RESEARCH ARTICLE
Trajectories of aggressive and depressive
symptoms in male and female overweight
children: Do they share a common path or do
they follow different routes?
Luca Cerniglia1
*, Silvia Cimino2
, Michela Erriu2
, Stanislav Jezek3
, Carlos A. Almenara3,4
,
Renata Tambelli2
1 International Telematic University Uninettuno, Psychology Faculty, Department of Psychology, Rome, Italy,
2 Sapienza, University of Rome, Psychology and Medicine Faculty, Department of Dynamic and Clinical
Psychology, Rome, Italy, 3 Institute for Research on Children, Youth and Family, Faculty of Social Studies,
Masaryk University, Brno, Czech Republic, 4 Universidad Peruana de Ciencias Aplicadas, Faculty of
Psychology, Chorrillos, Lima, Peru´
* l.cerniglia@uninettunouniverisity.net
Abstract
The prevalence of childhood overweight is a major social and public health issue, and primary
assessment should focus on early and middle childhood, because weight gain in these
phases constitutes a strong predictor of subsequent negative outcomes. Studies on community
samples have shown that growth curves may follow linear or non-linear trajectories
from early to middle childhood, and can differ based on sex. Overweight children may exhibit
a combination of physiological and psychosocial issues, and several studies have demonstrated
an association between overweight and internalizing/externalizing behavior. Nevertheless,
there is a dearth of longitudinal studies on depressive and aggressive symptoms in
children with high BMI. This study adopted a growth curve modeling over three phases to:
(1) describe BMI trajectories in two groups of children aged 2–8 (overweight and normal
weight) from a community sample; (2) describe the developmental trajectories of children’s
aggressive and depressive symptoms from 2 to 8 years of age. Results indicate higher BMI
in 2-year-old girls, with males catching up with them by age 8. While overweight females’
BMIs were consistently high, males’ increased at 5 and 8 years. The mean scores for
aggressive symptoms at T1 (2 years of age) were the same in all subjects, but a significant
deviation occurred from T1 to T2 in both samples, in divergent directions. With regards to
children’s depressive symptoms, the two groups had different starting points, with normal
weight children scoring lower than overweight youths. Overweight females showed lower
depressive scores than overweight males at T1, but they surpassed boys before T2, and
showed more maladaptive symptoms at T3. This study solicits professionals working in
pediatric settings to consider overweight children’s psychopathological risk, and to be aware
that even when children’s BMI does not increase from 2 to 8 years, their psychopathological
symptoms may grow in intensity.
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 1 / 18
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Cerniglia L, Cimino S, Erriu M, Jezek S,
Almenara CA, Tambelli R (2018) Trajectories of
aggressive and depressive symptoms in male and
female overweight children: Do they share a
common path or do they follow different routes?
PLoS ONE 13(1): e0190731. https://doi.org/
10.1371/journal.pone.0190731
Editor: Jacobus P. van Wouwe, TNO,
NETHERLANDS
Received: October 29, 2016
Accepted: December 19, 2017
Published: January 5, 2018
Copyright: © 2018 Cerniglia et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data from study are
available in Mendeley Datasets (DOI: 10.17632/
93cdm7mc3d.1/).
Funding: This work was supported by the Czech
Science Foundation (grant no. THINLINE—GA15-
05696S) to CAA and the Institute for Research on
Children, Youth and Family, Faculty of Social
Studies, Masaryk University to SJ. The funders had
no role in study design, data collection and
Background
Several studies have demonstrated that the prevalence of overweight (OW) in children has significantly
increased over the last two decades [1]. Overweight in childhood has been found to
predict severe negative health outcomes in adolescence and adulthood, such as cardiovascular
diseases, Type II diabetes, asthma, obstructive sleep apnea, and hyperlipidemia [2]. The
increasing prevalence of childhood overweight constitutes such a major social and public
health issue that the World Health Organization has strongly recommended applying growthmonitoring
policies to all children aged under 17 to inform prevention and intervention strategies
[3]. Moreover, it has been posited that primary assessment and intervention should focus
on children in early to middle childhood, because weight gain in this phase constitutes a strong
predictor of adiposity in later life [4]. In the present paper, we use the definition of overweight
developed by the International Obesity Task Force (IOTF), which proposed an international
Body Mass Index (BMI, kg/m2) reference standard to define underweight, overweight, and
obesity according to children’s age, weight, and height [5,6]. Furthermore, we also consider
overweight children whose mothers experienced no peri- or post-partum difficulties or health
problems, who were not born preterm, and who were of average socioeconomic status [7].
It has been suggested that BMI of children in the first years of life may follow different
developmental trajectories in the general population, particularly from 3 years of age [8]. Studies
examining these growth trajectories have identified diverse patterns of change in weight
status during infancy and childhood [9,10,11]. These studies suggest that growth curves may
follow linear or non-linear trajectories, with children belonging to overweight groups maintaining
a high BMI over time, or else linearly decreasing or increasing from 2 to 10 years of
age. In other cases, overweight children’s BMI has been shown to decrease from early to middle
childhood and then “rebound,” increasing again at 6–8 years [12]. Several studies have
focused on how these changes in BMI during growth are attributable to genetic or early life
factors [13,8,14], as well as how these changes can predict obesity later in life [15]. It is important
to note that these changes in growth are accompanied by physiological and psychological
changes that can have interactive effects and most likely play an important role in the establishment
and development of psychopathology [16].
However, the developmental relationship between weight status and mental health is very
complex [17]. Overweight children frequently show a combination of physiological and psychosocial
issues, such as abnormal development of interoceptive awareness (i.e., deficits in recognizing
hunger and satiety cues [18]), peer problems, mood disorders, and low self-esteem,
among other issues that undermine their quality of life [19,20,21,22,23]. They also tend to suffer
from social stigma connected to their weight, which can have negative psychosocial outcomes,
especially in late childhood and adolescence [24,25]. According to Vila et al. [26], more
than 60% of overweight children suffer from some form of psychological difficulties, but this
figure suggests that there is still a substantial proportion of overweight children who do not
face psychological problems. In line with this, the “Jolly Fat” hypothesis proposes that some
obese individuals display better mental health than otherwise similar normal weight individuals
[27]. However, the impact of being overweight, particularly in the case of emotional difficulties,
seems to be due to the extent of the negative impact of this condition on children’s selfconcept
[26,28]. During early childhood, the self-concept is characterized by inaccurate selfappraisals
mostly due to the immature cognitive development at this early age [29]. For
instance, early age children lack the ability to engage in social comparisons to conclude if they
are more or less competent than their peers [30]. But later, preschool children become much
more aware of the negative attitudes and prejudices toward obese individuals and start to
engage in social comparisons more frequently. Thus, the developmental interplay of selfPsychopathological
profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 2 / 18
analysis, decision to publish, or preparation of the
manuscript.
Competing interests: The authors have declared
that no competing interests exist.
Abbreviations: BMI, Body Mass Index; CBCL, Child
Behavior Check-List; IOTF, International Obesity
Task Force; NWg, Normal Weight group; OWg,
Underweight group; WHO, World Health
Organization.
appraisals and social comparisons [31] could explain the variation in how being overweight
affects children’s self-esteem and in turn their mental health and quality of life. Indeed, the
association between obesity and lower self-esteem seems much more established in later ages
such as adolescence (e.g., [1]). Similarly, developmental interplay with other factors such as
gender or cultural norms can influence the association between obesity and poorer mental
health [2,3].
Looking at the development of behavioral and emotional problems, Camfferman et al. [32]
have demonstrated an association between overweight and internalizing/externalizing behavior
in early childhood based on several different mechanisms. Externalizing behavior and high
BMI in children may share individual risk factors such as impaired self-regulation skills and
difficult temperament, but children’s overweight can be also associated with parent-child relationship
issues, e.g. inadequate parental behavior and feeding practices. For instance, parents
could respond to a child with a difficult temperament by proposing sedentary activities (e.g.,
watching television), or they could use food as a positive or negative reinforcement to calm
down the child, which may eventually result in overweight [33]. Similarly, Internalizing problems
may share individual risk factors with high BMI, such as dysregulated serotonin and cortisol
levels, which can be associated with overweight as a consequence of weight
stigmatization, rejection by peers, and frustration about the impossibility of participating in
certain physical activities due to excessive weight [34]. It has also been shown that children
may use food as a means to control and dump negative emotions (e.g., depressive symptoms),
and that parents may offer food to comfort their children and reduce their anxiety and stress
[35]. Children may therefore find themselves locked in a maladaptive circular process, where
internalizing symptoms foster overweight, which in turn leads to more internalizing problems,
and so forth. Although an association between overweight and internalizing/externalizing
problems has been widely demonstrated, few studies have focused on this association in early
childhood and from a longitudinal perspective [36]. The use of a prospective standpoint is crucial,
as Sawyer et al. [37] posited, in that the association between overweight children’s BMI
and psychological problems may only emerge in middle childhood, meaning that this correlation
could not be present in earlier years of life. Moreover, few studies have addressed specific
psychopathological symptoms in overweight children, as most authors considered overall
internalizing and externalizing problems [38]. A limited (but growing) body of literature has
investigated the association between overweight in children and specific “core” aspects of
internalizing and externalizing problems in longitudinal studies (such as, for example, depressive
symptoms). However, these studies mostly addressed the predictive power of children’s
[39,40] or adolescents’ [41] depression (and psychopathology in general) on the risk of obesity
in adolescence and adulthood [42], rather than focusing on trajectories of psychopathological
profiles and their associations with overweight within the childhood developmental stages
(early childhood; middle childhood). Several authors have addressed depression in children
and its associations with overweight in longitudinal studies, but they mostly concentrated on
samples of youths from 6 to 18 years of age [43,44]. Some scholars have indeed studied trajectories
of overweight and psychopathological symptoms from early childhood to adolescence,
but their research included an assessment of depression only in adolescence, whereas other
measures (e.g., children’s BMI) were administered from the start of the study [45,46].
Importantly, most studies on BMI trajectories have examined boys and girls together,
whereas fewer authors have focused on different trajectories in males and females in the first
years of life [47]. The few exceptions have revealed gender differences in BMI trajectory patterns
[48]. Girls, for example, may be more significantly affected by factors associated with biological
variations in the onset of puberty and by the strong relationship between sexual
maturation and changes in adiposity. Moreover, it has been demonstrated that sex plays a role
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 3 / 18
in predicting the association of psychopathological symptoms and high BMI. Externalizing
symptoms have been predominantly found in overweight males, whereas internalizing problems
are more prevalent in females [49]. The fact that literature in the field has widely accepted
that aggressive behaviors are more prevalent in boys than girls means that there is a dearth of
recent studies on conduct problems in overweight females [50], impeding the development of
informed prevention and intervention programs. A gap in the literature likewise exists concerning
middle childhood and the paths BMI may follow in that specific developmental stage
[51]. Due to mental health concerns, most research linking children’s BMI and psychopathological
risk has been conducted on clinical samples [52]. Nevertheless, it has been suggested
that assessing maladaptive emotional and behavioral patterns in children from community
samples could better inform prevention and intervention policies [53].
In this paper we adopted a growth curve model with two objectives: (1) to describe BMI trajectories
from age 2 to 8 years in two groups of children (overweight and normal weight) of
both sexes from a community sample; (2) to describe the developmental trajectories of children’s
aggressive and depressive symptoms from 2 to 8 years.
Methods
Sample and procedure
In 2006, our research group started a screening program in collaboration with pediatricians
working in public and private kindergartens and schools in Central Italy. The aim was to
detect overweight children and to examine their trajectories over time in terms of both BMI
and emotional-behavioral functioning. The study protocol was approved by the Ethical Committee
of the Psychology Faculty of Sapienza—University of Rome, in accordance with the
guidelines approved by the Helsinki Declaration. Additionally, participating schools obtained
ethical clearance through their respective institutional review bodies. The study was conducted
in three phases each three years from ages 2 to 8 (T1 to T3). Our collaborating group of pediatricians
and psychologists carried out the physical and psychological assessments (see Tools
section below). At T1, in 2006, over a period of 6 months, we recruited 90 children (data are
available in Mendeley Datasets at 10.17632/93cdm7mc3d.1). The sample of overweight children
was balanced by sex and randomly selected using computer software from among all the
two-year-old children above the 85th percentile for weight included in the screening program
(Overweight group/OWg). The assessment was made by pediatricians at kindergartens and
schools using the World Health Organization (WHO) growth curves [6], in the absence of any
referred medical and/or psychiatric diagnosis. OWg was paired with a sample of N = 90 children
(randomly selected from among the same school population) in which 2-year-old children
presented with adequate weight and without further medical and/or psychiatric
impairment (Normal Weight group/NWg). Informed written consent was obtained from all
children’s parents for the aims of the study, and at each phase of the study, both parents completed
a scale measuring their child’s emotional-behavioral functioning (see Tools section
below). This procedure was repeated for each phase, and the sample was not affected by attrition
thanks to the collaboration of pediatricians and schools, which strongly supported the
program. The team of pediatricians and psychologists were the same for every assessment
point. Screening results were sent to parents after each measurement and contained the child’s
BMI-for-age percentile and a description of the outcomes and suggested actions, if any.
Tools
Body Mass Index (BMI). BMI is a composite measure of children’ weight and height considering
age. To identify overweight children, we used international cut-off points [6].
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 4 / 18
Children’s emotional/behavioral problems. We used the two Italian versions of the
Child Behavior Checklist (CBCL) [54]. One is designed for 1½–5-year-old children and the
other one is for 6–18-year-olds [55]. Both versions contain items assessing internalizing/externalizing
problems, although the formulation of some items differs across versions to be coherent
with the developmental changes within these domains. As this paper uses a growth curve
model, we needed measurement equivalence across time points [56]. Thus, consistent with the
study of Sterb et al. [57], we chose items from both versions of the CBCL known to have
greater developmental invariance [58]. Moreover, we identified “core” aspects of internalizing
and externalizing syndromes that are developmentally appropriate and phenotypically
expressed from preschool to pre-puberty [59]. Consequently, we created subsets named
“Depression” and “Aggression.” The Depression subset was composed of items 47, 68, 70, 87,
98 in the version of the test for 1½–5-year-olds and 5, 50, 65, 70, 71 111 in the version for 6–
18-year-olds. The Aggression subset was composed of 18, 20, 40, 53, 66, 81, 85, 96 in the version
of the test for 1½–5-year-olds and 19, 21, 22, 37, 57, 68, 86, 95 in the version for 6–
18-year-olds. Examples of items from the first subset are: “Too fearful or anxious,” “Self-conscious
or easily embarrassed,” “Shy or timid,” “Unhappy, sad, or depressed,” “Withdrawn,
doesn’t get involved with others,” and “Worries.” Examples from the second subset are: “Can’t
sit still, restless, or hyperactive,” “Cruel to animals,” “Destroys his own things,” “Destroys
things belonging to his family or others,” “Disobedient,” “Doesn’t seem to feel guilty after misbehaving,”
“Get in many fights,” “Physically attacks people,” and “Temper tantrums or hot
temper.” The CBCL uses a 3-point Likert scale: 0 = not true, 1 = somewhat or sometimes true,
and 2 = very true or often true. Internal consistency was high for the externalizing Aggression
scale; as estimated by Cronbach’s α, it was .69 in the 1½–5 version and .95 in the 6–18 version.
Internal consistencies for the Depression scale were .70 in the 1½–5 version and .86 in the
6–18 version. (Cronbach’s α = .78 to .82 across time points), and moderate for the internalizing
scale (Cronbach’s α = .72 to .78).
Analysis. We used the Mixed procedure in IBM SPSS 23 to estimate multi-level growth
curve models of BMI and CBCL symptoms of aggression and depression.
Results
Demographic characteristics of the sample. In the overweight group and normal-weight
group (OWg, NWg), respectively, 93% and 95% of children were firstborn, 96% and 94% of
households were intact and all the children were of homogeneous nationality and biological
children of their parents. Most families had a middle-class socioeconomic status (SES; 94%)
[7]. No female subjects in either the OWg or the NWg reached menarche.
Table 1 presents basic descriptive statistics for individual variables. Whereas BMI was
approximately normally distributed in both overweight and normal-weight groups, the distribution
of both CBCL scales was markedly positively skewed in the NW group and approximately
normal in the OW group.
Developmental trajectories of BMI. To describe the developmental trajectories of BMI,
we used a growth-curve modelling approach as described by Singer and Willet [59]. In this
approach, individual age at the time of measurement is used instead of the rough ordinal
phase variable. The small sample size allows us to display all the individual observed growth
curves in Fig 1. In the overweight group, the observed individual trajectories started higher
than in the normal-weight group and remained higher until the last phase. There is noticeably
more variance in the overweight group. Over the course of the study, BMI variance in both
groups tended to decrease.
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 5 / 18
In contrast to clear between-group differences, the differences among individuals within
groups appear to be much less systematic. This is also evidenced by the very low correlations
(n.s.) between BMIs across phases in the normal-weight group (see Table 2). This raises doubts
about whether to model the intercept of BMI developmental trajectories as random, as
opposed to fixed. But in the overweight group, the correlations are around .3 (p < .05). Since
at least in the overweight group the differences in BMIs among children have same stability,
we chose to try to model this variability by including random intercept and slope parameters
in the model.
Based on the observed growth trajectories, we specified a linear growth-curve model conditioned
on group membership in Model 1 and on group membership and sex in Model 2.
Although in an unconditional model it was possible to estimate the variance of both intercepts
and slopes, variance of slopes was very small. Further, adding group to the model caused the
slope variance to become inestimable. Thus, models presented here include in their stochastic
part only residual variance and variance of intercepts; slopes are fixed conditional on group
and sex. The results of growth-curve modelling of BMI are reported in Table 3. Both models
capture the majority of variance in BMI across phases and individuals. Model 1 captures 89%
and Model 2 90% of variance in BMI. The inclusion of sex significantly improved the model in
terms of information criteria, but the sex differences are very small.
Table 1. Descriptive statistics for study variables across the three phases.
Age BMI AGG DEP
Group Phase M SD M SD M SD M SD
NWg 1 2.22 .26 16.05 1.53 3.86 2.29 1.54 1.41
2 5.10 .49 16.10 .91 3.61 2.93 1.86 1.91
3 7.71 .46 16.16 .91 1.04 1.17 .91 1.17
Total 5.01 2.28 16.10 1.15 2.84 2.58 1.44 1.57
OWg 1 2.40 .53 24.48 2.77 5.44 3.21 3.82 2.55
2 5.29 .36 26.01 2.38 6.43 2.97 4.54 2.24
3 7.80 .42 25.93 1.56 10.24 5.14 6.13 2.67
Total 5.16 2.26 25.47 2.39 7.37 4.40 4.83 2.67
Note. N = 90 in each cell. AGG, CBCL Aggression subscale; DEP, CBCL Depression subscale.
https://doi.org/10.1371/journal.pone.0190731.t001
Fig 1. Individual BMI growth curves in the normal-weight and overweight groups.
https://doi.org/10.1371/journal.pone.0190731.g001
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 6 / 18
To facilitate interpretation, mean growth curves are plotted in Fig 2. Given that the overweight
group was coded 1 and the normal-weight group was coded 0 and age in years was
used as the time-variable, the intercept in Model 1 is the mean BMI at age 0 in the normalweight
group—the level. The age parameter in Model 1 represents the annual BMI increase/
decrease in the normal-weight group—the growth rate. The age parameter is not significantly
different from 0, the predicted linear trajectory in the normal-weight group is flat. Because the
intercept is modelled as a random parameter, development in the normal-weight group can be
described as stable, with the levels of BMI having a normal distribution with mean = 15.97 and
SD = .77 (variance in intercepts is estimated at .60). The next two parameters in Table 3 represent
the differences in level and growth rate for the overweight group with respect to the normal
weight group. The intercept of the mean linear growth trajectory in OWg is 8.06 higher
than in NWg (group-overweight), i.e. the mean level of the BMI trajectories in OWg is higher
than in NWg. The mean growth rate in the OWg is also significantly higher by .25 BMI units
per year. Together, the mean intercept in the OWg group is 24.02 (SD = .77) with .28 mean
annual increase. Because the growth is not flat, it is important to interpret only mean BMIs
within the age range of the study, i.e. from 2 to 8 years.
With sex coded 1 for female and 0 for male in Model 2, the above described parameters represent
males’ development in the NWg and OWg groups. The four newly added parameters
describe how the mean growth trajectory differs for females in NWg and in OWg. Thus, we
can see the mean modelled trajectory level in NWg for females is 1.39 lower than for males (p
Table 2. Correlations of BMI across phases. Normal-weight group above diagonal, overweight below.
BMI 1 BMI 2 BMI 3
BMI 1 -.14 -.09
BMI 2 .30 -.05
BMI 3 .28 .25
https://doi.org/10.1371/journal.pone.0190731.t002
Table 3. Summary of BMI growth-curve models.
Parameter Model 1 Model 2
Intercept 15.972 ÃÃ
16.609 ÃÃ
Age .026 -.057
Group-Overweight 8.055 ÃÃ
6.206 ÃÃ
Group-OverweightÃ
Age .253 ÃÃ
.483 ÃÃ
Sex -1.393 ÃÃ
Group-OverweightÃ
Sex 3.801 ÃÃ
SexÃ
Age .180 Ã
Group-OverweightÃ
SexÃ
Age -.472 ÃÃ
Residual VAR 2.72 ÃÃ
2.60 ÃÃ
Intercept VAR .60 ÃÃ
.51 ÃÃ
Information criteria
-2LL 2163.8 2132.2
AIC 2175.8 2152.2
BIC 2201.5 2195.1
Parameters 6 10
Note. Group coded 1 for overweight group and 0 for normal-weight group. Sex coded 1 for female and 0 for male. Variance of BMI across individuals and phases = 25.4.
ÃÃ
p < .01,
Ã
p < .05
https://doi.org/10.1371/journal.pone.0190731.t003
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 7 / 18
< .01) but in contrast to males’ stable level females’ trajectory is slightly increasing, specifically
by .12 annually (p < .05). In OWg, females’ modelled mean BMI is 3.80 higher than males’ (p
< .01). This is, however, compensated for by the fact that the females’ growth rate is significantly
slower than males (.13 annually, compared to .42 in males). The variance in intercepts is
slightly smaller thanks to the introduction of sex into the model; the growth trajectories are
normally distributed with SD = .71, so the majority of the growth trajectories of children
within the groups defined by sex and weight status should fit into a range +/-1.4 around the
estimated intercept.
Developmental trajectories of aggression and depression. To describe the development
of behavioral and emotional problems as measured by CBCL over the span of the study, we
used the same multilevel approach as for BMI. The individual empirical growth curves are presented
in Figs 3 and 4. Aggression trajectories in the normal weight group are more homogenous
and generally lower than in the overweight group. Also, the majority shows a decreasing
trend, albeit not always a linear one. Finding patterns in the overweight group is more difficult.
Yet, it is obvious that there is more aggression reported in the overweight group, and that differences
among children in this group increase over time. Although many of the individual
trajectories are not linear, having only three measurements allows us to estimate only linear
trends. Some of the unexplained variance in the following models is thus due to this nonlinearity
of the individual trajectories. Phase-to-phase correlations (Table 4) are very small (and n.s.)
in the normal-weight group. In the overweight group, only the correlation between first two
measurements (ages 2 and 5) is statistically significant, and it is still small in size. This further
documents that within groups children experience quite different development trajectories.
Similar to BMI models we specified the growth-curve models of aggression and depressive
symptoms as linear multi-level models. The models include a linear trend conditioned on
group membership in Model 1 and on group membership and sex in Model 2. In the stochastic
part of the aggression models, slope variance was fixed; in depression symptom models, it was
possible to estimate variance in intercepts and slopes, and their covariance. That means, that
unlike in the previous models in this paper, not only the intercept but also the growth rate is
modelled as a distribution with a mean at the estimated parameter and some variance.
Fig 2. Modelled BMI growth curves from age 2 to 8 by group and sex.
https://doi.org/10.1371/journal.pone.0190731.g002
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 8 / 18
However, in Model 2, none of these variances is significantly different from 0. The summary
of the models is reported in Table 5. Both models of aggression captured a substantial proportion
of variance in aggression scores across phases and individuals. Model 1 captured 48% and
Model 2 57% of variance in aggression. The inclusion of sex significantly improved the model
both in terms of interpretability and information criteria. The trajectories for boys and girls in
the overweight group substantially differ in the rate of change. To facilitate interpretation
mean growth curves are plotted in Fig 4.
The models also explained a substantial proportion of variance in depressive symptoms
scores across phases and individuals. Model 1 captured 61% and Model 2 63% of variance.
Again, the effect of sex was significant in terms of information criteria and interpretability. To
facilitate interpretation of aggressive and depressive symptoms, mean growth curves are plotted
in Figs 5 and 6.
Discussion and conclusion
The literature has widely demonstrated that overweight in children can be related to a series of
biological, contextual factors [60], but few studies have focused on possible associations
between high BMI and mental health in young children. Some studies did suggest that overweight
in youths can be associated with psychopathological risk, and in particular with depressive
and aggressive symptoms [61]. Nevertheless, most longitudinal studies have focused on
BMI and psychological profile trajectories from childhood to adolescence and adulthood in
clinical samples, whereas few authors have addressed shorter periods, such as within childhood
in community samples [62]. Indeed, studies conducted on community samples recruited in
schools can play a crucial role in preventing overweight in children, as the majority of children
Fig 3. Individual aggression growth curves in the normal weight and overweight groups.
https://doi.org/10.1371/journal.pone.0190731.g003
Fig 4. Individual depressive-symptoms growth curves in the normal weight and overweight groups.
https://doi.org/10.1371/journal.pone.0190731.g004
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 9 / 18
in a given community attend school [63]. School-based screening programs can measure children’s
BMI to identify those at risk for overweight, offering parents information about their
offspring’s weight and psychological status to help them take appropriate actions. Previous
research has also indicated the necessity of studying associations between children’s BMI and
psychopathological symptoms from an early age, separately for males and females, with BMI
repeatedly measured throughout childhood. Since several studies demonstrated that parents
and children frequently are not aware of youths’ weight status and psychological well-being,
school-based screening programs in this field offer the two-fold benefit of objectively evaluating
both children’s BMI and possible associated psychological difficulties over time (see, for
example, [64]).
In this paper, we aimed to evaluate BMI trajectories from ages 2 to 8 years in two groups of
male and female children (overweight and normal weight; OWg/NWg). Moreover, we
intended to describe the development over time of children’s aggressive and depressive symptoms
during the same time span. In our results, when observing the trajectories of children’s
Table 4. Correlations of aggression and depressive symptoms scores across phases. Normal weight group above diagonal, overweight below.
AGG 1 AGG 2 AGG 3 DEP 1 DEP 2 DEP 3
AGG 1 .16 -.03 DEP 1 .40 -.01
AGG 2 .33 -.01 DEP 2 .20 -.05
AGG 3 .09 -.01 DEP 3 -.15 .24
Note. Correlations over .30 are p < .01 and those over .21 are p < .05.
https://doi.org/10.1371/journal.pone.0190731.t004
Table 5. Summary of aggression and depressive symptoms growth-curve models.
Aggression Depression
Parameter Model 1 Model 2 Model 1 Model 2
Intercept 4.513 ÃÃ
4.284 ÃÃ
1.425 ÃÃ
1.460 ÃÃ
Age -.389 ÃÃ
-.353 ÃÃ
-.083 -.075
Group-Overweight -1.967 ÃÃ
-3.630 ÃÃ
.568 1.952 ÃÃ
Group-OverweightÃ
Age 1.308 ÃÃ
1.921 ÃÃ
.548 ÃÃ
.166
Female .480 -.078
Group-OverweightÃ
Female 3.860 ÃÃ
-2.834 ÃÃ
FemaleÃ
Age -.075 -.016
Group-OverweightÃ
FemaleÃ
Age -1.251 ÃÃ
.782 ÃÃ
Residual VAR 9.52 ÃÃ
7.84 ÃÃ
2.73 ÃÃ
2.56 ÃÃ
Intercept VAR .73 .18 2.11 1.60
Int-Slope COV -.34 -.16
Slope VAR .07 Ã
.02
Information criteria
-2LL 2786.5 2656.7 2203.4 2115.6
AIC 2798.5 2676.7 2219.4 2139.6
BIC 2824.2 2719.7 2253.7 2191.1
Parameters 6 10 8 12
Note. Group coded 1 for overweight group and 0 for normal weight group. Sex coded 1 for female and 0 for male. Variance of aggression across individuals and
phases = 18.4. Variance of depressive symptoms across individuals and phases = 6.9.
ÃÃ
p < .01,
Ã
p < .05.
https://doi.org/10.1371/journal.pone.0190731.t005
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 10 / 18
BMI in both groups, without specifically considering sex differences, the weight status of OWg
and NWg appeared substantially stable across the three assessment points. Our results indicate
that girls show a higher BMI than boys at 2 years old, but boys catch up with them by age 8. So,
while females’ BMI was constantly high in the overweight group, males’ BMI was increasing.
Our first results are consistent with studies conducted on young children [65], as well as with
studies on elementary school children [66,67] suggesting the relative stability of weight status
growth during infancy and childhood. However, it is important to note that there are several
factors driving differences in growth trajectories [68], such as the effect of genetic variants
[69], and early life contextual factors [8]. Moreover, the number of data points, analytic methods,
sample characteristics, and other factors also account for differences among studies in
growth trajectories [66,67]. Therefore, when examining sex dimorphism, although some studies
have found gender differences in diverse samples (e.g., [70]), it is not surprising to observe
inconsistent findings of gender differences in weight status trajectories [66]. In summary,
Fig 5. Modelled aggression growth curves from ages 2 to 8 by group and sex.
https://doi.org/10.1371/journal.pone.0190731.g005
Fig 6. Modelled depression symptoms growth curves from ages 2 to 8 by group and sex.
https://doi.org/10.1371/journal.pone.0190731.g006
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 11 / 18
considering the diversity of methodologies within human growth research [71], it is important
to standardize and reach a consensus about the best model(s) for the study of weight status
growth in infancy and childhood [72].
Following Fernandez Castelao and Kro¨ner-Herwig [73], we also verified whether boys and
girls in both the OW and NW groups had different trajectories with regards to their aggressive
and depressive symptoms. In doing so, we aimed at filling a gap in the literature since a number
of studies have demonstrated differences in the phenomenological manifestation of psychopathology
in male and female children [74,75] but no study to our knowledge has verified
this pattern in the trajectories of overweight children from a very young age to 8 years. With
regards to the trajectories of aggressive symptoms, our analyses showed that at T1 (2 years of
age) the means of all children were the same. A significant deviation then occurred from the
relative stability observed from T1 to T2 in both samples, with divergent directions. In the normal
weight group, aggressive symptoms decreased at T3 (8 years of age), with males and
females in the NWg remaining on an overlapping trajectory. Conversely, at the same assessment
point, in the overweight group these symptoms significantly increased and, specifically
for OWg boys, parents reported highest aggressive symptoms. In the case of depressive symptoms,
the two groups showed different starting points, with NWg scores significantly lower
than OWg scores. Very interestingly, overweight females showed lower depressive scores than
OWg males at T1, but they surpassed boys (who remained relatively stable over time) even
before T2, showing significantly more maladaptive symptoms at T3. On one hand, the
decrease of NWg scores for both aggressive and depressive symptoms is consistent with previous
literature indicating that around age 8, children show less problematic emotional/behavioral
functioning [76]. On the other hand, while previous longitudinal research on overweight
children has suggested that increasing trajectories of maladaptive symptoms are observed in
association with linear increasing BMI in childhood, our data shows a higher degree of aggressive
and depressive symptoms even with a relatively stable BMI in OWg. Thus, even if children’s
BMI does not increase over time, their aggressive and depressive symptoms do. This
result should alert both pediatricians and families to be attentive to the psychological wellbeing
of their overweight children, even if their physical conditions do not worsen. Our finding
of an early association between aggression and overweight is consistent with previous studies,
which demonstrated higher externalizing scores in 2-year-old overweight children.
Nevertheless, our work is the first to find this association specifically in overweight males and
throughout childhood, from 2 to 8 years. Another study found such behavioral problems in
boys, but focused on subjects aged 9–16 [42].
We are not able to ascertain a direction in the association between early aggressive symptoms
(at 2 years of age) and high BMI, and therefore, based on our analyses, we cannot affirm
what comes first, overweight or aggressive symptoms. Our results only allow us to state that
there is a variation in BMI due to contextual factors [77]. Nevertheless, we can speculate that
high BMI may result from child management techniques used by parents or caregivers in the
rearing of a challenging child (possibly a child with difficult temperament). In an attempt to
respond to their offspring’s behavior, parents may engage the child in sedentary activities (e.g.
gaming or television watching) or offer food as a reward for behaving, which can negatively
alter eating patterns. McEwen [78] has also argued that biological factors influencing brain
development could simultaneously be the basis of aggressive behavior and weight gain, and
that the developmental paths governing appetite and emotion regulation are interdependent.
In our study, we did not observe a difference between male and female children in the shape of
increasing trajectories of aggression and depression. This is inconsistent with previous literature,
which suggested sex-specific paths in the onset of psychopathological symptoms due to
the diverse development of emotional regulation processes in males and females, based on
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 12 / 18
different pathways of brain frontal lobe maturation [79]. It is possible that the age range considered
in our study did not allow us to appreciate these differences.
Although we obtained interesting results, our study has some limitations that should be
kept in mind. First, we relied upon parental reports of children’s emotional and behavioral
functioning, using the CBCL, which is not a diagnostic tool. Nevertheless, several studies have
demonstrated that mothers and fathers are reliable sources of information about their offspring’s
psychological profiles, and no study has yet shown that parents of overweight children
are influenced or biased in describing them [80]. Second, we did not have access to important
variables that could be related to or predictive of overweight in children, such as birth weight
for gestational age [81]. However, we did have an indication from the parents of whether or
not their children were born preterm, ill, or subsequent to a problematic delivery (these conditions
were adopted as exclusion criteria). Third, our longitudinal design did not include pubertal
age, so we could not verify possible modifications in the trajectories of BMI in males and
females, which are likely, as indicated by Dunger, Ahmed, & Ong [82]. At the moment, a further
assessment point (T4; 11 years of age) is being performed on the same sample, and the
results will be included in a future paper. Fourth, due to the design of the study using groups
selected by BMI, the within-group between-subjects variances are artificially low. That prevents
the interpretation of individual level differences and correlations. Within-group correlations
between BMI and aggression/depression are range-restricted nearly to zero. Only group
differences could be interpreted. The across-groups correlations only reflect group differences
reflecting the design of the study. While this complex pattern of data is well represented by the
growth curve models it may not be so clear to readers with limited experience with growthcurve
models. Last, the result showing early high depressive and aggressive symptoms in overweight
children should be taken with caution. These symptoms could be linked to other issues
(e.g., parental maltreatment) that have not been possible to assess in our study. Thus, further
research is needed to clarify the mechanism underpinning the early maladaptive symptoms in
OW children.
Notwithstanding the above limitations, this paper has several strengths. First, traditionally,
population-based BMI statistical analyses have taken into account whole samples. We chose
instead an approach focusing on subgroups of children with an atypical growth. Such an
approach is crucial to capturing the heterogeneity in the trajectories of children’s BMIs and
psychopathological symptoms [83]. Second, this study examined the developmental trajectories
of BMI and emotional-behavioral functioning from early childhood to 8 years of age,
which has rarely examined in previous literature on overweight children [84]. Third, we
employed widely used tools to measure the study variables and operated a selection of the
items in CBCL on the basis of previous literature (as described above), which guaranteed its
scores from T1 to T3 to be comparable for both aggressive and depressive symptoms. Finally,
our results have important clinical implications. Professionals working in pediatric settings
must consider that overweight children need attention both from a biological and psychological
standpoint, and be aware that even if BMI does not increase over time in children from 2 to
8 years, their psychopathological symptoms may grow in intensity. Clearly, this issue must be
addressed by prevention as well as intervention efforts. Moreover, clinicians must bear in
mind that the classical view in which males are more aggressive than females at every age
seemingly does not apply to overweight offspring. OW girls, in fact, can be more aggressive
than males at 2 years of age (even though higher aggressive symptoms in OW females at T1
could be due to parental over-rating due to cultural stereotypes making them more attuned to
girls’ aggressive behavior; [85]). The same logic applies to depressive symptoms, as in our
study overweight males at T1 showed a higher degree of depressive symptoms than OW
females. From this standpoint, professionals should consider interventions in overweight
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 13 / 18
children’s psychopathological symptoms, tailoring strategies to children’s age and sex alongside
their families.
Author Contributions
Conceptualization: Luca Cerniglia, Silvia Cimino.
Data curation: Michela Erriu, Stanislav Jezek.
Formal analysis: Stanislav Jezek.
Methodology: Stanislav Jezek, Carlos A. Almenara.
Software: Michela Erriu.
Supervision: Renata Tambelli.
Writing – original draft: Luca Cerniglia, Silvia Cimino.
Writing – review & editing: Luca Cerniglia, Carlos A. Almenara.
References
1. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national
prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis
for the Global Burden of Disease Study 2013. Lancet. 2014; 384(9945): 766–81. https://doi.org/10.
1016/S0140-6736(14)60460-8 PMID: 24880830
2. Kelsey M, Zaepfel A, Bjornstad P, Nadeau KJ. Age-related consequences of childhood obesity. Gerontology.
2014; 60(3): 222–8. https://doi.org/10.1159/000356023 PMID: 24434909
3. Lobstein T, Jackson-Leach R, Moodie ML, Hall KD, Gortmaker SL, Swinburn BA, et al. Child and adolescent
obesity: part of a bigger picture. Lancet. 2015; 385(9986): 2510–20. https://doi.org/10.1016/
S0140-6736(14)61746-3 PMID: 25703114
4. de Kroon ML, Renders CM, van Wouwe JP, van Buuren S, Hirasing RA. The Terneuzen Birth Cohort:
BMI change between 2 and 6 years is most predictive of adult cardiometabolic risk. PLoS One. 2010; 5
(11): e13966. https://doi.org/10.1371/journal.pone.0013966 PMID: 21103047
5. Cole TJ, Flegal KM, Nicholls D, Jackson AA. Body mass index cut offs to define thinness in children and
adolescents: international survey. BMJ. 2007; 335(7612): 194–7. https://doi.org/10.1136/bmj.39238.
399444.55 PMID: 17591624
6. Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight
and obesity. Pediatr Obes. 2012; 7(4): 284–94. https://doi.org/10.1111/j.2047-6310.2012.00064.x
PMID: 22715120
7. Bornstein MH, Bradley RH, editors. Socioeconomic status, parenting, and child development. Routledge;
2014.
8. Bo¨rnhorst C, Siani A, Russo P, Kourides Y, Sion I, Molna´r D, et al. Early life factors and inter-country
heterogeneity in BMI growth trajectories of European children: The IDEFICS Study. PLoS One. 2016;
11(2): e0149268. https://doi.org/10.1371/journal.pone.0149268 PMID: 26901773
9. Evensen E, Wilsgaard T, Furberg A, Skeie G. Tracking of overweight and obesity from early childhood
to adolescence in a population-based cohort—the Tromsø Study, Fit Futures. BMC Pediatr. 2016; 16
(1): 64. https://doi.org/10.1186/s12887-016-0599-5 PMID: 27165270
10. Li C, Goran MI, Kaur H, Nollen N, Ahluwalia JS. Developmental trajectories of overweight during childhood:
role of early life factors. Obesity. 2007; 15(3): 760–71. https://doi.org/10.1038/oby.2007.585
PMID: 17372328
11. Wheaton N, Millar L, Allender S, Nichols M. The stability of weight status through the early to middle
childhood years in Australia: a longitudinal study. BMJ Open. 2015; 5(4): e006963. https://doi.org/10.
1136/bmjopen-2014-006963 PMID: 25922101
12. Garthus-Niegel S, Hagtvet KA, Vollrath ME. A prospective study of weight development and behavior
problems in toddlers: the Norwegian Mother and Child Cohort Study. BMC Public Health 2010; 10(1).
https://doi.org/10.1186/1471-2458-10-626 PMID: 20961446
13. Beyerlein A, von Kries R, Ness AR, Ong KK. Genetic markers of obesity risk: stronger associations with
body composition in overweight as compared to normal weight children. PLoS One. 2011; 6(4): e19057.
https://doi.org/10.1371/journal.pone.0019057 PMID: 21526213
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 14 / 18
14. Howe LD, Firestone R, Tilling K, Lawlor DA. Trajectories and transitions in childhood and adolescent
obesity. In: A life course perspective on health trajectories and transitions. Springer International Publishing;
2015. pp.19–37. https://doi.org/10.1007/978-3-319-20484-0_2 PMID: 27683930
15. Cole T. Children grow and horses race: is the adiposity rebound a critical period for later obesity? BMC
Pediatr. 2004(1): 6. https://doi.org/10.1186/1471-2431-4-6 PMID: 15113440
16. Yu ZB, Han SP, Zhu GZ, Zhu C, Wang XJ, Cao XG, et al. Birth weight and subsequent risk of obesity: a
systematic review and meta-analysis. Obes Rev. 2011; 12(7): 525–42. https://doi.org/10.1111/j.1467-
789X.2011.00867.x PMID: 21438992
17. Small L, Aplasca A. Child obesity and mental health. Child Adolesc Psychiatr Clin N Am. 2016; 25(2):
269–82. https://doi.org/10.1016/j.chc.2015.11.008 PMID: 26980129
18. Harrist AW, Topham GL, Hubbs-Tait L, Page MC, Kennedy TS, Shriver LH. What developmental science
can contribute to a transdisciplinary understanding of childhood obesity: an interpersonal and
intrapersonal risk model. Child Dev Perspect. 2012; 6(4): 445–55. https://doi.org/10.1111/cdep.12004
19. Geoffroy M, Li L, Power C. Depressive symptoms and body mass index: co-morbidity and direction of
association in a British birth cohort followed over 50 years. Psychol Med. 2014; 44(12): 2641–52.
https://doi.org/10.1017/S0033291714000142 PMID: 25055177
20. Jansen PW, Mensah FK, Clifford SA, Tiemeier H, Nicholson JM, Wake M. Development of mental
health problems and overweight between ages 4 and 11 years: a population-based longitudinal study of
Australian children. Acad Pediatr. 2013; 13(2): 159–67. https://doi.org/10.1016/j.acap.2012.12.001
PMID: 23356960
21. Jansen PW, Mensah FK, Clifford S, Nicholson JM, Wake M. Bidirectional associations between overweight
and health-related quality of life from 4–11 years: longitudinal study of Australian children. Int J
Obes. 2013; 37(10): 1307–13. https://doi.org/10.1038/ijo.2013.71 PMID: 23736370
22. Mu¨hlig Y, Antel J, Fo¨cker M, Hebebrand J. Are bidirectional associations of obesity and depression
already apparent in childhood and adolescence as based on high-quality studies? A systematic review.
Obes Rev. 2016; 17(3): 235–49. https://doi.org/10.1111/obr.12357 PMID: 26681065
23. Sanders RH, Han A, Baker JS, Cobley S. Childhood obesity and its physical and psychological co-morbidities:
a systematic review of Australian children and adolescents. Eur J Pediatr. 2015; 174(6): 715–
46. https://doi.org/10.1007/s00431-015-2551-3 PMID: 25922141
24. Puhl RM. Weight stigmatization toward youth: a significant problem in need of societal solutions. Child
Obes. 2011; 7(5): 359–63. https://doi.org/10.1089/chi.2011.0500.pers
25. Pine DS, Cohen P, Brook J, Coplan JD. Psychiatric symptoms in adolescence as predictors of obesity
in early adulthood: a longitudinal study. Am J Public Health. 2007; 87(8): 1303–310.
26. Vila G, Zipper E, Dabbas M, Bertrand C, Robert JJ, Ricour C, et al. Mental disorders in obese children
and adolescents. Psychosom Med. 2004; 66(3): 387–94. https://doi.org/10.1097/00006842-
200405000-00018 PMID: 15184702
27. Magallares A, Pais-Ribeiro JL. Mental health and obesity: A meta-analysis. App Res Qual Life. 2014; 9
(2), 295–308.
28. Liu M, Wu L, Ming Q. How does physical activity intervention improve self-esteem and self-concept in
children and adolescents? Evidence from a meta-analysis. Plos One. 2015; 10(8), e0134804. https://
doi.org/10.1371/journal.pone.0134804 PMID: 26241879
29. Bjorklund DF, Ellis BJ. Children, childhood, and development in evolutionary perspective. Develop Rev.
2014; 34(3), 225–264.
30. Lu H, Tarasenko YN, Asgari-Majd FC, Cottrell-Daniels C, Yan F, Zhang. More overweight adolescents
think they are just fine: Generational shift in body weight perceptions among adolescents in the US. Am
j prev med. 2015; 49(5), 670–677. https://doi.org/10.1016/j.amepre.2015.03.024 PMID: 26163169
31. Shankaran S, Bann C, Das A, Lester B, Bada H, Bauer CR, . . . & Higgins RD. Risk for obesity in adolescence
starts in early childhood. J perin. 2011; 31(11), 711–716.
32. Camfferman R, Jansen PW, Rippe RC, Mesman J, Derks IP, Tiemeier H, et al. The association
between overweight and internalizing and externalizing behavior in early childhood. Soc Sci Med. 2016;
168: 35–42. https://doi.org/10.1016/j.socscimed.2016.09.001 PMID: 27639050
33. Rodgers RF, Paxton SJ, Massey R, Campbell KJ, Wertheim EH, Skouteris, et al. Maternal feeding practices
predict weight gain and obesogenic eating behaviors in young children: a prospective study. Int J
Behav Nutr Phys Act. 2013; 10(1): 24. https://doi.org/10.1186/1479-5868-10-24 PMID: 23414332
34. Trost SG, Sirard JR, Dowda M, Pfeiffer KA, Pate RR. Physical activity in overweight and nonoverweight
preschool children. Int J Obes. 2003; 27(7): 834–39. https://doi.org/10.1038/sj.ijo.0802311 PMID:
12821970
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 15 / 18
35. Ouwens MA, van Strien T, van Leeuwe J F. Possible pathways between depression, emotional and
external eating. A structural equation model. Appetite. 2009; 53(2): 245–48. https://doi.org/10.1016/j.
appet.2009.06.001 PMID: 19505515
36. Mackenbach JD, Tiemeier H, Ende JV, Nijs IM, Jaddoe VW, Hofman A, et al. Relation of emotional and
behavioral problems with body mass index in preschool children. J Dev Behav Pediatr. 2012; 33(8):
641–48.
37. Sawyer MG, Miller-Lewis L, Guy S, Wake M, Canterford L, Carlin JB. Is there a relationship between
overweight and obesity and mental health problems in 4- to 5-year-old Australian children? Ambul
Pediatr. 2006; 6(6): 306–11. PMID: 17116602
38. Martin-Storey A, Crosnoe R. Trajectories of overweight and their association with adolescent depressive
symptoms. Health Psychol 2015; 34(10): 1004–12. https://doi.org/10.1037/hea0000201 PMID:
25603419
39. Hasler G, Pine DS, Kleinbaum DG, Gamma A, Luckenbaugh D, Ajdacic V, Angst J. Depressive symptoms
during childhood and adult obesity: the Zurich Cohort Study. Mol Psych. 2005; 10(9): 842–850.
https://doi.org/10.1038/sj.mp.4001671 PMID: 15838533
40. Pine DS, Cohen P, Brook J, Coplan JD. Psychiatric symptoms in adolescence as predictors of obesity
in early adulthood: a longitudinal study. Am J Pub Health. 1997; 87(8): 1303–1310. https://doi.org/10.
2105/AJPH.87.8.1303
41. Goodman E, Whitaker RC. A prospective study of the role of depression in the development and persistence
of adolescent obesity. Pediatrics. 2002; 110(3): 497–504. https://doi.org/10.1542/peds.110.3.497
PMID: 12205250
42. Mustillo S, Worthman C, Erkanli A, Keeler G, Angold A, Costello EJ. Obesity and psychiatric disorder:
developmental trajectories. Pediatrics. 2003; 111(4): 851–859.
43. Schwartz BS, Glass TA, Pollak J, Hirsch AG, Bailey-Davis L, Moran TH, Bandeen-Roche K. Depression,
its comorbidities and treatment, and childhood body mass index trajectories. Obesity. 2016; 24
(12): 2585–2592. https://doi.org/10.1002/oby.21627 PMID: 27804225
44. Pryor L, Brendgen M, Boivin M, Dubois L, Japel C, Falissard B, Trembley RE, Coˆte´ SM. Overweight during
childhood and internalizing symptoms in early adolescence: The mediating role of peer victimization
and the desire to be thinner. J Affect Disord. 2016; 202: 203–209. https://doi.org/10.1016/j.jad.2016.05.
022 PMID: 27267292
45. Sanders RH, Han A, Baker JS, Cobley S. Childhood obesity and its physical and psychological co-morbidities:
a systematic review of Australian children and adolescents. Europ J Pediat. 2015; 174(6): 715–
746. https://doi.org/10.1007/s00431-015-2551-3 PMID: 25922141
46. Martin-Storey A, Crosnoe R. Trajectories of overweight and their association with adolescent depressive
symptoms. Health Psy. 2015; 34(10): 1004–1012.
47. Nonnemaker JM, Morgan-Lopez AA, Pais JM, Finkelstein EA. Youth BMI trajectories: evidence from
the NLSY97. Obesity. 2009; 17: 1469–78. https://doi.org/10.1038/oby.2009.5 PMID: 19584884
48. Pryor LE. Developmental trajectories of body mass index in early childhood and their risk factors. Arch
Pediatr Adolesc Med. 2011; 165(10): 906–12. https://doi.org/10.1001/archpediatrics.2011.153 PMID:
21969392
49. Cimino S, Cerniglia L, Almenara CA, Jezek S, Erriu M, Tambelli R. Developmental trajectories of body
mass index and emotional-behavioral functioning of underweight children: a longitudinal study. Sci.
Rep. 2016; 6: 20211. https://doi.org/10.1038/srep20211 PMID: 26806123
50. Mustillo S, Worthman C, Erkanli A, Keeler G, Angold A, Costello EJ. Obesity and psychiatric disorder:
developmental trajectories. Pediatrics. 2003; 111(4): 851–9.
51. Garnett SP, Hogler W, Blades B, Baur LA, Peat J, Lee J, et al. Relation between hormones and body
composition, including bone, in prepubertal children. Am J Clin Nutr. 2012; 80: 966–72.
52. Csa´bi G, Te´nyi T, Molna´r D. Depressive symptoms among obese children. Eat Weight Disord-St. 2000;
5(1): 43–5. https://doi.org/10.1007/bf03353437
53. Jones DJ, Gonzalez M, Ward DS, Vaughn A, Emunah J, Miller L, et al. Should child obesity be an issue
for child protective services? A call for more research on this critical public health issue. Trauma Violence
Abuse. 2014; 15(2): 113–25. https://doi.org/10.1177/1524838013511544 PMID: 24231942
54. Frigerio A, Montirosso R. La valutazione su base empirica dei problemi emotivo-comportamentali in età
evolutiva. Infanzia e adolescenza. 2002; 1(1): 38–48.
55. Achenbach TM, Rescorla LA. Manual for the ASEBA preschool forms & profiles: child behavior checklist
for ages 1/2–5, language development survey, caregiver-teacher report form; an integrated system of
multi-informant assessment. Burlington, VT: ASEBA; 2000.
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 16 / 18
56. Willett JB, Sayer AG. Using covariance structure analysis to detect correlates and predictors of individual
change over time. Psychol Bull. 1994; 116(2): 363–81. https://doi.org/10.1037/0033-2909.116.2.
363
57. Sterb SK, Prinstein MJ, Cox MJ. Trajectories of internalizing problems across childhood: heterogeneity,
external validity, and gender differences. Dev Psychopathol. 2007; 19(02): 345–66. https://doi.org/10.
1017/s0954579407070174 PMID: 17459174
58. McConaughy SH, Stanger C, Achenbach TM. Three-year course of behavioral/emotional problems in a
national sample of 4- to 16-year-olds: agreement among Informants. J Am Acad Child Adolesc Psychiatry.
1992; 31(5): 932–40. https://doi.org/10.1097/00004583-199209000-00023 PMID: 1400128
59. Singer JD, Willett JB. Applied longitudinal data analysis: modeling change and event occurrence.
Oxford, UK: Oxford University Press; 2003.
60. Anderson SE, He X, Schoppe-Sullivan S, Must A. Externalizing behavior in early childhood and body
mass index from age 2 to 12 years: longitudinal analyses of a prospective cohort study. BMC Pediatrics.
2009; 10(49). https://doi.org/10.1186/1471-2431-10-49 PMID: 20630082
61. de Wit L, Luppino F, van Straten A, Penninx B, Zitman F, Cuijpers P. Depression and obesity: a metaanalysis
of community-based studies. Psychiat Res. 2010; 178(2): 230–5. https://doi.org/10.1016/j.
psychres.2009.04.015 PMID: 20462641
62. Wang Z. Body mass index and all-cause mortality. JAMA. 2016; 316(9): 991–2. https://doi.org/10.1001/
jama.2016.10288 PMID: 27599337
63. Nihiser AJ, Lee SM, Wechsler H, McKenna M, Odom E, Reinold C, et al. Body mass index measurement
in schools. J School Health. 2007; 77(10): 651–71. https://doi.org/10.1111/j.1746-1561.2007.
00249.x PMID: 18076411
64. Baughcum AE, Chamberlin LA, Deeks CM, Powers SW, Whitaker RC. Maternal perceptions of overweight
preschool children. Pediatrics. 2000; 106(6): 1380–6. https://doi.org/10.1542/peds.106.6.1380
PMID: 11099592
65. Stuart B, Panico L. Early-childhood BMI trajectories: evidence from a prospective, nationally representative
British cohort study. Nutr Diabetes. 2016; 6(3): e198. https://doi.org/10.1038/nutd.2016.6 PMID:
26950479
66. Chen T, Baranowski T, Moreno JP, O’Connor TM, Hughes SO, Baranowski J, et al. Obesity status trajectory
groups among elementary school children. BMC Public Health. 2016; 16(1): 526. https://doi.org/
10.1186/s12889-016-3159-x PMID: 27387030
67. Moreno-Black G, Boles S, Johnson-Shelton D, Evers C. Exploring categorical body mass index trajectories
in elementary school children. J School Health. 2016; 86(7): 495–506. https://doi.org/10.1111/josh.
12402 PMID: 27246674
68. Boone-Heinonen J, Messer LC, Andrade KE, Takemoto EE. Connecting the dots in childhood obesity
disparities: a review of growth patterns from birth to pre-adolescence. Curr Epidemiol Rep. 2016; 3(1):
113–24. https://doi.org/10.1007/s40471-016-0065-9 PMID: 27172171
69. Beyerlein A, von Kries R, Ness AR, Ong KK. Genetic markers of obesity risk: stronger associations with
body composition in overweight compared to normal-weight children. PLoS One. 2011; 6(4): e19057.
https://doi.org/10.1371/journal.pone.0019057 PMID: 21526213
70. Munthali RJ, Kagura J, Lombard Z, Norris SA. Childhood adiposity trajectories are associated with late
adolescent blood pressure: birth to twenty cohort. BMC Public Health. 2016; 16(1): 665. https://doi.org/
10.1186/s12889-016-3337-x PMID: 27473865
71. Johnson W. Analytical strategies in human growth research. Am J Hum Biol. 2015; 27(1): 69–83.
https://doi.org/10.1002/ajhb.22589 PMID: 25070272
72. Regnault N, Gillman MW, Kleinman K, Rifas-Shiman S, Botton J. Comparative study of four growth
models applied to weight and height growth data in a cohort of US children from birth to 9 years. Ann
Nutr Metab. 2014; 65(2–3): 167–74. https://doi.org/10.1159/000365894 PMID: 25413655
73. Fernandez Castelao C, Kro¨ner-Herwig B. Developmental trajectories and predictors of externalizing
behavior: a comparison of girls and boys. J Youth Adolesc. 2013; 43(5);775–89. https://doi.org/10.
1007/s10964-013-0011-9 PMID: 24002677
74. Coˆte´ SM, Boivin M, Liu X, Nagin DS, Zoccolillo M, Tremblay RE. Depression and anxiety symptoms:
onset, developmental course and risk factors during early childhood. J Child Psychol Psychiatr. 2009;
50(10): 1201–8. https://doi.org/10.1111/j.1469-7610.2009.02099.x PMID: 19519755
75. Karriker-Jaffe KJ, Foshee VA, Ennett ST, Suchindran C. The development of aggression during adolescence:
sex differences in trajectories of physical and social aggression among youth in rural areas. J
Abnorm Child Psychol. 2008; 36(8):1227–36. https://doi.org/10.1007/s10802-008-9245-5 PMID:
18521738
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 17 / 18
76. Cleverley K, Szatmari P, Vaillancourt T, Boyle M, Lipman E. Developmental trajectories of physical and
indirect aggression from late childhood to adolescence: sex differences and outcomes in emerging
adulthood. J Am Acad Child Adolesc Psychiatry. 2012; 51(10):1037–51. https://doi.org/10.1016/j.jaac.
2012.07.010 PMID: 23021479
77. Ulijaszek S. Variation in human growth patterns due to environmental factors. In Muehlenbein MP, editor.
Human evolutionary biology. Cambridge, UK: Cambridge University Press; 2010. pp. 396–404.
78. McEwen BS. Understanding the potency of stressful early life experiences on brain and body function.
Metabolism. 2008; 57(2):S11–5. https://doi.org/10.1016/j.metabol.2008.07.006 PMID: 18803958
79. Caldwell JZ, Armstrong JM, Hanson JL, Sutterer MJ, Stodola DE, Koenigs M, et al. Preschool externalizing
behavior predicts gender-specific variation in adolescent neural structure. PLoS One. 2015; 10(2):
e0117453. https://doi.org/10.1371/journal.pone.0117453 PMID: 25658357
80. Anderson SE, Cohen P, Naumova EN, Must A. Association of depression and anxiety disorders with
weight change in a prospective community-based study of children followed up into adulthood. Archives
of Pediatrics & Adolescent Medicine. 2006; 160(3): 285. https://doi.org/10.1001/archpedi.160.3.285
PMID: 16520448
81. Zhao Y, Wang S, Mu M, Sheng J. Birth weight and overweight/obesity in adults: a meta-analysis. Eur. J.
Pediatr. 2012; 171(12):1737–46. https://doi.org/10.1007/s00431-012-1701-0 PMID: 22383072
82. Dunger DB, Lynn Ahmed M, Ong KK. Effects of obesity on growth and puberty. Best Pract Res Clin
Endocrinol Metab. 2005; 19(3):375–90. https://doi.org/10.1016/j.beem.2005.04.005 PMID: 16150381
83. Nagin D. Group-based modeling of development. Cambridge, MA: Harvard University Press; 2005.
84. Connell A, Bullock BM, Dishion TJ, Shaw D, Wilson M, Gardner F. Family intervention effects on cooccurring
early childhood behavioral and emotional problems: a latent transition analysis approach. J
Abnorm Child Psychol. 2008; 36(8): 1211–25. https://doi.org/10.1007/s10802-008-9244-6 PMID:
18473160
85. Harris JR. The nurture assumption: Why children turn out the way they do. New York: Free Press;
1998.
Psychopathological profiles of overweight children
PLOS ONE | https://doi.org/10.1371/journal.pone.0190731 January 5, 2018 18 / 18