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Abstract

Background: Personality pathology does not have to be a contraindication to a bariatric surgery if a proper pre-surgical assessment is done. Indicating subgroups of patients with their specific needs could help tailor interventions and improve surgical treatment outcomes. Objectives: Using the Alternative DSM-5 model for personality disorders (AMPD) and the ICD-11 model for PDs to detect subgroups of patients with obesity based on a specific constellation of maladaptive personality traits and the level of overall personality impairment. Methods: 272 consecutively consented patients who underwent a standard pre-surgical psychological assessment. The majority were women (58.0%), age range was 22–79 years ( M = 48.06, SD = 10.70). Patients’ average body mass index (BMI) was 43.95 kg/m ² . All participants were administered the Personality Inventory for DSM-5 (PID-5) from which Level of Personality Functioning Scale-Self Report (LPFS-SR) and Standardized Assessment of Severity of Personality Disorder (SASPD) scores were gained using the “crosswalk” for common metric for self-reported severity of personality disorder. The k-means clustering method was used to define specific subgroups of patients with obesity and replicated for equality testing to the samples of non-clinical respondents and psychiatric patients. Results: The cluster analysis detected specific groups in the sample of patients with obesity, which differed quantitatively from the samples of non-clinical respondents and psychiatric patients. A vast majority of patients with obesity showed above-average values in most of the PID-5 facets compared to the United States representative general community sample. In two out of the three clusters defined, patients demonstrated moderate (> M + 1.5 × SD ) to severe (> M + 2.0 × SD ) personality psychopathology within the Detachment and Negative Affectivity domains according to PID-5, which in one of the clusters corresponded to the mild overall impairment in both, LPFS-SR ( M = 2.18, SD = 0.27) and SASPD ( M = 8.44, SD = 2.38). Moreover, higher levels of psychopathology prove to be associated with higher age and use of psychiatric medication. Conclusions: The dimensional DSM-5 and ICD-11 trait models are suitable procedures for defining specific “characters” of patients in a pre-bariatric setting. As such, they help to identify subgroups of patients with obesity who are different from general population and psychiatric patients. Implications for clinical practice and further research are discussed.
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ORIGINAL RESEARCH
published: 05 January 2022
doi: 10.3389/fpsyg.2021.814421
Edited by:
Gina Rossi,
Vrije Universiteit Brussel (VUB),
Belgium
Reviewed by:
Tim Bastiaens,
University Psychiatric Center KU
Leuven, Belgium
Bo Bach,
Psychiatry Region Zealand, Denmark
*Correspondence:
Karel D. Riegel
kareldobroslav.riegel@vfn.cz
Specialty section:
This article was submitted to
Psychology for Clinical Settings,
a section of the journal
Frontiers in Psychology
Received: 13 November 2021
Accepted: 13 December 2021
Published: 05 January 2022
Citation:
Riegel KD, Konecna J,
Matoulek M and Rosova L (2022)
Implementation of the DSM-5
and ICD-11 Dimensional Models
of Maladaptive Personality Traits Into
Pre-bariatric Assessment.
Front. Psychol. 12:814421.
doi: 10.3389/fpsyg.2021.814421
Implementation of the DSM-5 and
ICD-11 Dimensional Models of
Maladaptive Personality Traits Into
Pre-bariatric Assessment
Karel D. Riegel1*, Judita Konecna2,3 , Martin Matoulek2and Livia Rosova4
1Department of Addictology, 1st Faculty of Medicine, Charles University and General University Hospital, Prague, Czechia,
23rd Department of Medicine—Department of Endocrinology and Metabolism, 1st Faculty of Medicine, Charles University
and General University Hospital, Prague, Czechia, 3Department of Psychiatry, 1st Faculty of Medicine, Charles University
and General University Hospital, Prague, Czechia, 4Department of Applied Mathematics and Statistics, Faculty
of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia
Background: Personality pathology does not have to be a contraindication to a
bariatric surgery if a proper pre-surgical assessment is done. Indicating subgroups of
patients with their specific needs could help tailor interventions and improve surgical
treatment outcomes.
Objectives: Using the Alternative DSM-5 model for personality disorders (AMPD)
and the ICD-11 model for PDs to detect subgroups of patients with obesity based
on a specific constellation of maladaptive personality traits and the level of overall
personality impairment.
Methods: 272 consecutively consented patients who underwent a standard pre-
surgical psychological assessment. The majority were women (58.0%), age range was
22–79 years (M= 48.06, SD = 10.70). Patients’ average body mass index (BMI) was
43.95 kg/m2. All participants were administered the Personality Inventory for DSM-5
(PID-5) from which Level of Personality Functioning Scale-Self Report (LPFS-SR) and
Standardized Assessment of Severity of Personality Disorder (SASPD) scores were
gained using the “crosswalk” for common metric for self-reported severity of personality
disorder. The k-means clustering method was used to define specific subgroups of
patients with obesity and replicated for equality testing to the samples of non-clinical
respondents and psychiatric patients.
Results: The cluster analysis detected specific groups in the sample of patients with
obesity, which differed quantitatively from the samples of non-clinical respondents and
psychiatric patients. A vast majority of patients with obesity showed above-average
values in most of the PID-5 facets compared to the United States representative general
community sample. In two out of the three clusters defined, patients demonstrated
moderate (>M+ 1.5 ×SD) to severe (>M+ 2.0 ×SD) personality psychopathology
within the Detachment and Negative Affectivity domains according to PID-5, which
in one of the clusters corresponded to the mild overall impairment in both, LPFS-
SR (M= 2.18, SD = 0.27) and SASPD (M= 8.44, SD = 2.38). Moreover, higher
levels of psychopathology prove to be associated with higher age and use of
psychiatric medication.
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Riegel et al. DSM-5 and ICD-11 Traits in Bariatrics
Conclusions: The dimensional DSM-5 and ICD-11 trait models are suitable procedures
for defining specific “characters” of patients in a pre-bariatric setting. As such, they
help to identify subgroups of patients with obesity who are different from general
population and psychiatric patients. Implications for clinical practice and further
research are discussed.
Keywords: bariatric surgery, obesity, PID-5, AMPD, cluster analysis, personality trait, ICD-11
INTRODUCTION
The level of personality pathology and mental disorders including
personality disorders (PDs) is discussed in regard to obesity
treatment, especially when the risks and profits of bariatric
surgery are in question (Walfish et al., 2007;Chalopin et al.,
2020). Some findings indicate a less successful outcome for
obesity patients suffering from adjustment disorders, depression
and/or PDs, compared to patients who are not mentally ill (Kinzl
et al., 2006). Although the PD diagnosis is one of the three most
frequently cited factors that negatively affect weight reduction
after bariatric surgery (Livhits et al., 2012), the way personality
pathology is reported prevents a systematic comparison of results.
First, studies address personality variables by various tools, which
may cause inconsistencies in the presented findings (Rowe et al.,
2000;Guisado et al., 2002;Tsushima et al., 2004;De Panfilis et al.,
2006;Leombruni et al., 2007). Another problem is that there is no
clear consensus among psychologists as to whether (Larsen et al.,
2004) and how to assess the overall personality psychopathology
in pre-bariatric evaluations (Bauchowitz et al., 2005;Fabricatore
et al., 2006).
There is already some evidence that the reliance on the
categorical approach to the classification of psychological
variables, in addition to the existence of methodological
differences between studies, is likely to have contributed to
the reports of inconsistent findings regarding the role of
psychopathology on the bariatric treatment outcomes (Oltmanns
et al., 2020). Pre-surgical use of dimensional assessment of the
hierarchical model of psychopathology is important in predicting
post-surgical outcomes, because it provides more statistical
power to detect change through enhancing measurement
reliability (Marek et al., 2016). The issue of reliability can be
considered as one of the main reasons why the dimensional
classification of personality and psychopathology is increasingly
promoted (Kotov et al., 2017). To date, several studies have
been published using the Minnesota Multiphasic Personality
Inventory-2 (MMPI-2) and its restructured form (MMPI-2-
RF) in bariatric settings (e.g., Tsushima et al., 2004;Marek
et al., 2015a,b,2017). The last of the cited studies provided
strong supporting evidence that disinhibited and depressed
personality along with the presence of borderline, antisocial,
and narcissistic personality traits predicts less longitudinal body
mass index (BMI) reduction 5 years after bariatric surgery
(Marek et al., 2017). Similarly, borderline personality problems
along with anxiety-related disorders and higher proneness to
stress have been associated with less optimal BMI and weight
outcomes 5 years after bariatric surgery in a recent study using
the Personality Assessment Inventory (PAI) in a representative
sample of bariatric patients (Oltmanns et al., 2020). Put together,
using the dimensional assessment tools such as MMPI-2-RF
and PAI supports the clinical utility of dimensional systems
for pre-surgical personality assessment of bariatric patients. The
Alternative DSM-5 model for personality disorders (AMPD)
might be one of such systems.
The AMPD has been introduced in the 5th edition of the
Diagnostic and Statistical Manual of Mental Disorders (DSM-5)
section III as a dimensional alternative for measuring personality
psychopathology to the current categorical classification
maintained in DSM-5 section II [American Psychiatric
Association [APA], 2013]. The key innovation of the AMPD
is defining PDs on the basis of impairments in personality
functioning (criterion A) and the presence of maladaptive
personality traits (criterion B). This makes it possible to create
a more plastic and vivid image of a personality than simply
reaching a diagnostic threshold for the presence or absence of a
PD diagnosis by meeting a limited number of criteria. Moreover,
the AMPD approach to PDs is largely commensurate with
the 11th edition of the International Classification of Diseases
(ICD-11) [World Health Organization [WHO], 2019], which
also adopts a dimensional approach to the classification of PDs
that focuses on global level of severity and five trait qualifiers.
In both classifications trait domains are used as specifiers that
contribute to the individual expression of personality disturbance
in addition to the overall classification of severity (e.g., mild,
moderate, or severe) (Zimmermann et al., 2019). Both ICD-11
and AMPD describe trait domains of Negative Affectivity,
Detachment, Antagonism/Dissociality, and Disinhibition.
In addition, the AMPD also includes a separate domain of
Psychoticism, whereas the ICD-11 includes a separate domain of
Anankastia. Furthermore, the 25 trait facets in the AMPD model
may provide more detailed description of the subtle nuances
of the patient’s personality (Bach and First, 2018), which can
be enriching also for the ICD-11’s five trait domains. The high
complementarity of the two classifications makes it possible
to use uniform instruments for this purpose, for example the
Personality Inventory for DSM-5 (PID-5), a self-assessment tool
designed to directly evaluate the proposed system of personality
traits in the AMPD.
Since its release in 2013, the AMPD has stimulated extensive
research with promising findings (Zimmermann et al., 2019),
which go well beyond the scope of this article. However,
with regard to some broadband tools for assessing personality
psychopathology in bariatric settings, we consider it important to
mention some conclusions regarding the convergent validity of
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the AMPD. Previous research has shown significant convergence
between the Personality Psychopathology-Five (PSY-5) domains
of MMPI-2-RF and the domains and facets of the PID-5
(Anderson et al., 2013). Moreover, there is also empirical
evidence about the substantial convergence between the DSM-5
pathological traits and a range of clinical issues as instantiated in
the PAI (Hopwood et al., 2013). The broad convergence between
personality and psychopathology constructs is consistent with
the hypothesis that the higher-order domains that describe
covariation in normal personality, personality disorder, and
clinical constructs more generally, might be thought of
as psychological systems, as might be seen e.g., in the
Research Domain Criteria (RDoC) (Insel et al., 2010) or the
Hierarchical Taxonomy of Psychopathology (HiTOP) (Kotov
et al., 2017) framework.
To date, some studies (e.g., Marek et al., 2015b, 2016) have
explained the pre-surgical psychological risk factors for poor
bariatric surgery outcomes associated with PSY-5 on basis of
the RDoC, but to the best of our knowledge, there is only one
study using tools designed for the AMPD in patients with obesity.
Its authors demonstrated a specific constellation of maladaptive
personality traits of emotional lability, anhedonia, impulsivity
and depressivity in a relatively small sample (n= 55) of obese
female patients with a binge eating disorder (BED) using a
forward stepwise linear regression analysis (Aloi et al., 2020).
The authors conclude that these preliminary findings could be
beneficial in clinical practice where specialists should evaluate
the presence of specific personological traits to develop specific
therapeutic approaches to offer a tailor-made treatment for
patients with comorbid obesity and BED.
It has been pointed out (Gerlach et al., 2015), that the best way
how to treat obesity is to make the treatment as individualized as
possible, which seems particularly meaningful for patients with
a more severe personality psychopathology (Kinzl et al., 2006).
Following the previous research focused on the dimensional
evaluation of psychopathology in patients with obesity (Aloi et al.,
2020) and its importance for the prediction of bariatric outcomes
(Marek et al., 2015b;Oltmanns et al., 2020), the first goal of this
study is to use PID-5 to detect subgroups of patients based on
a specific constellation of maladaptive personality traits. As the
a priori internal structure of the PID-5 is not taken into account,
methods such as confirmatory factor analysis are not applicable.
Also, feature extraction methods are most commonly used to a
different purpose, i.e., simplification of the set of variables. For
these reasons we found cluster analysis to be the most suitable
approach. We assume that, based on a cluster analysis, we will
be able to detect several clusters in a sample of patients with
obesity which will be quantitatively different from the general
population sample and the sample of psychiatric patients. Given
the fact that the ICD-11 is the only authoritative nomenclature
for all WHO countries, while the AMPD remains an “alternative”
to the existing categorical model retained in DSM-5 Section II,
we applied cluster analysis not only to the AMPD, but also the
ICD-11 trait model for PDs. Although the specific constellation
of personality traits may not in itself indicate the overall degree of
personality impairment, previous findings have shown that the
PID-5 score is consistent with various measures of personality
functioning (Zimmermann et al., 2019). Therefore, as the second
goal of the study, we use a crosswalk between PID-5 and the
Level of Personality Functioning Scale-Self Report (LPFS-SR) and
the Standardized Assessment of Severity of Personality Disorder
(SASPD) scores (Zimmermann et al., 2020) to verify differences
in the severity of personality psychopathology between individual
clusters. Consequently, we hypothesize that based on the AMPD
criteria, we will be able to detect groups of patients with
obesity with different care needs, which may have a significant
prospective impact on the effectiveness of obesity treatment.
MATERIALS AND METHODS
Participants
Three samples were used in the study. Sample 1 was
composed of patients with obesity, Sample 2 contained university
students from various fields of study, working volunteers
and pensioners, and Sample 3 was composed of psychiatric
patients. Samples 2 and 3 were used to compare the equality
of the cluster analysis with Sample 1. To be included in
a sample, all participants had to be over 18 years of age.
Participation in the study was voluntary and anonymous
for all respondents. Participants were not rewarded for their
participation in the study. As seen in the Plan of analysis
section, we used the PID-5 Response Inconsistency Scale
(PID-5-RIS) for removing participants with invalid data. The
number of removed participants within the samples was as
follows: Sample 1 n= 80; Sample 2 n= 37; Sample 3
n= 37.
Sample 1 consisted of patients with severe obesity (n= 272;
average BMI was 43.95 kg/m2). Patients were consecutively
recruited from both the outpatient and inpatient units of the
Department of Endocrinology and Metabolism of the General
University Hospital in Prague. These individuals were considered
as potential candidates for a bariatric surgery between 1/2017
and 3/2020. The exclusion criteria were: age <18 years,
BMI <30.10 kg/m2, a neurological condition or acute psychotic
illness that could affect cognitive functioning, presence of
type 1 diabetes mellitus. Gender representation was almost
balanced: women slightly prevailed over men (n= 159, 58.0%).
The age range was 22–79 years (M= 48.06, SD = 10.70).
Distribution according to the highest attained level of education
was as follows: primary education 4.78%, secondary education
57.35%, some college 4.04%, university degree 25.74%, not
specified 8.09%.
Sample 2 consisted of respondents from the general
population included in the international study by Bach et al.
(2020) (n= 335). Gender representation was not balanced:
women prevailed over men (n= 217, 64.8%). The age range
was 18–84 years (M= 32.07, SD = 12.81). Distribution
according to the highest attained level of education in this
group was as follows: primary education 1.2%, secondary
education 53.4%, some college 6.3%, undergraduate degree
15.8%, graduate degree 23.3%.
Sample 3 consisted of psychiatric patients included in the
study by Riegel et al. (2018) (n= 106). Gender representation
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Riegel et al. DSM-5 and ICD-11 Traits in Bariatrics
was not balanced: again, women prevailed over men (n= 70,
66.0%). The age range was 18–65 years (M= 36.58, SD = 11.53).
Distribution according to the highest attained level of education
in this group was as follows: primary education 11.3%, secondary
education 65.1%, some college 6.6%, undergraduate degree 0.9%,
graduate degree 16.0%.
Procedures
Each patient from Sample 1 was examined by a multidisciplinary
team consisting of a surgeon, gastroenterologist, dietician,
and clinical psychologist. As part of a standard psychological
examination, all subjects enrolled in this study were asked
to fill the PID-5 and sociodemographic questionnaires either
online after the assignment via a unique identification code,
or by the paper-and-pencil method. Potential differences
between the types of administration were assessed by
comparing all of the mean scores. As no statistically
significant differences were found, the subgroups were
merged. Only complete and valid PID-5 protocols were
included in the analysis (see Plan of analysis). Prior to the
evaluation, patients were informed about the objective of
the study and asked to consent to having their medical
and psychological data used for research purposes. The
study protocol and the informed consent form were
approved by the ethics committee of the General University
Hospital in Prague.
Measures
Sociodemographic Questionnaire
All respondents were asked to answer questions regarding their
age, weight, height, gender, level of education, presence of type
2 diabetes mellitus, history of bariatric surgery, and use of
psychiatric medication.
Personality Inventory for DSM-5
The PID-5 (Krueger et al., 2012) is a self-administered of
220 items that measures 25 personality trait facets according
to the criterion B of the AMPD. The facets are grouped
into five broad domains: Negative Affectivity, Detachment,
Antagonism, Disinhibition, and Psychoticism. Participants are
asked to evaluate each item on a Likert scale ranging from
0 (“very untrue or often untrue”) to 3 (“very true or often
true”). Higher average scores indicate greater dysfunction in
a specific facet or domain. In the present study, in Sample
1, the Cronbach’s alphas for all 25 facets ranged from
0.56 (suspiciousness) to 0.94 (eccentricity) indicating good
internal consistency. For the purpose of comparing the PID-
5 scores with LPFS-SR and SASPD we used an algorithm
which has been recently developed to evaluate the combined
AMPD and the ICD-11 personality traits model based on six
higher-order domains (i.e., Negative affectivity, Detachment,
Antagonism, Disinhibition, Anankastia and Psychoticism),
covering 17 of the lower-order facets and featuring a total
number of 34 items. This algorithm is captured by the
Personality Inventory for DSM-5-Brief Form Plus (PID-5BF+)
(Kerber et al., 2020).
Level of Personality Functioning Scale-Self Report
The LPFS-SR (Morey, 2017) is a comprehensive self-report
measure for assessing criterion A of the AMPD. It features
descriptions of five different levels of impairment in the domains
of identity, self-direction, empathy, and intimacy. It includes 80
items that are rated on four-point Likert scales ranging from
1 (“totally false, not at all true”) to 4 (“very true”). Since the
LPFS-SR was not administered to patients, as the evaluation of
the general personality impairment was not an inherent part of
pre-bariatric assessment, we have worked—for the purposes of
this study—exclusively with the total (average) scores based on
the simplified scoring scheme proposed by Zimmermann et al.
(2020).
Standardized Assessment of Severity of Personality
Disorder
The SASPD (Olajide et al., 2018) is a self-report measure that
provides an index of PD severity. It includes 9 items that are
rated using 0–3 response options with unique descriptions and
captures 9 distinct PD features, which are separately rated in
terms of severity. As in the case of LPFS-SR, we’ve also worked
for SASPD exclusively with the total (sum) scores based on the
scoring scheme proposed by Zimmermann et al. (2020).
Plan of Analysis
To ensure the validity of data in all three samples, we used the
PID-5-RIS developed by Keeley et al. (2016), which has proven
successful in detecting random responses in the original version
of PID-5 and has been verified by a number of recent studies
(Bagby and Sellbom, 2018;Somma et al., 2018;Lowmaster et al.,
2020). In line with these studies, we excluded respondents with a
PID-5-RIS score 17. Subsequently, the internal consistency of
each trait was examined by calculating Cronbach’s alpha.
Afterward, based on the measured patient scores, subgroups
were formed which associated patients with similar scores. The
division was performed using the well-known k-means clustering
method, in which nobservations are divided into kclusters,
where each observation belongs to the cluster with the closest
mean (cluster center). The number of clusters was chosen
using the silhouette method (based on how much a point is
similar to its own cluster compared to other clusters) supported
by the elbow method (based on the average distance of each
point in a cluster to its cluster’s center). One-way ANOVA
was used to analyze whether the scores of the three clusters
differed significantly.
In the next step the algorithm developed by Bach et al. (2017)
was used to derive ICD-11 trait domains using PID-5 trait facet
scores. Finally, relationships between the established clusters and
the sociodemographic data were examined through the cross-
tabulation analysis.
To obtain information about the potential results of the LPFS-
SR and SASPD questionnaires, we used a crosswalk table linking
total scores of different measures (Zimmermann et al., 2020).
Because this connection is made only through PID-5BF+ we
first needed to abbreviate the full 220-item PID-5 following the
algorithm proposed by Kerber et al. (2020).
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FIGURE 1 | Elbow diagram (left) and silhouette diagram (right) for all three datasets—patients with obesity (Sample 1), respondents from the general population
(Sample 2) and psychiatric patients (Sample 3).
The statistical program R 4.1.0 (R Core Team, 2021) was used
in the analysis.
RESULTS
Defining Trait-Based Personality Clusters
Figure 1 shows the elbow diagram and the silhouette diagram
for all three datasets. For Samples 2 and 3 a division into
two clusters is proposed, while three clusters are recommended
as the optimal solution for the examined Sample 1. In the
following paragraphs, we refer to the results relating exclusively
to Sample 1.
Table 1 and Figure 2 provide more detailed information about
the average PID-5 facet scores and the corresponding standard
deviations across all clusters. Clusters differed by the number
of participants. The lowest number of respondents after the
division into three clusters was in Cluster 1 (n= 20, 7.4%). In
this cluster, the lowest average values of the PID-5 facet scores
were achieved in the range from M= 0.27 (SD = 0.23) for
callousness to M= 1.15 (SD = 0.70) for separation insecurity.
Cluster 3 (n= 107, 39.3%) was the cluster with the highest values
of the average score, which ranged from M= 1.54 (SD = 0.38)
for callousness to M= 2.79 (SD = 0.63) for emotional lability.
Cluster 2 comprised the highest number of respondents (n= 145,
53.3%) and its average score values ranged from M= 1.23
(SD = 0.21) for perceptual dysregulation to M= 2.01 (SD = 0.48)
for restricted affectivity. In all cases, the hypothesis of equality of
the scores of the three clusters was tested and in all cases it was
rejected (p<0.05).
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TABLE 1 | Cronbach’s alphas, average scores and standard deviations of PID-5 facets and domains within clusters.
Facet/domain Clusters
1 (n= 20) 2 (n= 145) 3 (n= 107)
No. items Cr. alpha M SD M SD M SD
Anhedonia 8 0.82 0.79 0.51 1.81 0.35 2.33 0.43
Anxiousness 9 0.89 0.94 0.55 1.67 0.44 2.57 0.64
Attention seeking 8 0.88 0.66 0.81 1.41 0.49 1.89 0.75
Callousness 14 0.75 0.27 0.23 1.34 0.20 1.54 0.38
Deceitfulness 10 0.81 0.50 0.56 1.47 0.33 1.83 0.50
Depressivity 14 0.92 0.52 0.51 1.32 0.28 2.14 0.67
Distractibility 9 0.88 0.77 0.71 1.57 0.46 2.44 0.65
Eccentricity 13 0.94 0.46 0.53 1.25 0.35 2.05 0.68
Emotional lability 7 0.86 1.09 0.67 1.74 0.50 2.79 0.63
Grandiosity 6 0.77 0.35 0.46 1.28 0.37 1.60 0.59
Hostility 10 0.82 0.84 0.53 1.71 0.40 2.50 0.47
Impulsivity 6 0.76 1.04 0.61 1.74 0.53 2.40 0.60
Intimacy avoidance 6 0.70 0.61 0.64 1.91 0.51 2.13 0.52
Irresponsibility 7 0.72 0.51 0.40 1.60 0.27 2.02 0.47
Manipulativeness 5 0.65 0.50 0.52 1.44 0.44 1.69 0.56
Perceptual dysregulation 12 0.81 0.32 0.25 1.23 0.21 1.81 0.49
Perseveration 9 0.77 0.97 0.56 1.74 0.39 2.42 0.46
Restricted affectivity 7 0.58 0.79 0.44 2.01 0.48 2.08 0.54
Rigid perfectionism 10 0.86 1.02 0.66 1.89 0.52 2.36 0.63
Risk taking 14 0.82 0.99 0.40 1.94 0.29 2.21 0.27
Separation insecurity 7 0.81 1.15 0.70 1.82 0.56 2.51 0.68
Submissiveness 4 0.70 0.99 0.68 1.87 0.57 2.44 0.66
Suspiciousness 7 0.56 0.91 0.36 1.89 0.40 2.37 0.42
Unusual beliefs and experiences 8 0.81 0.46 0.50 1.32 0.38 1.73 0.63
Withdrawal 10 0.84 0.76 0.37 1.78 0.52 2.30 0.60
Negative Affectivity 1.06 0.54 1.74 0.37 2.62 0.47
Detachment 0.72 0.40 1.83 0.34 2.25 0.38
Antagonism 0.45 0.44 1.40 0.31 1.71 0.46
Disinhibition 0.77 0.49 1.64 0.31 2.29 0.39
Psychoticism 0.41 0.33 1.27 0.24 1.86 0.50
NEGATIVE AFFECTIVITY 0.85 0.49 1.58 0.31 2.50 0.49
DETACHMENT 0.72 0.39 1.90 0.38 2.17 0.41
DISSOCIALITY 0.49 0.36 1.44 0.24 1.83 0.36
DISINHIBITION 0.83 0.39 1.71 0.26 2.27 0.31
ANANKASTIA 0.99 0.51 1.81 0.36 2.39 0.42
Scores above M + 1.0 ×SD (normative United States values) are italicized; scores above M + 1.5 ×SD are italicized and bolded; scores above M + 2.0 ×SD are
italicized, bolded and underlined.
ICD-11 trait domains are capitalized.
As can be seen in the PID-5 domain values in Figure 2
and Table 1, the lowest value in Cluster 1 was acquired by the
Psychoticism domain (M= 0.41, SD = 0.33) while the Negative
Affectivity domain had the highest value (M= 1.06, SD = 0.54).
This is similar to Cluster 3, where the highest value was also
Negative Affectivity (M= 2.62, SD = 0.47), however, the lowest
was Antagonism (M= 1.71, SD = 0.46). Cluster 2 differed in the
highest value achieved by Detachment (M= 1.83, SD = 0.34), with
the lowest value being analogous to Cluster 1 with Psychoticism
(M= 1.27, SD = 0.24). As with the individual facet scores testing,
equality between cluster scores was not confirmed for domains.
From the perspective of the ICD-11 domain scores, the
lowest value in Cluster 1 was obtained by the Dissociality
domain (M= 0.49, SD = 0.36) and the highest by Anankastia
(M= 0.99, SD = 0.51). In Cluster 2 was also the lowest
value Dissociality (M= 1.44, SD = 0.24), however, the highest
value was acquired by the Detachment domain (M= 1.90,
SD = 0.38). In line with both previous clusters, Dissociality was
the lowest domain also in Cluster 3 (M= 1.83, SD = 0.36)
while the Negative Affectivity domain had the highest value
(M= 2.50, SD = 0.49). For more details we refer to Table 1
and Figure 3.
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FIGURE 2 | The average PID-5 facet and domain scores within clusters for patients with obesity (Sample 1).
FIGURE 3 | The average ICD-11 trait domain scores within clusters for patients with obesity (Sample 1).
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Due to the absence of representative Czechia normative values
of the PID-5, we compared the average scores across the clusters
with the normative data from the United States representative
sample (Krueger et al., 2012). This comparison helped us in
defining the most clinically significant features within the clusters
based on the height of the standard deviations for each individual
facet and domain. In Cluster 1, none of the facets or domains
exceeded the value of the normative score mean plus one
standard deviation (M+ 1.0 ×SD). In Cluster 2, 20 out of 25
PID-5 facets and all five domains scored >M+ 1.0 ×SD, of
which eight facets and four domains scored >M+ 1.5 ×SD
and 1 facet scored >M+ 2.0 ×SD. In Cluster 3, all 25
facets and all five domains exceeded M+ 1.0 ×SD, of which
seven facets scored >M+ 1.5 ×SD and 16 facets and all five
domains scored >M+ 2.0 ×SD. Detailed information about
the distribution of the facet and domain scores are provided in
Table 1.
Associations Between Clusters and
Sociodemographic Data
From the perspective of age, the lowest average age (M= 41.22,
SD = 12.45) was observed in Cluster 1, followed by Cluster 3
(M= 47.66, SD = 9.50) and Cluster 2 (M= 49.23, SD = 11.03).
The male-female ratio was almost balanced in Cluster 1 (47.4%
vs. 52.6%) and Cluster 2 (44.8% vs. 55.2%), whereas in Cluster
3 women predominated over men (35.5% vs. 64.5%). From the
perspective of psychiatric medication use, the proportions of
patients between clusters were unbalanced: Cluster 1 (10.0%);
Cluster 2 (28.3%); and Cluster 3 (47.7%). The average BMI was
almost balanced between the clusters: Cluster 1 (M= 45.46,
SD = 12.24); Cluster 2 (M= 44.49, SD = 8.60); and Cluster 3
(M= 43.72, SD = 7.89). The differences between clusters and
sociodemographic data were not statistically significant, except
for age (F= 4.72, df = 2, p= 0.0097) and the use of psychiatric
medication (χ2= 15.99, df = 2, p<0.05).
Matching the Clusters With the Level of
Overall Psychopathology
After the transformation of PID-5 to PID-5BF+, the cluster with
the lowest acquired values of average facet scores became Cluster
2 (except for intimacy avoidance and withdrawal, both contained
the lowest facet values). Subsequently, this was reflected in the
average value of LPFS-SR, which was M= 1.79 (SD = 0.37) for
Cluster 1, M= 1.63 (SD = 0.22) for Cluster 2, and M= 2.18
(SD = 0.27) for Cluster 3 and SASPD, which was M= 4.75
(SD = 3.45) for Cluster 1, M= 3.48 (SD = 2.17) for Cluster 2,
and M= 8.44 (SD = 2.38) for Cluster 3. Detailed PID-5BF+ facet
scores for each cluster are provided in Table 2.
DISCUSSION
In line with the first goal of the current investigation based on
the k-means clustering method, we defined three clusters with
a specific distribution of the AMPD maladaptive personality
traits and varying degrees of psychopathology within Sample
1. As the three-cluster solution did not prove to be optimal in
TABLE 2 | Average scores and standard deviations of PID-5BF+ facets
within clusters.
Facet Clusters
1 (n= 20) 2 (n= 145) 3 (n= 107)
M SD M SD M SD
Anhedonia 0.68 0.63 0.61 0.69 1.31 0.87
Anxiety 0.52 0.60 0.48 0.68 1.48 1.01
Deceitfulness 0.82 0.89 0.57 0.65 1.11 0.84
Distractibility 0.68 0.67 0.57 0.68 1.47 0.82
Eccentricity 0.58 0.75 0.24 0.43 0.93 0.82
Emotional lability 1.32 0.83 0.67 0.67 1.85 0.79
Grandiosity 0.35 0.56 0.23 0.45 0.55 0.69
Impulsivity 0.95 0.84 0.80 0.73 1.60 0.81
Intimacy avoidance 0.45 0.67 0.53 0.69 0.76 0.78
Irresponsibility 0.55 0.63 0.27 0.44 0.81 0.76
Manipulativeness 0.28 0.47 0.18 0.34 0.37 0.56
Perceptual dysregulation 0.15 0.40 0.15 0.36 0.52 0.69
Perseveration 0.68 0.67 0.50 0.57 1.16 0.69
Rigid perfectionism 0.95 0.78 0.77 0.67 1.25 0.83
Separation insecurity 0.85 0.75 0.59 0.64 1.35 0.78
Unusual beliefs and experiences 0.75 0.90 0.35 0.57 0.96 0.85
Withdrawal 0.62 0.58 0.65 0.71 1.29 0.75
the remaining Samples 2 and 3, we can assume that the more
heterogeneous sample of patients with obesity contains certain
specifics that allow for more precise definition of personality
indicators than the mere distinction between greater and lesser
severity, which proved typical for more homogeneous samples
of non-clinical respondents and psychiatric patients. On the
one hand, the difference between Clusters 1 and 2 in Sample 1
underlines to some extent the fact that obesity is a multifactorial
physical health problem, which is primarily a consequence
of a sustained positive energy balance (Gerlach et al., 2015)
to which psychological factors (such as personality variables)
of an individual may or may not contribute. On the other
hand, the difference between Clusters 2 and 3 suggests that
another specific group of individuals can be detected among
obese patients with higher psychopathology, whose personality
variables significantly interfere with adaptive psychological
functioning. This is confirmed not only by the prescription
of psychiatric medication in almost 50% of respondents from
Cluster 3, but also by the degree of overall personality impairment
according to the SASPD in this cluster. Considering that the
relationship between the degree of personality impairment, the
use of psychiatric medication and obesity has been already
pointed out (Frankenburg and Zanarini, 2006), the distinction
of Sample 1 into three specific clusters has significant clinical
justification, especially in relation to more serious personality
problems such as borderline psychopathology.
Although the clusters within the Sample 1 proved to be
mutually unequal from the quantitative point of view, we are
aware that from the perspective of clinical practice, qualitative
differences in facet/domain distribution between the clusters
have informative value only when tested for their clinical
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Riegel et al. DSM-5 and ICD-11 Traits in Bariatrics
significance. Interestingly, 93% of respondents in Sample 1 were
included in clusters with above-average mean values of the
majority of PID-5 facets and domains in comparison to the
values of a representative United States sample. This result
is in line with some previous studies which have shown that
the incidence of psychiatric disorders, including personality
disorders, increases in patients with obesity (Baumeister and
Härter, 2007;Petry et al., 2008). The only exception were
the respondents included in Cluster 1, who did not show
above-average values in any of the PID-5 facets and domains.
Reflecting that it was a cluster with a distinctly lower number
of patients who were in addition significantly younger compared
to the other two clusters, the lower proportion of maladaptive
personality traits observed in this cluster can be seen as an
exception rather than a rule, considering our Sample 1 as
a whole, in which higher age seems to be connected with
higher psychopathology. Although it can be expected that
patients belonging to this cluster may cooperate well in both
the pre-surgical and post-surgical phase of bariatric treatment
without the need to undergo any additional psychological
intervention, a somewhat higher proneness to the Negative
Affectivity traits of separation insecurity and emotional lability
should always be investigated in the course of a pre-bariatric
psychological evaluation. Interestingly, when the algorithm for
deriving ICD-11 domains was applied, the Anankastia domain
became the most prominent trait domain in this cluster.
We assume, that was probably due to the omission of the
separation insecurity facet, which is not included in a triplet
of PID-5 facets primarily defining Negative Affectivity domain
according to this algorithm (Bach et al., 2017). In this case,
it seems desirable to confirm the role of the Anankastia
domain in Cluster 1 via more specific tools for the ICD-11
trait model, such as Personality Inventory for ICD-11 (PiCD)
(Oltmanns and Widiger, 2018).
In the case of Cluster 2, the restricted affectivity, intimacy
avoidance, risk taking, suspiciousness, rigid perfectionism, and
irresponsibility facets deserve special attention. In regard to
specific PDs according to the AMPD (American Psychiatric
Association [APA], 2013), this constellation of personality traits
most corresponds to the criteria of an obsessive-compulsive
personality disorder, in which compulsivity for repetitive
behavior despite its negative consequences is a central feature.
Behavioral patterns of compulsive eating are common across
several eating-related conditions (Moore et al., 2017). Kakoschke
et al. (2019) indicated some evidence of deficits across the
compulsivity-related cognitive processes among individuals with
excessive eating-related problems. According to their findings,
there were differences in terms of the valence of impaired reversal
learning in patients with obesity and those with comorbid
obesity and BED. While obese individuals without BED may be
more likely to avoid responding based on previously punished
behaviors, an increased sensitivity to rewards, and enhanced
risk taking in relation to reward expectation might be common
features in obese individuals with BED. Those patients’ risk-
taking behavior in the reward domain shows similarities to
substance use disorders (Voon et al., 2015). This distinction
may lead to a consideration of the presence of two subtypes
of patients with a predominance of obsessive-compulsive traits
within Cluster 2, which is to some extent supported by the
distribution of domains according to ICD-11. While the first
subtype would be represented by patients with a predominance of
Detachment and Anankastia domains, the second subtype would
be represented by patients with a predominance of Detachment
and Disinhibition domains. In terms of clinical implications,
different pre-surgical interventions should be considered for both
the subtypes of patients. While obese patients with concomitantly
increased Anankastia might benefit from dieticians’ educational
programs, in some cases supplemented by specific psychological
interventions decreasing their anxiety about the surgery, in case
of patients with increased Disinhibition, treatment approaches
should seek to use explicit knowledge of the contingencies
between actions and outcomes to update the maladaptive eating
behavior, especially when comorbid eating disorder such BED
is in question (Heriseanu et al., 2020). These interventions may
be reminiscent of those used in the treatment of substance use
disorders and should precede bariatric considerations.
Finally, in Cluster 3, clinically significant scores of M+ 2 ×SD
in the emotional lability, anxiousness, separation insecurity,
depressivity, impulsivity, risk taking and hostility facets—
i.e., all of the PID-5 facets defining borderline personality
disorder (BPD) within the AMPD (American Psychiatric
Association [APA], 2013)—support the previous findings about
the prevalence of borderline personality symptomatology among
gastric surgery patients (Sansone et al., 2008). In addition, the
degree of psychopathology in this cluster appears to be related to
the significantly higher proportion of patients taking psychiatric
medication in comparison to the other two groups. More
recently, borderline personality problems, namely problems with
the self and identity, and rocky and unstable interpersonal
relationships, have been associated with higher 5-year outcomes
in both BMI and weight after a bariatric surgery (Oltmanns
et al., 2020). These findings are in line with another predictive
study published by Marek et al. (2017), in which borderline
features connected with behavioral/externalizing dysfunction,
such as disinhibition and aggression, were one of the predictors
responsible for a higher BMI at the 5-year outcome after a
bariatric surgery. In this regard, our results provide further
evidence of the clinical utility of dimensional tools for assessing
personality traits such as PAI, MMPI-2-RF, and now also PID-5,
in detecting borderline psychopathology in obesity patients. Early
detection of borderline features in bariatric candidates might be
crucial for treatment planning. Although there is already some
evidence that mental health treatment after bariatric surgery
influences short-term outcomes (Shen et al., 2016), in case of
patients with more severe mental health problems such as BPD,
long-term success of therapy is likely to be impeded given
their personality structure (Gerlach et al., 2015). Thus, in such
cases, specific treatment options focused on strengthening self-
control skills should be applied not only after, but also before the
bariatric surgery.
As the severity of impairment in the areas of self and
interpersonal functioning is considered to be a core of
personality psychopathology in the AMPD (Pincus et al., 2020)
as well as in the ICD-11 model for PDs [Morey, 2017;
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Riegel et al. DSM-5 and ICD-11 Traits in Bariatrics
Bach and First, 2018;World Health Organization [WHO], 2019],
we tried—in line with the second goal of our investigation—
to examine the overall level of personality impairment across
the defined clusters via LPFS-SR and SASPD based on the
PID-5BF+ scores. Although the averaging of psychopathology
in Sample 1 hypothetically corresponded to Kernberg’s model
of personality psychopathology (Kernberg, 1984), since Cluster
1 resembled normal, Cluster 2 neurotic, and Cluster 3 a
borderline personality organization, this assumption was partially
confirmed by LPFS-SR and SASPD only for Cluster 3, in which
the trait-based borderline psychopathology corresponded with
a mild impairment in the personality functioning, which is
considered as the minimal threshold for yielding a Personality
Disorder diagnosis according the ICD-11 [Bach and First,
2018;World Health Organization [WHO], 2019]. It could be
assumed that although PID-5BF+ is a good screening tool for
distinguishing between mild and more severe psychopathology,
a 34-item measure cannot provide the diagnostic precision
and coverage of a 220-item measure, especially with respect
to the facet traits that are assessed with only two items
(Kerber et al., 2020).
Our findings need to be considered with respect to certain
limitations that may inspire future research. First of all, it
is necessary to take into account the subjectivity of the
respondents’ statements as PID-5 is a tool based on self-
assessment. Moreover, from the perspective of the AMPD and
the ICD-11 model for PDs as a whole, we consider the way
of assessing overall personality impairment on the basis of
the crosswalk between PID-5 and LPFS-SR and SASPD as
another significant limit of the presented study. Further research
would benefit from the inclusion of clinician-guided structured
interviews focused on the AMPD criterion A, such as the
Structured Clinical Interview for the DSM-5 AMPD (Clarkin
et al., 2020), or at least from the administration of the LPFS-
SR as a stand-alone measure. With regard the ICD-11 trait
model, employment of the instruments specifically developed
for ICD-11 as PiCD could provide further verification of our
findings based on the PID-5. Another limitation might be seen
in using representative United States norms as thresholds of
clinical significance. Although the previous research has shown
significant differences between the average PID-5 scores of
the Czechia and American populations (Riegel et al., 2017),
it should be borne in mind that this data were not obtained
from a representative Czechia sample and thus their use could
significantly underestimate the severity of psychopathology. With
regard to the proposed treatment implications, another limit
of the study can be seen in the absence of specific tools
for the diagnosis of BED and a confirmation of obsessive-
compulsive and borderline psychopathology in our Sample
1. Future research in this regard could provide important
information on whether our hypothesis of the two subtypes
of patients in our largest Cluster 2 is justified. In addition,
our analysis did not aim to find specific score patterns
for bariatric patients, which also creates room for a future
study focusing on a variable-centered approach. Finally, we
consider it important to mention that with regard to the
cross-sectional study design, our results have only a limited
predictive value. In this respect, we consider the presented
research to be exploratory. Nevertheless, our study can be deemed
an important first step for future confirmatory studies on a
longitudinal basis.
CONCLUSION
Overall, in line with the previous studies (Marek et al.,
2015b, 2017;Oltmanns et al., 2020), the current investigation
has provided yet more support for the utility of using
empirically-grounded, dimensional psychological assessments in
pre-surgical evaluation. In contrast to the study by Larsen
et al. (2004) and in line with study by Gerlach et al. (2015),
our findings suggest a personality assessment to be a valuable
procedure for delineation of specific “characters” of the bariatric
patients that can provide important clinical information for
tailoring obesity treatment planning. PID-5 seems to be a
reliable instrument for identifying different groups of patients
with obesity, which are quantitatively different from general
community individuals and psychiatric patients. Our results
support the effectiveness of the dimensional AMPD and ICD-
11 models of maladaptive personality traits in terms of the
distinction between none, mild and more severe personality
pathologies within the population of bariatric candidates, and
as such provide further evidence about the clinical utility of
the AMPD and ICD-11 model for PDs outside of a standard
psychiatric setting.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Ethics committee of the General University Hospital
in Prague. The patients/participants provided their written
informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
KDR drafted the manuscript and was responsible for the final
version of the manuscript. JK and MM were responsible for the
data collection. LR conducted all the data analyses. All authors
have read and approved the manuscript.
FUNDING
The APC was funded by the Open Access Fund of the General
University Hospital in Prague, Czechia. This publication has
been supported by the Ministry of Health of the Czechia via the
RVO program (project VFN 6416) and the PROGRES program
(Progres = C4 = 8D.Q 06/LF1 = 20).
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Riegel et al. DSM-5 and ICD-11 Traits in Bariatrics
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