Journal of Affective Disorders 184 (2015) 51–59

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Temperament and character profiles in bipolar I, bipolar II and majordepressive disorder: Impact over illness course, comorbidity patternand psychopathological features of depression

Leonardo Zaninotto a,b, Daniel Souery c, Raffaella Calati d, Marco Di Nicola a,Stuart Montgomery e, Siegfried Kasper f, Joseph Zohar g, Julien Mendlewicz h,C. Robert Cloninger i, Alessandro Serretti b,n, Luigi Janiri a

a Institute of Psychiatry and Psychology, Catholic University of the Sacred Heart, Rome, Italyb Department of Biomedical and Neuro-Motor Sciences, University of Bologna, Italyc Laboratoire de Psychologie Medicale, Université Libre de Bruxelles and Psy Pluriel, Centre Européen de Psychologie Medicale, Brussels, Belgiumd INSERM U1061, University of Montpellier, FondaMental Foundation, Montpellier, Francee Imperial College, University of London, London, United Kingdomf Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austriag Chaim Sheba Medical Center, Tel-Hashomer, Israelh Université Libre de Bruxelles, Brussels, Belgiumi Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, USA

a r t i c l e i n f o

Article history:Received 1 February 2015Received in revised form19 May 2015Accepted 20 May 2015Available online 28 May 2015

Keywords:TemperamentCharacterDepressionMood disordersBipolar disorderDepressive disorder

x.doi.org/10.1016/j.jad.2015.05.03627/& 2015 Elsevier B.V. All rights reserved.

esponding author. Tel.: þ39 051 6584233; fax

a b s t r a c t

Background: Studies comparing temperament and character traits between patients with mood dis-orders and healthy individuals have yielded variable results.Methods: The Temperament and Character Inventory (TCI) was administered to 101 bipolar I (BP-I), 96bipolar II (BP-II), 123 major depressive disorder (MDD) patients, and 125 HS. A series of generalized linearmodels were performed in order to: (a) compare the TCI dimensions across groups; (b) test any effect ofthe TCI dimensions on clinical features of mood disorders; and (c) detect any association between TCIdimensions and the psychopathological features of a major depressive episode. Demographic and clinicalvariables were also included in the models as independent variables.Results: Higher Harm Avoidance was found in BP-II and MDD, but not in BP-I. Higher Self-Transcendencewas found in BP-I. Our models also showed higher Self-Directedness in HS, either vs MDD or BP-II. Noassociation was found between any TCI dimension and the severity of symptoms. Conversely, a positiveassociation was found between Harm Avoidance and the overall burden of depressive episodes duringlifetime.Limitations: The cross-sectional design and the heterogeneity of the sample may be the main limitationsof our study.Conclusion: In general, our sample seems to support the view of a similar profile of temperament andcharacter between MDD and BP-II, characterized by high Harm Avoidance and low Self-Directedness. Incontrast, patients with BP-I only exhibit high Self-Transcendence, having a near-normal profile in termsof Harm Avoidance or Self-Directedness.

& 2015 Elsevier B.V. All rights reserved.

1. Introduction

The relationship between personal disposition and affectivedisorders has been debated for centuries, since the old medicaltheory of humors and the original description of the melancholictemperament by Aristotle (Problemata XXX) (Aristotle, 1984). EmilKraepelin (1856–1926) was the first to describe and arrange into acoherent nosographic system the “personal disposition” of manic-

: þ39 051 521030.

depressive illness (Kraepelin, 1899): a depressive, irritable, manicor cyclothymic disposition which he connected to the pre-dominant clinical manifestation of the disorder (von Zerssen andAkiskal, 1998). The Kraepelinian model subsequently inspiredmost theories about the role of personality in the development ofaffective disorders, such as those by Kretschmer (1936) and Akis-kal et al. (1977, 1998).

The psychobiological model of personality developed by Clo-ninger (1994a, 1994b), Cloninger and Svrakic (1997) and Cloningeret al. (1993), and the corresponding instrument of evaluation, the

L. Zaninotto et al. / Journal of Affective Disorders 184 (2015) 51–5952

Temperament and Character Inventory (TCI), was inspired by adimensional model of the personality–psychopathology relation-ship, and has been widely applied to mood disorder patients.According to Cloninger's theory, temperament is regarded as theemotional core of personality and refers to automatic responses toemotional stimuli that are moderately stable throughout life andrelated to individual differences in associative conditioning tobasic stimuli such as reward and punishment. Temperamentincludes four largely independent dimensions: Novelty Seeking(NS), which is described as an intense excitement in response tonovel stimuli and signals of reward; Harm Avoidance (HA), whichrefers to a tendency to respond intensely to signals of aversivestimuli leading to cautious, inhibited and apprehensive behavior;Reward Dependence (RD), which reflects the maintenance ofsocially rewarded behavior; and Persistence (P), which describesthe maintenance of behavior despite only intermittent reinforce-ment. Character, on the other hand, is defined in terms of indivi-dual differences in goals and values that develop across the life-span in response to socio-cultural influences on conceptual andautobiographical learning. Both temperament and character areequally heritable but differ in the underlying type of learning andmemory by which they are regulated in the human brain (Clo-ninger, 2004, 2009). The three character dimensions are: Self-Directedness (SD), which is the ability to regulate and adaptbehavior to the demands of a situation in order to achieve per-sonally chosen goals; Cooperativeness (CO), which expresses thedegree to which a person is generally helpful and agreeable in his/her relations with others; and Self-Transcendence (ST), which isassociated with ability to recall the past and imagine the future invivid detail in developing one's life narrative, as well as toexperience a unity with nature and to develop spiritual values(Cloninger, 1994a, 1994b; Cloninger and Svrakic, 1997; Cloningeret al., 1993).

Studies comparing the temperament and character dimensionsbetween patients with mood disorders and healthy individualshave yielded variable results. Most studies report a higher HA inmood disorder patients, both bipolar and unipolar (Engstrom et al.,2004; Nowakowska et al., 2005; Osher et al., 1996; Young et al.,1995), while a higher NS has been detected in bipolar subjects only(Nowakowska et al., 2005; Young et al., 1995). Other studies reportbipolar patients to be lower in P (Osher et al., 1996, 1999), SD and C(Engstrom et al., 2004), and higher in RD (Osher et al., 1996) andST (Nowakowska et al., 2005).

The relationship of personality to affective disorders is com-plex, since many traits appear to be unstable, varying with changesin symptom levels (Hirschfeld et al., 1983; Peselow et al., 1995), orbeing affected by a sort of “scarring” effect due to multiple moodepisodes (Dunayevich et al., 1996; Hirschfeld et al., 1989). Fur-thermore, most studies are widely heterogeneous in terms ofsample sizes, methods of assessment (i.e. operational definitions ofeuthymia (Engstrom et al., 2004; Osher et al., 1996)), and selectionof participants, including either inpatients (Strakowski et al., 1992),or outpatients (Engstrom et al., 2004; Nowakowska et al., 2005;Osher et al., 1999; Young et al., 1995), and combining bipolar dis-order, type I (BP-I) and type II (BP-II) (Nowakowska et al., 2005;Osher et al., 1996, 1999). Finally, most investigators do not con-sistently control for covariates, such as demographic and clinicalfactors known to influence TCI dimensions' scores (i.e. age, gender,and psychiatric symptoms).

To fill this gap, the primary aim of the current study was todetect any difference in temperament and character dimensionsbetween healthy individuals and mood disorder patients, includ-ing BP-I, BP-II and major depressive disorder (MDD). The second-ary aim was to detect any association between temperament andcharacter traits and some clinical features of mood disorders.Finally, we aimed to examine the relationship between

temperament and character dimensions and the psychopatologicalfeatures of a major depressive episode (MDE).

2. Methods

2.1. Sample selection

Our sample comprised 445 individuals 18–89 years of age (180males and 265 females; mean age 44.71714.28), including 320patients affected by a mood disorder and 125 healthy subjects (HS).

Mood disorder patients came from two different study samples.The first sample included all the subjects consecutively recruited inthe period between June, 2006 and December, 2013, among thepatients referred to the Psychiatric Day-Care unit at Gemelli Hospital,Department of Psychiatry and Addiction Center, Catholic University ofRome. For the present study, only those subjects meeting the fol-lowing criteria were included: (1) a diagnosis of mood disorderaccording to DSM-IV diagnostic criteria (A.P.A., 1994); and (2) anageZ18 years. Exclusion criteria were: (1) a diagnosis of dementia,mental retardation or documented IQo70; (2) an unstable generalmedical condition; (3) a diagnosis of a schizophrenia-spectrum dis-order; and (4) the presence of alcohol/substance intoxication at thetime of assessment (as revealed by toxicological tests).

The second sample was recruited within a large multicenter,multinational study project, named the “Patterns of treatment resis-tance and switching strategies in unipolar affective disorder”, conceivedin the context of the Group for the Study of Resistant Depression(GSRD). This project lasted from January, 2000 to February, 2004, andaimed at defining some key issues in Treatment Resistant Depression(TRD), such as diagnosis, clinical features and treatment adequacy (fora detailed description, see Souery et al., 2007, 2011). Six centers tookpart in this project: the Department of Psychiatry, Erasme Hospital,Université Libre de Bruxelles, Brussels, Belgium; the Department ofPsychiatry, University Hospital Gasthuisberg, Leuven, Belgium; theSint-Truiden Psychiatric Center, Sint-Truiden, Belgium; the Depart-ment of Psychiatry, Istituto Scientifico San Raffaele, Milan, Italy; theDepartment of Psychiatry, Chaim Sheba Medical Center, Tel-Hasho-mer, Israel; and the Department of General Psychiatry, Medical Uni-versity Vienna, Vienna, Austria. For the present study, we consideredonly the epidemiological screening phase of the project, whoseinclusion criteria were: (a) an ageZ18 years; (b) a diagnosis of MajorDepressive Episode (MDE) according to DSM-IV diagnostic criteria (A.P.A., 1994); and (c) at least one antidepressant trial of adequate doseand duration. An antidepressant trial was defined as adequate if (1) itwas at least 4 weeks in duration and (2) the dose used was equal to orhigher than the lowest dose defined as effective in the productdatasheet. The only exclusion criterion was the presence of a schi-zophrenia-spectrum disorder. Thus, the final sample included bothbipolar and unipolar subjects, and both treatment-resistant and non-treatment-resistant depressed patients.

Healthy individuals were recruited within blood donors refer-ring to the blood bank of Gemelli Hospital, Catholic University ofRome, in the period between January, 2011 and December, 2013.Exclusion criteria for the control group were: any illness of thecentral nervous system, a lifetime diagnosis of major psychiatricillness, and a history of psychopharmacological or psychother-apeutic treatments. To be included in the control group, subjectshad to be free of any Axis I disorder, as determined by the SCID-I,non-patient edition (SCID-I/NP) (First et al., 2002).

The research protocol was approved by the Institutional ReviewBoard of Gemelli Hospital, for the Rome sample, and by the ethicscommittees of all participating centers, for the GSRD study. Eachstudy was conducted in accordance with the ethical principles of theDeclaration of Helsinki (W.M.A., 1989), and followed the guidelines ofGood Clinical Practice. All participants were voluntarily enrolled,

L. Zaninotto et al. / Journal of Affective Disorders 184 (2015) 51–59 53

anonymity was guaranteed, and written informed consent wasobtained after a complete description of the study was provided.

2.2. Assessment

Current and lifetime diagnosis, course of illness and psychiatriccomorbidities according to DSM-IV diagnostic criteria (A.P.A., 1994)were assessed by specifically trained psychiatrists on the basis ofstructured diagnostic interviews: (a) the Structured Clinical Interviewfor DSM-IV Axis I Disorder (SCID-I) (First et al., 1995), for the Romesample; (b) a modified version of the Mini International Neu-ropsychiatric Interview (MINI)-version 5.0.0 (Sheehan et al., 1998), forthe GSRD sample. Validation and reliability studies have been donecomparing the MINI to the SCID-P (First et al., 2002a), showingacceptably high validation and reliability scores (Sheehan et al., 1997).

For both samples, diagnosis, psychiatric familial antecedentsand somatic comorbidites were also reviewed by clinical investi-gation using all possible sources of information, including previouscharts, family members and previous treating clinicians. Since ourstudy was a cross-sectional investigation, we only consideredbaseline assessment, and did not examine changes in diagnosisover time (i.e. fromMDD to BP). Also, medication/treatment effectsor adherence to treatments were not considered.

Severity of mood symptoms for the current mood episode wasassessed at the intake by using the 21 item Hamilton DepressionRating Scale (HAM-D) (Hamilton, 1960) in all patients, and theYoung Mania Rating Scale (YMRS) (Young et al., 1978), in bipolarsubjects. The YMRS (Young et al., 1978) was also used to assess thepresence of intra-depressive manic symptoms in bipolar patients(Mazza et al., 2012; Souery et al., 2012; Zaninotto et al., 2014).

Diagnostic interviews and assessment of symptom severitywere conducted within the first 48 h after admission. Participantswere also administered the Temperament and Character Inventory(TCI), either in its version 9 (Cloninger, 1992), for the GSRD study,or in the Italian translation of its revised version (TCI-R) (Clo-ninger, 1999; Martinotti et al., 2008), for the Rome sample. In bothstudies, the TCI was administered after a condition of stableeuthymia was ascertained by a HAM-D total score r8 and a YMRSscore r7 lasting for at least three months. Each subject wasallowed to take as much time as needed to complete the ques-tionnaire. Since the two samples used different versions of the TCI,T-scores were computed for all the TCI dimensions. Outliers (i.e.,values representing impossible responses or erroneous data) wereexcluded, and T-scores were created to reflect a mean score of 50and standard deviation (SD) of 10.

2.3. Outcome variables

For the present investigation, we considered only subjects(n¼320) meeting DSM-IV diagnostic criteria (A.P.A., 1994) for aprimary (i.e. not secondary to another Axis I disorder or to amedical condition) mood disorder, including BP-I, BP-II, and MDD.

At first, the standardized scores (T-scores) of all the TCI scaleswere compared across groups (HS, BP-I, BP-II, and MDD), in orderto detect any difference in the distribution of temperament andcharacter dimensions. Then, we tried to detect any associationbetween the TCI dimensions and some clinical features of themood disorder sample, such as: age at onset, number of episodes,history of suicide attempts, and axis I comorbidites. The effect ofTCI scores was tested against other possible predictors, includingage, gender, diagnosis, and setting of treatment (inpatient vsoutpatient). The latest variable was included to account for thedifferent distribution of patients in the two datasets, since all ofthe inpatients were from the GSRD study sample, while mostoutpatients (93.4%) were from the Rome sample.

Finally, we tried to detect any effect of temperament and char-acter dimensions on the psychopathological features of a depressiveepisode. Thus, we considered only those subjects having a MDE atthe intake (n¼249), and addressed the following outcome variables:depressive severity, duration of the episode, melancholic or psychoticfeatures, double depression (MDEþdysthymic disorder), suicide risk,abrupt onset (within 24 h) of the episode, and intra-depressive manicsymptoms (in bipolar patients only). Predictors were the same as inthe previous analyses, and depressive severity (HAM-D total score)was used as the covariate.

2.4. Statistical analysis

All statistical analyses were performed by using the STATISTICAsoftware package (Dell Software, Tulsa, OK, USA). Differences indemographic and/or clinical variables among groups were deter-mined by Chi-square test (with Fisher's correction, when neces-sary) for categorical variables, and by Student T-test and one-wayanalysis of variance (ANOVA) for continuous variables. Post-hocanalyses (LSD test) were also performed to discriminate individualeffects. Any association between continuous variables, includingage, years of education, and T-scores of TCI dimensions wasdetermined by Pearson correlation.

All p values were 2-tailed, and statistical significance wasconservatively set at .001 level (.05 divided by 5 socio-demo-graphic and 34 clinical variables, including the seven TCI dimen-sions), also considering that the present sample has been pre-viously investigated (Souery et al., 2007, 2011, 2012).

A series of generalized linear models were then performed totest: (1) the effects of age, gender, and diagnosis (BP-I, BP-II, MDDand HS) on temperament and character scores; and (2) the effectsof TCI dimensions, age, gender, and setting of treatment (inpatientvs outpatient) on some clinical and psychopathological features ofthe sample. Family-wise Bonferroni post-hoc corrections wereused to adjust for multiple comparisons.

3. Results

3.1. Demographic and clinical characteristics of study sample

Our sample (n¼445) was made of 320 (71.9%) subjects affectedby a mood disorder and 125 (28.1%) healthy individuals. Half of themood disorder sample was recruited within the Psychiatric Day-Care unit at Gemelli Hospital (n¼155; 48.4%), the rest came fromthe epidemiological screening phase of the GSRD study project(n¼165; 51.6%). Over half of the subjects were Italians (n¼288;64.7%), the others being from Austria (n¼81; 18.2%), Belgium(n¼68; 15.3%), and Israel (n¼8; 1.8%).

Healthy individuals resulted to be younger (mean age40.23716.47 vs 46.46712.94; t¼4.21; po .0001), and with a higherlevel of instruction (years of education 13.7272.96 vs 11.6974.59;t¼�4.57; po .0001) than mood disorder patients. The latter showeda higher prevalence of employed subjects (63.1% vs 16.8%) and a lowerprevalence of retirees (7.8% vs 32%) and students (3.8% vs 32%) thanHS (Chi-sq¼137.31; d.f.¼3; po .0001). No difference was foundbetween groups in terms of gender or civil status (Table 1).

The three diagnostic groups were almost equally representedwithin the mood disorder sample (BP-I, n¼101, 31.6%; BP-II, n¼96,30%; MDD, n¼123; 38.4%), and most patients (n¼249; 77.8%)were diagnosed as having a MDE at the intake. The remainingsubjects were reported to be either euthymic (n¼47; 14.7%),mixed (n¼18; 5.6%), or manic/hypomanic (n¼6; 1.9%) at the timeof assessment. One hundred fifty-four subjects (48.1%) wereassessed as inpatients, most of them having MDD (Table 2).

L. Zaninotto et al. / Journal of Affective Disorders 184 (2015) 51–5954

3.2. Differences in temperament and character among groups

The standardized scores (T-scores) of all the TCI dimensionswere compared across groups, showing differences in HA, SD andST (Fig. 1). Post-hoc analyses confirmed that either BP-I(po .0001), BP-II (po .0001) and MDD (p¼ .0010) scored higherthan HS on HA. Conversely, HS exhibited higher SD scores thaneither BP-I (po .0001) or BP-II (po .0001). Differences on ST wereconfirmed between BP-I and MDD (po .0001), between BP-I andHS (po .0001) and between BP-II and HS (p¼ .0002). Finally, asmall difference at the limit of significance (p¼ .0012) was found

Table 1Demographic variables of the study sample.

(% Column)/Mean (SD) Total445 HS125 (28.1%) BP-I101 (

Mean age 44.71 (14.28) 40.23 (16.47) 45.85 (12Males 180 (40.4%) 61 (48.8%) 43 (42

Civil status (n¼429)Single 121 (28.2%) 41 (32.8%) 32 (32Married/cohabiting 237 (55.2%) 74 (59.2%) 45 (45Divorced/separated 67 (15.6%) 10 (8.0%) 20 (20Widowed 4 (1.0%) – 2 (2.0

EmploymentEmployed 223 (50.1%) 21 (16.8%) 59 (58Unemployed 105 (23.6%) 24 (19.2%) 35 (34Retired 65 (14.6%) 40 (32.0%) 5 (4.9Student 52 (11.7%) 40 (32.0%) 2 (2.0

Education (years) (n¼442) 12.27 (4.29) 13.72 (2.96) 12.24 (4.3

HS¼Healthy Subjects; BP-I¼Bipolar Disorder-type I; BP-II¼Bipolar Disorder-type II; MD

Table 2Differences in family history, illness course and comorbidity pattern across different mo

(% Column)/Mean (SD) Total320 BP-I101 (31.6%)

Inpatients 154 (48.1%) 25 (24.8%)

Family historyMDD 94 (29.4%) 19 (18.8%)BP 36 (11.3%) 18 (17.8%)SUD 13 (4.1%) 2 (2.0%)Suicide 20 (6.3%) 6 (5.9%)

Illness courseMDE Lt (nr.) (n¼246) 4.12 (6.47) 4.19 (6.33)Man. Lt (nr.) (n¼120) 0.73 (1.58) 2.22 (2.37)Age at onset (n¼314) 31.53 (12.06) 26.83 (10.38)Mood episodes Lt (nr.) 4.32 (6.16) 5.32 (6.64)History of SA (n¼204) 79 (38.8%) 20 (40.8%)

ComorbiditiesAnxiety disorders (n¼221) 109 (49.3%) 22 (40.0%)PD (n¼275) 60 (21.8%) 9 (11.7%)SP (n¼165) 26 (15.8%) 2 (7.4%)OCD (n¼165) 11 (6.7%) 2 (7.4%)PTSD (n¼165) 10 (6.1%) 2 (7.4%)GAD (n¼257) 32 (12.5%) 4 (5.5%)SUD 82 (25.6%) 21 (20.8%)Drug (n¼275) 31 (11.3%) 9 (11.7%)Alcohol (n¼275) 68 (24.7%) 16 (20.8%)Axis II (n¼193) 92 (47.7%) 40 (47.1%)

SUD¼Substance Use Disorders; Lt¼Lifetime; SA¼Suicide Attempts; PD¼Panic DisordeStress Disorder; GAD¼Generalized Anxiety Disorder.

for higher P in HS compared to patients with BP-II. No significantcorrelation was found between any TCI dimension and symptomseverity (as expressed by HAM-D and YMRS total scores).

Since both age (Chen et al., 2013; Cloninger, 1994a; Cloningeret al., 1993; Mikolajczyk et al., 2008) and gender (Gutierrez-Zoteset al., 2004; Hansenne et al., 2005; Pelissolo and Lepine, 2000),were previously reported to affect temperament and character, aseries of univariate analyses were performed to test their asso-ciations with TCI dimensions. Age resulted to have a mild negativecorrelation with NS (r¼� .25; po .0001) and RD (r¼� .16;po .0001), and a mild positive correlation with ST (r¼ .19;

22.7%) BP-II96 (21.6%) MDD123 (27.6%) χ2/F/t P

.45) 47.13 (12.78) 46.44 (13.52) 6.03 .0005

.6%) 39 (40.6%) 37 (30.1%) 9.30 .0256

.3%) 29 (30.5%) 19 (17.3%)

.5%) 45 (47.4%) 73 (66.4%) 24.01 .0043

.2%) 19 (20.0%) 18 (16.3%)%) 2 (2.1%) –

.4%) 58 (60.4%) 85 (69.1%)

.7%) 19 (19.8%) 27 (21.9%) 148.25 o .0001%) 13 (13.5%) 7 (5.7%)%) 6 (6.3%) 4 (3.3%)

3) 12.77 (3.84) 10.39 (5.04) 14.13 o .0001

D¼Major Depressive Disorder.

od disorders.

BP-II96 (30.0%) MDD123 (38.4%) χ2/F/t P

14 (14.6%) 115 (93.5%) 166.79 o .0001

22 (22.9%) 53 (43.1%) 18.51 .000113 (13.5%) 5 (4.1%) 11.23 .003611 (11.5%) – 19.81 o .00013 (3.1%) 11 (8.9%) 3.14 .2082

2.76 (4.93) 4.78 (7.15) 1.88 .15450.74 (1.12) – 4.40 o .000132.20 (10.28) 35.01 (13.43) 13.76 o .00013.04 (4.04) 4.50 (6.95) 3.50 0.03159 (26.5%) 50 (41.3%) 2.59 .2745

26 (60.5%) 61 (49.6%) 4.05 .13197 (9.2%) 44 (36.1%) 26.23 o .00013 (18.8%) 21 (17.2%) 1.72 .42302 (12.5%) 7 (5.7%) 1.07 .58632 (12.5%) 6 (4.9%) 1.53 .46519 (12.2%) 19 (17.3%) 5.61 .060642 (43.8%) 19 (15.5%) 24.47 o .000117 (22.4%) 5 (4.1%) 15.65 .000437 (48.7%) 15 (12.3%) 34.21 o .000136 (45.0%) 16 (57.1%) 1.25 .5357

r; SP¼Social Phobia; OCD¼Obsessive-Compulsive Disorder; PTSD¼Post-Traumatic

42

44

46

48

50

52

54

56

NS HA RD PE SD CO ST

T-s

core

TCI dimensions

HS

BP-I

BP-II

MDD

Fig. 1. Distribution of temperament and character dimensions across groups. HS¼Healthy Subjects; BP-I¼Bipolar Disorder-type I; BP-II¼Bipolar Disorder-type II;MDD¼Major Depressive Disorder; NS¼Novelty Seeking; HA¼Harm Avoidance; RD¼Reward Dependence; PE¼Persistence; SD¼Self-Directedness; CO¼Cooperativeness;ST¼Self-Transcendence. ***po .0001.

L. Zaninotto et al. / Journal of Affective Disorders 184 (2015) 51–59 55

po .0001), while females resulted to have significantly higherscores on HA (51.4179.67 vs 47.93710.11; t¼�3.65; p¼ .0003).Although previous studies also included education (Loftus et al.,2008; Mendlowicz et al., 2000) as an independent predictor, wefound no correlation between this variabile and any TCI dimen-sion. Thus, all the seven TCI dimensions were further tested asdependent variables by a series of generalized linear models,including age, gender, and diagnosis as the independent variables.Three different kinds of models were built for each TCI dimension,in order to include all the possible pairwise comparisons betweengroups (BP-I, BP-II and MDD vs HS; BP-I and MDD vs BP-II; BP-I vsMDD). Then, each model included a maximum of five predictors,and a Bonferroni post-hoc correction was applied to adjust formultiple comparisons (alpha-value .001/5¼ .0002).

From our models, age was associated with lower NS scores(Est.¼� .004; S.E.¼ .001; Wald.¼30.21; corrected po .0001),while no association was found between gender and any TCIdimension. When compared to HS, both BP-II (Est.¼ .074; S.E.¼ .016; Wald.¼20.96; corrected po .0001) and MDD(Est.¼0.074; S.E.¼ .016; Wald.¼20.96; corrected po .0001)reported higher HA scores, while no association was detectedbetween BP-I diagnosis and HA. Conversely, a positive and inde-pendent effect on ST score was confirmed for the BP-I group, eithervs HS (Est.¼ .070; S.E.¼ .015; Wald.¼21.36; corrected po .0001), vsBP-II (Est.¼ .070; S.E.¼ .015; Wald.¼21.36; corrected po .0001), orvs MDD (Est.¼ .070; S.E.¼ .015; Wald.¼21.36; corrected po .0001).Finally, our models showed that HS may report higher SD, whencompared to either BP-II (Est.¼ .081; S.E.¼ .015; Wald.¼29.16;corrected po .0001) or MDD (Est.¼ .081; S.E.¼0.015;Wald.¼29.16; corrected po .0001), but not to BP-I.

3.3. Effect of temperament and character on clinical variables

A large number of generalized linear models were performed inorder to test for all the possible associations between TCI dimensionsand clinical features of mood disorders. Each model included asindependent variables: (a) age; (b) gender; (c) diagnosis; (d) setting oftreatment (inpatient vs outpatient); and (e) TCI dimensions (separatemodels for each dimension). Two different kinds of models were builtfor each outcome variable, in order to include all the possible pairwisecomparisons between diagnostic groups (BP-I and BP-II vs MDD; BP-Ivs BP-II). Then, eachmodel included amaximum of six predictors, and

a Bonferroni post-hoc correction was applied to adjust for multiplecomparisons (alpha-value.001/6¼ .0002).

At first, we considered the whole sample (n¼320) andaddressed the following outcome variables: (1) age at illnessonset; (2) number of mood episodes; (3) number of depressiveepisodes; and (4) number of manic/hypomanic episodes duringlifetime. Other outcome variables were: (5) history of suicideattempts (yes vs no); (6) presence of comorbid anxiety disorders(yes vs no), with separate models for each disorder (PD, SP, OCD,PTSD and GAD); (7) presence of comorbid SUDs (yes vs no), withseparate models for alcohol use and drug use.

After Bonferroni post-hoc correction, BP-I diagnosis was con-firmed to be associated with younger age at onset, either vs MDD(in all models, corrected po .0001), or vs BP-II (in all models,corrected po .0001). No effect was found for any variable onoverall number of mood episodes during the subject's lifetime, buta positive association was found between the number of depres-sive episodes and HA score (corrected po .0001). Also, beingdiagnosed as BP-I (vs MDD; in all models, corrected po .0001) orbeing an inpatient (in all models, corrected po .0001) was asso-ciated to a higher number of depressive episodes. BP-I diagnosiswas also associated with more manic/hypomanic episodes duringthe subject's lifetime (in all models, corrected po .0001). Noassociation was found with a lifetime history of suicide attempts.

Concerning the comorbidity pattern, a positive association wasfound either between BP-II diagnosis and SUD comorbidity ingeneral (in all models, corrected po .0001), and between BP-II andalcohol use (in all models, corrected po .0001). No association wasfound between our predictors and any other axis I disorder (PD, SP,OCD, PTSD, GAD and drug use).

3.4. Effect of temperament and character on the psychopathologicalfeatures of a depressive episode

In order to detect any possible effect on the psychopathologicalfeatures of major depression, we considered only those subjectshaving a MDE at intake (n¼249; features of the current MDE aredescribed in Table 3), and addressed the following outcome vari-ables: (1) depressive severity (HAM-D total score); (2) duration ofthe episode (days); (3) presence of melancholic features (yes vsno); (4) presence of dysthymic disorder (yes vs no); (5) presence ofpsychotic features (yes vs no); (6) presence of suicide risk (yes vs

Table 3Features of the current Major Depressive Episode (MDE) across different mood disorders.

(% Column)/Mean (SD) Total249 BP-I59 (58.4%) BP-II80 (83.3%) MDD110 (89.4%) χ2/F/t P

Rapid onseta (n¼142) 33 (23.2%) 6 (33.3%) 5 (31.3%) 22 (20.4%) 2.10 .3496Psychotic features (n¼237) 24 (10.1%) 5 (9.3%) 11 (14.7%) 8 (7.4%) 2.62 .2697Melancholic features (n¼238) 126 (52.9%) 16 (29.6%) 31 (41.3%) 79 (72.5%) 32.53 o .0001Dysthymic disorder (n¼144) 13 (9.0%) 2 (10.5%) 2 (12.5%) 9 (8.3%) .37 .8329Ep. duration (days) (n¼214) 143.83 (189.49) 128.98 (214.97) 94.61 (157.57) 188.37 (188.47) 5.32 .0056HAM-D total score (n¼249) 17.60 (6.28) 15.95 (6.25) 15.83 (5.29) 19.77 (6.34) 12.94 o .0001

Suicide risk levelb (n¼238)No suicide risk 88 (37.0%) 21 (38.9%) 44 (58.7%) 23 (21.1%)Low suicide risk 70 (29.4%) 21 (38.9%) 22 (29.3%) 27 (24.8%) 46.11 o .0001Medium suicide risk 24 (10.1%) 3 (5.6%) 4 (5.3%) 17 (15.6%)High suicide risk 56 (23.5%) 9 (16.7%) 5 (6.7%) 42 (38.5%)

BP-I¼Bipolar Disorder-type I; BP-II¼Bipolar Disorder-type II; MDD¼Major Depressive Disorder; HAM-D¼Hamilton Depression Rating Scale.a Rapid onset was defined as the onset of acute mood symptoms within 24 h.b Suicide risk level was defined according to Mini International Neuropsychiatric Interview (MINI)-version 5.0.0 (Sheehan et al., 1998).

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no); (7) abrupt onset (within 24 h) of the episode (yes vs no); and(8) intra-depressive hypomanic symptoms (in bipolar depressedonly, YMRS total score). Each model included as independentvariables: (a) age; (b) gender; (c) diagnosis; (d) setting of treat-ment (inpatient vs outpatient); and (e) TCI dimensions (separatemodels for each dimension). Again, two different kinds of modelswere built for each outcome variable, in order to include all thepossible pairwise comparisons between diagnostic groups (BP-Iand BP-II vs MDD; BP-I vs BP-II). In all models, except the first,depressive severity (HAM-D total score) was also included as acovariate.

After Bonferroni post-hoc correction (alpha-value .001/7¼ .0001), no effect was found for any variable on either depres-sive severity or episode duration. A positive association was foundbetween melancholic features and being an inpatient (in allmodels, corrected po .0001) and between melancholic featuresand HAM-D total score (in all models, corrected po .0001). Thepresence of suicide risk was also positively associated withdepressive severity (in all models, corrected po .0001). No effectwas found for any variable on the presence of dysthymic disorder,psychotic features or abrupt onset of the current depressive epi-sode. Finally, an inverse relationship was found between HAM-Dscore and YMRS score in bipolar depressed patients (in all models,corrected p¼ .0001).

4. Discussion

To our knowledge this is one of the largest study samplescomparing temperament and character dimensions betweenmood disorder patients and healthy individuals. In general, ourfindings confirmed that BP-I, BP-II, MDD, and HS may have a dif-ferent profile in some specific TCI dimensions, such as HA, SD andST. The study also examined the possibile connections betweentemperament and character traits and some clinical features ofmood disorders, showing an association between HA and theburden of depressive episodes during lifetime. Otherwise, wecould not detect any association between the TCI dimensions andthe psychopathological features of the current depressive episode.

As regards the effect of demographic variables on temperamentand character, we found an inverse relationship between age andNS score (Chen et al., 2013; Cloninger, 1994a; Cloninger et al.,1993; Mikolajczyk et al., 2008), but, in contrast with previous lit-erature (Gutierrez-Zotes et al., 2004; Hansenne et al., 2005;Pelissolo and Lepine, 2000; Sasayama et al., 2011), our multivariate

analyses found no independent effect of gender on any TCIdimension.

Among the seven TCI scales, scores of HA, SD and ST resulted tobe significantly different across groups. In line with previous lit-erature (Engstrom et al., 2004; Evans et al., 2005; Farmer et al.,2003; Hansenne et al., 2005; Harley et al., 2011; Janowsky et al.,1999; Loftus et al., 2008; Mula et al., 2008; Nery et al., 2008;Nowakowska et al., 2005; Osher et al., 1996; Sasayama et al., 2011;Young et al., 1995), both BP-II and MDD patients reported higherHA and lower SD scores than HS. Mood disorder patients seem todescribe themselves as more fatigable, shy, worry-prone, andpessimistic than healthy individuals. A high HA has been inter-preted as a characteristic of susceptibility to mood disorders ingeneral, and not unique to either MDD or bipolar disorder (Harleyet al., 2011). However, in contrast with other studies on bipolarsubjects (Engstrom et al., 2004; Evans et al., 2005; Harley et al.,2011; Mula et al., 2008; Nowakowska et al., 2005), in our sampleboth high HA and low SD appeared to be specific of BP-II, but notof BP-I patients.

This finding seems to be indirectly contradicted by the positiveassociation between HA scores and the burden of depressivesymptomatology over lifetime, since BP-I subjects reported ahigher number of depressive episodes. Nonetheless, the latestfinding may also depend on the younger age at onset of thesesubjects. Thus, while for BP-II and MDD a high HA may be a stable,trait-like dimension, independent of age, gender and setting oftreatment, for BP-I, HA may be more an effect of illness duration ornumber of depressive episodes during lifetime. Taken together, allthese findings seem to suggest the need to control for the numberof depressive episodes when addressing the TCI dimension of HAin bipolar subjects. Another possible explanation is that in BP-I thepresence of a sub-threshold hypomanic symptomatology maycounteract the effects of modest depressive symptoms on HA andSD.

Other findings regarding either higher (Evans et al., 2005;Janowsky et al., 1999; Nowakowska et al., 2005; Young et al., 1995)or lower (Osher et al., 1999) NS, and lower P (Osher et al., 1996,1999) in bipolar patients, could not be confirmed. This lack ofreplication might be due to differences in current mood stateamong the samples, limited sample sizes, or lack of control fordemographic variables in the other studies (Mendlowicz et al.,2000).

A positive and independent effect on ST score was found for BP-I, either vs HS, BP-II, or MDD (Evans et al., 2005; Harley et al., 2011;Loftus et al., 2008; Nery et al., 2008; Nowakowska et al., 2005).According to Cloninger's theory (Cloninger, 1994a), high ST in the

L. Zaninotto et al. / Journal of Affective Disorders 184 (2015) 51–59 57

presence of high SD and CO, may be an adaptive personality trait,leading to a mature creativity and spirituality. When high ST is notfound with high SD and CO, the unusual, imaginative and idio-syncratic interpretations to events may be associated with psy-chotic thought processes (Cloninger et al., 1993). Thus, a high ST inthe presence of low SD may suggest either a proneness to psy-chosis (Bayon et al., 1996), or the presence of residual psychoticsymptoms. However, as for the other features we found in BP-I (i.e.near-normal HA and SD traits), a high ST may also depend on thepresence of sub-threshold thought disorders deriving from mildhypomanic symptoms.

Our study reported no association between any TCI dimensionand the severity of depressive symptoms. This finding seems to bein contrast with previous studies reporting that severity ofdepression positively correlates with HA and negatively with SDscores (Bayon et al., 1996; Brown et al., 1992; Cloninger et al., 1998;Farmer et al., 2003; Hansenne et al., 1999; Hirano et al., 2002;Naito et al., 2000; Peirson and Heuchert, 2001; Richter et al., 2000;Sasayama et al., 2011; Spittlehouse et al., 2010). Nonetheless, weused strict criteria for euthymia, also based on symptom severity(HAM-D total score r8 and YMRS score r7), which could haveminimized the effect of sub-threshold symptomatology on TCIdimensions.

On the other hand, we found a positive association between HAscore and the overall burden of depressive episodes during life-time, independently of any residual inter-episode depressivesymptomatology (Judd et al., 2002). Thus, in line with thehypothesis by Akiskal et al. (1983), a sort of “scarring” effect ofdepressive illness on this temperament trait could be hypothe-sized. However, the greater burden of lifetime episodes may alsoindicate that HA is the best indicator of vulnerability to futuredepression, since it was found to be better at predicting accumu-lated dysphoria (number of future episodes) than current mood(Rosenstrom et al., 2014). In fact, it is unlikely that the associationwith risk of depression of HA is the result of scarring, only, becausehigher HA is present in never-depressed siblings of depressivepatients compared to never depressed controls (Farmer et al.,2003). The study of siblings of depressives and the heritability ofHA indicate that HA is a substantial predictor of vulnerability todepression, but does not rule out some additional contribution ofscarring.

There were some limitations in the current study. The cross-sectional design did not allow any definitive conclusions as towhether the TCI score profiles were premorbid or the results ofillness. Secondly, the heterogeneity of the mood disorder group,including subjects from two different populations and from dif-ferent study centers, may also have limited the validity of ourfindings. However, we tried to control for a “dataset effect” byadding treatment setting (inpatients vs outpatients) to all ourpredictive models. This variable was considered both as an alter-native measure of severity and as an indirect expression of the twodifferent study populations. We did not control for family historyof psychiatric disorders in our sample of healthy individuals, andthis may be regarded as a further limitation to the generalizationsof our results. The more complex comorbidity pattern of somepatients may also have impacted over the temperament profile, aspatients with PD yield higher HA scores and lower SD scores thanHS (Mula et al., 2008; Wachleski et al., 2008). Controlling forcomorbidities may help disentangling the specific effect of diag-nosis on TCI dimensions. However, this operation was not feasiblefor our study, because data about comorbidites were randomlydistributed within the sample (i.e. part of the Rome sample did notreport information about anxiety disorders), and our GLMs couldnot fit with this lack of information. Some MDD subjects may besubsequently re-diagnosed as affected by bipolar disorder (Akiskalet al., 1995; Coryell et al., 1995)though the mean age of unipolar

subjects in our sample was beyond the peak period of risk for afirst manic episode (Perlis et al., 2004). Finally, the use of blooddonors as a control group while studying personality may havesome limitations, since there is some evidence that blood donorstend to have a specific personality pattern, characterized by lowself-esteem (Fernandez-Montoya et al., 1998; Golding et al., 2013).Thus, they may be a more motivated cohort with higher levels ofCO, possibly SD and lower HA, in the very process of volunteeringto donate blood.

In general, our study seems to support the view of a similarprofile of temperament and character between MDD and BP-II,characterized by high HA and low SD. In contrast, BP-I patientsexhibit high ST, while describing themselves as near normal in HAand SD. In the study by Engstrom et al. (2004), when compared toBP-II, BP-I patients were lower in TCI sub-scales for impulsivityand rapid fatigability, while they were higher in resourcefulnessand impulse control. Further, a previous study by Akiskal et al.(2006) using a complex personality battery, showed a similarprofile between BP-II and MDD, in terms of interpersonal sensi-tivity, neuroticism and ruminativeness, while BP-I resulted to below on neuroticism, and have near-normal levels of extroversionand sociability. However, it may also be that, as in our study, thoseresults depend on the uncontrolled presence of sub-thresholdhypomanic symptoms in BP-I patients.

Future studies both prospective and longitudinal in nature arenecessary to disentangle the state-trait relationship in order tobetter define possible endophenotypes for mood disorders.Another way to circumvent the state dependency of measures ofpersonality and emotionality, especially as regards bipolar dis-order, may be the use of sibling studies, in order to see what traitsare different in never ill sibs of mood disorder patients comparedto controls or sibs of controls. Such designs may help us determinewhether there is a specific vulnerability pattern-according toCloninger's model of personality-to developing a mood disorder(Farmer et al., 2003), either unipolar or bipolar in nature, whetherpersonality is influenced by mood episodes, or whether thereexists a complex interplay of both processes (Loftus et al., 2008).

Role of the funding source

The sponsors had no role in the study design, analysis, andwriting of the paper.

Financial disclosure

This study has been supported by an unrestricted grant to theGroup for the Study of Resistant Depression (GSRD) by LundbeckA/S and the Belgian National Fund for Scientific Research (FNRS)(FNRS; 3.4.530.07 F).

Conflict of interestAs regards potential conflicts of interest:Leonardo Zaninotto, Raffaella Calati, and Marco Di Nicola declare no conflict of

interest for this paper. Daniel Souery has received grant/research support fromGlaxoSmithKline and Lundbeck; has served as a consultant or on advisory boardsfor AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen and Lundbeck. Luigi Janirihas been a member of the advisory board for Lundbeck. Stuart Montgomery hasbeen a consultant or served on Advisory boards: AstraZeneca, Bionevia, BristolMyers Squibb, Forest, GlaxoSmithKline, Grunenthal, Intellect Pharma, Johnson &Johnson, Lilly, Lundbeck, Merck, Merz, M's Science, Neurim, Otsuka, Pierre Fabre,Pfizer, Pharmaneuroboost, Richter, Roche, Sanofi, Sepracor, Servier, Shire, Syno-sis, Takeda, Theracos, Targacept, Transcept, UBC, Xytis and Wyeth. Siegfried Kasperhas received grant/research support from Eli Lilly and Company, Lundbeck, Bristol-Myers Squibb, GlaxoSmithKline, Organon, Sepracor and Servier; has served as aconsultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, Glax-oSmithKline, Eli Lilly, Lundbeck, Pfizer, Organon, Schwabe, Sepracor, Servier,

L. Zaninotto et al. / Journal of Affective Disorders 184 (2015) 51–5958

Janssen, and Novartis; and has served on speakers' bureaus for AstraZeneca, Eli Lily,Lundbeck, Schwabe, Sepracor, Servier, Pierre Fabre, and Janssen. Joseph Zohar hasreceived grant/research support from Lundbeck, Servier and Pfizer, has served as aconsultant or on advisory boards for Servier, Pfizer, Solvay and Actelion, and hasserved on speakers' bureaus for Lundbeck, GSK, Jazz and Solvay. Julian Mendlewiczis a member of the Board of the Lundbeck International Neuroscience Foundationand of Advisory Board of Servier. Alessandro Serretti is or has been consultant/speaker for: Abbott, AstraZeneca, Clinical Data, Boheringer,Bristol Myers Squibb, EliLilly, GlaxoSmithKline, Janssen, Lundbeck, Pfizer, Sanofi, Servier. Claude RobertCloninger holds the ownership of the TPQ and TCI instruments.

AckowledgementsWe have no specific acknowledgments for this paper.

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