A Causal and Mediation Analysis of the Comorbidity Between Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD)
A Causal and Mediation Analysis of the Comorbidity Between Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD)
Elena Sokolova 0 1 2 3 4 5
Anoek M. Oerlemans 0 1 2 3 4 5
Nanda N. Rommelse 0 1 2 3 4 5
Perry Groot 0 1 2 3 4 5
Catharina A. Hartman 0 1 2 3 4 5
Jeffrey C. Glennon 0 1 2 3 4 5
Tom Claassen 0 1 2 3 4 5
Tom Heskes 0 1 2 3 4 5
Jan K. Buitelaar 0 1 2 3 4 5
0 Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen , Groningen , The Netherlands
1 Institute for Computing and Information Sciences, Radboud University , Nijmegen , The Netherlands
2 Radboud University , Postbus 9010, 6500 Nijmegen , The Netherlands
3 Department of Psychiatry, Donders Institute for Brain , Cognition and Behaviour , Radboud University Medical Center , Nijmegen , The Netherlands
4 Karakter Child and Adolescent Psychiatry University Centre , Nijmegen , The Netherlands
5 Department of Cognitive Neuroscience, Donders Institute for Brain , Cognition and Behaviour , Radboud University Medical Center , Nijmegen , The Netherlands
Autism spectrum disorder (ASD) and Attention-deficit/hyperactivity disorder (ADHD) are often comorbid. The purpose of this study is to explore the relationships between ASD and ADHD symptoms by applying causal modeling. We used a large phenotypic data set of 417 children with ASD and/or ADHD, 562 affected and unaffected siblings, and 414 controls, to infer a structural equation model using a causal discovery algorithm. Three distinct pathways between ASD and ADHD were identified: (1) from impulsivity to difficulties with understanding Elena Sokolova and Anoek M. Oerlemans are joint first authors on this work.
ADHD; ASD; Inattention; Social interaction; Comorbidity
Autism spectrum disorders (ASD) and attention-deficit/
hyperactivity disorder (ADHD) are regarded as distinct
disorders in the Diagnostic and Statistical Manual of Mental
Disorders (DSM-5). ASD symptoms include impairments
in interaction, communication and restricted, stereotyped,
and repetitive behavior, whereas ADHD is characterized
by symptoms of inattention and hyperactivity/impulsivity
(American Psychiatric Association 2013). In previous
versions of the DSM, ASD was an exclusion criterion to be
diagnosed as having ADHD. As a result, these disorders
were studied separately from each other for many years.
However, recent research recognizes considerable
clinical, genetic, and neuropsychological overlap between ASD
and ADHD (Rommelse et al. 2011, 2010a) and within the
DSM-5, ADHD can now be diagnosed in conjunction with
ASD. Various studies showed that 22–83% of children with
ASD have symptoms that satisfy the DSM-IV criteria for
ADHD (Ronald et al. 2008; Matson et al. 2013), and vice
versa, 30–65% of children with ADHD have clinically
significant symptoms of ASD (Clark et al. 1999; Ronald et al.
2008). In clinical practice, it is sometimes difficult to
differentiate between ASD and ADHD, partly due to the
entanglement of symptom descriptions of both disorders
(Luteijn et al. 2000). This might explain why a substantial
proportion of children have been alternatively given a
diagnosis of one or the other disorder throughout development
(Fein et al. 2005). A strong body of twin-, family-, and
linkage studies have consistently shown that ASD and ADHD
share a portion of their heritable etiology (Lichtenstein
et al. 2010b). About 50–72% of the contributing genetic
factors overlap between ASD and ADHD (Lichtenstein
et al. 2010a; Rommelse et al. 2010b). Furthermore, similar
deficits in executive function, social cognition, and motor
speed have been linked to both ASD and ADHD (see for
an extensive review, Rommelse et al. 2011). Relationships
between ASD and ADHD appear to be stronger during
certain developmental periods than others, with rather strong
ASD/ADHD constellations during adolescence and weaker
correlations in early childhood and at adult age. This might
be due to that optimal social adaptation and EF skills
matter most in adolescence (Hartman et al. 2016).
The main goal of this paper is to investigate what is
now needed to resolve this issue of symptom
entanglement and alternating diagnoses. Some studies have tried to
examine to which degree different symptom domains
cluster together, and to which extent these domains are caused
by the same genetic and environmental influence
(Polderman et al. 2013; Ronald et al. 2014; Taylor et al. 2015). It
has been proposed that the association between ASD and
ADHD traits is primarily due to shared attention-related
problems (inattention and attentional switching capacity),
suggesting that biological pathways involving attentional
control may be a key factor in unraveling the genetic causes
of these disorders (Polderman et al. 2013). However, it is
controversial to assume that attentional switching deficits
belong solely to ASD and not ADHD. Impulsivity and
inattention are often present in individuals with symptoms of
ASD and these symptoms have a strong phenotypic and
genetic overlap with non-social autistic traits, such as
repetitive behavior (Ronald et al. 2014). In contrast, another
study showed that genetic overlap was strongest between
communication difficulties typical of ASD and ADHD,
while repetitive behavior and social difficulties showed
only moderate genetic overlap (Taylor et al. 2015). Thus,
these studies provide different explanations of comorbidity
between ADHD and ASD.
These studies did not assess whether or not the observed
links between specific ASD and ADHD traits were due to
direct associations or indirect associations. That is, whether
or not traits are correlated due to the causal effect of one
variable on another or an unobserved common cause for
both traits (direct paths) or due to an indirect association
mediated via another trait (indirect paths). For example,
the finding that social problems were only moderately
correlated with hyperactivity, yet strongly correlated with
inattention (Ronald et al. 2014), may suggest that the
former correlation is explained by an indirect path from social
problems to hyperactivity mediated via inattention. Being
able to differentiate between direct and indirect paths may
greatly improve our understanding of the co-occurrence
of ASD and ADHD. In clinical practice it is often unclear
what amplifies what, i.e., whether the ADHD related
impulsivity is causing the social problems, or reversely,
whether the repetitive behaviors are mistaken for
hyperactivity. Answering these questions of direction and causation
may have significant clinical implications, as it may inform
Standard research methods such as correlation analysis
or clustering do not provide the possibility to infer
directionality from cross-sectional data. In the current study, the
aim is to build a causal model describing the direction of
the associations between specific behavioral symptoms of
ASD, ADHD, and general factors via a structural equation
model (SEM), using the Bayesian Constraint-based Causal
Discovery (BCCD) algorithm (Claassen and Heskes 2012).
This is an exploratory approach that learns the structure of
a SEM from the observed data instead of the more
commonly published confirmatory approach that tests a priori
defined hypothetical networks. The idea of exploratory
structure learning algorithms (Pearl 2000) is based on the
connection between conditional independencies and causal
relationships. Thus, by finding conditional independencies
in cross-sectional data, it is possible in particular cases to
infer parts of the structure of a SEM and make
(preliminary) predictions about causation. BCCD infers the
skeleton of the SEM that describes direct associations as well
as the direction of effects from data (a detailed
description is provided in the Supplementary material). While the
skeleton can be accurately inferred from a relatively small
sample size, the accurate inference of causal directions
requires larger sample sizes (Claassen and Heskes 2012)
and the presence of particular patterns to be able to infer
the directions. As a second step, standard mediation
analysis is applied to test direct or indirect relationships obtained
through causal modeling.
In sum, our aim is to explore the relationships between
specific ASD and ADHD symptoms by applying causal
modeling to a large set of observed data (n = 1393)
including children with ADHD and/or ASD, their siblings and
control children. Some generic factors are included in our
analysis that are known to be associated with ASD and
ADHD, namely age, gender, and IQ (Gardener et al. 2009;
Mill and Petronis 2008). The current approach primarily
determines whether the association between variables is
direct, rather than determining the direction of this
association, but inferred directions are also included as
preliminary hypotheses that should be further tested in
Materials and Methods
Participants from two large-scale family-genetic studies,
the Biological Origins of Autism (BOA, data collected
between 2008 and 2012) study and the Dutch part of the
International Multicenter ADHD Genetics (IMAGE data
collected between 2004 and 2008) study (van Steijn et al.
2012), were included in the current study. Inclusion
criteria for all participants were at least two biological
siblings (in case of families: at least one child with a
clinical diagnosis of ASD or ADHD), offspring age between
4 and 20 years, European Caucasian descent, offspring
IQ ≥70, and no diagnosis of epilepsy, brain disorders,
or known genetic disorders, such as Down-syndrome or
All participants were carefully phenotyped for ASD
and ADHD using validated and standardized
questionnaires and diagnostic interviews. Briefly, both the
children already clinically diagnosed with ASD and/
or ADHD, their siblings, and the control children were
screened for the presence of ASD and ADHD
symptoms using the parent- and teacher-reported Social
Communication Questionnaire (SCQ)(Rutter et al. 2003)
and the parent-, and teacher-reported Conners Rating
Scales-Revised (CPRS; CTRS), respectively
(Conners 1996). Raw scores of ≥10 on the parent-rated SCQ
Total score, ≥ 15 on the teacher-rated SCQ Total score
and T-scores ≥63 on the Conners’ DSM-IV Inattention,
Hyperactivity-Impulsivity, or Combined scales were
considered as clinical. A lower cutoff was used for the
parent reported SCQ to avoid false negatives in their
undiagnosed offspring (Corsello et al. 2007). All children
scoring above cut-off on any of the screening
questionnaires underwent full clinical ASD and ADHD
assessment, including the Autism Diagnostic Interview-Revised
(ADI-R) structured interview for ASD (Le Couteur et al.
2003) and the Parental Account of Childhood Symptoms
ADHD subversion (PACS) for ADHD (Taylor 1986).
Control children were required to obtain non-clinical
scores (i.e., a raw score <10 on the SCQ and T-score <63
on both parent and teacher reported CRS-R DSM-IV
scales) in order to be accepted in this study.
The total sample contained 1393 participants,
including 586 patients (317 ADHD only, 130 ASD only, and
139 combined ASD+ADHD), 393 unaffected siblings,
and 414 controls. Demographics of the study sample
are shown in Supplementary Table S1. A more detailed
description of participant selection can be found in
papers by Steijn et al. and Oerlemans et al. (van Steijn
et al. 2012; Oerlemans et al. 2014).
To apply causal discovery using the BCCD algorithm, the
following variables were selected.
• Age of the participant
• Current ADHD symptoms assessed with the parent and
teacher reported CRS-R scales.
• Inattention symptoms (CRS DSM-IV inattention
• Hyperactivity symptoms (hyperactivity items of the
CRS DSM-IV hyperactivity/impulsivity subscale).
• Impulsivity symptoms (impulsivity items of the CRS
DSM-IV impulsivity subscale).
• Current ASD symptoms assessed with four subscales
of the parent-reported Child Social Behavior
Questionnaire (CSBQ) (Hartman et al. 2015). A full list of
CSBQ items is provided in supplementary material. For
clarity we provide a few examples items for each
symptom type of CSBQ.
• Reduced contact and social interests (Has little or no
need for contact with others, makes little eye contact,
• Difficulties in understanding social information, referred
to as social ineptness further in the text (Takes things
literally, e.g., does not understand certain expressions,
Does not fully understand what is being said, i.e., tends
to miss the point, etc.)
• Fear of/and resistance to changes (Remains clammed
up in new situations or if change occurs, panics in new
situations or if change occurs, etc.)
• Stereotyped, repetitive behavior (Constantly feels
objects, smells objects, etc.)
• Intelligence as measured using the Wechsler Intelli
gence Scale for Children (WISC-III) or the Wechsler
Adult Intelligence Scale (WAIS-III), depending on
child’s age (Wechsler 2002, 2000).
• Verbal IQ, prorated by subtests Similarities and Vocab
• Performance IQ, prorated by the subtests Block Design
and Picture Completion.
In this study we considered the raw data ADHD
symptoms for our analyses instead of the T-scale score, since
T-scale scores are adjusted for the effect of gender and age.
BCCD can model the effect of age and gender into account,
and so we avoided unwanted ‘double correction’. Moreover,
we separated impulsivity and hyperactivity subscales based
on item scores, instead of using the ‘standard’ DSM
hyperactivity/impulsivity subscale to examine the effect of each
specific trait. For ADHD symptoms, scores assessed by
parents and teachers were provided. To increase the reliability
of the symptom assessment, for each subject we averaged
the symptom scores from parent and teacher. The main two
reasons for that are: (1) ADHD is diagnosed when several
symptoms are prevalent in at least two or more settings,
thus many clinicians find that parent and teacher ratings are
helpful in the diagnostic process; (2) parent- and teacher
ratings are highly correlated (R = 0.64, p < 0.0001), which
makes it difficult to compare them independently.
Unfortunately, it was not possible to obtain information about ASD
symptoms from a second observer, thus ASD symptoms
were assessed only based on parents report.
The CSBQ contains items refer directly to the DSM-IV
criteria for autistic disorder, but also represent less severe
variations of these criteria as well as ASD-associated
problem such as executive function problems and
disruptive behavior in social settings (Hartman et al. 2012). We
opted for the CSBQ instead of the SCQ to assess ASD
symptoms, because we were specifically interested in
current behavior, whereas the SCQ mainly refers to
behavior at age 4–5 years. Multiple studies have shown that the
CSBQ has good psychometric properties with regard to
test–retest and interrater reliability, internal consistence of
the scales (all reliability indices >0.75), and good criterion
validity both for high-functioning children and for children
with mild to moderate mental retardation (Hartman et al.
2006; de Bildt et al. 2009; Noordhof et al. 2015; Jaspers
et al. 2013; Greaves-Lord et al. 2013). For ASD
symptoms only parent scores were provided, so it was not
possible to combine them with teacher scores. The reason that
only parent-reported ASD symptoms were included in the
study is that there is no teacher version of the CSBQ
available. The selected WISC-III/WAIS-III subtests are known
to correlate between 0.90 and 0.95 with the full-scale IQ
In the first step of the analysis a causal discovery algorithm
was used to learn the structure of a SEM, and to formulate
hypotheses about direct and indirect relations between
variables. Supplementary material describes the link between
SEM and causal discovery, provides a description of
existing algorithms for causal discovery as well as our
motivation for using Bayesian Constraint-based Causal Discovery
(BCCD) (Claassen and Heskes 2012). This algorithm infers
statements representing causal relationships and estimates
the reliability of these statements. The method outputs
information about potential interactions between observed
variables and does so in two ways: through the skeleton
and through orientation. The skeleton describes
mediation: two variables are connected if the association between
them is not mediated by any other observed variable. Tails
and arrows provide information about the direction of the
association. The results are visualized through a causal
graph by considering statements with reliability higher than
50%. Our method can incorporate prior knowledge about
the domain. Here the assumption that gender and
participant’s age cannot be caused by any other observed
variables in the model was implemented, since chronologically
the former are present in the lifespan earlier than the latter.
In the second step of the analysis standard mediation
analysis was applied to explicitly check some of the
hypotheses generated by our causal analysis. Mediation analysis
distinguishes between independent variable, dependent
variable, and potential mediators (Baron and Kenny 1986).
To test whether the effect of the independent variable is
indirect, a regression model was built that aimed to predict
the dependent variable from the independent variable and
potential mediators. If the regression coefficient was
statistically significant for the potential mediators, but not for the
independent variable, a conclusion can be made that there
was not enough evidence to reject the hypothesis that the
effect of the independent variable is indirect.
Note here that the same data is used twice: to generate
hypotheses and to test them. Consequently, the reported
p-values of the mediation test should be treated with care.
These p-values only indicated the significance as if the
specific hypothesis had been coined prior to observing any
data. Also note that the data set contains siblings from the
same family. To test whether there is an effect of familiality
on the resulting causal model, a sensitivity analysis using a
subsample of singletons was performed, including only one
subject per family. Due to the reduction of the sample size
the reliability of the causal links tends to drop. However, if
the model is stable, the main links will be preserved in the
Running the BCCD algorithm we inferred reliability
estimates of the causal relations between variables
(Supplementary Tables S1, S2, S3) and built a graph
summarizing these relationships presented in Fig. 1. In this graph an
edge between two variables suggests that no other variable
in the model can make these variables independent, which
we call here a direct relationship. This can be either an
effect of one variable on another (“A → B”), unobserved
common cause (“A ↔ B”) or a selection bias (“A − B”). If
the direction of an edge between two variables is uncertain
it has a circle mark “◦”.
The general structure of the network matches other
studies in the literature: gender influences symptom counts with
males having higher scores than females (Cantwell 1996;
Ramtekkar et al. 2010); age influences hyperactivity level
with older subjects having lower level of hyperactivity than
Fig. 1 Causal model representing causal relationships between
variables in our combined ADHD and ASD data set. Edge directions
represent either a causal effect (“A → B”), an unobserved common cause
“A ↔ B” or a selection bias “A − B”. Non-identifiable edge directions
are marked with a circle mark “◦”. Notation “A◦B” is either a causal
effect “A → B”, or a selection bias “A − B”; “A◦ → B” is either a
causal effect “A → B”, or a common cause “A ↔ B”; “A◦ −◦B” (“A◦
younger subjects (Biederman et al. 2000); ADHD
symptoms are associated with ASD symptoms (Ronald et al.
2008) and both are associated with IQ (with children
having ASD, ADHD, or both having lower IQs in general than
children without the disorder) (Vaida et al. 2013).
Moreover, both ASD and ADHD symptoms are strongly
interconnected, resulting in a separate cluster (also called a clique:
a complete subgraph, in which all variables are pairwise
interconnected) of ADHD and ASD symptoms. The same
holds for IQ.
The inferred network suggests that the ASD traits
‘social ineptness’ and ‘stereotyped, repetitive behaviors’
are directly and differentially associated with ADHD
symptoms. Social ineptness is associated with
inattention and impulsivity, while stereotyped, repetitive
behavior is associated with hyperactivity (but not impulsivity).
Our network also shows that verbal IQ is a linking factor
between ADHD and ASD, since there is a link from verbal
IQ to both ADHD and ASD symptom traits. To get a
better understanding of these observed direct associations, we
zoom in on each link. The direction of these causal links
contains circle marks, indicating uncertainty in the causal
directions. For example, a link ‘◦−’ between impulsivity
− ◦B”) is either a causal effect “A → B” or “A ← B”, a selection bias
“A − B” or a common cause “A ↔ B”. No edge between variables
means that these variables are conditionally independent given the
other variables in the network. Reliability estimates for the presence
of an edge are depicted as percentages. Direct links between ASD,
ADHD, and IQ are marked in red. (Color figure online)
and social ineptness and between hyperactivity and
repetitive behavior is either a causal link or a selection bias. A
link ‘◦→’ between inattention and social ineptness is either
a causal link or an unobserved common cause.
Based on the inferred model, there is a direct
association of the social ineptness with inattention and
impulsivity. Both links have a very strong reliability for a direct
link (>99%), providing strong evidence of a direct
association. Mediation analysis confirmed that there was no direct
link between hyperactivity and social ineptness (β = 0.01,
p = 0.79) (Fig. 2). We provide the first figure of the
regression analysis as an example in the main text, other figures
of this type of analysis can be found in the Supplementary
Another direct association between ASD and ADHD
traits can be seen between hyperactivity and stereotyped,
repetitive behaviors (reliability for direct link >99%). No
direct causal links are found between repetitive behavior
and inattention or impulsivity (Fig. 1). Mediation analysis
confirmed that there are no direct paths between
inattention and repetitive behavior (β = 0.03, p = 0.11), and
impulsivity and repetitive behavior (β = 0.01, p = 0.86), but an
indirect one mediated through social ineptness, which may
Fig. 2 Regression model for mediation analysis that predicts
dependent variable (in grey) social ineptness using inattention and
impulsivity as a mediator and hyperactivity as independent predictor. The
explain the correlations observed between these variables
(Supplementary Fig. S1).
Our analyses also indicated that inattention and social
ineptness are associated via verbal IQ due to direct
links between inattention and IQ (reliability for direct
link >79%), and between social ineptness and IQ (reliability
for direct link >99%). Taking into account the link between
inattention and social ineptness mentioned before, all three
variables are pairwise connected, which can be a sign of an
unobserved common cause for these variables. Mediation
analysis showed that verbal IQ is only indirectly associated
with impulsivity (β = 0.09, p = 0.74) and hyperactivity (β
=−0.25, p = 0.16) and that this is mediated through
inattention (Supplementary Fig. S2). Mediation analysis also
revealed that there is no direct link between verbal IQ and
repetitive behavior (β =−0.27, p = 0.12), reduced contact (β
= 0.02, p = 0.87), and fear of change (β = 0.04, p = 0.89),
but that these effects are mediated through social ineptness
and hyperactivity (Supplementary Fig. S3).
Our model makes preliminary predictions about the
directions of the causal links between ASD and ADHD
traits. According to the model, hyperactivity may have
a causative effect on repetitive behavior, with reliability
of the link direction >91%. The direction of this link is
inferred from (1) the assumption that hyperactivity does
not cause age, and (2) the dependency between age and
stereotypic behavior became insignificant when
controlling for hyperactivity (R =−0.01, p = 0.86). Moreover,
our model indicates that impulsivity may have a causative
effect on social ineptness (and not visa-versa) with
reliability >85%. This direction is inferred from (1) the assumption
regression model is presented at the top of the figure, the significance
of the regression coefficient is shown next to the edge
that impulsivity does not cause age, and (2) the
dependency between age and social ineptness became insignificant
when controlling for impulsivity (R = 0.01, p = 0.79).
In the current study we applied exploratory causal
modeling to investigate the co-occurrence of ADHD and ASD
by incorporating their core symptom domains into a single
integral model. Since ASD and ADHD symptom domains
are all significantly pairwise correlated, raw
correlationbased methods would not provide any insight into the direct
and indirect association between these symptom domains.
The causal method applied in this paper builds a more
complete model, distinguishes between direct and indirect
associations, and allows us to make preliminary
predictions about causation. These predictions were corroborated
by mediation analysis. The results suggest at least three
separate pathways between ADHD and ASD: (a) a
pathway from impulsivity to social ineptness, and (b) a pathway
from hyperactivity to stereotyped behavior (c) a cluster of
inattention, social ineptness and verbal IQ, with a possible
Our findings suggest that there are multiple distinct
pathways and causes for the co-occurrence between ASD and
ADHD. The strongest link was found between social
communication difficulties, inattention and impulsivity. This
corroborates previous reports based on both cross-sectional
(Polderman et al. 2014) and longitudinal data (St Pourcain
et al. 2014) that part of the association between ASD and
ADHD may be due to shared attention-related problems.
This is also in accordance with the outcome of reviews by
our group (Visser et al. 2016) as well as others (Jones et al.
2014) that attentional problems at a very early age may
precede the onset of clinical manifestations of ASD, ADHD,
or both disorders. These attentional problems may include,
for example, problems in attentional shifting and
disengaging impairments (Jones et al. 2014; Visser et al. 2016). As a
novel finding, our model putatively suggests that
impulsivity has a causative effect on social ineptness. Such a causal
link from impulsivity to social ineptness would make
intuitive sense. To interact effectively with others, an individual
must be able to control impulsive behaviors. Impulsive
symptoms may lead a person to miss social cues, for
example, because they act prematurely or interrupt the other
person (Leitner 2014), which in turn may result in social
difficulties. The relevance of impulsivity is reflected in
cognitive studies that describe deficits in executive functioning
in young children with ADHD and/or ASD, as measured in
tests of response inhibition and interference control.
Our model does not make (preliminary) predictions on
the causal direction between inattention and social
ineptness. It does put inattention and social ineptness in one
cluster with verbal IQ, which can be an indication of an
unobserved common cause between these variables, for
example a shared genetic factor. Verbal IQ refers to the
capacity to use language in order to express oneself,
comprehend stories, and understand other people, but also to
self-directed speech that supports self-control. Previous
studies have reported on language problems in both ASD
and ADHD (Geurts and Embrechts 2008; Geurts et al.
2004; Leonard et al. 2011). Children with ASD often have
a delayed development of spoken language, fail in normal
back-and forth conversations, and use language in a
stereotypic and repetitive manner. The diagnostic criteria for
ADHD also include behaviors suggesting
social-communication dysfunction, such as talking excessively,
interrupting others, and not listening to what is being said
(American Psychiatric Association 2013). These communication
deficiencies may contribute to social interaction problems
that are typical for individuals with ASD and ADHD. A
number of studies have reported on chromosomal regions
that may harbor quantitative trait loci (QTLs) for language
and communication problems in ASD, including
chromosome 7q (Alarcon et al. 2002), which was also identified in
a study looking for potential pleiotropic loci for ASD and
ADHD (Nijmeijer et al. 2010). Nijmeijer et al. (2010) also
found suggestive linkage on chromosome 15q for the SCQ
communication subscale in their sample of ADHD
families. Furthermore, relatively poor verbal comprehension
is more often found in children with ASD (Charman et al.
2011; Rundblad and Annaz 2010). Further study is needed
to increase our knowledge on possible pleiotropic (genetic)
risk factors that underlie the complex associations between
inattention, social ineptness, and verbal competence.
A second pathway identified was between
hyperactivity and repetitive behavior. In most studies, impulsivity and
hyperactivity are regarded as one combined feature, but our
results suggest that these symptoms may be differentially
associated with ASD symptoms. Some studies have
previously reported on the link between repetitive behaviors and
hyperactivity (Polderman et al. 2014, 2013; Gabriels et al.
2005; Ronald et al. 2014). It has been argued that repetitive
behavior and ADHD are due to a lack of inhibitory control,
but contrasting findings have also been reported (Rommelse
et al. 2011). Our model putatively suggests that individuals
who are hyperactive and therefore less able to inhibit motor
behaviors may, as a result, engage also more often in
various motor behaviors that are classified as stereotypic, such
as flapping arms/hand when excited or making odd and fast
movements with fingers or hands (all items from the CSBQ
‘stereotypic behavior’ subscale). However, Polderman and
colleagues (Polderman et al. 2014) proposed that the
association may be conversely explained by repetitive behaviors
interfering with the ability to switch attention from one task
to another. Furthermore, inhibitory control is also
associated with impulsivity, which was only indirectly related
to repetitive behaviors according to our model. Further
research on the direction of the link between hyperactivity
and repetitive behavior is therefore needed.
Our putative predictions about the causal directions in
the two pathways between ADHD and ASD (from ADHD
inattention/impulsivity to ASD social ineptness, and
from ADHD hyperactivity to ASD stereotyped, repetitive
behavior) suggest that interventions that decrease
inattention/impulsivity related difficulties are also likely to have
a beneficial effect on social functioning, but not the other
way around; interventions that affect social functioning
cannot be expected to also have a positive effect on the
level of inattention/impulsivity. The same logic is
applicable for the effect of hyperactivity on repetitive behavior.
These findings are consistent with results from
longitudinal study by St. Pourcain et al. (2011). They showed that
children with high probability for persistent
hyperactiveinattentive symptoms had a high probability for
persistent social communication deficits, but not vice versa (St
Pourcain et al. 2011). Our results may also fit well with the
gradient overarching disorder theory, which proposes that
ADHD is a less severe subtype within the ASD spectrum
(van der Meer et al. 2012). As a consequence, individuals
with (more severe forms of) ADHD are also highly likely
to have increased (sub)clinical levels of ASD symptoms. It
is important to note however, that our findings are based
on just one (albeit rather large) sample, which needs to be
replicated in other, independent samples, ideally in a
Several strengths and limitations should be taken into
account when assessing the results of the current study.
The main strength of this study is the application of a novel
causal discovery method for data analysis. This method
considers all variables together, infers both direct and
indirect dependencies between variables, provides a reliability
measure for each edge in the network, and is able to detect
latent common causes. This method does not require
longitudinal or interventional data and can infer causal
statements based on cross-sectional data (Pearl 2000). Another
strength is the use of a large, carefully phenotyped sample
of affected and unaffected siblings and control children,
allowing us to study the full spectrum of ASD and ADHD
symptoms. A limitation of our causal discovery method is
that it is an exploratory analysis—it provides new
hypotheses that need to be tested using other methods and requires
independent replication through experiments or additional
data. Another limitation of our study is that the
conclusions mainly apply to individuals with average IQ, as we
excluded participants with an IQ below 70. This is not
representative of the ASD population at large that includes a
considerable proportion of individuals with ASD with an
intellectual disability. Furthermore, we excluded
individuals with known epilepsy, brain disorders, or genetic
syndromes, and who were not of European Caucasian descent.
Thus, caution is warranted when interpreting our results. In
addition, including data of genetically related individuals
may cause interpretation problems, due to the possibility of
unobserved latent associations between variables that were
not taken into account. We tackled this problem by
running a sensitivity analysis using a subsample of singletons
- including only one subject per family - to evaluate the
impact of familiarity. We obtained a highly similar network
structure with only a few missing edges due to reduced
statistical power as a consequence of the reduction of the
sample size by half, indicating the robustness of our approach
and our findings.
In conclusion, our results indicate that the often reported
co-occurrence of ASD and ADHD might be explained by
three distinct pathways: (a) between
inattention/impulsivity and social ineptness, and (b) between hyperactivity
and stereotypic, repetitive behaviors (c) through verbal IQ.
These findings may inform future studies on understanding
the (pathophysiological) mechanisms behind the overlap
between ASD and ADHD.
Funding The research leading to these results has received
funding from the European Community’s Seventh Framework
Programme (FP7/2007–2013) under Grant agreement n° 278948
(TACTICS) and the Netherlands Organisation (NWO) Grant
MoCoCaDi (612.001.202), a Veni Grant assigned by NWO (#91610024
and #1750102007010); a Grant assigned by the National
Institute of Mental Health (NIH grant # R01 MH62873-01A1); a Grant
assigned by the European Community’s Horizon 2020 Program
(H2020/2014–2020, Grant #643051 (MiND) and #642996
(BRAINVIEW)), grant from the Innovative Medicines Initiative Joint
Undertaking under grant agreement n° 115300 (EU-AIMS), resources of
which are composed of financial contribution from the European
Union’s Seventh Framework Program (FP7/2007–2013) and the
European Federation of Pharmaceutical Industries and Associations
(EFPIA) companies’ in kind contribution.
Author contributions AO concieved the study and together with
ES drafted the manusrcipt. ES perfomed statistical analysis and
modeling. TH, TC, PG participated in data analysis, model interpretation
and manuscript draft. NR, CH, JG and JB worked on results
interpretation, literature study and helped with manuscript draft. All authors
read and approved the final manuscript.
Compliance with Ethical Standards
Conflict of interest Jan K. Buitelaar has been in the past 3 years a
consultant to / member of advisory board of / and/or speaker for
Janssen Cilag BV, Eli Lilly, Shire, Lundbeck, Medice and Servier. He is not
an employee of any of these companies, and not a stock shareholder of
any of these companies. He has no other financial or material support,
including expert testimony, patents, royalties. All other authors report
no biomedical financial interests or potential conflicts of interest.
Ethical Approval The study was approved by the local medical
ethics board and parents and children (12 years and older) signed for
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
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