Dự án - 1

1

Research report
Temperament and character t raits predict future burden of depression
Tom Rosenström
a, d,n
, Pekka Jylhä
b,e
, C. Robert Cloninger
c
, Mirka Hintsanen
a, h
,
Marko Elovainio
a,d
, Outi Mantere
b,f ,g
, Laura Pulkki-RÃ¥back
a
, Kirsi Riihimäki
b
,
Maria Vuorilehto
b ,f
, Liisa Keltikangas-Järvinen
a
, Erkki Isometsä
b, f, g
a
IBS, Unit of Personality, Work and Health Psychology, University of Helsinki, Helsinki, Finland
b
Department of Mental Health and Substance Abuse Services, National Institute of Health and Welfare, Helsinki, Finland
c
Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
d
National Institute for Health and Welfare, Helsinki, Finland
e
Department of Psychiatry, Jorvi Hospital, Helsinki University Central Hospital, Espoo, Finland
f
Department of Psychiatry, Institute of Clinical Medicine, University of Helsinki, Helsinki, Finland
g
Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland
h
Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland
article info
Article history:
Received 28 October 2013
Received in revised form
27 January 2014
Accepted 28 January 2014
Available online 11 February 2014
Keywords:
Personality
Major depressive disorder
Bipolar disorder
Mood disorders
Longitudinal data
Prevention
abstract
Background: Personality traits are associated with depressive symptoms and psychiatric disorders.
Evidence for their value in predicting accumulation of future dysphoric episodes or clinical depression
in long-term follow-up is limited, however.
Methods:Within a 15-year longitudinal study of a general-population cohort (N ¼ 751), depressive
symptoms were measured at four time points using Beck's Depression Inventory. In addition, 93 primary
care patients with DSM-IV depressive disorders and 151 with bipolar disorder, diagnosed with SCID-I/P
interviews, were followed for fi ve and 1.5 years with life-chart methodology, respectively. Generalized
linear regression models were used to predict future number of dysphoric episodes and total duration of
major depressive episodes. Baseline personality was measured by the Temperament and Character
Inventory (TCI).
Results: In the general-population sample, one s.d. lower Self-directedness predicted 7.6-fold number of
future dysphoric episodes; for comparison, one s.d. higher baseline depressive symptoms increased the
episode rate 4.5-fold. High Harm-avoidance and low Cooperativeness also implied elevated dysphoria
rates. Generally, personality traits were poor predictors of depression for specifi c time points, and in
clinical populations. Low Persistence predicted 7.5% of the variance in the future accumulated depression
in bipolar patients, however.
Limitations: Degree of recall bias in life charts, limitations of statistical power in the clinical samples, and
21– 79% sample attrition (corrective imputations were performed).
Conclusion:TCI predicts future burden of dysphoric episodes in the general population, but is a weak
predictor of depression outcome in heterogeneous clinical samples. Measures of personality appear more
useful in detecting risk for depression than in clinical prediction.
& 2014 Elsevier B.V. All rights reserved.
1. Introduction
Depression is a common disorder with a high risk of episode
recurrence over time ( Vos et al., 2012; Hardeveld et al., 2013 ).
Predicting future chronicity and recurrence of depression is clini-cally important, for targeting treatment. Preceding episodes, family
history of depression ( Hardeveld et al., 2013), and comorbidity
(Melartin et al., 20 04) predict recurrence; less obvious factors, such
as body-image dissatisfaction (Rosenström et al., 2013 ), may con-tribute. Previous studies have also found that personality traits,
such as those de fined by the Psychobiological Model of Personality
(Cloninger, 1987; Cloninger et al., 1993 ), are predictive of depressive
symptoms measured 3 months (Na i t o e t a l. , 2 0 0 0), a year (Cloninger
et al., 20 06), and 4 years ( Elovainio et al., 20 04; Farmer and Seeley,
20 09) later,suggestinga more generalbackgroundbehindaccu-mulation of depressive and dysphoric episodes. Other evidence that
personality predicts risk of depression has been obtained with
Contents lists available at ScienceDirect
journal homep age: www.elsevier.com/loc at e/jad
Journal of Affective Disorders
htt p ://dx.doi.org/10.1016/j.jad.2014.01.017
0165-0327 & 2014 Elsevier B.V. All rights reserved.
n
Correspondence to: University of Helsinki, Siltavuorenpenger 1A , P.O. Box 9,
Finland. Tel.: þ 358 9 1912 9396; fax: þ 358 9 1912 9521.
E-mail address: tom.rosenstrom@helsinki. fi (T. Rosenström).
Journal of Affective Disorders 158 (2014) 139 – 14 7
measures of coping in relation to concurrent and future depression
in community samples ( Rohde et al., 1990) and with antecedent
personality traits in never-depressed siblings of depressives com-pared to never-depressed siblings of controls (Farmer et al., 20 03 ).
Prior work with Cloninger's psychobiological model of person-ality shows that the risk of depression is associated with high
Harm Avoidance, low Self-directedness, and low Persistence
(Cloninger et al., 2012, 2010; Farmer et al., 20 03 ). Conversely,
resilience is associated with low scores in Harm Avoidance, and
high scores in Self-directedness, Cooperativeness, and Persistence
(Elye et al., 2013). A brain imaging study showed that these
personality traits can be linked with a speci fic brain circuit that
modulates mood and reward-seeking behavior (Gusnard et al.,
20 03; Cloninger et al., 2012 ). Dysfunctional attitudes that increase
the risk of depression are largely explained by low Self-directed-ness, as expected from the cognitive theory of depression, but the
other personality variables in fl uence in particular circumstances
(Luty et al., 1999; Richter and Eisemann, 20 02; Otani et al., 2013).
Dysphoric, or subclinical, symptoms are strongly associated
with functional impairment ( Karsten et al., 2010), and show no
clear empirical boundary with respect to more severe forms of
depression ( Haslam et al., 2012 ). Sample differences among gen-eral and clinical populations are likely, however. Simultaneously
studying longitudinal accumulation in clinical and general popula-tions offers the opportunity to examine which personality traits
have prognostic value under what starting points (e.g., for ran-domly chosen individual versus randomly chosen mood-disorder
patient). The potential differences among different clinical popula-tions are studied herein using two separate clinical populations;
one with bipolar disorder and another with unipolar depressive
disorder. We concentrate on the predictive value of personality
traits for future dysphoric/depressive episode accumulation rather
than on future depression at single time points. In the general
population, the outcome is rate of future dysphoric episodes; in
clinical populations, the outcome is the proportion of follow-up
with participant fulfi lling the DSM-IV criteria for a major depres-sive episode.
The aim of this study was to provide an answer to two
questions. First, are there personality traits that predispose people
to a higher or lower rate of future dysphoric episodes compared to
the base rate in the general population? Second, which personality
traits predict future burden of major depressive episode for
unipolar and bipolar mood disorder patients? These results may
have clear and immediate clinical utility, as the importance of
prevention efforts for depression has been recently emphasized
(Ghaemi et al., 2013). Personality is an attractive candidate for
detection of at-risk groups, as it is malleable, yet more stable than
the actual target of prevention— depressive episodes ( Klein et al.,
2011).
2. Methods
This study used one data set with a random sample from the
general population and two samples from clinical populations of
psychiatric patients.
2.1. Participants from Young Finns study (YFS)
YFS is an ongoing prospective study with the fi rst data collec-tion in 1980 (Raitakari et al., 20 0. The original sample consists of
3596 healthy Finnish children and adolescents (1832 women, 176 4
men) sampled from six birth cohorts with approximately equal
frequency (born 1962, 1965, 196 8, 1971, 1974, or 1977). In order to
select a b roadly sociodemographically representative sample,
Finland was divided into fi ve areas according to locations of
university cities with a medical school (Helsinki, Kuopio, Oulu,
Tampere, and Turku). In each area, urban and rural boys and girls
were randomly selected on the basis of their unique personal
social-security number. All participants gave written informed
consent and the study was approved by the ethical committee of
the Varsinais-Suomi's hospital district's federation of municipali-ties. The sample has been followed subsequently in 8 data collec-tion waves in 1983, 1986, 1989, 1992, 1997, 20 01, 20 08, and 2012,
but only data from the four latter waves contained the required
measures of both depressive symptoms and personality. Data from
the year 1997 formed the baseline data, whereas the 20 01, 20 08,
and 2012 follow-ups were used for evaluating future dysphoria
and depressive symptoms.
Altogether 751 participants (256 men and 4 95 women) pro-vided all data needed for the intended analyses in YFS data. The
study attrition was 79% from the initial year-1980 sample, and 56%
from those with baseline data available ( n ¼ 1690). Of ten, those
who lack data in YFS have more psychopathology-related person-ality traits and depressive symptoms, and are more likely to be
young and male, compared to retained participants ( Rosenström
et al., 2012a, 2012b). Correlates of attrition are same in clinical
studies (Melartin et al., 20 0 4 ). Supplementary on-line material
presents an imputation analysis, indirectly testing the sensitivity
of the fi ndings for missing observations. For simplicity, the main
manuscript presents non-imputed estimates; both should be
provided in some form, when possible (White et al., 2011).
2.2. Participants from Vantaa Primary Care Depression Study (PC-VDS)
Baseline data collection of the PC-VDS was based on stratifi ed
sampling from two districts within the city of Vantaa, Finland,
during the year 20 02 (population 63,40 0). Primary care patients
aged 20– 69 from general practitioners' waiting rooms were
screened by using Primary Care Evaluation of Mental Disorders,
PRIME-MD ( Spitzer et al., 1994), from three health centers and two
maternity clinics. A total of 1119 participants were addressed, of
which 402 screened positive for depressive symptoms; 37 of these
refused to participate in the study and the rest gave their written
informed consent. In the second phase, a diagnosis was made by a
psychiatrist using the Structured Clinical Interview for DSM-IV
axis I disorders (SCID-I/P; First et al., 20 02). All available informa-tion from face-to-face interviews and psychiatric records was
used; if the diagnosis was uncertain, other informants were
contacted. To exclude substance-induced mood disorder, patients
who were currently abusing alcohol or other substances were
interviewed af ter 2 – 3 weeks of abstinence. The fi nal baseline
cohort consisted of 137 depressive disorder patients. Two thirds
had major depressive disorder (MDD), the rest being diagnosed
with dysthymia, subsyndromal MDD with 2– 4 symptoms (mini-mum one core symptom) and lifetime MDD, or minor depression
otherwise similar to subsyndromal MDD, but without history of
MDD. Distress or functional impairment was required. Interrater
reliability for current depressive disorder, evaluated from 20
randomly selected videotaped interviews, was perfect [ κ ¼ 1. 0
(Vuorilehto et al., 20 05, 20 09; Riihimäki et al., 2011 )].
The participants were followed again af ter 6 and af ter 18
months, and af ter 5 years from the baseline. A life chart of the
entire 5-year follow-up period was constructed for the patients by
one of the two interviewers to determine the duration of the index
episode and the timing of possible relapses and recurrences using
all available medical and psychiatric records to complement the
information. Altogether 93 participants provided the necessary
personality and depression inventories at the baseline, and the full
life-chart information. Hence, study attrition was between 32%
and 47%, depending on the unknown clinical status of refused
patients. Further details of the sample can be found from previous
T. Rosenström et al. / Journal of Af fective Disorders 158 (2014) 139 – 14 7 14 0
publications (Vuorilehto et al., 20 05, 20 09; Jylhä et al., 2011;
Riihimäki et al. , 2011).
2.3. Participants from Jorvi Bipolar Study (JoBS)
The patients for the JoBS were screened from those of the
Department of Psychiatry at the Jorvi Hospital (part of Helsinki
University Central Hospital), serving the adjacent cities of Espoo,
Kauniainen, and Kirkkonummi in Finland during the year 20 02
(population 261,10 0). All patients, excluding those with schizo-phrenia (n ¼ 1630), were screened with the Mood Disorder Ques-tionnaire (MDQ); and 546 positive screens for bipolar disorder
(BD) were found; 91 participants refused and the rest gave their
written informed consent. In the second phase, a diagnosis was
made by one of six psychiatrist using the Structured Clinical
Interview for DSM-IV axis I disorders (SCID-I/P; First et al.,
20 02). All available information from face-to-face interviews and
psychiatric records was used; if the diagnosis was uncertain, other
informants were contacted. Altogether 191 patients were assigned
a research diagnosis of DSM-IV type I or type II BD; interrater
reliability of BD and type I or II diagnoses, evaluated by 20
randomly selected interviews, was perfect ( κ ¼ 1.0). Details of
baseline methodology have been published elsewhere (Mantere
et al., 20 0 4).
The participants were followed again af ter 6 and af ter 18
months. Graphic life charts of the follow-up period were con-structed individually for each patient, as in PC-VDS. Altogether 151
patients provided the necessary personality and depression inven-tories at the baseline, and the full life-chart information. Hence,
study attrition was between 21% and 46%, depending on the
unknown diagnostic status of refused patients. Further methodo-logical details can be found from previous publications ( Jylhä et al.,
2011; Mantere et al., 20 0.
2.4. Measures
A modifi ed version of the Beck's Depression Inventory (mBDI)
was used in the general-population YFS to measure depressive
symptoms (Cronbach's α ¼ 0.91 in 1997, 0.92 in 20 01, 0.93 in 20 08,
and 0.93 in 2012). In the modifi ed version, subject rank s to what
degree (a 5-point scale from‘no’ to ‘ very much ’) he or she suffers
from the ailment presented in the second mildest symptom
description of the original Beck's Depression Inventory; such
mo dified versions of clinical scales are frequently used because
they better represent the general-population variation in the
symptoms than the original clinically oriented scales ( Rosenström
et al., 2012b ). In year 20 08, the participants also fulfilled Beck's
Depression Inventory II (BDI-II,α ¼ 0.8 for which a national
standardization has been published (Beck et al., 20 04 ); BDI and
BDI-II are highly similar measures that are strongly correlated (at
0.93) with each other (Beck et al., 1996). Using the 20 08 mBDI and
BDI-II measures, a general relationship between the mBDI and BDI-II scales was established (see beginning of the Results section).
Further psychometric analyses of relationships between mBDI and
BDI has been published elsewhere, including Item Response Theory
modeling and various attrition analyses (Rosenström et al., 2012b;
Rosenström, 2013b).
In the national standardization, BDI-II scores above 13 points
signify at least mild depression, a state referred to as dysphoric
episode herein. Via the established mBDI to BDI-II relationship, it
was possible to count the dysphoric episodes across all the three
follow-ups af ter the baseline. The total number of dysphoric
episodes within given number of assessments/follow-ups is
referred to as caseness , as in previous studies ( Jokela et al., 2011).
Caseness is, for the population-based YFS, a related measure to the
proportion of time a person suffers from a depressive episode as
measured from the life chart for the clinical data.
In the clinical data, all collected information was integrated into
a graphic of a life chart together with the patient. In addition to
symptom ratings, change points in psychopathological states were
inquired using probes related to important life-events in order to
improve accuracy of the assessment. From the life charts, propor-tions of time in the follow-up during which the participants
fulfi lled DSM-IV criteria for MDE (5 or more of the 9 symptoms;
SCID-I/P;First et al., 20 02 ) were computed ( Holma et al., 20 08;
Vuorilehto et al., 20 09). Accuracy of, or information in, life charts
must considerably exceed simple interpolation from face-to-face
follow-up assessments (see Sections 2.2 and 2.3), but cannot be
quanti fi ed further, as the patients were not under full-time
continuous surveillance. The participants also fi lled in the Beck's
Depression Inventory [BDI (Beck and Steer, 1993), 0.86 r α r 0.95].
In addition to the depression assessments, personality as
defi ned by the Psychobiological Model of Personality ( Cloninger,
1987; Cloninger et al., 1993 ) was assessed in the baseline follow-up of both the population-based YFS study and the clinical studies.
The PC-VDS and JoBS used the Revised version of Temperament
and Character Inventory (Cronbach's α ¼ 0.81  0.94), while YFS
used the Temperament and Character Inventory (internal consis-tencies below) modifi ed to correspond to the revised version with
a 5-point Likert scale (Cloninger et al., 1994). A personality trait is a
continuous measure for individual differences occurring along
certain dimension of behavior and thought. The main personality
traits that were used are briefl y described below, and more
detailed description has been published elsewhere ( Cloninger
et al., 1993).
Novelty seeking is a tendency toward excitement and activation
of behavior in response to novel stimuli, or in response to cues of
potential rewards or potential relief of punishment (40 items,
α ¼ 0.85 in YFS). Harm avoidanceis a tendency to inhibit behavior
in response to signals of aversive stimuli or frustrative non-reward
(35 items, α ¼ 0.92). Reward dependenceis a tendency to form
social attachments in response to signals of reward (especially to
signals of social approval; 24 items, α ¼ 0.80). Persistence is a
tendency to maintain or resist extinction of behavior previously
associated with intermittent rewards or relief from punishment (8
items, α ¼ 0.6 4). Self-directedness is a tendency to set and to strive
towards self-determined rather than externally infl uenced life
goals, and to attribute causes for the consequences of one's actions
to oneself rather than to other peoples or external circumstances
(4 4 items, α ¼ 0.89). Cooperativenessrefers to ability and desire to
co-operate with other people (42 items, α ¼ 0.91). Finally, Self-transcendence is a tendency to be aware of connections with what
is beyond the individual self, referring to personal qualities such as
spirituality and universal values (33 items, α ¼ 0.91).
2.5. Statistical analyses
Regression models for future accumulation of depression were
estimated, with baseline personality traits as predictors. First,
‘individual effects’ of traits and depression scores in predicting
the amount of time a person was depressed were estimated
(Model 0). Then, Model I assessed what personality traits con-tribute when adjustment is made for the baseline depressive-symptoms summary score. In YFS, we also adjusted for the
presence of a dysphoric episode as a dichotomous variable at
baseline (Model II). Finally, Model III assessed the contribution of
each variable controlling for all the other traits and/or the
depression score, that is, a full multiple regression was estimated.
Despite this conceptual division, every regression model was
adjusted for sex and age (single continuous variable in clinical
data; five cohort indicators in YFS).
T. Rosenström et al. / Journal of Af fective Disorders 158 (2014) 139 – 14 7 14 1
As the outcome variable was either a count (in YFS) or a
proportion (in PC-VDS and JoBS), generalized linear regression
models were used (Gelman and Hill, 20 07). In YFS, Quasi-Poisson
regression was applied (sensitivity analysis with Ordered Logistic
regression in on-line supplement). In the clinical data sets, two
complementary approaches were taken. Proportions are fre-quently modeled by transforming them into a continuous variable
by Logit transformation, but this does not work when 0 or
1 proportions exist in the data, as the Logit transformation for
the former is minus in fi nity and for the latter plus infi nity [the
Logit transformation is the map p-log{ p /(1-p )} from open interval
(0, 1) to the real line]. Therefore, we also applied Infl ated Beta
Regression, which can handle the extreme values as well (Ospina
and Ferrari, 2010; Stasinopoulos and Rigby, 20 07 ), thereby allow-ing the use of all eligible data. In addition to being a sensitivity
analysis, the approach with an explicit Logit transformation also
allows for presenting the coefficient of determinations (R
2
) and the
change resulting from adding an independent variable into a
regression model (ΔR
2
). R
2
value signifies the proportion of
outcome-variable variance explained by the model; herein, “ R
2
”
always refers to the covariate-number “adjusted R
2
” (Gelman and
Hill, 20 07). Notice that change ΔR
2
for adjusted R
2
can be negative
for a bad predictor.
All analyses were performed using R-sof tware 6 4-bit version
2.15.3, and for the Infl ated Beta Regression, GAML SS R-package
(Core Team, 2012; Stasinopoulos and Rigby, 20 07). Statistical
comparisons between two linear models were based either on
the classical F-test or on the Akaike's Information Criterion [AIC
(Stasinopoulos and Rigby, 20 07)]. Continuous independent vari-ables in regression models were standardized z -scores. The statis-tical p -values in Table 1 are from two-tailed t -test of equal means.
2.6. On the interpretation of outcome variables
In the clinical samples (JoBS and PC-VDS), we were able to
explicitly compute the proportion of follow up that a participant
suffered from symptoms fulfi lling Major Depressive Episode cri-teria by using the life charts and hospital records. Hence, direct
associations between personality measured at baseline and pro-portion of time depressed could be evaluated. In the general
population (YFS), however, the participants were sampled only
in discrete time points, without knowledge of their emotional
states in between. As the temporal sampling points determined by
the study protocol can be considered unrelated to individuals'
emotional processes, the number of dysphoric states observed
during the sampling times should be monotonically related to
their general rate of occurrence. Therefore, estimated changes in
base rates due to a covariate should be reasonably comparable
across partially observed general-population trajectories and more
fully observed clinical trajectories. It should be kept in mind,
however, that quasi-Poisson models assess relative increases in
base rate of dysphoric episodes rather than absolute increases.
In addition to comparability between samples, there was
another reason for studying accumulation of discrete dysphoric
episodes instead simple sums of symptom sums over several time
points. This way one avoids confounding cases of repeatedly
elevated scores with cases of a single very high score and several
quite low scores.
3. Results
Because the distances among the levels of depressive-symptom
severity are encoded differently by mBDI and BDI-II ( Rosenström
et al., 2012b), the inclusion of a quadratic component was required
in modeling the relationship between the mBDI and the clinically
oriented BDI-II ( β
quadratic¼ 1. 4 8 , S.E. ¼ 0.07,p o 0.0 01, ΔR
2
¼ 0.085;
see Fig. 1A). Further nonlinear components were not needed in the
model (p ¼ 0.998 and ΔR
2
¼ 0.0 0 for a cubic term; other relevant
estimates were: R
2
¼ 0.695; β
linear ¼ 4.24 andβ
intercept¼ 4.02). Cor-relation between the established model estimate and measured
BDI-II was 0.83, which is close to the maximum possible [Cron-bach's (alpha) reliability of the BDI-II was 0.88]. A dysphoric
episode was defi ned by this estimated quadratic transformation
(i.e., BDI-II modeled by the mBDI) exceeding 13 points of BDI-II
score (see Measures section 2.4). Af ter the baseline-year 1997,
there were 3 non-baseline follow-ups, and hence three dysphoric
episodes were maximum number of ‘ future ’ episodes in YFS
(Fig. 1.
As can be seen fromFig. 1A , the predictor/proxy for the BDI-II-defined mild depression had higher speci fi city (0.9 than sensi-tivity (0.65). Speci fi city implied that episodes detected by the
model almost always re flected at least mild episodes according to
BDI-II. Sensitivity implied that we missed 35% of such episodes,
suggesting that regression estimates below are underestimates
rather than overestimates. The issue of sensitivity is pertinent,
however, only so far as one prefers BDI-II over mBDI in de fining
dysphoric episodes.
Table 1 presents the basic characteristics of the studied sam-ples. Whereas the proportion of time as depressed is shown for
clinical PC-VDS and JoBS data sets, the number of dysphoric
episodes in the three non-baseline follow-ups is shown for YFS
general-population data that lacks the life chart methodology. On
average, the clinical patients had approximately 14– 17 points
higher BDI compared to the YFS participants' (BDI-II proxy).
Table 1
Basic sample characteristics and their comparison.
Variable PC-VDS YFS p-value
Median Mean s.d. Median Mean Range/s.d.
Age at the baseline 46.15 4 4.27 13.85 29 27.61 20– 35 o 0.0 01
Age at the fi nal follow up 51.15 49. 27 13.85 4 4 42.61 35– 50 –
Proportion/number of episode(s) 0.18 0.32 0.34 0 0.32 0.71 –
BDI or BDI-II proxy at baseline 17 19.54 10.51 3.79 5.63 5.30 o 0.0 01
JoBS
Age at the baseline –––37.93 38.54 11.72 0.0 01
Age at the fi nal follow up –––39.43 40.0 4 11.72 –
Proportion of derpressive episode(s) –––0.27 0.35 0.32 –
Logit of depressive-episode proportion  1.32  1. 17 1. 8 9  0.55  0.65 1.65 0.056
BDI or BDI-II proxy at baseline –––23 22.07 11.78 0.082
Note: “ PC-VDS” ¼ Primary Care Vantaa Depression Study; “ YFS ” ¼ Young Finns (general-population) Study; “ JoBS ” ¼ Jorvi Bipolar Study. p -value is provided for a column-wise
t-test when such test made sense; “ s.d. ” ¼ standard deviation, range is given for age at YFS that had six approximately equally large cohorts.
T. Rosenström et al. / Journal of Af fective Disorders 158 (2014) 139 – 14 7 14 2
3.1. Main results for general population
Table 2 shows regression coeffi cients from quasi-Poisson mod-els predicting the number of future dysphoric episodes with the
baseline measures in general-population participants. Baseline
measures included score of depressive symptoms (mBDI), indica-tor of dysphoric episode at baseline, and personality traits; all
independent variables were standardized except dichotomous
indicator variables. For all models, low current Self-directedness
predicted the greatest increase in the rate of future dysphoric
episodes, among personality traits. Also high Harm avoidance, low
Cooperativeness, and depressive symptoms contributed strongly.
The presence of a dysphoric episode at baseline was a non-signifi cant predictor af ter adjusting for the continuous depression
Fig. 1. Outcome Variables. (A) Dysphoric episodes in Young Finns Study's (YFS) general population were defined by Beck's Depression Inventory II (BDI-II) score higher than
13 (a national cut-off). BDI-II existed from single year, but was well-predicted (solid line) by quadratic model on more frequently observed modi fi ed BDI (mBDI; x-axis is for
standardized z -score). Points/circles represent observed values, with jitter (uniformly distributed random values on interval [ 1/40, 1/40]) added to both axes so that
overlapping points can be discerned. ( Distribution of the number of dysphoric episodes ( ‘caseness ’ score) across the three non-baseline follow-ups. (C) Histogram for the
proportions of time that the clinical participants in Primary Care Vantaa Depression Study (PC-VDS) satis fi ed DMS-IV criteria for major depressive episode. Circles represent
numbers of participants without episode accumulation (zero proportion) or with a single full five-year long episode (proportion is one). (D) Similar histogram as in panel C,
but for the participants of Jorvi Bipolar Study (JoBS), followed for 18 months.
Table 2
Quasi-Poisson regression coeffi cients for models predicting number of future dysphoric episodes with prior dysphoria and personality in general population.
Model 0 M odel I Model II Model III
mBDI 1.51 (0.10)
nnn
– 1.52 (0.16)
nnn
1.05 (0.20)
nnn
Dysphoric episode 1.77 (0.16)
nnn
 0.02 (0.22) –  0.11 (0.23)
Novelty seeking  0.02 (0.21) 0.23 (0.17) 0.0 0 (0.1 0.36 (0.20)
Harm avoidance 1.42 (0.14)
nnn
0.46 (0.16)
nn
0.94 (0.15)
nn n
0.58 (0.20)
nn
Reward dependence  0.20 (0.20) 0.11 (0.16)  0.0 6 (0.1  0.0 6 (0.20)
Persistence 0.02 (0.14) 0.09 (0.12) 0.03 (0.13) 0.28 (0.13)
n
Self-directedness  2.03 (0.16)
nnn
 0.88 (0.22)
nnn
 1.55 (0.19)
nnn
 0.65 (0.26)
nn
Cooperativeness  1.15 ( 0 .19 )
nnn
-0.18 (0.19)  0.61 (0.19)
nn
0.05 (0.23)
Self-transcendence 0.38 (0.14)
nn
0.15 (0.12) 0.27 (0.13) 0.22 (0.13)
Note: Standard errors in parentheses.
“ mBDI” ¼ modifi ed version Beck's Depression Inventory score; “ Dysphoric episode” ¼ a dichotomous variable for an episode at baseline, preceding the
episodes that contributed to the outcome variable; “ Model 0” ¼ regression coef fi cients for model with only age and sex as covariates;“ Model I” ¼ Model
0 further adjusted for baseline mBDI; “ Model II” ¼ Model 0 further adjusted for a dysphoric episode at baseline; “ Model III” ¼ Multiple regression with all
predictor variables included in the same model, adjusted for age and sex.
n
p -value o 0.05.
nn
p-value o 0.01.
nnn
p-value o 0.0 01.
T. Rosenström et al. / Journal of Af fective Disorders 158 (2014) 139 – 14 7 14 3
score at baseline. All personality-trait coeffi cients were at least
partially attenuated by adjusting for baseline depression score,
particularly so for Cooperativeness.
The regression-coeffi cient values in the Table 2 imply, for
example, that a one standard deviation lower Self-directedness
predicted e
0.65
¼ 1.92 times higher rate of‘ dysphoric-episode case-ness ’ for the following fi f teen years compared to the population
average (Gelman and Hill, 20 07), (linearly) controlling for the
present state of the other traits and depressive symptoms. When
not considering other covariates, observing the same one standard
deviation lower Self-directedness translated to 7.61-fold rate of
dysphoric episodes compared to average-population base rate.
Current Self-directedness alone was better at predicting the future
number of dysphoric episodes than the sum of current depressive
symptoms alone (4.53-fold rate for 1 s.d. higher depression score
compared to base rate). The effects of Self-directedness and
depressive-symptom counts on number of future episodes were
only slightly overlapping (Model I in Table 2); that is, complemen-tary rather than redundant.
In addition to predicting the number of dysphoric episodes, one
may ask how much variance in a single future time-point's
depressive-symptom score (mBDI) the baseline personality traits
linearly explain, and how much this adds over the baseline
symptom score? The score af ter four years from the baseline was
examined (i.e., in the 20 01 follow-up). The seven baseline person-ality traits explained 35.1% of the later symptom score, adding
considerably to the explained variance achieved by sex and age/
cohort effects alone ( Δ R
2
¼ 34.7%, F
7, 7 3 6 ¼ 58.55, p o 0.0 01), but
only little compared to that achieved by sex, age/cohort, and
baseline mBDI (Δ R
2
¼ 1.6%, F
7,736 ¼ 4.13,p o 0.0 01). Baseline mBDI
alone explained 45.5% of the variance in the mBDI measured four
years later. Hence, the information in baseline mBDI and person-ality traits was redundant rather than complementary when
predicting mBDI-values at single future time point.
An online supplementary sensitivity analysis presents results
for imputed data, and for an alternative model to quasi-Poisson
regression that cannot be biased by the ceiling effect on caseness.
Both the sensitivity analyses provided qualitatively corresponding
results, indicating that the non-imputed YFS analyses presented
herein were reliable, perhaps conservative, estimates.
3.2. Main results for clinical populations
Table 3 shows regression coef ficients in the mood disorder
patients for the baseline variables predicting proportion of time as
depressed during the follow-up (Fig. 1 C and D), or its Logit
transformation. The Logit transformation allows for using ordinary
least squares regression, but applying the Infl ated Beta Regression
model allows for also using the participants with zero or unit
proportions (circles inFig. 1C and D). Results for both models are
shown in the Table 3. The ordinary linear regression model with
Logit-transformed outcome variable provided highly similar
results compared to In fl ated Beta regression models. Continuous
baseline depression scores explained 9 –11% of variance in the
accumulated time as depressed during the subsequent 5 or
1.5 years. Personality showed predictive value in JoBS data, but
not in PC-VDS. In the ordinary linear model in PC-VDS, there was
only a slight chance for detecting small effects for individual
personality traits, however [statistical power was 21.1% for small
(f
2
¼ 0.02) effect]; for large effects the power was adequate [99.8%
for f
2
¼ 0.35 ( Cohen, 1988 )].
In the linear model predicting the depression-time proportion in
JoBS data, baseline Persistence explained 7.5% of variance; most of it
(7.2%) non-overlapping with baseline depression score that
explained 11.0% by itself and 10.6% adjusting for persistence. Base-line Persistence and baseline BDI did not correlate signi ficantly
(r ¼0.02,p ¼ 0.792). Together baseline Persistence and BDI explai-ned 18.2% of variance in the proportion of future time as depressed.
This result was specific to m ajor d ep res sive ep is od es , a s we ver i fied
that Persistence alone did not significantly predict accumulated
ma ni a (β ¼0.20, s.e. ¼ 0.17, p ¼ 0.241 in Inflated Beta regression;
JoBS included analogous life-chart data on manic episodes).
4. Discussion
In this study we examined whether personality traits, defined by
the Cloninger's Psychobiological Model of Personality ( Cloninger,
1987; Cloninger et al., 1993 ), predicted the future number of
dysphoric episodes in a general population, and whether the same
traits predicted the amount of future time a person suffered from
major depressive episode given a current diagnosis of mood dis-order (unipolar and/or bipolar). In the general population, person-ality was better at predicting accumulated dysphoria (number of
future episodes) than at predicting depression score values at a
single future time point. For example, one standard deviation lower
current Self-directedness led to 7.61-fold rate of future dysphoric
episodes across 15 years compared to base rate, whereas one
standard deviation higher depression score implied only 4.53-fold
rate; in contrast, all current personality traits together predicted
only 35.1% of the depression-score variance four years later, whereas
the current depression score predicted 45.5%. This observation
is plausible because past depression scores certainly assess a
similar construct to present depression score, whereas personality
traits are more temporally stable than depression scores ( Cloninger
et al., 20 06), thereby exerting their potential effects in a more
prolonged manner. In addition to the strongest predictor, low Self-directedness, also high Harm avoidance predicted elevated rates of
future dysphoria; low Cooperativeness was predictive, but not
significantly so after accounting for the baseline depressive symp-to m s . T he s e findings con firm and extend prior work showing that
the risk of depression is predicted by high Harm Avoidance, low
Self-directedness, and low Persistence, variables that interact in the
modulation of a brain circuit that regulates mood and reward-seeking behavior in the general population (Cloninger et al., 2012;
Elye et al., 2013).
In primary-care depression patients, current personality was
not particularly informative about future prognosis. For Bipolar
patients, however, the baseline level of the trait Persistence
predicted 7.5% of the variance in the future accumulated major
depressive episodes up to 18 months. This was close to predictive
value of baseline depressive symptoms (11.0%), and mostly inde-pendent information with respect to the baseline depressive
symptoms (a similar effect on mania was not observed). Our
finding about the importance of low Persistence in Bipolar patients
extends earlier observations that Persistence is of ten low in
Bipolar patients, even when they are in full remission ( Osher
et al., 1996, 1999 ).
Our fi ndings need to be evaluated in light of the methodology
of the study. The major strengths of the study were prospective
design, outcome measures related to temporal durations of illness
states, and a comprehensive picture drawn from three hetero-geneous samples. The general-population sample was followed for
15 years; comparable follow-up times with the studied variables
are rare or non-existent. The clinical screening-based representa-tive cohorts, diagnosed using SCID-I/P interviews with excellent
reliability, were also followed for 1.5 or 5 years. In the clinical
samples, the life-chart methodology allowed measures related to
temporal durations of illness states. In the general-population
sample, predictors for deviations from population-average rate of
illness-like states were studied using 751 four-sample time series
T. Rosenström et al. / Journal of Af fective Disorders 158 (2014) 139 – 14 7 14 4
and statistical models (the plural is due to supplementary analyses
available on-line).
The most important limitations include sample attrition, some
degree of recall bias (likely underestimates of psychopathology in
the life charts), and some limitations of statistical power in clinical
samples. In the YFS data that had the largest attrition, supple-mentary on-line imputation analysis was provided, and did not
suggest major changes to results. Such imputations are never
perfect, however, and some degree of regression-coef fi cient in fla-tion due to association between depressive symptoms and study
attrition (e.g., Rosenström et al., 2012b) is possible. In contrast, the
ceiling of three observed episodes and sensitivity-to-specifi city
imbalance in episode detection may have attenuated rather than
in fl ated the coefficients, promoting conservative estimates.
Our results suggested that some personality traits (especially
low Self-directedness) predispose one for higher future rate of
dysphoric episodes compared to base rate in the general popula-tion. In line with present fi ndings, a previous categorical analysis
with a baseline and one follow-up measurement suggested that
low Self-directedness and Cooperativeness index ones vulnerabil-ity to future depressive episodes ( Farmer and Seeley, 20 09). That
study also found a similar role for low Reward Dependence as well,
which was not strongly implicated here. Present study is much
stronger in assessing vulnerability to future episodes, however, as
it in cluded three follow- ups in stead of j ust a s ingle one. Present a nd
prev ious s tudies are generally congruent with p redict ive ro l e o f b ot h
low S elf-directednes s ( Cloninge r e t a l., 20 0 6; Farmer and S eeley,
20 09; Naito e t a l., 20 0 0) a nd h ig h Ha r m avo i da nc e ( Cloninger et al.,
20 0 6; Farmer and S eeley, 2 0 0 9 ) for future depression.
Teasing apart the“precursor” (shared or similar etiology) and
“predisposition” (personality predicts depression onset with other
variables mediating/moderating) models for the effects of person-ality on subsequent depression is a diffi cult task (Klein et al., 2011).
The present results support the predisposition model rather than
the precursor model, because in the shared-etiology case there
should be no qualitative dissociation between personality- and
depression-based predictions for future point-estimates of depres-sion versus estimates for future accumulation. The predisposing
role of personality traits for risk of depression has also been well-documented in a study of never-ill siblings of depressives: never-depressed siblings of depressives are intermediate between cases
and controls for Self-directedness and Harm Avoidance, indicating
that these traits in fl uence the predisposition to major depression
(Farmer et al., 20 03 ). Similar evidence regarding other disorders is
summarized elsewhere (Cloninger et al., 2010 ).
While importance of prevention efforts for depression has been
recently emphasized (Ghaemi et al., 2013 ), expenses of prevention
can of ten be effectively carried out mainly for well-de fi ned at-risk
Table 3
Regression models predicting accumulated DSM-IV major depressive episodes in diagnosed patients with baseline personality and depression.
PC-VDS (5 yr follow) Variable Model 0 M odel I Model III ΔR
2
ΔR
2
MI
Logitþ linear, n ¼ 71 BDI 0.34 (0.12)
nn
– 0.32 (0.14)
n
0.09 –
Novelty seeking  0.11 (0.12)  0.0 6 (0.12)  0.0 6 (0.14) 0.0 0  0.01
Harm avoidance 0.18 (0.12) 0.11 (0.12) 0.13 (0.17) 0.02 0.0 0
Reward dependence 0.05 (0.13) 0.11 (0.12) 0.10 (0.15)  0.01 0.0 0
Persistence 0.0 0 (0.12)  0.03 (0.12) 0.02 (0.14)  0.02  0.01
Self-directedness  0.12 (0.12)  0.03 (0.11)  0.01 (0.17) 0.0 0  0.01
Cooperativeness 0.05 (0.12) 0.09 (0.12) 0.07 (0.15)  0.01  0.01
Self-transcendence 0.01 (0.12) 0.02 (0.12) 0.0 4 (0.13)  0.02  0.01
In fl ated Beta, n ¼ 93 BDI 0.40 (0.15)
nn
– 0.39 (0.16)
n
–– Novelty seeking  0.11 (0.13)  0.07 (0.13)  0.07 (0.14) –– Harm avoidance 0.19 (0.15) 0.12 (0.14) 0.17 (0.20) –– Reward dependence 0.0 6 (0.14) 0.14 (0.14) 0.14 (0.17) –– Persistence 0.01 (0.14)  0.02 (0.14) 0.03 (0.16) –– Self-directedness  0.13 (0.14)  0.02 (0.14) 0.0 0 (0.19) –– Cooperativeness 0.05 (0.14) 0.09 (0.14) 0.05 (0.16) –– Self-transcendence 0.01 (0.15) 0.0 4 (0.15) 0.0 6 (0.16) ––
JoBS (1.5 yr follow)
Logitþ linear, n ¼ 11 8 BDI 0.38 (0.10)
nnn
– 0.35 (0.11)
nn
0.11 –
Novelty seeking  0.10 (0.09)  0.08 (0.09)  0.13 (0.10) 0.0 0 0.0 0
Harm avoidance 0.31 (0.09)
nnn
0.19 (0.10)  0.03 (0.13) 0.09 0.02
Reward dependence  0.11 (0.09)  0.05 (0.09) 0.03 (0.11) 0.0 0  0.01
Persistence  0.28 (0.09)
nn
 0.27 (0.09)
nn
 0.26 (0.09)
nn
0.08 0.07
Self-directedness  0.21 (0.09)
n
 0.12 (0.09)  0.10 (0.11) 0.0 4 0.01
Cooperativeness  0.11 (0.09)  0.10 (0.09)  0.10 (0.12) 0.0 0 0.0 0
Self-transcendence  0.01 (0.09)  0.01 (0.09) 0.03 (0.09)  0.01  0.01
In fl ated Beta, n ¼ 151 BDI 0.40 (0.11)
nnn
– 0.39 (0.12)
nn
–– Novelty seeking  0.11 (0.10)  0.10 (0.10)  0.14 (0.11) –– Harm avoidance 0.31 (0.10)
nn
0.19 (0.11)  0.0 4 (0.14) –– Reward dependence  0.11 (0.10)  0.05 (0.10) 0.02 (0.12) –– Persistence  0.31 (0.10)
nn
 0.30 (0.10)
nn
 0.29 (0.10)
nn
–– Self-directedness  0.25 (0.11)
n
 0.15 (0.11)  0.12 (0.14) –– Cooperativeness  0.11 (0.10)  0.10 (0.10)  0.12 (0.13) –– Self-transcendence  0.01 (0.10)  0.02 (0.10) 0.0 4 (0.11) ––
Note: Standard errors in parentheses, hyphens indicate impossible computations.
n ¼ available sample size; “ BDI ” ¼ Beck's Depression Inventory score; “ Logitþ linear ” ¼ ordinary regression applied af ter Logit transformation to non-infi nite transformed
values; “ In fl ated Beta” ¼ In fl ated Beta Regression applied to all observed proportions/participants; “ PC-VDS” ¼ Vantaa Primary Care Depression Study;“ JoBS ” ¼ Jorvi Bipolar
Study; “ Model 0” ¼ regression coefficients for model with only age and sex as covariates; “ Model I” ¼ Model 0 further adjusted for baseline BDI;“ Model III” ¼ Multiple
regression with all predictor variables included in the same model, adjusted for age and sex; “ ΔR
2
” ¼ change in adjusted R
2
due to adding the individual-effect predictor to
regression with age and sex; “ ΔR
2
MI
” ¼ the contribution of the predictor to Model I.
n
p-value o 0.05.
nn
p-value o 0.01.
nnn
p -value o 0.0 01.
T. Rosenström et al. / Journal of Af fective Disorders 158 (2014) 139 – 14 7 14 5
groups rather than for entire populations, or for indicated/sub-threshold cases where “ treatment ” might be a more adequate
term ( Clarke et al., 1995; Lewinsohn et al., 1998; Klein et al., 2011).
The support that present study gives for “the predisposition ”
rather than “ the precursor” model underlines the fact that person-ality is not an equivalent of depression, but is potentially use-ful in defi ning at-risk groups. Also, personality traits like Self-directedness can be modi fi ed by cognitive-behavioral therapy, and
hence directly targeted in an intervention (Anderson et al., 20 02 ;
Cloninger, 20 0 6). Hence, personality is an attractive candidate for
detection of at-risk groups, because it is more stable than depres-sive episodes but can be altered ( Klein et al., 2011 ).
In addition to single traits, combinations of personality traits
(personality profi les) may offer ef fi cient future predictors
(Josefsson et al., 2011; Rosenström et al., 2012a ), but their deriva-tion is subject to analytical dif ficulties commonly known as the
“curse of dimensionality ” (Hastie et al., 20 09; Wasserman, 20 0 6).
The ensuing problem is reminiscent of the statistical challenges in
molecular genetics, where either a huge number of observations
or solid a priori functional information is often needed. Never-theless, progress is being made towards detection of dynamic
interactions among multiple variables that in fl uence the develop-ment of complex phenotypes like mood disorders and schizo-phrenic psychoses (Arnedo et al., 2013 ).
While pastfi ndings regarding genetic associations and cross-sectional factor loadings have suggested that some traits in the
Psychobiological theory of personality might not represent sepa-rate entities, recent longitudinal research has demonstrated that
these traits do have different developmental trajectories (Josefsson
et al., 2013). Speci fi cally regarding the two traits associated with
depression, Harm avoidance and Self-directedness, the former
shows no mean-level changes as a function of age while the latter
grows by age (Josefsson et al., 2013). In addition, twin studies
show that the genetic determinants of each of the TCI dimensions
are largely independent ( Gillespie et al., 20 03). Hence any correla-tions observed among the dimensions could be associations
produced by self-organization during the development as a com-plex adaptive system (Cramer et al., 2012b; Cloninger et al., 1997 ;
Rosenström et al., 2012a; van der Maas et al., 20 0 6). Suchfi ndings
call into question the adequacy of describing personality in terms
of traits identifi ed by linear factor analysis ( Cloninger, 20 08;
Cervone, 20 05 ), but modern personality inventories actually over-lap extensively in their information content and predictive validity
despite these theoretical differences (Grucza and Goldberg, 20 07).
Debate is going on regarding the true nature and origins of both
individual differences in behavior in general ( Cramer et al., 2012b;
Brown et al., 2011; Buss, 20 09) and depression specifi cally ( Cramer
et al., 2012a; Hagen, 2011; Rosenström, 2013a), and we do not
imply having used a fl awless model of personality; just that these
personality variables have been shown to contain information
about other constructs of interest in psychiatry and other fi elds
(e.g., Cloninger et al., 2010; Grucza and Goldberg, 20 07; Määttänen
et al., 2013; Svrakic et al., 20 02 ), and are of interest due to their
predictive value. Hence, our present contribution is not a theore-tical one, but provides empirical facts to be explained by future
theory, and possible prognostic tools.
In summary, personality traits were found to be strong pre-dictors for future accumulation of dysphoric episodes in a general-population sample, but weak predictors of future accumulation of
depressive episodes in primary-care Depression patients. In the
general population, low Self-directedness was the strongest pre-dictor of future burden of dysphoric episodes. In the clinical
sample of Bipolar-disorder patients, low Persistence was a strong
predictor of future depressive-episode burden. Persistence was
also predictive independently of the baseline level of depression,
providing prognostic value. Overall, measures of personality (TCI
main traits) appeared more useful in detecting risk for future
burden of depression than in clinical prediction of future DSM-IV
depressive-episode burden in diagnosed cases of unipolar or
bipolar mood disorder.
Role of funding source
No special agreements or policies for any of the involved authors or data
projects. The sponsors of the study (mentioned in acknowledgments) did not have
a role in writing of the manuscript, or in decision to publish.
Con fl ict of interest
No confl ict of interest exists for any of the involved authors or data projects.
Acknowledgments
This work was fi nancially supported by the Academy of Finland (L.K.J., Grant no.
258711; M.H., Grant no. 258578; and Grants for JoBS and PC-VDS); the Department
of Psychiatry at Helsinki University Central Hospital (JoBS and PC-VDS); Signe and
Ane Gyllenberg Foundation (L.K.J. and M.H.); Alli Paasikivi Foundation (M.H.); Emil
Aaltonen Foundation (M.H.); and the Juho Vainio Foundation (L.P.R.).
Appendix A. Supplementary material
Supplementary data associated with this article can be found in
the online version at http://dx.doi.org/10.1016/j.jad.2014.01.017.
References
Anderson, C.B., Joyce, P.R., Carter, F.A ., McIntosh, V.V., Bulik, C.M., 20 02. The effect of
cognitive-behavioral therapy for bulimia nervosa on temperament and char-acter as measured by the temperament and character inventory. Compr.
Psychiatry 43, 182– 18 8 .
Arnedo, F.J., del Val, C., Erausquin, G.A ., Romero-Zaliz, R., Svrakic, D.M., Cloninger, C.
R., 2013. A web server for (Phenotype x Genotype) many-to-many relation
analysis in GWAS. Nucleic Acids Res. 41, W142– W14 9.
Beck, A .T., Steer, R.A ., 1993. Manual for the Beck Depression Inventory. Psycholo-gical Corporation, San Antonio.
Beck, A .T., Steer, R.A ., Ball, R., Ranieri, W.F., 1996. Comparison of Beck's Depression
Inventories-IA and -II in psychiatric outpatients. J. Personal. Assess. 67,
588 – 597 .
Beck, A .T., Steer, R.A ., Brown, G.K., 20 0 4. BDI-II – Beck Depression Inventory
s
-II:
Finnish Standardization. Psychological Corporation and Psykologien Kustannus
Oy, Helsinki.
Brown, G.R., Dickins, T.E., Sear, R., Laland, K.N., 2011. Evolutionary accounts of
human behavioural diversity. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 366,
313 – 324 .
Buss, D.M., 20 09. How can evolutionary psychology successfully explain personality
and individual differences? Perspect. Psychol. Sci. 4, 359 – 36 6 .
Cervone, D., 20 05. Personality architecture: within-person structures and pro-cesses. Annu. Rev. Psychol. 56, 423– 452 .
Clarke, G.N., Hawkins, W., M urphy, M., Sheeber, L.B., Lewinsohn, P.M., Seeley, J.R.,
1995. Targeted prevention of unipolar depressive disorder in an at-risk sample
of high school adolescents: a randomized trial of group cognitive intervention.
J. Am. Acad. Child Adolesc. Psychiatry 34, 312 – 321 .
Cloninger, C.R., 1987. A systematic method for clinical description and classifi cation
of personality variants. A proposal. Arch. Gen. Psychiatry 4 4, 573 – 588 .
Cloninger, C.R., 20 06. The science of well-being: an integrated approach to mental
health and its disorders. World Psychiatry 5, 71 – 76.
Cloninger, C.R., 20 08. The psychobiological theory of temperament and character:
comment on Farmer and Goldberg (20 0. Psychol. Assess. 20, 292 – 299 .
Cloninger, C.R., Przybeck, T.R., Svrakic, D.M., Wetzel, R.D., 1994. The Temperament
and Character Inventory (TCI): a guide to its development and use, Center for
Psychobiology of Personality. Washington University, St. Louis.
Cloninger, C.R., Svrakic, N.M., Svrakic, D.M., 1997. Role of personality self-organiza-tion in development of mental order and disorder. Dev. Psychopathol. 9,
881 – 906 .
Cloninger, C.R., Svrakic, D.M., Przybeck, T.R., 1993. A psychobiological model of
temperament and character. Arch. Gen. Psychiatry 50, 975 – 990 .
Cloninger, C.R., Svrakic, D.M., Przybeck, T.R., 20 06. Can personality assessment
predict future depression? A twelve-month follow-up of 631 subjects. J. Affect.
Disord. 92, 35– 44.
Cloninger, C.R., Zohar, A .H., Cloninger, K.M., 2010. Promotion of well-being in
person-centered mental health care. Focus 8, 165 – 17 9 .
Cloninger, C.R., Zohar, A .H., Hirschman, S., Dahan, D., 2012. The psychological costs
and benefi ts of being highly persistent: personality pro fi les distinguish mood
disorders from anxiety disorders. J. Affect. Disord. 136, 758– 76 6 .
Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.
Lawrence Erlbaum Associates, New Jersey.
T. Rosenström et al. / Journal of Af fective Disorders 158 (2014) 139 – 14 7 14 6
Cramer, A .O.J., Borsboom, D., Aggen, S.H., Kendler, K.S., 2012a. The pathoplasticity of
dysphoric episodes: differential impact of stressful life events on the pattern of
depressive symptom inter-correlations. Psychol. Med. 42, 957– 965 .
Cramer, A .O.J., van der Sluis, S., Noordhof, A ., W hichers, M., Geschwind, N., Aggen, S.
H., Kendler, K.S., Bosboom, D., 2012b. Dimensions of normal personality as
network s in search of equilibrium: you can’ t like parties if you don ’ t like
people. Eur. J. Personal. 26, 414– 431 .
Elovainio, M., Kivimäki, M., Puttonen, S., Heponiemi, T., Pulkki, L., Keltikangas-Järvinen, L., 20 0 4. Temperament and depressive symptoms: a population-based
longitudinal study on Cloninger's psychobiological temperament model. J.
Affect. Disord. 83, 227232 .
First, M.B., Spitzer, R.L., Gibbon, M., W illiams, J.B.W., 20 02. Structured Clinical
Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition
With Psychotic Screen (SCID-I/P W/PSY SCREEN). Biometrics Research, New
York State Psychiatric Institute: New York.
Farmer, A ., Mahmood, A ., Redman, K., Harris, T., Sadler, S., McGuffi n, P., 20 03. A sib-pair study of the Temperament and Character Inventory scales in major
depression. Arch. Gen. Psychiatry 60, 4 90– 496 .
Farmer, F.F., Seeley, J.R., 20 09. Temperament and character predictors of depressed
mood over a 4-year interval. Depress. Anxiety 26, 371– 381 .
Gelman, A ., Hill, J., 20 07. Data Analysis Using Regression and Multilevel/Hierarch-ical Models. Cambridge University Press, New York.
Ghaemi, S.N., Vöhringer, P.A ., W hitham, E.A., 2013. Antidepressants from a public
health perspective: re-examining effectiveness, suicide, and carcinogenicity.
Acta Psychiatr. Scand. 127, 89– 93.
Gillespie, N.A ., Cloninger, C.R., Heath, A .C., Martin, N.G., 20 03. The genetic and
environmental relationship between Cloninger's dimensions of temperament
and character. Personal. Individ. Differ. 35, 1931– 1946.
Grucza, R.A ., Goldberg, L.R., 20 07. The comparative validity of 11 modern person-ality inventories: predictions of behavioral acts, informant reports, and clinical
indicators. J. Personal. Assess. 89, 167– 18 7 .
Gusnard, D.A ., Ollinger, J.M., Schulman, G.L., Cloninger, C.R., Price, J.L., van Essen, D.
C., Raichle, M.E., 20 03. Persistence and brain circuitry. Proc. Natl. Acad. Sci. USA
10 0, 3479– 3484.
Hagen, E.H., 2011. Evolutionary theories of depression: a critical review. Can. J.
Psychiatry 56, 716– 72 6 .
Hardeveld, F., Spijker, J., De Graaf, R., Hendrik s, S.M., Licht, C.M.M., Nolen, W.A .,
Penninx, B.W.J.H., Beekman, A .T.F., 2013. Recurrence of major depressive
disorder across different treatment settings: results from the NESDA study. J.
Affect. Disord. 147, 225 – 231 .
Haslam, N., Holland, E., Kuppens, P., 2012. Categories versus dimensions in
personality and psychopathology: a quantitative review of taxometric research.
Psychol. Med. 42, 903– 920 .
Hastie, T., Tibshirani, R., Friedman, J., 20 09. The Elements of Statistical Learning:
Data Mining, Inference, and Prediction, 2nd ed. Springer-Verlag, New York.
Holma, K.M., Holma, I.A .K., Melartin, T.K., Rytsälä, H.J., Isometsä, E.T., 20 08. Long-term outcome of Major Depressive Disorder in psychiatric patients is variable. J.
Clin. Psychiatry 69, 196 – 205 .
Jokela, M., Singh-Manoux, A ., Shipley, M.J., Ferrie, J.E., Gimeno, D., Akbaraly, T.N.,
Head, J., Elovainio, M., Marmot, M.G., Kivimäki, M., 2011. Natural course of
recurrent psychological distress in adulthood. J. Affect. Disord. 130, 454 – 461 .
Josefsson, K., Merjonen, P., Jokela, M., Pulkki-Råback, L., Keltikangas-Järvinen, L.,
2011. Personality profi les identify depressive symptoms over ten years? A
population-based study. Depress. Res. Treat. 2011, 1 – 11.
Josefsson, K., Jokela, M., Cloninger, C.R., Hintsanen, M., Salo, J., Hintsa, T., Pulkki-Råback, L., Keltikangas-Järvinen, L., 2013. M aturity and change in personality:
developmental trends of temperament and character in adulthood. Dev.
Psychopathol. 25, 713 – 72 7 .
Jylhä, P., Mantere, O., Melartin, T., Suominen, K., Vuorilehto, M., Arvilommi, P.,
Holma, I., Holma, M., Leppämäki, S., Valtonen, H., Rytsälä, H., Isometsä, E., 2011.
Differences in temperament and character dimensions in patients with bipolar I
or II or major depressive disorder and general population subjects. Psychol.
Med. 41, 1579– 15 9 1.
Karsten, J., Hartman, C.A ., Ormel, J., Nolen, W.A ., Penninx, B.W.J.H., 2010. Subthres-hold depression based on functional impairment better defi ned by symptom
severity than by number of DSM-IV symptoms. J. Affect. Disord. 123, 230 – 237 .
Klein, D.N., Kotov, R., Bufferd, S.J., 2011. Personality and depression: explanatory
models and review of evidence. Ann. Rev. Clin. Psychol. 7, 269– 295 .
Lewinsohn, P.M., Rohde, P., Seeley, J.R., 1998. Major depressive disorder in older
adolescents: prevalence, risk factors, and clinical implications. Clin. Psychol.
Re v. 18 , 76 5– 794 .
Luty, S.E., Joyce, P.R., Mulder, R.T., Sullivan, P.F., McKnezie, J.M., 1999. The relation-ship of dysfunctional attitudes to personality in depressed patients. J. Affect.
Disord. 54, 75– 80.
Mantere, O., Suominen, K., Leppämäki, S., Valtonen, H., Arvilommi, P., Isometsä, E.,
20 04. The clinical characteristics of DSM-IV bipolar I and II disorders: baseline
fi ndings from the Jorvi Bipolar Study (JoBS). Bipolar Disord. 6, 395 – 405 .
Mantere, O., Suominen, K., Valtonen, H.M., Arvilommi, P., Leppämäki, S., Melartin,
T., Isometsä, E., 20 08. Differences in outcome of DSM-IV bipolar I and II
disorders. Bipolar Disord. 10, 413– 425 .
Melartin, T.K., Rytsälä, H.J., Leskelä, U.S., Lestelä-Mielonen, P.S., Sokero, T.P.,
Isometsä, E.T., 20 0 4. Severity and comorbidity predict episode duration and
recurrence of DSM-IV major depressive disorder. J. Clin. Psychiatry 65, 810 – 819 .
Määttänen, I., Jokela, M., Hintsa, T., Fitser, S., Kähönen, M., Jula, A ., Raitakari, O.T.,
Keltikangas-Järvinen, L., 2013. Testosterone and temperament traits in men:
longitudinal analysis. Psychoneuroendocrinology 38, 2243 – 2248.
Naito, M., Kijima, N., Kitamura, T., 20 0 0. Temperament and character inventory
(TCI) as predictors of depression among Japanese college students. J. Clin.
Psychol. 56, 1579 – 1585.
Osher, Y., Cloninger, C.R., Belmaker, R.H., 1996. TPQ in euthymic manic-depressive
patients. J. Psychiatr. Res. 30, 353– 357 .
Osher, Y., Lefkifker, E., Kotler, M., 1999. Low persistence in euthymic manic-depressive patients: a replication. J. Affect. Disord. 53, 87– 90.
Ospina, R., Ferrari, S.L.P., 2010. Infl ated Beta distributions. Stat. Pap. 51, 111 – 12 6 .
Otani, K., Suzuki, A ., M atsumoto, Y., Shibuya, N., Sadahiro, R., Enokido, M., Kamata,
M., 2013. Relationship of the 24-item dysfunctional attitude scale with the
temperament and character inventory in healthy subjects. Nord. J. Psychiatry
67, 388– 392 .
Raitakari, O.T., Juonala, M., Rönnemaa, T., Keltikangas-Järvinen, L., Räsänen, L.,
Pietikäinen, M., Hutri-Kähönen, N., Taittonen, L., Jokinen, E., Marniemi, J., Jula,
A ., Telama, R., Kähönen, M., Lehtimäki, T., Åkerblom, H.K., Viikari, J.S.A., 20 08.
Cohort profi le: the cardiovascular risk in Young Finns study. Int. J. Epidemiol.
37, 1220 – 1226.
R. Core Team, 2012. R: A Language and Environment for Statistical Computing. R
Foundation for Statistical Computing: Vienna, Austria. Retrieved from: 〈 http://
www.R-project.org〉
Richter, J., Eisemann, M., 20 02. Self-directedness as a cognitive feature in depres-sive patients. Personal. Individ. Differ. 32, 1327 – 13 3 7.
Riihimäki, K.A. , Vuorilehto, M.S., Melartin, T.K., Isometsä, E.T., 2011. Five-year
outcome of major depressive disorder in primary health care. Psychol. Med.
1 – 11, E-pub ahead of print , htt p ://dx.doi.org/10.1017/S0 0332917110 02303
Rohde, P., Lewinsohn, P.M., Tilson, M., Seeley, J.R., 1990. Dimensionality of coping
and its relation to depression. J. Personal. Soc. Psychol. 58, 499– 511 .
Rosenström, T., 2013a. Bargaining models of depression and evolution of coopera-tion. J. Theor. Biol. 331, 54– 65.
Rosenström, T., 2013b. Temporal and Population Dynamics of Depressive Symp-toms: Empirical and Modeling Approaches. University of Helsinki, Faculty of
Behavioural Sciences, Institute of Behavioural Sciences, Studies in Psychology,
95. Available from: 〈 http://urn. fi /URN:ISBN:978-952-10-9339-5〉 (last retrieved
17th Jan 2014).
Rosenström, T., Hintsanen, M., Jokela, M., Cloninger, C.R., Juonala, M., Raitakari, O.T.,
Viikari, J., Keltikangas-Jarvinen, L., 2012a. Associations between dimensional
personality measures and preclinical atherosclerosis: the Cardiovascular Risk in
Young Finns study. J. Psychosom. Res. 72, 336– 343 .
Rosenström, T., Jokela, M., Puttonen, S., Hintsanen, M., Pulkki-Råback, L., Viikari, J.S.,
Raitakari, O., Keltikangas-Järvinen, L., 2012b. Pairwise measures of causal
direction in the epidemiology of sleep problems and depression. PLoS One 7,
e50841.
Rosenström, T., Jokela, M., Hintsanen, M., Josefsson, K., Juonala, M., Kivimäki, M.,
Pulkki-Råback, L., Viikari, J.S.A., Hutri-Kähönen, N., Heinonen, E., Raitakari, O.T.,
Keltikangas-Järvinen, L., 2013. Body-image dissatisfaction is strongly associated
with chronic dysphoria. J. Affect. Disord. 150, 253 – 260 .
Spitzer, R.L., Williams, J.B.W., Kroenke, K., Linzer, M., de Gruy, F.V., Hahn, S.R., Brody,
D., Johnson, J.G, 1994. Utility of a new procedure for diagnosing mental
disorders in primary care: The PRIME-MD 10 0 0 study. JAMA 272, 1749 – 1756.
Stasinopoulos, M.D., Rigby, R.A ., 20 07. Generalized additive models for location
scale and shape (GAML SS). R. J. Stat. Softw. 23, 1 – 46.
Svrakic, D.M., Draganic, S., Hill, K., Bayon, C., Przybeck, T.R., Cloninger, C.R., 20 02.
Temperament, character, and personality disorders: etiologic, diagnostic, treat-ment issues. Acta Psychiatr. Scand. 10 6, 189– 19 5 .
van der Maas, H.L.J., Dolan, C.V., Grasman, R.P.P.P., Wicherts, J.W., H