Temperament Clusters in a Normal Population: Implications for Health and Disease

Article · July 2012with48 Reads
DOI: 10.1371/journal.pone.0033088 · Source: PubMed
Abstract
The object of this study was to identify temperament patterns in the Finnish population, and to determine the relationship between these profiles and life habits, socioeconomic status, and health. A cluster analysis of the Temperament and Character Inventory subscales was performed on 3,761 individuals from the Northern Finland Birth Cohort 1966 and replicated on 2,097 individuals from the Cardiovascular Risk in Young Finns study. Clusters were formed using the k-means method and their relationship with 115 variables from the areas of life habits, socioeconomic status and health was examined. Four clusters were identified for both genders. Individuals from Cluster I are characterized by high persistence, low extravagance and disorderliness. They have healthy life habits, and lowest scores in most of the measures for psychiatric disorders. Cluster II individuals are characterized by low harm avoidance and high novelty seeking. They report the best physical capacity and highest level of income, but also high rate of divorce, smoking, and alcohol consumption. Individuals from Cluster III are not characterized by any extreme characteristic. Individuals from Cluster IV are characterized by high levels of harm avoidance, low levels of exploratory excitability and attachment, and score the lowest in most measures of health and well-being. This study shows that the temperament subscales do not distribute randomly but have an endogenous structure, and that these patterns have strong associations to health, life events, and well-being.
9 Figures
Temperament Clusters in a Normal Population:
Implications for Health and Disease
Jaana Wessman
1,2
, Stefan Scho
¨nauer
1
, Jouko Miettunen
17
, Hannu Turunen
2
, Pekka Parviainen
1
,
Jouni K. Seppa
¨nen
3
, Eliza Congdon
4
, Susan Service
4
, Markku Koiranen
5
, Jesper Ekelund
2,6
,
Jaana Laitinen
7
, Anja Taanila
5,8
, Tuija Tammelin
7,9
, Mirka Hintsanen
10
, Laura Pulkki-Ra
˚back
10
,
Liisa Keltikangas-Ja
¨rvinen
10
, Jorma Viikari
11
, Olli T. Raitakari
12
, Matti Joukamaa
13
, Marjo-
Riitta Ja
¨rvelin
14
, Nelson Freimer
4
, Leena Peltonen
2,15,16{
, Juha Veijola
17
, Heikki Mannila
3
, Tiina Paunio
2,6
*
1Helsinki Institute for Information Technology and Department of Computer Science, University of Helsinki, Helsinki, Finland, 2Institute for Molecular Medicine Finland
and National Institute for Health and Welfare, Helsinki, Finland, 3Helsinki Institute for Information Technology and Department of Information and Computer Science,
Aalto University, Espoo, Finland, 4University of California Los Angeles Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, California,
United States of America, 5Institute of Health Sciences, University of Oulu, Oulu, Finland, 6Department of Psychiatry, Helsinki University Central Hospital, Helsinki,
Finland, 7Finnish Institute of Occupational Health, Helsinki, Finland, 8Unit of General Practice, University Hospital of Oulu, Oulu, Finland, 9LIKES Research Center for
Sport and Health Sciences, Jyva
¨skyla
¨, Finland, 10 Department of Psychology, University of Helsinki, Helsinki, Finland, 11 Department of Medicine, Turku University Central
Hospital and University of Turku, Turku, Finland, 12 Department of Clinical Physiology, Turku University Central Hospital, and Research Center of Applied and Preventive
Cardiovascular Medicine, University of Turku, Turku, Finland, 13 Tampere School of Public Health, University of Tampere, and Psychiatric Department, Tampere University
Hospital, Tampere, Finland, 14 Department of Epidemiology and Public Health, Imperial College, London, United Kingdom and Department of Public Health Science and
General Practice, University of Oulu, Oulu, Finland, 15 Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom, 16 Department of Medical Genetics,
University of Helsinki, Helsinki, Finland, 17 Department of Psychiatry, University of Oulu, Oulu, Finland
Abstract
Background:
The object of this study was to identify temperament patterns in the Finnish population, and to determine the
relationship between these profiles and life habits, socioeconomic status, and health.
Methods/Principal Findings:
A cluster analysis of the Temperament and Character Inventory subscales was performed on
3,761 individuals from the Northern Finland Birth Cohort 1966 and replicated on 2,097 individuals from the Cardiovascular
Risk in Young Finns study. Clusters were formed using the k-means method and their relationship with 115 variables from
the areas of life habits, socioeconomic status and health was examined.
Results:
Four clusters were identified for both genders. Individuals from Cluster I are characterized by high persistence, low
extravagance and disorderliness. They have healthy life habits, and lowest scores in most of the measures for psychiatric
disorders. Cluster II individuals are characterized by low harm avoidance and high novelty seeking. They report the best
physical capacity and highest level of income, but also high rate of divorce, smoking, and alcohol consumption. Individuals
from Cluster III are not characterized by any extreme characteristic. Individuals from Cluster IV are characterized by high
levels of harm avoidance, low levels of exploratory excitability and attachment, and score the lowest in most measures of
health and well-being.
Conclusions:
This study shows that the temperament subscales do not distribute randomly but have an endogenous
structure, and that these patterns have strong associations to health, life events, and well-being.
Citation: Wessman J, Scho
¨nauer S, Miettunen J, Turunen H, Parviainen P, et al. (2012) Temperament Clusters in a Normal Population: Implications for Health and
Disease. PLoS ONE 7(7): e33088. doi:10.1371/journal.pone.0033088
Editor: Petter Holme, Umea
˚University, Sweden
Received September 12, 2011; Accepted February 2, 2012; Published July 18, 2012
Copyright: ß2012 Wessman et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by research grants from the National Institutes of Health (NIH) (NIH Roadmap grants for the Consortium for Neuropsychiatric
Phenomics, UL1-DE019580 and RL1MH083268, for EC, SS, NBF; an NIH/National Institute of Neurological Disorders and Stroke training grant T32 NS048004: NBF,
PI, for EC); the The Brain & Behavior Research Foundation: Brain and Behavior Research Fund (Independent Investigator Award to JM); and a grant from the
Academy of Finland (#125853 for JM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: tiina.paunio@thl.fi
{
Deceased
Introduction
Temperament refers to early-appearing individual differences in
emotional responding and central to its definition is the notion that
temperament is innate [1]. There is now substantial data
supporting the biologically based nature of these individual
differences in emotional experience and regulation, with herita-
bility estimates ranging from 50 to 65% [2,3,4,5]. These stable and
organized patterns of behavioral responses across a range of
PLoS ONE | www.plosone.org 1 July 2012 | Volume 7 | Issue 7 | e33088
contexts are thus believed to form the basis of more complex
psychological structures.
Several models have been proposed for classifying tempera-
ment, including Cloninger’s psychobiological model of four
higher-order temperament dimensions distinguished by their
stimulus-response characteristics [6,7]. The widely used Temper-
ament and Character Inventory (TCI) [7] measures individual
differences along four main temperament dimensions: novelty
seeking (NS), harm avoidance (HA), reward dependence (RD) and
persistence (P). NS is a tendency to respond with intense
excitement to novel stimuli, or cues for potential rewards or
potential relief of punishment and thereby activating behavior. HA
is a tendency to respond intensively to signals of aversive stimuli,
thereby inhibiting behavior. RD is a tendency to respond intensely
to signals of reward, especially social rewards, thereby maintaining
and continuing particular behaviors. P is a tendency to persevere
in behaviors that have been associated with reward or relief from
punishment. Scores measured by the TCI distribute normally in
the population with sex-dependent differences [8].
The importance of temperament to mental health [7] and to
some extent to somatic health [9,10] has been previously
established. In particular, high levels of HA are associated with
a number of psychiatric disorders [11,12,13,14,15]. A more
complete understanding of the influence of temperament on health
outcome will require dissection of the specific pathways between
temperament and outcome. However, despite the original
hypothesis that temperament dimensions are independent behav-
ioral systems [6], accumulating evidence suggests correlations
between dimensions [16]. Organizing personality traits as profiles
or clusters (person-oriented approach) provides an opportunity to
examine the context of an individual’s traits, as opposed to
considering individual traits in isolation (variable-centered ap-
proach), and does not require the assumption that personality
dimensions operate independently (Crockett et al., 2006). While
there is growing support that such personality clusters predict a
number of health outcomes, much of this work has been focused
on clusters defined in childhood or adolescence (e.g., Crockett et
al., 2006) or has been focused on a single diagnostic outcome (e.g.,
Muthen & Muthen, 2000).
In order to examine the inter-relationships between dimensions
of temperament, as defined by Cloninger’s model, and relation-
ships between temperament and adult outcome, we conducted a
series of analyses in the Northern Finland Birth Cohort 1966. This
longitudinal birth-cohort provided the opportunity to assess the
relationship between profiles of temperament and overt expression
of psychiatric illness, health outcome, and lifestyle in a large,
relatively genetically homogeneous population.
Specifically, we hypothesized that individuals from this large
birth cohort could be stratified into functionally meaningful groups
according to their temperament profiles. As temperament is an
important determinant for affective regulation and behavior, we
hypothesized that individuals from these separate temperament
groups would also differ in their life habits, socioeconomic status,
and psychiatric and somatic health. To test these hypotheses, we
performed a cluster-based analysis of responses on the TCI in
3,761 men and women from the Northern Finland Birth Cohort
1966, and tested for differences between the resulting clusters on a
wide range of life domains. In order to test the stability of these
clusters in a separate sample, we also replicated the results of the
cluster structure analysis among 2,097 participants of the
Cardiovascular Risk in Young Finns study.
Materials and Methods
Participants and Measures
The Northern Finland Birth Cohort 1966 (NFBC 1966) is a
longitudinal one-year birth cohort originally including all males
and females whose expected year of birth was in 1966 in Finland’s
two northernmost provinces, Oulu and Lapland (N = 12,058 live-
born individuals) [17]. The cohort members have been carefully
monitored prospectively from the prenatal period onwards. The
current study sample is based on cohort members who lived in
Finland at the age of 16 years (N = 10,526:5,365 male, 5,161
female), as validated psychiatric diagnoses from the Finnish
Hospital Discharge Register are available for these subjects [18].
In the 31-year follow-up of the cohort, all subjects alive at that
time with a known address were sent a postal questionnaire
(N = 10,526). For subjects then living in the Oulu or Lapland
Figure 1. Bayesian information criterion (BIC) scores for various cluster sizes. Clustering of the subscales showed stable results with an
optimum of four clusters in both females (left) and males (right).
doi:10.1371/journal.pone.0033088.g001
Temperament Clusters in a Normal Population
PLoS ONE | www.plosone.org 2 July 2012 | Volume 7 | Issue 7 | e33088
Temperament Clusters in a Normal Population
PLoS ONE | www.plosone.org 3 July 2012 | Volume 7 | Issue 7 | e33088
provinces or in Helsinki area (N = 8,463), the questionnaire
included an invitation to take part in a clinical examination.
The subjects who participated (N = 5,960 (70%): 2863 male (65%),
3097 female (76%)) were also asked to fill in another questionnaire
on temperament, health, and occupation [19].
This study was limited to those individuals for whom a complete
personality questionnaire had been returned and who had not
been diagnosed with mental retardation (N = 3761 out of the 5084
who returned the questionnaire). Of all subjects who were
provided the temperament questionnaire, 63% (60% of the males,
66% of the females) participated.
Health-related quality of life was assessed with the 15D measure
[20]. The HSCL-25-depression questionnaire, which is a 25-item
shortened version of the original 90-item questionnaire [21], was
used for measurement of symptoms for depression and anxiety as
described by Veijola et al [22], and the Twenty Item Toronto
Alexithymia Scale (TAS-20), which has been translated into
Finnish [23], was used to measure the three facets of alexithymia
[24]. Subjects were asked whether they had ever been diagnosed
by a physician as having depression (yes/no), while a diagnosis of
schizophrenia was based on validated diagnosis [17] data from
hospital discharge register up until the end of year 1997.
The second questionnaire included a Finnish translation of the
107-item Temperament and Character Inventory (TCI) for measurement
of four dimensions of temperament (NS, HA, RD and P) and their
respective subscales (HA1: anticipatory worry, HA2: fear of
uncertainty, HA3: shyness, HA4: fatigability; NS1: exploratory
excitability, NS2: impulsiveness, NS3: extravagance, NS4: disor-
derliness; RD1: sentimentality, RD3: attachment, RD4: depen-
dence, and P: persistence). Specifically, this was the Tridimen-
sional Personality Questionnaire (TPQ) subset of the TCI version
9, with 107 binary items. Although the TPQ originally measured
three dimensions, these original items were rearranged to calculate
summary scores for the revised four dimensions of the TCI (with
five items originally contributing to RD now analyzed separately
as Persistence, and one of the original RD items now contributing
to NS). In addition, the questionnaire included the following: the
Perceptual Aberration Scale (PER),Physical Anhedonia Scale (PAS) and
Social Anhedonia Scale (SAS) were included to measure traits which
indicate a predisposition toward psychosis [25]; the Schizoid Scale
(SCHD), extracted from the Minnesota Multiphasic Personality
Inventory, to measure psychotic traits [26]; the Hypomanic
Personality Scale (HPS) [27]; and a scale referred to here as ‘‘Bipolar
II scale’’ to identify those depressed subjects who are at risk for later
conversion to bipolar disorders [28]. For reference of collection
and application of these scales, see Miettunen et al. [29,30].
All subjects included in the present study gave written consent
for their data to be used. Permission to gather registry data was
obtained by the Finnish Ministry of Social Welfare and health.
The study was approved by the Ethics Committee of the Faculty of
Medicine, University of Oulu.
An additional sample was examined in order to replicate the
cluster structure identified in the NFBC1966 cohort. In the
Cardiovascular Risk in Young Finns study (YF), which began in 1980,
subjects for the original sample (N = 3,596) were selected randomly
from six different age cohorts (3 to 18 years) in the population
register of the Social Insurance Institution, a database covering the
whole population of Finland. The design of the study and the
selection of the sample have been described in detail by Raitakari
et al [31]. Measurements for the present study were carried out in
year 2001 when 2,105 participants (59% of the 1980 cohort)
completed the TCI-version 9 questionnaire. As there was no
significant association of the TCI scales to age group in this cohort,
we used the TCI variables without any further adjustment for age.
Data Analysis
In cluster analysis, a set of individuals is divided into groups
such that individuals designated to the same group are as similar to
each other as possible while being as different from individuals in
other groups as possible. Clustering methods applied to psycho-
logical data have lead to biologically meaningful results in previous
work on schizophrenia by members of our group [32].
A large number of different clustering methods exist, often
tailored to specific data types or applications. We performed initial
tests with the probabilistic Gaussian mixture model clustering [33],
a density-based clustering method [34], and the k-means method
[35], and chose k-means for further analysis. We chose k-means for
further analysis, as this method is well established in the literature
and, compared to the other two methods, produces very robust
and stable clusterings of temperament data, and delivers easily
interpretable prototypical descriptions for the clusters found in the
form of cluster centers.
Before clustering, all the scales were normalized to mean 0 and
SD 1. The Euclidean distance between the 12-dimensional
temperament subscale vectors was used as similarity measure. As
the distribution of the subscales in the two genders differ
significantly [7,8], clustering and subsequent analyses were
performed separately for both genders. The k-means clustering
algorithm requires the user to select k, the number of clusters. We
Figure 2. Star plots describing the clusters. Cluster results for females (left column) and males (right column) are presented as star plots, with 0
as the sample mean and 1 as the sample standard deviation. The average score of each cluster (I-IV) on each of the twelve TCI subscales are indicated
by the thick black line, with the line closer to the middle of the plot representing lower scores and the line closer to the edge of the plot representing
higher scores.
doi:10.1371/journal.pone.0033088.g002
Table 1. Number and proportion of variables significantly
associated with clusters, any scale, and each individual scale,
after correction for multiple testing.
Females Males
Grouping Number Proportion Number Proportion
Clusters 47 0.41 42 0.37
Any Scale 48 0.42 51 0.44
HA1 24 0.21 29 0.25
HA2 24 0.21 21 0.18
HA3 27 0.23 30 0.26
HA4 31 0.27 30 0.26
NS1 11 0.096 19 0.17
NS2 6 0.052 4 0.035
NS3 13 0.11 15 0.13
NS4 8 0.07 4 0.035
P 4 0.035 5 0.043
RD1 11 0.096 14 0.12
RD3 20 0.17 19 0.17
RD4 9 0.078 6 0.052
doi:10.1371/journal.pone.0033088.t001
Temperament Clusters in a Normal Population
PLoS ONE | www.plosone.org 4 July 2012 | Volume 7 | Issue 7 | e33088
computed clusterings for 2–12 clusters and selected the best model
from among those by the Bayesian information criterion [36].
Replication Analysis
To further analyze the validity of the clusters, we performed a
cluster analysis on the TCI temperament subscales in the YF
sample. Assigning a cluster to the individuals in the replication
sample based on the model obtained on original NFBC1966 data,
and vice versa, enabled us to analyze whether these two models
represent the same underlying population structure. Further
details of this method and results are presented in (Materials S1),
including Figure S1 and Tables S1, S2, S3.
Association Analyses
In order to test differences between clusters on outcome
variables representing a range of life domains, we conducted
one-way analyses of variance for continuous variables and a chi-
square tests of independence for discrete variables. A total of 115
variables representing occupation, lifestyle, socioeconomic status,
and mental and physical health were examined. The collection of
data in the NFBC1966 is extensive; in order to span the range of
adult health and outcome while including those variables with
adequate data, we selected variables if 1) sufficient information was
available about the nature of the variable (i.e., how information
was collected and measured), 2) more than 50% of cohort
members had data available for that variable, and 3) we
considered that variable to be meaningful for psychological and
somatic health. An un-weighted Bonferroni correction [37] for p-
values was applied to compensate for multiple hypothesis testing.
In order to compare results between temperament clusters and
original subscale scores, we also tested differences between each
individual subscale and the same variables representing adult
outcome across a range of life domains. All individuals were
ordered based on their score in the subscale in question and split
into four groups of sizes equal to the clusters. For these groups, we
performed the same statistical test as was used for the clusters,
corrected again with the Bonferroni correction, and compared the
strength of the resulting association with that obtained from
analyses using the clusters.
Results
Cluster Analyses
Clustering of the NFBC1966 gender-specific samples according
to the TCI main scales did not yield evidence for a cluster structure.
In contrast, clustering of the 12 TCI subscales showed stable results
with an optimum of four clusters in both genders (Figure 1). In the
replication of the TCI subscale clustering in the YF sample,
Cohen’s kappa values were between 0.7 and 0.9, indicating a
strong agreement between the models based on the separate
samples (see Materials S1).
Despite genders having been clustered separately, we found
similar clusters in the gender-specific models. Figure 2 shows these
clusters as star plots (with 0 as sample mean and 1 as sample
standard deviation). For females, Clusters I, II, III and IV include
Table 2. Means and standard deviations from 15D questionnaire for females.
Clusters Subscales
I II III IV First
a
Second
b
Other
c
Mean SD Mean SD Mean SD Mean SD
Mobility 1.017 0.169 1.031 0.173 1.044 0.214 1.039 0.193
Vision 1.081 0.342 1.083 0.330 1.070 0.262 1.109 0.381
Hearing 1.029 0.179 1.056 0.255 1.055 0.266 1.063 0.262
Breathing* 1.096 0.338 1.136 0.399 1.179 0.429 1.191 0.429
Sleeping*** 1.250 0.480 1.368 0.584 1.421 0.601 1.433 0.590 HA1 HA4 NS4
Eating 1.000 0.000 1.004 0.064 1.002 0.043 1.010 0.098
Speech*** 1.029 0.168 1.014 0.120 1.031 0.174 1.101 0.318 HA2 HA4
RD4
Elimination 1.160 0.406 1.186 0.439 1.203 0.425 1.196 0.438
Usual activities* 1.027 0.194 1.043 0.214 1.060 0.246 1.089 0.294 HA4
Mental function*** 1.071 0.257 1.085 0.293 1.121 0.326 1.196 0.409 HA4 HA1 HA3
Discomfort and symptoms*** 1.516 0.576 1.477 0.570 1.590 0.578 1.661 0.580 HA4 HA1
Depression*** 1.277 0.473 1.324 0.565 1.485 0.615 1.652 0.664 HA2 HA3
RD3
Distress*** 1.215 0.421 1.254 0.506 1.375 0.555 1.582 0.665 HA2 NS1
RD1 RD3
Vitality*** 1.329 0.513 1.357 0.588 1.497 0.601 1.640 0.652 HA3 RD3
Sexual activity** 1.104 0.353 1.081 0.288 1.161 0.424 1.184 0.472 HA4 HA1
*p,0.05 uncorrected.
**p,0.05 corrected.
***p,0.01 corrected.
a
First scale: the individual TCI scale with the strongest significant association to the variable.
b
Second scale: any other individual TCI scale with a significant association to the variable that is stronger than the clusters.
c
Other scale(s): any other individual TCI scale that is significantly associated to the variable. For significant associations between clusters and variables from the 15D
questionnaire, the mean value for the cluster with the highest scores are in bold.
doi:10.1371/journal.pone.0033088.t002
Temperament Clusters in a Normal Population
PLoS ONE | www.plosone.org 5 July 2012 | Volume 7 | Issue 7 | e33088
26%, 25%, 28% and 21% of the subjects, whereas for males these
numbers are 26%, 22%, 30% and 22%.
Association Analyses
The number and proportion of variables that are significantly
associated with cluster membership and TCI scales are presented
in Table 1. We examined the associations of temperament and the
health-related quality of life as measured with the 15D measure [20]
(Tables 2–3). Cluster IV consistently reported the most problems
across the 15 dimensions measured, while Clusters I and II
reported the least problems both in males and females. Of the
subscales, HA-1 and HA-4 had power equal to the clusters to
‘‘predict’’ these variables, with RD-1, RD-3, NS-1, NS-3 and the
other HA scales also having some associations, although with less
power than the clusters.
We tested for differences in education, work and socioeconomic status
between the four clusters, and found significant differences in
education (Tables S4–S5). For example, only 41% and 26% of
women and men in Cluster IV had finished secondary school,
while 56% and 35% in Cluster II had finished secondary school.
These clusters also represent the two extremes in higher level of
education. In concordance, Cluster IV reported lowest working
capacity in both genders. Marital status differed between clusters,
with the highest rate of marriage in Cluster I for both females
(62%) and males (50%), and the highest rate of divorce in Cluster
II females (7%) and males (5%). For males, the TCI subscales most
strongly associated to these variables were HA-3 and HA-4. For
females, there were very few strong associations of the individual
scales, with the exception of a strong association of rate of
marriage to NS-3 (in females only).
Individuals from Cluster II reported the best, while those from
Cluster IV the worst, physical functional capacity both in females
and males. Over 10% of Cluster II females, but only 4% of Cluster
IV females, reported no trouble running 2 or 5 kilometers, with a
similar pattern observed for males. Self-reported physical activity
also followed the pattern of self-reported physical functional
capacity.
As indicators of taking care of oneself, Cluster IV individuals
tended to report brushing their teeth less often than other clusters,
while members from Cluster II reported using more alcohol and
more members reported smoking regularly (52% of females and
55% males). Almost no statistically significant differences could be
observed in the physical measurements of the individuals,
including height, weight, BMI, blood pressure, and levels of
fasting sugar, insulin, and cholesterol. Triglyceride levels were
slightly higher in Cluster IV (1.35 mmol/dL) and lower in Cluster
I (1.20 mmol/dL) males. Females, particularly those in Cluster II,
had a tendency to underestimate their weight in the postal
questionnaire compared to the measurements at the physician’s
office.
In terms of the individual temperament dimensions, for females,
smoking and alcohol were associated to NS-3 almost exclusively,
while the HA scales were associated to self-reported physical
capacity. NS-3 and the HA scales also dominated the pattern in
males, but the performance of clusters vs. scales was the opposite
that seen in females. The lack of associations indicate that using
Table 3. Means and standard deviations from 15D questionnaire for males.
Clusters Subscales
I II III IV First
a
Second
b
Other
c
Mean SD Mean SD Mean SD Mean SD
Mobility* 1.023 0.165 1.016 0.147 1.053 0.310 1.062 0.343
Vision 1.094 0.357 1.060 0.279 1.056 0.229 1.091 0.324
Hearing* 1.069 0.311 1.046 0.222 1.053 0.225 1.099 0.326
Breathing 1.085 0.311 1.081 0.274 1.115 0.410 1.118 0.323 HA4
Sleeping*** 1.229 0.447 1.323 0.549 1.313 0.515 1.444 0.623 RD1
Eating 1.005 0.068 1.000 0.000 1.000 0.000 1.019 0.186
Speech*** 1.046 0.241 1.019 0.137 1.016 0.127 1.126 0.349 NS1 RD3
Elimination* 1.106 0.330 1.070 0.256 1.078 0.305 1.161 0.429 HA1
Usual activities*** 1.030 0.239 1.041 0.224 1.047 0.264 1.126 0.412 HA4 HA1
Mental function*** 1.067 0.285 1.049 0.216 1.082 0.275 1.199 0.433 HA1 HA3
Discomfort and symptoms*** 1.353 0.507 1.428 0.604 1.416 0.564 1.553 0.540 HA1
Depression*** 1.150 0.377 1.233 0.448 1.265 0.499 1.617 0.685 HA2 NS1
RD1 RD3
Distress*** 1.155 0.387 1.203 0.435 1.263 0.490 1.566 0.622 HA2 NS1
RD1 RD3
Vitality*** 1.180 0.430 1.233 0.454 1.307 0.524 1.539 0.662 HA2 HA3
NS1 RD1 RD3
Sexual activity*** 1.025 0.172 1.038 0.191 1.056 0.255 1.143 0.481 HA3 HA4
*p,0.05 uncorrected.
**p,0.05 corrected.
***p,0.01 corrected.
a
First scale: the individual TCI scale with the strongest significant association to the variable.
b
Second scale: any other individual TCI scale with a significant association to the variable that is stronger than the clusters.
c
Other scale(s): any other individual TCI scale that is significantly associated to the variable. For significant associations between clusters and variables from the 15D
questionnaire, the mean value for the cluster with the highest scores are in bold.
doi:10.1371/journal.pone.0033088.t003
Temperament Clusters in a Normal Population
PLoS ONE | www.plosone.org 6 July 2012 | Volume 7 | Issue 7 | e33088
only subscales without clustering would miss the associations to
physical activity, frequency of brushing teeth, lifetime abstinence
from alcohol, calcium intake, and difference in self-reported and
measured BMI in females. In males, while using the scales instead
of clusters would miss associations to the brushing of teeth, using
the clusters alone would miss associations certain physical
variables, all strongly associated to NS-3.
In terms of confirmed diagnoses, we found minor differences in
physical health, with a considerable difference between males and
females (Table 4). For females, individuals in Cluster IV reported
less allergic rhinitis and eczemas, and more rheumatoid arthritis
than individuals in other clusters. Among males, Cluster II
individuals had almost double the prevalence of asthma compared
to the other clusters, while individuals in Cluster II and IV had
hypertension more often. For both genders, self-reported lifetime-
depression and register-based diagnosis of schizophrenia were over
twice as common in Cluster IV, while Cluster I clearly had the
lowest prevalence. In terms of the individual subscales, depression
was associated to HA-4 in both genders, while there were no
additional associations between other diagnoses and individual
subscales.
We also analyzed associations to certain validated psychological
scales completed by the participants (Tables 5–6). Individuals from
Cluster IV scored the highest and individuals from Cluster I or II
the lowest on all scales measuring traits predisposing to psychosis,
including the PER,PAS,SAS and SCHD. Similar findings were
obtained with SCL and TAS-20 factors, with individuals in Clusters
I and II scoring consistently lowest and individuals from Cluster
IV highest on symptoms related to anxiety, depression and
alexithymia. Interestingly, individuals from Cluster II scored
highest on the HPS (mean in females 16.2 and in males 16.4)
while individuals from Cluster III scored lowest (mean in females
9.5 and in males 8.5).
These psychological scales were also associated to many of the
TCI subscales (Tables 5–6). For example, in females, we found
strong associations of PAS to RD-1 and HA-1, and SCL symptoms
to HA-1 and HA-4. It is interesting to note that while for males the
HA scales dominate the association picture, there are two scales
that were not associated to any temperament dimension: the
schizoid scale, and the social anhedonia scale. In addition, in
females, the cluster association to the HPS would be missed by an
analysis using the subscales alone.
Discussion
We present a stable and robust clustering of domains of
temperament in a population-based sample. Our results on
.2,000 females and .1,700 males from a longitudinal birth
cohort from Northern Finland demonstrate that the analyzed
temperament dimensions of the TCI do not distribute randomly
among individuals but have a consistent, endogenous pattern, and
this structure is supported by a replication analysis in a separate,
representative population sample of .2,000 Finnish individuals.
In addition, our results provide further evidence for the
importance of temperament to health and well being, with
statistically significant differences between these temperament
clusters across a number of life domains.
Properties of the Temperament Clusters
A stable and robust clustering was found with the TCI subscales
with an optimum of four clusters (I-IV) for both males and females.
The results proved to be quite similar for both genders, despite the
separate analyses, and in agreement in a replication sample,
providing further support for the stability of these temperament
Table 4. Confirmed diagnoses.
Clusters Subscales
I II III IV First
a
Females
Asthma 8.0% 11% 9.5% 7.1%
Allergic rhinitis* 26% 29% 27% 19%
Eczema* 38% 34% 38% 30%
Allergic eye
symptoms
20% 22% 21% 16%
Hypertension 13% 14% 13% 15%
Diabetes 2.2% 1.8% 1.8% 1.8%
Thyroiditis 3.0% 3.0% 2.3% 1.8%
Gastric ulcer 1.3% 1.4% 2.7% 2.1%
Epilepsy 0.6% 2.2% 1.2% 1.4%
Migraine 17% 21% 20% 20%
Other neurologic
disease
0.7% 0.2% 1.1% 1.6%
Rheumatoid
arthritis*
1.3% 0.6% 1.4% 3.0%
Other arthritic
condition
4.7% 5.6% 5.7% 4.4%
Degenerative or
other back
condition
14% 15% 12% 13%
Schizophrenia* 0.4% 1.2% 1.6% 3.0%
Depression*** 2.4% 3.6% 3.7% 8.5% HA4
Males
Asthma* 7.7% 13% 7.2% 7.9%
Allergic rhinitis 20% 19% 17% 22%
Eczema 24% 24% 21% 23%
Allergic eye
symptoms
17% 14% 13% 14%
Hypertension* 9% 12% 10% 15%
Diabetes 0.2% 0.0% 0.8% 0.8%
Thyroiditis 0.2% 1.3% 0.4% 0.8%
Gastric ulcer 2.2% 2.6% 2.5% 3.4%
Epilepsy 0.9% 0.8% 0.8% 2.6%
Migraine 6.0% 6.0% 7.0% 7.9%
Other neurologic
disease
0.2% 0.3% 1.0% 1.1%
Rheumatoid
arthritis
0.7% 0.3% 0.8% 0.3%
Other arthritic
condition
4.7% 5.7% 5.8% 2.9%
Degenerative or
other back
condition
17% 20% 19% 16%
Schizophrenia** 0.9% 1.3% 2.0% 3.7%
Depression*** 1.1% 2.1% 2.1% 5.5% HA4
*p,0.05 uncorrected.
**p,0.05 corrected.
***p,0.01 corrected.
a
First scale: the individual TCI scale with the strongest significant association to
the diagnosis. For significant associations between clusters and diagnoses, the
values for the cluster with the highest percentage are in bold.
doi:10.1371/journal.pone.0033088.t004
Temperament Clusters in a Normal Population
PLoS ONE | www.plosone.org 7 July 2012 | Volume 7 | Issue 7 | e33088
profiles. We did not, however, find evidence for a stable clustering
pattern based on the four TCI scales alone; this is likely because
the use of subscales provided more information that the clustering
algorithm could use to partition the individuals into stable and
robust clusters.
Individuals from Cluster I can be described as stable, persistent
and not very impulsive. They report a high quality of life and self-
reported working capacity, and a relatively high level of education.
Both females and males are more often married than individuals
from the other clusters. Their life habits are healthy: they brush
their teeth, do not drink very much alcohol and only rarely smoke.
They score lowest in most of the scales for psychosis proneness and
symptoms for depression and anxiety, and this cluster has a lower
prevalence of depression and schizophrenia than other clusters.
Consequently, our results suggest that this temperament profile,
which is characterized by remarkably average levels on most of the
temperament traits except particularly low levels of impulsivity
(HA2) and disorderliness (HA4), may possess features enabling
mental stability and psychological adaptability, leading to practice
of healthy life habits, stable life features, and decreased risk for
mental disorders.
Individuals from Cluster II can be characterized as outgoing,
energetic people who tend to be impulsive. Like individuals from
Cluster I, they have a high quality of life and self-reported working
capacity. They have the highest level of education, their annual
income is on average higher than other clusters, and they also
report the best physical functional capacity. Divorces are more
common and they have a tendency for higher consumption of
alcohol and more smoking, particularly in females. Cluster II is
characterized by relatively low levels of depression and schizo-
phrenia, supported by low scores for all psychological scales that
measure traits predisposing toward psychosis, anxiety or depres-
sion, except on the hypomania personality scale, on which Cluster
II members score the highest. It is noteworthy that we observed
here a tendency of Cluster II individuals to embellish reality. This
is consistent with this cluster’s temperament profile, as well as the
high scores on the HPS [38]. Individuals with hypomanic
personality have been reported to provide high estimates of their
future academic and occupational performance [27,38], which
leads to a need for caution when interpreting the self-reports of
positive lifestyle and health-related variables in Cluster II.
In terms of quality of life, socioeconomic status, mental and
physical health, individuals from Cluster III do not show any
extreme characteristics. The education, working capacity and
physical functional capacity are higher in Cluster III than in
Cluster IV but generally lower than for individuals from Clusters I
or II. They score low in scales for psychosis proneness as well as for
the hypomania personality scale, the latter being likely to reflect
the low energy level of these individuals.
Individuals from Cluster IV could be described as shy and
pessimistic persons who prefer routine and privacy. They score the
lowest in most fields of health and well-being and are more often
unemployed. They also report lowest working capacity scores and
their annual income is lowest among the four clusters. Members of
Cluster IV are the least physically fit. Virtually all indicators for
psychological health show signs for increased mental health
problems, both in levels symptoms as well as manifest disorders.
Thus, based on the particular traits measured in the NFBC1966,
our results suggest that a profile characterized by excessively high
HA and low NS, RD and P (representing approximately 20% of
Table 5. Psychological scales for females.
Clusters Subscales
I II III IV First
a
Second
b
Other
c
Females Mean SD Mean SD Mean SD Mean SD
Schizoid scale*** 2.5 1.2 2.5 1.2 2.8 1.4 3.5 1.3 NS1
Perceptional aberration
scale***
2.1 2.8 2.4 2.9 2.3 3.1 3.8 4.4 HA1 RD1 HA2 HA3 HA4 NS2 NS4
P RD3 RD4
Physical anhedonia scale*** 12.1 5.2 11.1 5.4 12.6 5.2 16.0 6.3 HA1 HA4 NS3 P RD1
RD4
Social anhedonia scale*** 7.2 3.9 7.3 4.2 6.6 3.5 12.5 5.5 NS3
Bipolar scale*** 10.4 4.5 11.7 4.8 10.7 4.8 14.9 5.1
Hypomania personality
scale***
11.0 6.3 16.3 7.5 9.6 6.3 10.1 6.0 HA1
Symptoms of anxiety*** 1.3 0.2 1.3 0.3 1.3 0.3 1.4 0.4 HA1 HA4 HA2 HA3 NS2 RD1 RD3
Symptoms of depression*** 1.3 0.3 1.3 0.4 1.4 0.4 1.5 0.4 HA2 HA3 RD1 RD3
Alexithymia, factor 1*** 12.7 5.6 12.6 5.1 13.5 5.3 16.9 5.8 NS1 RD1 RD4
Alexithymia, factor 2*** 12.1 3.6 11.7 3.1 11.9 3.1 14.6 3.1 HA1 HA4 NS3
Alexithymia, factor 3** 27.6 4.9 27.2 3.8 27.0 4.2 28.1 3.5 RD3
*p,0.05 uncorrected.
**p,0.05 corrected.
***p,0.01 corrected.
a
First scale: the individual TCI scale with the strongest significant association to the variable.
b
Second scale: any other individual TCI scale with a significant association to the variable that is stronger than the clusters.
c
Other scale(s): any other individual TCI scale that is significantly associated to the variable. For significant associations between clusters and variables from the
psychological scales, the mean value for the cluster with the highest scores are in bold. Psychological scales included the Schizoid Scale (from the Minnesota Multiphasic
Personality Inventory); Perceptual Aberration Scale,Physical Anhedonia Scale and Social Anhedonia Scale; Bipolar II scale;Hypomanic Personality Scale;Symptoms of anxiety
and symptoms of depression from the HSCL-25; and Alexithymia factors 1–3 from the Twenty Item Toronto Alexithymia Scale (Factor 1: difficulties in identifying feelings;
Factor 2: difficulties in describing feelings; and Factor 3: externally oriented thinking).
doi:10.1371/journal.pone.0033088.t005
Temperament Clusters in a Normal Population
PLoS ONE | www.plosone.org 8 July 2012 | Volume 7 | Issue 7 | e33088
cohort members) may capture a profile of increased physical and
mental health risk.
Overall, our results are in line with the previously published
findings. In a previous study on NFBC1966, all four domains of
temperament were found to be associated to socioeconomic status,
alcohol consumption and smoking behavior in varying configura-
tions [10]. There was a negative gradient between HA and level of
education and a tendency towards higher RD and P with increasing
socioeconomic status. Previous studies have also demonstrated
relationships between high HA (and other related personality traits,
such as high Neuroticism and dysthymic temperament) with
depression and anxiety [39,40,41,42,43]. In our current analyses,
we also found that the clusters do as well as, and in many cases
better than, the individual dimensions to find associations with
outcome variables. A further advantage of using these clusters to
investigate the relationship between temperament and health
outcomes is that clusters have an additional value of simpler data
structure (as each individual has only one definition), meaning also
that fewer statistical tests are needed.
Strengths and Limitations
The primary strength of our analyses stems from the prospective
design of the study and follow-up of a large birth cohort, which
allows for control of recall bias. Furthermore, a previous analysis
has demonstrated that participation in this cohort does not vary
across specific disorders, nor do gender or education explain the
association of psychiatric disorders with participation [19]. An
additional strength of the present study is the relative homogeneity
of its population: all subjects were of the same age and ethnicity.
This implication of this is that the TCI scores in young Finns are
likely not biased by cross-cultural issues associated with temper-
ament measurement [28,29].
One potential limitation is that temperament in the NFBC1966
was assessed only at one time point. However, although absolute
scores in temperament may change over time, inter-individual
differences typically remain relatively stable [42]. Nevertheless,
application of repeated measures of temperament would likely add
to the accuracy of the results.
A final limitation is that clustering itself cannot answer the
question of whether the clusters found reflect real clusters in the data
or are artifacts of the method. However, to begin to address this
limitation, we replicated the clustering analysis in a separate sample
that represents the Finnish population well, thereby providing
additional support for this pattern of temperament profiles.
Implications
To our knowledge, this is the first study to investigate
temperament patterns using cluster analysis tools in population-
based samples of both females and males. One previous study
reported an association between temperament profiles and a high
level of physiological CHD risk factors; however, this study
included only men and was comprised of a relatively small sample
of 190 individuals [44].
Our results further question the assumption that temperament
domains are independent. The heritability of each temperament
factor has been estimated to range from 50 to 65% [2,3,4,5].
Although these factors have been suggested to be independent of
each other, contradictory results have also been reported [45]. For
example, a recent meta-analysis supports temperament dimension
inter-relatedness, particularly given the consistent negative correla-
tion between NS and HA in a number of studies [3]. These results
Table 6. Psychological scales for males.
Clusters Subscales
I II III IV First
a
Second
b
Other
c
Females Mean SD Mean SD Mean SD Mean SD
Schizoid scale*** 1.9 1.2 2.1 1.3 2.2 1.4 3.0 1.5 NS1 NS2 RD3
Perceptional aberration
scale***
1.3 2.1 2.3 3.4 1.6 2.5 3.0 3.9 RD1 HA1 HA4 NS2 NS4 RD3
RD4
Physical anhedonia scale*** 18.0 6.7 15.0 6.4 17.5 6.6 21.9 7.9 HA1 HA2 HA4 NS3 P
Social anhedonia scale*** 10.7 5.0 8.9 5.0 9.2 4.1 16.3 6.4 NS3 RD1
Bipolar scale*** 9.8 4.5 11.7 4.9 9.6 4.9 14.6 5.0 HA4 NS1 NS3 NS4 P
Hypomania personality
scale***
8.9 5.6 16.3 7.6 8.4 5.8 9.0 5.9 HA1 NS3
Symptoms of anxiety *** 1.2 0.2 1.3 0.3 1.2 0.2 1.4 0.3 HA2 HA3 HA4 RD1
Symptoms of depression*** 1.2 0.3 1.3 0.3 1.3 0.3 1.5 0.4 HA2 HA3 NS3 RD1 RD3
Alexithymia, factor 1*** 11.7 5.3 12.0 5.4 12.7 5.2 16.0 5.4 HA2 NS1 RD1 RD3 RD4
Alexithymia, factor 2*** 13.5 3.7 12.6 3.2 12.8 3.1 15.3 2.9 HA1 HA2 RD4
Alexithymia, factor 3* 28.6 4.3 28.6 4.0 27.9 3.8 28.5 3.1
*p,0.05 uncorrected.
**p,0.05 corrected.
***p,0.01 corrected.
a
First scale: the individual TCI scale with the strongest significant association to the variable.
b
Second scale: any other individual TCI scale with a significant association to the variable that is stronger than the clusters.
c
Other scale(s): any other individual TCI scale that is significantly associated to the variable. For significant associations between clusters and variables from the
psychological scales, the mean value for the cluster with the highest scores are in bold. Psychological scales included the Schizoid Scale (from the Minnesota Multiphasic
Personality Inventory); Perceptual Aberration Scale,Physical Anhedonia Scale and Social Anhedonia Scale; Bipolar II scale;Hypomanic Personality Scale;Symptoms of anxiety
and symptoms of depression from the HSCL-25; and Alexithymia factors 1–3 from the Twenty Item Toronto Alexithymia Scale (Factor 1: difficulties in identifying feelings;
Factor 2: difficulties in describing feelings; and Factor 3: externally oriented thinking).
doi:10.1371/journal.pone.0033088.t006
Temperament Clusters in a Normal Population
PLoS ONE | www.plosone.org 9 July 2012 | Volume 7 | Issue 7 | e33088
also lend support to a person-centered approach. While the majority
of studies to combine independent dimensions and examine the
ability of resulting temperament profiles to predict mental health
have focused on samples of children and adolescents
[46,47,48,49,50], our results support the argument that additional
information can be obtained by considering cluster profiles. Indeed,
in a manuscript submitted concurrently, we demonstrate significant
relationships between these same cluster profiles and life course
measures (e.g., early environment, neurobehavioral development,
and adolescent behavior)(Congdon et al., submitted concurrently).
Obtaining a better understanding of these relationships will be
critical for understanding the underlying genetic architecture and
other possible etiological processes predisposing individuals to a
particular temperament profiles, as well as the relationship
between genetic factors, patterns of temperament, and ultimately
psychiatric and somatic health outcomes.
Supporting Information
Figure S1 Histograms of chi-square values. Chi-square
values of 100 experiments using generated data and of results
based on cross-tabulation of 4-cluster solutions (presented in Table
S2), with green for females and red for males.
(TIF)
Table S1 Clusterings based on NFBC66 four-cluster
model vs. YF two-cluster mode.
(DOC)
Table S2 Clusterings based on NFBC66 four-cluster
model vs. YF four-cluster mode.
(DOC)
Table S3 Clusterings based on NFBC66 two-cluster
model vs. YF two-cluster mode.
(DOC)
Table S4 Self-rated physical capacity, life habits, health
and stress reactivity data from the 31-year follow-up in
NFBC66 females.
(DOC)
Table S5 Self-rated physical capacity, life habits, health
and stress reactivity data from the 31-year follow-up in
NFBC66 males.
(DOC)
Materials S1
(DOC)
Author Contributions
Conceived and designed the experiments: JW JM NBF J. Veijola HM TP.
Analyzed the data: JW S. Scho¨nauer PP JKS HM TP. Contributed
reagents/materials/analysis tools: HT PP MK JE JL AT TT MH LPR
LKJ J. Viikari OTR MJ MRJ LP J. Veijola. Wrote the paper: JW JM EC
S. Service MJ NBF J. Veijola HM TP.
References
1. Oliver JP, Robins RW, Pervin LA (2008) Handbook of Personality, Third
Edition: Theory and Research. New York: The Guilford Press.
2. Ando J, Ono Y, Yoshimura K, Onoda N, Shinohara M, et al. (2002) The genetic
structure of Cloninger’s seven-factor model of temperament and character in a
Japanese sample. Journal of Personality 70: 583–609.
3. Gillespie NA, Cloninger CR, Heath AC, Martin NG (2003) The genetic and
environmental relationship between Cloninger’s dimensions of temperament
and character. Personality and Individual Differences 35: 1931–1946.
4. Heath AC, Cloninger CR, Martin NG (1994) Testing a model for the genetic
structure of personality: A comparison of the personality systems of Cloninger
and Eysenck. J Pers Soc Psychol 66: 762–775.
5. Stallings MC, Hewitt JK, Cloninger CR, Heath AC, Eaves LJ (1996) Genetic
and environmental structure of the Tridimensional Personality Questionnaire:
Three or four temperament dimensions? J Pers Soc Psychol 70: 127–140.
6. Cloninger CR (1987) A systematic method for clinical descri ption and
classification of personality variants. A proposal. Archives of General Psychiatry
44: 573–588.
7. Cloninger CR, Przybeck TR, S
ˇvrakic
´DM, Wetzel RD (1994) The Temper-
ament and Character Inventory (TCI): A Guide to Its Development and Use. St.
Louis: Washington University, Center for Psychobiology of Personality.
8. Miettunen J, Veijola J, Lauronen E, Kantoja¨rvi L, Joukamaa M (2007) Sex
differences in Cloninger’s temperament dimensions – a meta-analysis.
Comprehensive Psychiatry 48: 161–169.
9. Smith TW, MacKenzie J (2006) Personal ity and risk of physical illness. Annu
Rev Clin Psychol 2: 435–467.
10. Sovio U, King V, Miettunen J, Ek E, Laitinen J, et al. (2007) Cloninger’s
Temperament dimensions, socio-economic and lifestyle factors and metabolic
syndrome markers at age 31 years in the Northern Finland Birth Cohort 1966.
J Health Psychol 12: 371–382.
11. Battaglia M, Przybeck TR, Bellodi L, Cloninger CR (1996) Temperament
dimensions explain the comorbidity of psychiatric disorders. Comprehensive
Psychiatry 37: 292–298.
12. Engstro¨ m C, Bra¨ ndstro¨ m S, Sigvardsson S, Cloninger R, Nylander PO (2004)
Bipolar disorder: I. Temperament and character. Journal of Affective Disorders
82: 131–134.
13. Fassino S, Abbate-Daga G, Amianto F, Leombruni P, Boggio S, et al. (2002)
Temperament and character profile of eating disorders: A controlled study with
the temperament and character inventory. International Journal of Eating
Disorders 32: 412–425.
14. Klump KL, Strober M, Bulik CM, Thornton L, Johnson C, et al. (2004)
Personality characteristics of women before and after recovery from an eating
disorder. Psychological Medicine 34: 1407–1418.
15. Ritsner M, Susser E (2004) Temperament types are associat ed with weak self-
construct, elevated distress and emotion-oriented coping in schizophrenia:
Evidence for a complex vulnerability marker? Psychiatry Research 128: 219–
228.
16. Miettunen J, Veijola J, Lauronen E, Kantoja¨ rvi L, Joukamaa M (2008) Inter-
correlations between Cloninger’s temperament dimensions – a meta-analysis.
Psychiatry Research 160: 106–114.
17. Rantakallio P (1969) Groups at risk in low birth weight infants and perinatal
mortality. Acta Paediatrica Scandinavica Supplement 193: 1–71.
18. Moilanen K, Veijola J, La¨ ksy K, Ma¨ kikyro¨ T, Miettunen J, et al. (2003) Reasons
for the diagnostic discordance between clinicians and researchers in schizo-
phrenia in the Northern Finland 1966 Birth Cohort. Social Psychiatry and
Psychiatric Epidemiology 38: 305–310.
19. Haapea M, Miettunen J, La¨a¨ra¨ E, Joukamaa MI, Ja¨ rvelin MR, et al. (2008)
Non-participation in a field survey with respect to psychiatric disorder s.
Scand J Public Health 36: 728–736.
20. Sintonen H, Pekurinen M (1989) A generic 15 dimensional measure of health-
related quality of life (1SD). J Soc Med 26: 85–96.
21. Derogatis LR, Lipman RS, Covi L (1973) SCL-90: An outpatient psychiatric
rating scale–preliminary report. Psychopharmacological Bulletin 9: 13–28.
22. Veijola J, Jokelainen J, La¨ksy K, Kantoja¨rvi L, Kokkonen P, et al. (2003) The
Hopkins Symptom Checklist-25 in screening DSM-III-R axis-I disorders. Nordic
Journal of Psychiatry 57: 119–123.
23. Joukamaa M, Miettunen J, Kokkonen P, Koskinen M, Julkunen J, et al. (2001)
Psychometric properties of the Finnish 20-item Toronto Alexithymia Scale.
Nordic Journal of Psychiatry 55: 123–127.
24. Bagby RM, Parker JDA, Taylor GJ (1994) The twenty-item Toronto
Alexithymia Scale-I. Item selection and cross-validation of the factor structure.
Journal of Psychosom Res 38: 23–32.
25. Chapman LJ, Chapman JP, Raulin ML (1976) Scales for physical and social
anhedonia. Journal of Abnormal Psychology 85: 374–382.
26. Golden RR, Meehl P (1979) Detection of the schizoid taxon with MMPI
indicators. Journal of Abnormal Psychology 88: 217–233.
27. Eckblad M, Chapma n LJ (1986) Development and validation of a scale for
hypomanic personality. Journal of Abnormal Psychology 95: 214–222.
28. Akiskal HS, Maser JD, Zeller PJ, Endicott J, Coryell W, et al. (1995) Switching
from ‘unipolar’ to bipolar II. An 11-year prospective study of clinical and
temperamental predictors in 559 patients. Archives of General Psychiatry 52:
114–123.
29. Miettunen J, Kantoja¨ rvi L, Ekelund J, Veijola J, Karvonen JT, et al. (2004) A
large population cohort provides normative data for investigation of tempera-
ment. Acta Psychiatrica Scandinavica 110: 150–157.
30. Miettunen J, Veijola J, Freim er N, Lichtermann D, Peltonen L, et al. (2008)
Data on schizotypy and affective scales are gender and education dependent –
study in the Northern Finland 1966 Birth Cohort. Psychiatry Research 178:
408–413.
Temperament Clusters in a Normal Population
PLoS ONE | www.plosone.org 10 July 2012 | Volume 7 | Issue 7 | e33088
31. Raitakari OT, Juonala M, Ro¨ nnemaa T, Keltikangas-Ja¨ rvinen L, Ra¨sa¨ nen L, et
al. (2008) Cohort profile: The Cardiovascular Risk in Young Finns Study.
International Journal of Epidemiology 37: 1220–1226.
32. Wessman J, Paunio T, Tuulio-Henriksson A, Koivisto M, Partonen T, et al.
(2009) Mixture model clustering of phenotype features reveals evidence for
association of DTNBP1 to a specific subtype of schizophrenia. Biological
Psychiatry 66: 990–996.
33. McLachlan G, Peel D (2000) Finite Mixture Models. Hoboken, NJ: John Wiley
& Sons, Inc.
34. Ester M, Kriegel HP, Sander J, Xu X (1996) A Density-Based Algorithm for
Discovering Clusters in Large Spatial Databases with Noise. Proc 2nd Int Conf
on Knowledge Discovery and Data Mining. 226–231.
35. MacQueen J (1967) Some methods for classification and analysis of multivariate
observations. Proc 5th Berkeley Symp Math Statist Prob. 281–297.
36. Schwarz G (1978) Estimating the dimension of a model. The Annals of Statistics
6: 461–464.
37. Shaffer JP (1995) Multiple hypothesis testing. Annu Rev Psychol 46: 561–584.
38. Meyer TD, Krumm-Merabet C (2003) Academic performance and expectations
for the future in relation to a vulnerability marker for bipolar disorders: the
hypomanic temperament. Personality and Individual Differences 35: 785–796.
39. Farmer A, Mahmood A, Redman K, Harris T, Sadler S, et al. (2003) A sib-pair
study of the Temperament and Character Inventory scales in major depression.
Arch Gen Psychiatry 60: 490–496.
40. Elovainio M, Kivimaki M, Puttonen S, Heponiemi T, Pulkki L, et al. (2004)
Temperament and depressive symptoms: A population-based longitudinal study
on Cloninger’s psychobiological temperament model. J Affect Disord 83: 227–
232.
41. Nyman E, Miettunen J, Freimer N, Joukamaa M, Maki P, et al. (2011) Impact of
temperament on depression and anxiety symptoms and depressive disorder in a
population-based birth cohort. J Affect Disord 131: 393–397.
42. Cloninger CR, S
ˇvrakic
´DM, Przybeck TR (2006) Can personality assessment
predict future depression? A twelve-month follow-up of 631 subjects. Journal of
Affective Disorders 92: 35–44.
43. Jylha P, Isometsa E (2006) Temperament, character and symptoms of anxiety
and depression in the general population. Eur Psychiatry 21: 389–395.
44. Keltikangas-Ja¨ rvinen L, Ravaja N, Viikari J (1999) Identifying Cloninger’s
temperament profiles as related to the early development of the metabolic
cardiovascular syndrome in young men. Arterioscler Thromb Vasc Biol 19:
1988–2006.
45. Herbst JH, Zonderman AB, Mccrae RR, Costa PT (2000) Do the dimensions of
the temperament and character inventory map a simple genetic architecture?
Evidence from molecular genetics and factor analysis. American Journal of
Psychiatry 157: 1285–1290.
46. Janson H, Mathiesen KS (1008) Temperament profiles from infancy to middle
childhood: development and associations with behavior problems. Develop-
mental Psychology 44: 1314–1328.
47. Komsi N, Ra¨ikko¨ nen K, Pesonen AK, Heinonen K, Keskivaara P, et al. (2006)
Continuity of temperament from infancy to middle childhood. Infant Behav Dev
29: 494–508.
48. Caspi A, Moffitt TE, Newman DL, Silva P (1996) Behavioral observations at age
3 years predict adult psychiatric disorders. Longitudinal evidence from a birth
cohort. Archives of General Psychiatry 53: 1033–1039.
49. Crockett LJ, Moilanen KL, Raffaelli M, Randall BA (2006) Psychological
profiles and adolescent adjustment: A person-centered approach. Development
and Psychopathology 18: 195–214.
50. Rettew DC, Althoff RR, Dumenci L, Ayer L, Hudziak JJ (1008) Latent profiles
of temperament and their relations to psychopathology and wellness. Journal of
the American Academy of Child & Adolescent Psychiatry 47: 273–281.
Temperament Clusters in a Normal Population
PLoS ONE | www.plosone.org 11 July 2012 | Volume 7 | Issue 7 | e33088
  • [Show abstract] [Hide abstract] ABSTRACT: This study investigated the factor structure of observational indicators of children's temperament that were collected across the first three years of life in the Family Life Project (N=1205) sample. A four-factor model (activity level, fear, anger, regulation), which corresponded broadly to Rothbart's distinction between reactivity and regulation, provided an acceptable fit the observed data. Tests of measurement invariance demonstrated that a majority of the observational indicators exhibited comparable measurement properties for male vs. female, black vs. white, and poor vs. not-poor children, which improved the generalizability of these results. Unadjusted demographic group comparisons revealed small to moderate sized differences (Cohen ds=|.23-.42|) in temperamental reactivity and moderate to large sized differences (Cohen ds=-.64--.97) in regulation. Collectively, demographic variables explained more of the variation in regulation (R(2)=.25) than in reactivity (R(2)=.02-.06). Follow-up analyses demonstrated that race differences were substantially diminished in magnitude and better accounted for by poverty. These results help to validate the distinction between temperamental reactivity and regulation using observational indicators. Copyright © 2015 Elsevier Inc. All rights reserved.
    Full-text · Article · Feb 2015
  • [Show abstract] [Hide abstract] ABSTRACT: Drug addictions are debilitating disorders that are highly associated with personality abnormalities. Early life stress (ELS) is a common risk factor for addiction and personality disturbances, but the relationships between ELS, addiction, and personality are poorly understood. Ninety-five research participants were assessed for and grouped by ELS history and cocaine dependence. NEO-FFI personality measures were compared between the groups to define ELS- and addiction-related differences in personality traits. ELS and cocaine dependence were then examined as predictors of personality trait scores. Finally, k-means clustering was used to uncover clusters of personality trait configurations within the sample. Odds of cluster membership across subject groups was then determined. Trait expression differed significantly across subject groups. Cocaine-dependent subjects with a history of ELS (cocaine+/ELS+) displayed the greatest deviations in normative personality. Cocaine dependence significantly predicted four traits, while ELS predicted neuroticism and agreeableness; there was no interaction effect between ELS and cocaine dependence. The cluster analysis identified four distinct personality profiles: Open, Gregarious, Dysphoric, and Closed. Distribution of these profiles across subject groups differed significantly. Inclusion in cocaine+/ELS+, cocaine-/ELS+, and cocaine-/ELS- groups significantly increased the odds of expressing the Dysphoric, Open and Gregarious profiles, respectively. Cocaine dependence and early life stress were significantly and differentially associated with altered expression of individual personality traits and their aggregation as personality profiles, suggesting that individuals who are at-risk for developing addictions due to ELS exposure may benefit from personality centered approaches as an early intervention and prevention. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Article · Mar 2015