanceand learningacross the task impaired[8]. For instance, a recent study conductedwith GD participants describesa strong preference for Decision Making in OB, SUD & GD PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 2 / 11 Andaluz de Salud (SAS) (B-0033-2014). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: I have read the journal’s policy and one of the authors of this manuscript have the following competing interests: Susana Jimenez Murcia is an academic editor of this journal. choices featuring high rewards rather than higherlosses during the IGT. The authors of this study suggest that this might reflect an hypersensitivity of their reward systems[13]. Additionally, GD and SUD are usually associated;being the decision-makingpatterns worse when both diagnosesare present[14]. Similarly, OB and GD display neurocognitiveand clinicalassociations. For instance OB is associated with decision making and sustained attention impairments in gamblers, along with greater monetarylosses due to gambling [15]. Direct comparisons of decisionmakingprofiles among SUD, GD and OB groups have yet to be conducted. However, direct comparisons can provide a valuable insight into the similarities and/or singularitiesof different addictiverelated behaviours. Aims of the study The present study aims to furtherexplore the decision making profiles of Substance use disorder, Gambling disorderand Obesitywhen assessedby the Iowa gambling taskand compares them with healthy controls (HC). The specificaims of the study are the following: (1) compare the overall performanceof the three clinical groups and the healthy controls, (2) observeand compare specificpatterns of learning across the task in the three clinical conditions and the healthy controls. It is hypothesizedthat the clinical samples willpresent poorerperformances on the IGT when compared to the HC group. Also, specificpatterns of learningacross the task will be observedin the four studied samples. The results have the potential of improving our understandingof the specificexecutive profiles (namely decisionmaking)underlyingthe association betweenobesity and addictive behaviours which in turn can also help improving current obesity treatments. Methods Sample The final sample consists of 591 participants (51.7% females) distributedas follow:194 HC, 178 GD, 113 OB and 106 SUD individuals.GD and SUD diagnosticcriterionwere assessedby an experiencedclinician(according to the DSM-IV-TR), by means of SCID-I[16]. Sevencenters, all involved in the CIBERobnSpanish ResearchNetwork, participated in the study: the Eating Disorders Unit (Departmentof Psychiatry, University Hospital of Bellvitge- IDIBELL,Barcelona), the Departmentof Endocrinology(University Hospital of Santiago, Santiago de Compostela); the Departmentof Diabetes, Endocrinologyand Nutrition (Clinic University Hospital Virgen de Victoria, Malaga); the Departmentof Endocrinologyand Nutrition (University of Navarra, Pamplona); the Diabetes,Endocrinologyand Nutrition Department, (BiomedicalResearch Institute of Girona IdIBGi-DoctorJosep Trueta Hospital, Girona); the ClinicalResearch Unit (Hospital del Mar Medical Research Institute-IMIM, Barcelona) and the Departmentof Basic Psychology, Clinic and Psychobiology (University Jaume I, Castellón). The GD, SUD and OB participants were patients that were consecutively referred to the clinics mentioned above. Recruitmentof the controls took place by means of word-of-mouth and advertisementsat local universities..All participants gave written signedinformedconsent and receivedno additional compensation for beingpart of the study. In accordance with the Helsinki Declaration of 1975 as revisedin 1983, the Ethics Committeeof all the institutions involved in the project approved the study: Comissió Deontológicade la Universitat Jaume I, Subcomisión Clínicadel Hospital Universitario “Virgen de la Victoria”, Málaga, Comite Etic de Investigacio Clinica Hospital Universitari de Girona DoctorJosep Trueta (048/10), Comite Etico de Investigacion Clinicadel ConsorciMar Parc de Salut de Barcelona-Parc de Salut Mar (2010/3914/I),Comité de Etica de la Investigación Universidad de Navarra (110/2010) and Comité Etico de Investigación Clínica del Hospital Universitari de Bellvitge(307/06)]. Decision Making in OB, SUD & GD PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 3 / 11 Exclusion criterionwere: (1) history of chronic medical illness or neurologicalcondition that might affect cognitivefunction;(2) head trauma, learningdisability or intellectualdisabilities; (3) individualswho have suffered a lifetime mental disorder according to DSM-IV-TR other than the specificdisorder of study (including the following: no OB individualhad a lifetime eating disorder, GD participants had no SUD, no SUD individualhad GD); (4) age under18 or over 65 (to discard neuropsychologicaldeficits associatedwith age); (5) having diabetesmellitus. There was one extra exclusion criteriafor all groups except from the SUD group: (6) historyof substanceabuse in the previous 3 months and use of psychoactivemedicationor drugs. The SUD individualshad not taken any drugsduring the last 72 hours prior to explorations (assessed by urine drug testing). Finally, in addition to the already mentioned criterion, for the OB group (7) patients with obesitywho had comorbid binge eating disorder (DSM-IV-TR) were also excluded. Neuropsychological assessment For the purposeof this study all individualswere assessed with the IGT [4]. This computer task evaluates decision-making,risk and reward as well as punishment value. The subject has to select 100 cards from four decks (A, B, C and D). After each card selectionan output is given: gain or a gain and loss of money. Two decks(A and B) are not advantageous as the final loss is higher than the final gain. DecksC and D, however, are advantageous since the punishments are smaller. The final objectiveof the task is to win as much money as possible. Before completing the task, all participants were instructedto try to win as much money as possible and avoid losing as much as money as possible and, they were also informed that some decks were worse than others. This test is scored by subtractingthe amount of cards selectedfrom decks A and B from the amount of cards selectedfrom decks C and D. It provides information about task learning(NET 1 to 5) as well as an overall performancescore(NET_Total). Statistical analysis Analyses were carried out with SPSS20 for Windows. Chi-square (χ 2 ) tests compared categorical variables betweenthe diagnosticsubtypes. Analysis of Variance (ANOVA), adjusted by the covariates age and years of education, compared the means for the cognitivemeasures (IGT scores). The ANOVA procedures included the between-subjectsfactors sex (two levels: men versus women) and diagnosticsubtype (four levels: HC, GD, OB and SUD), as well as the interaction group-by-sex to explore the potential moderator effect of sex into the relationships betweendiagnosisand cognitionoutcomes. The Holm-Bonferronimethod,which is one of the Familywise error rate stepwise procedures that offers more powerful tests than the classical Bonferroni-correction,was usedto control Type-I error due to multiple comparisons[17]. Results Table 1 includes the results of the χ 2 tests and the ANOVA to compare the sociodemographic variables betweengroups. Descriptivevariables of our sample (Table 1) show the inherent differences in sex, age and education among clinicalgroups (GD, SUD and OB groups). Accordingly and for the statistical aims of this study we controlled for the age and education level variables and used them as covariates in the ANOVA when comparing IGT scores. Table 2 includes the ANOVA model comparing the cognitivemean scores measured through the IGT, adjusted by the covariates age and years of education. The first step of the ANOVA proceduretested the interactionparameter ‘group-by-sex’. The absence of significant moderation effectsindicates no sex significantdifferences in the diagnosticsubtypes and it also indicates that differencesbetweensexes were statistically equal among the diagnosticsubtypes. Decision Making in OB, SUD & GD PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 4 / 11 Table 1. Sociodemographic descriptive. HC GD OB SUD Factor: group Pairwise comparisons: p-value n = 194 n = 178 n = 113 n = 106 1 Stat df p HC-GD HC-OB HC-SUD GD-OB GD-SUD OB-SUD Sex; n(%) Females 154 79.4% 17 9.6% 87 77.0% 32 30.2% 232.9 3 <.001 <.001 .623 <.001 <.001 <.001 <.001 Males 40 20.6% 161 90.4% 26 23.0% 74 69.8% Civil status; n(%) Single 155 79.9% 26 14.6% 13 11.5% - - - - - - 217.7 4 <.001 <.001 <.001 - - - .269 - - - - - - Married—in couple 35 18.0% 144 80.9% 90 79.6% - - - - - - Divorced—separated 4 2.1% 8 4.5% 10 8.8% - - - - - - Employment; n(%) Unemployed 133 68.6% 6 3.4% 16 14.2% - - - - - - 202.9 2 <.001 <.001 <.001 - - - .001 - - - - - - Employed 61 31.4% 172 96.6% 97 85.8% - - - - - - Education; n(%) Primary 56 28.9% 156 87.6% 19 16.8% 77 72.6% 243.9 6 <.001 <.001 <.001 <.001 <.001 <.001 <.001 Secondary 81 41.8% 18 10.1% 78 69.0% 8 7.5% University 57 29.4% 4 2.2% 16 14.2% 21 19.8% Age (years-old); Mean-SD 24.95 7.10 38.28 10.94 43.38 10.42 21.66 2.87 189.9 3;587 <.001 <.001 <.001 .002 <.001 <.001 <.001 Education (yrs); Mean-SD 14.53 3.73 12.17 2.89 12.87 3.95 11.69 2.85 21.8 3;587 <.001 <.001 <.001 <.001 .090 .245 .011 SD: standard deviation. 1 Statistic: χ2-statistic for proportion comparison and F-statistic for mean comparison. - - - Not available for this group. HC: healthy control. GD: gambling disorder. SUD: substance use disorder. OB: obesity. doi:10.1371/journal.pone.0163901.t001 Decision Making in OB, SUD & GD PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 5 / 11 Table 2. Comparison of the cognitive mean scores between diagnostic subtypes: ANOVA. Interaction HC GD OB SUD Factor Pairwise comparisons Women Men Factor group×sex n= 194 n= 178 n= 113 n= 106 group HC vs GD HC vs OB HC vs SUD GD vs OB GD vs SUD OB vs SUD n= 290 n= 301 Sex F 3;581 pMean SD Mean SD Mean SD Mean SD F 3;584 pMD pMD pMD pMD pMD pMD pMean SD Mean SD F 1;584 pMD NET_total 0.94 .419 16.07 27.7 2.14 23.8 4.33 22.8 6.45 21.7 6.51 .001 13.93 .001 11.74 .001 9.61 .003 -2.19 .518 -4.31 .231 -2.13 .615 3.02 26.3 11.48 23.8 10.76 .001 8.45 NET_1 0.28 .842 -1.69 6.3 -1.15 5.2 -1.25 5.3 -2.30 4.3 0.69 .559 -0.55 .493 -0.44 .582 0.61 .410 0.10 .895 1.15 .159 1.05 .272 -1.62 6.0 -1.58 5.0 0.01 .953 -0.03 NET_2 0.24 .868 2.25 7.3 0.83 6.6 -1.11 5.8 1.51 5.5 4.30 .016 1.42 .128 3.35 .001 0.74 .387 1.93 .032 -0.68 .479 -2.61 .020 0.53 7.0 1.21 6.2 0.97 .326 -0.67 NET_3 0.36 .784 5.00 8.1 0.80 7.7 1.39 7.0 2.05 6.3 6.42 .001 4.20 .001 3.61 .001 2.95 .003 -0.59 .569 -1.25 .258 -0.66 .611 1.51 7.7 3.11 7.5 4.07 .044 -1.59 NET_4 1.61 .185 5.17 9.3 2.12 8.1 2.50 7.3 2.23 7.2 3.56 .028 3.05 .010 2.67 .026 2.94 .007 -0.38 .740 -0.12 .924 0.26 .853 1.80 8.7 4.21 7.9 7.74 .006 -2.42 NET_5 0.75 .521 5.33 9.9 -0.20 8.8 2.84 8.0 2.88 8.1 6.29 .001 5.53 .001 2.49 .056 2.45 .039 -3.03 .014 -3.08 .020 -0.04 .978 0.86 9.1 4.57 9.1 15.56 .001 -3.72 SD: standard deviation. MD: mean difference. |d|: Cohen’s-dcoefficient. HC: healthy control. GD: gambling disorder. SUD: substance use disorder. OB: obesity. Bold: significant comparison. p-values includes Bonferroni-Holm correction. Results obtained in ANOVA adjusted by the participants’ age and years of education. doi:10.1371/journal.pone.0163901.t002 Decision Making in OB, SUD & GD PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 6 / 11 Consequently, the interactionparameter term was excluded from the modelingand the main effectsfor the factors group and sex were obtainedand interpreted.Regardingthe group factor analysis, differencesamong groups emerged in all NET scores but in NET-1. Additionally, HC participants achievedhighermean scores in NET-total, NET-3 and NET-4 when compared to the other clinical conditions (GD, OB and SUD). The HC participants also achieveda higher mean score in NET-5 when compared to GD and SUD individuals and, GD participants obtained a lower mean score compared to OB and SUD individuals.ConcerningNET-2, the OB group achievedthe lowest mean score and it was statisticallydifferent when compared to the other clinical conditions. Finally, with reference to the sex factor analysis, differences betweenmen and women emergedin all IGT scores except in NET-1 and NET-2 ones. Specifically, men achieved significantlyhigher means than women. Fig 1, displays the comparison of the mean scores in the cognitivelearningacross the task scalesbetweengroups. Discussion This study compares decisionmaking patterns when assessed by the IGT in three different clinicalsamples (SUD, OB and GD) and a HC. Results display impairments in the overall performance and in the task learning process in all clinical samples when individuallycompared to the HC. Fig 1. Mean cognitive measures in learning across the task between groups. doi:10.1371/journal.pone.0163901.g001 Decision Making in OB, SUD & GD PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 7 / 11 Resultsare in agreement with previousstudies reportingimpaired decisionmakingin SUD and GD individuals[13,14] as well as in individualswith OB[10,12] when independently assessed and compared to a HC group. Additionally, each of the three clinical groups of our sample present more impaired learningacross the task than the HC group. Data shows how the HC group presents a preference for the short term wins but after their first set of selections they progressively learn to choosethe decks that will lead to bigger long term wins. This is an expectedpattern in healthy individuals[4].In line with previous studies, the decision-making pattern of our HC sample cannot be extended to the clinical groups where learningstarts later and/or with a more erratic progress. For instance, OB individualsmaintain negative scores until the third set (i.e.: NET_3) where learning starts but to a lesser extent than in the HC group. Previous studies have also reported similar impairments in OB[7,18]. The SUD participants show an earlier learning across the task but improvement progression is slower when compared to the HC ones. The GD individualsdisplay more erratic or random choices. The learningeffectis quite small and ends with a score similar to the initial one. Accordingly, previous studies demonstrate how SUD individualstend to present an adaptive shift in decisionmakingperformancetowards the end of the task[19,20] whereas GD individuals do not[21,22]. Therefore, the decision-makingimpairment of individualswith SUD is probably more associated to a learning delay strategy rather than an inability to learn from the task and this pattern seems not to be extendedto GD participants. Finally, no significantdifferencesare found in the overall performanceamong the clinical groups. All groups achieve low but still positive scores. Results can reflectthat to some extent the three clinical samples respond to the punishment cues (although probably to a lesser extent than the HC group) and also that their deficit could be more due to difficultiesin reward processing. Resultsalso display no sexinfluenceamong groups. However, some differencesare observed when comparing the IGT performancebetweenmen and women in the whole sample. Specifically, men present a better performanceon the IGT. Previous studies report similar results and suggestthat men tend to have a better performancethan women in general population[23] and in SUD[24]. Although sex factor was controlled in our study, it would be interesting to explore furtherin future studies to specificallyexplore sex differencesin the studiedsamples. Our data adds to the current literature more evidenceof decisionmakingpatterns in SUD, OB and GD by comparing the three clinical samples. Importantly, in the analyses conducted none of the differencesamong the groups couldbe due to sex or other sociodemographicvariables, which adds extra value to the findings reportedhere. Our study supports the hypothesis that OB shares specificcognitiveand neurobiologicalpatterns with SUD and GD which are known to sufferfrom impairments in dopaminergicpathways that regulate neural systems associatedwith reward sensitivity and incentive motivation[25]. Accordingly, food addiction has beenassociated with obesity in previous behavioral and neuroimagingstudies[26] and the food intake management difficultiesobservedin some of the individualswith obesity could be associatedwith decision making impairments. The clinical implications of these results lie on the identifiedcognitivepatterns that suggestthe value of parallel therapeuticapproaches among OB, SUD and GD individuals[27,28].Importantly, the SUD and OB groups display some improvement in decision making; however, they are far from the HC group results (see Fig 1). Hence, cognitivestimulation protocols (through executive functionworking tools) could potentially benefitthese individualsby enhancing their adaptive decision makingstrategies when treating their disorder. Additionally, some specificdifferencesamong the clinical groups could be considered for improving current treatments. For instance, OB individuals take longer to learn the relationship betweendecks but once they do, their behavior conforms to that of HC. Still, their learningpattern remains less adaptive than the HC one and it should be considered for treatment approaches. Specifically, results may suggest that OB individuals Decision Making in OB, SUD & GD PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 8 / 11 couldprobably benefitfrom an early treatment extraemphasis on gaining more adaptive decision makingstrategies.On the other hand, SUD tend to learn faster than OB individualsand more similarto HC but they display verylittle improvement when compared to HC. This could be due to more risky behaviors and thus more emphasis should be given to these difficulties. Finally, GD individualsdisplay even more risky behaviors and move faster towards bigger rewards. These difficultiesshould be targeted in treatment. Future studies should test this hypothesis and furtherexplore these domains in order to (1) help disentangleto what extend these differencesare generalizableor constrained to our sample, (2) test the predictiveor mediating role of decision makingimpairments in a study which compares the here studied clinical samples. Furthermore,future studies couldfurtherexplore sex differenceswithin the studied groups. The present study has some limitations. Firstly, there are sociodemographicdifferencesconcerningeducation,sex and age across the groups which are representative of the studieddisorders. This is an expectedresult when working with a consecutiveclinical sample referred to GD, SUD and OB treatments. However, we have controlled these variables in all the statistical analysis. Although in this study we have paid specificattention to one of the most relevant neuropsychologicalfactors (namely decisionmaking),other cognitivefunctioningvariables or intelligence measuresnot assessedhere may better explain specificdifferences(more than commonalities) among the clinical groups. Finally, although participants did not present withdrawal symptoms or presented any life time mood-anxietydisorder or mental disorder that couldhinderthe assessment, the analyses conducted do not explicitly control for the participants’ hunger, anxiety or sadness feelings and these could also play a role in the participants’ performanceon the task. To our knowledge,we present the first study that compares decisionmaking in substance and behavioral related addictions,obeseindividualsand healthy controls. Results show similar impairments in decisionmaking in the three clinicalgroups. These impairments are statistically significantwhen compared to the healthy control group but not different among the three clinical groups. Finally, the clinical groups present significantdifficultiesin learning across the task when compared to the healthy controls and also some specificdifferenceswhen comparing clinical groups. Author Contributions Conceptualization:NMB ABF SJM FFA. Formal analysis:NMB RG. Funding acquisition: JM FFA RT CB FFC JCFG JMFR FJO. Investigation: NMB ABF RMB CB FFC ABC JCFG JMFR GF AR ITS FJO FJT EAM CO. Methodology:NMB ABF SJM RT JAM FFA. Project administration:NMB ABF SJM FFA. Resources:SJM FFA RT RMB CB FFC ABC JCFG JMFR GF FJO FJT EAM CO. Supervision:SJM FFA. Validation: SJM RT RMB CB FFC ABC FFA. Visualization: NMB ITS. Writing – originaldraft:NMB ABF RG. Decision Making in OB, SUD & GD PLOS ONE | DOI:10.1371/journal.pone.0163901 September 30, 2016 9 / 11