Evaluating Sex and Age Differences in ADI-R and ADOS Scores in a Large European Multi-site Sample of Individuals with Autism Spectrum Disorder
Journal of Autism and Developmental Disorders
Evaluating Sex and Age Differences in ADI-R and ADOS Scores in a Large European Multi-site Sample of Individuals with Autism Spectrum Disorder
0 Department of Clinical Sciences , Child and Adolescent Psychiatry , Medical Faculty, Lund University , Lund , Sweden
1 Department of Experimental-Clinical and Health Psychology, Ghent University , Ghent , Belgium
2 Mafalda Luce Center for Pervasive Developmental Disorders , Milan , Italy
Research on sex-related differences in Autism Spectrum Disorder (ASD) has been impeded by small samples. We pooled 28 datasets from 18 sites across nine European countries to examine sex differences in the ASD phenotype on the ADI-R (376 females, 1763 males) and ADOS (233 females, 1187 males). On the ADI-R, early childhood restricted and repetitive behaviours were lower in females than males, alongside comparable levels of social interaction and communication difficulties in females and males. Current ADI-R and ADOS scores showed no sex differences for ASD severity. There were lower socio-communicative symptoms in older compared to younger individuals. This large European ASD sample adds to the literature on sex and age variations of ASD symptomatology.
Autism Spectrum Disorder; Phenotype; Sex; Age; Symptom severity
Autism Spectrum Disorder (ASD) is one of the most
common neurodevelopmental conditions with a prevalence of
1–1.5% of children and adults
(Baird et al. 2006; Brugha
et al. 2011; Christensen et al. 2016)
. A consistent finding
from both clinical observation and empirical evidence is
that more males than females are diagnosed with ASD, and
current estimates range from 3:1–4.3:1 across the autism
(Loomes et al. 2017)
. This ratio, however, varies
as a function of IQ, with prevalence rates of 5.75:1 males:
females in samples composed of individuals in the
normative IQ range (> 70) compared to 1.9:1 in ASD associated
with low IQ (≤ 70)
(Baird et al. 2006; Fombonne 2009; Scott
et al. 2002; Kim et al. 2011)
. The reason for this discrepancy
Extended author information available on the last page of the article
in the sex ratio is unclear. While some have suggested that
females may require a greater genetic load to develop ASD
(Jacquemont et al. 2014), others have proposed that the
male-preponderance in ASD prevalence, particularly at the
intellectually able end of the spectrum, may be related to
females being better at compensating for their difficulties
(Attwood 2006; Lai et al. 2011;
Postorino et al. 2015; Rynkiewicz et al. 2016)
leading to under-recognition of females and delay in diagnosis
(Lai et al. 2015). Indeed, there is evidence from population
studies that girls with comparable levels of symptoms to
boys are less likely to be diagnosed or are later diagnosed
by community services
(Russell et al. 2011; Kirkovski et al.
, unless they present with more substantial behavioural
and/or cognitive difficulties (Dworzynski et al. 2012).
The way the core clinical symptoms of ASD—difficulties
in social communication and interaction and the presence of
restricted, repetitive, behaviours and interests and atypical
responses to sensory input
(DSM-5, American Psychiatric
—manifest may also be different for males
(Mandy et al. 2012; Van Wijngaarden-Cremers
et al. 2014)
. Yet, in contrast to the strong evidence of sex
differences in the prevalence of ASD, differences between
the sexes in the phenotypic presentation of ASD have been
found to be small in magnitude and available findings are
inconsistent, both in terms of the severity of core symptoms
and across age and level of functioning. While some studies
have found no significant sex differences in the behavioural
presentation of ASD using the ADOS
(Lord et al. 2000,
2012; Ratto et al. 2017)
(Rutter et al. 2003;
Holtmann et al. 2007; Pilowsky et al. 1998; Andersson et al.
2013; Reinhardt et al. 2015; Harrop et al. 2015; Ratto et al.
, others have reported some differences using a mixed
set of measures
(for reviews see Lai et al. 2015; Kirkovski
et al. 2013; Van Wijngaarden-Cremers et al. 2014)
example, a meta-analysis of smaller-scale studies (Van
Wijngaarden-Cremers et al. 2014) and multi-site large-scale
(Mandy et al. 2012; Szatmari et al. 2012; Frazier
et al. 2014; Supekar and Menon 2015; Charman et al. 2017)
demonstrated fewer restrictive and repetitive behaviours
(RRB) in females than males, consistent with findings both
in young children with varying cognitive abilities
(Lord et al.
1982; Hartley and Sikora 2009)
and intellectually able adults
(Wilson et al. 2016; Lai et al. 2011)
. In contrast, specific
sex differences in the severity of social and communication
impairments have not been conclusively presented. Some
studies have found girls to have more impaired social and/
or communicative functioning than boys
(Hartley and Sikora
2009; Carter et al. 2007)
, whereas others have found
(Wilson et al. 2016; Mandy et al. 2012; Supekar and
or superior social and communication skills
in females compared to males
(Lai et al. 2011; Park et al.
. Comparisons between studies are compromised by a
number of factors that potentially contribute to the
discrepancy in findings.
First, females with ASD are often underrepresented due
to small sample sizes that result in limited statistical power
to detect small to moderate effects. Studies involving
intellectually able adolescents and adults are particularly
affected by this problem, and while some have addressed
this issue by analysing large-scale datasets
(Mandy et al.
2012; Frazier et al. 2014; Howe et al. 2015; Supekar and
Menon 2015; Wilson et al. 2016; Charman et al. 2017)
these studies have been limited. Second, although the ASD
phenotype may present differently in males and females,
current defining (DSM) criteria are still mainly based
on male characteristics. This is true from both a
qualitative and a quantitative point of view, because
diagnostic thresholds are similar in males and females
Beisler 1983; McLennan et al. 1993; Holtmann et al. 2007;
Lai et al. 2015)
. This poses several problems. If current
diagnostic criteria are more tuned to the male phenotype
of ASD, the diagnosis of ASD in females may be missed
or the condition could be misdiagnosed
(Rivet and Matson
2011; Begeer et al. 2013; Dworzynski et al. 2012)
if these females present with a substantial clinical burden
and would benefit from support programmes. Moreover,
since sex differences in presentation may not lead to a
diagnosis in females, many ASD samples potentially miss
a large number of females resulting in an
overrepresentation of males in ASD research even if a small group of
females is included (but underrepresented) who fulfil DSM
criteria, although results are thought to be applicable to
(Lai et al. 2015)
Third, there is evidence that ASD symptoms may
present differently across development. Some studies highlight
reduced ASD symptoms with age, particularly in early
childhood, but also marked heterogeneity in the trajectory
of symptom expression over childhood and into early
adolescence with some individuals having relatively stable high
or low symptom levels across age, while others improve or
become more impaired over time
(Bölte and Poustka 2000;
Szatmari et al. 2009, 2015; Fountain et al. 2012; Gotham
et al. 2012; Lombardo et al. 2015; Bal et al. 2015)
symptoms also often persist into adulthood, but often
improve compared to adolescence
(Billstedt et al. 2007;
Shattuck et al. 2007; Howlin et al. 2013)
. Thus, comparing
samples of young children
(Hartley and Sikora 2009; Carter
et al. 2007)
to subjects across a broad age range (Pilowsky
et al. 1998) may mask sex differences due to developmental
Fourth, differences between males and females in the
behavioural presentation of ASD may also vary with IQ,
and whilst some studies have matched for IQ and age, others
have not. Finally, previous studies have differed in the choice
of measures used, from structured caregiver interviews
(ADI-R), clinician rated observational measures (ADOS),
to parent- or self-reported questionnaires, and this may
have contributed to the discrepant findings
Grantham et al. 2011)
. The ADI-R for example probes about
an individual’s current or past behaviour (ever and at
4-to5-years—considered historically to be the ‘prototypic age’
of presentation), while the ADOS measures current
symptom severity in a standardised behaviour sampling context.
These instruments are relevant in our clinical and
conceptual understanding of ASD symptomatology, but may yield
different insights into the ASD phenotype based on their
relative strengths and weaknesses in assessing symptom
presentation at different developmental time-points using
different informant and context-dependent assessment
(Charman and Gotham 2013)
Given these confounds, the pattern of sex differences in
the core symptomatology of ASD remains unclear,
potentially contributing to a male-bias in our understanding of
(for a recent special issue on this topic see Mandy and
. One potential avenue to advance our
understanding is to obtain large-scale samples which are difficult to
acquire from one site alone. While some efforts are
underway to actively pool clinical data from multiple sites for
analysis. Of these, 18 sites from nine European countries
contributed 28 datasets relevant for this study resulting in a
total sample of 2684 individuals with ASD (see Table 1 for
a summary of datasets by site).
Datasets from all participating sites were obtained from
a range of existing research programmes (e.g. early
screening studies, intervention programs, high-risk sibling studies,
genetic and imaging studies) and ascertained from a
variety of settings including volunteer databases and research
cohorts, clinical referrals from local outpatient centres,
special needs schools, mainstream schools and local
communities. Resembling DSM-5
, diagnostic classifications used in older systems
(DSM-IV/-TR, ICD-10; American Psychiatric Association
1994, 2000; World Health Organization 1992)
, i.e. autistic
disorder, Asperger’s syndrome, atypical autism versus
nonASD were collapsed into ASD versus non-ASD. Clinical
diagnosis of ASD was made according to DSM-IV
(American Psychiatric Association 2000)
Psychiatric Association 2000)
Psychiatric Association 2013)
or ICD-10 criteria
. Minimal requirements for inclusion of
datasets in the study were data on the Autism Diagnostic
Interview-Revised (ADI-R; summary or item-level data)
and/or data on the Autism Diagnostic Observation
Schedule (ADOS; item-level data), as well as basic demographic
Principal investigators and key contributors Males
(Simons Simplex Collection, Frazier
et al. 2014)
, similar large-scale collaborative efforts have
so far been largely neglected in Europe
(but see Bildt et al.
. In response, we set up a collaboration to collect
historical clinical data from ASD clinical and research
institutions across Europe that are part of the EU-AIMS
Clinical Network (https://www.eu-aims.eu/clinical-network/) to
examine differences across the ASD phenotype according
to sex and age including larger sample sizes of females with
ASD than previously examined. This circumvents the
previous limited size of populations studied, narrow age ranges,
level of abilities and ascertainment differences. While our
primary aim was to investigate sex differences in ASD
symptomatology, the size of this cross-sectional dataset and
broad age distribution also afforded to analyse differences in
symptomatology relating to age.
Sites in the EU-AIMS clinical network (100 sites in 37
countries; http://www.eu-aims.eu/clinical-network/) were
contacted between 2015 and 2017 to indicate their
willingness to share behavioural and cognitive data for secondary
Each contributing site and sample is assigned an alphabetical letter
aNumber in brackets indicates the number of males and females with ASD and Intellectual Disability (ID) for each site
information (e.g. age, sex). To allow comparability of data
across sites, data processing, coding and submission was
standardised across sites by developing a common data
sharing protocol and a data dictionary. Upon receipt, data were
checked for impossible data entries (for example data points
beyond published maxima and minima) and missing values.
When item-level data was available (45% for ADI-R, 100%
for ADOS), ADI-R standard algorithm scores for reciprocal
social interaction (Social), communication, and restricted,
repetitive and stereotyped behaviours and interests (RRB)
and ADOS comparison or Calibrated Severity Scores (CSS)
total, social affect (SA) and restricted and repetitive
behaviours (RRB) were recomputed from the original item scores.
There were no formal exclusion criteria of individuals (e.g.
presence of any DSM-5 axis I and II psychiatric disorders).
Institutional Review Board’s approval from King’s College
London (ethics reference number: PNM/13/14-174) was
obtained to collect fully anonymised data for secondary
analysis to ensure confidentiality of the shared data.
The Autism Diagnostic Observation Schedule
Lord et al. 2000, 2012; ADOS-2)
is a semi-structured
observational assessment designed to evaluate aspects of
communication, social interaction, play, and stereotyped behaviours
and restricted interests. Depending on an individual’s
language level and age, certified staff in ADOS administration
(e.g. clinicians, psychologists, research staff) administered to
participants one of several modules (modes of
implementation) of the ADOS (see Tables 2, 3 for a summary of
participants by module). The majority of individuals received
Module 1 for preverbal children who use no expressive language
(N = 484) or only single words (N = 374). The other modules
that were administered included Module 2 for children with
phrase speech (N = 199), Module 3 for more verbally
fluent and older children (N = 275), as well as Module 4 for
adolescents and adults with fluent speech (N= 88). Module
T from the ADOS-2 was not represented. Across sites, the
majority of individuals received the ADOS-G (N = 1383),
while some received the ADOS-2 (n = 37, Stockholm site).
To allow comparability across ADOS Modules, ADOS-G
raw scores were mapped onto ADOS-2 raw scores and CSS
(Gotham et al. 2009; Hus et al. 2014)
provide standardised ASD severity measures across the
different modules for the core symptom domains of social
communication (i.e. social affect, SA) and RRB, as well as an
overall indicator of ASD severity (CSS Total). This metric
has been shown to be less strongly associated with age and
language compared to raw ADOS-2 totals. CSS can range
from 1 to 10, with higher scores indicating more severe ASD
symptoms. Note that since the raw RRB total consists of
Chronological age [years:months]
Sex, % of male participants
Social interaction 0–10
Social interaction 0–2
Sample size 4a
ICC intraclass correlation coefficient, ADI—R Autism Diagnostic Interview—Revised, ADOS CSS Total, SA, RRB Autism Diagnostic
Observation Schedule Calibrated Severity Scores for total, social affect and restricted and repetitive behaviours, IQ intelligence quotient
aIndicators in row relate to ADOS datasets only
bIndicators in row relate to ADI-R 4–5 ever/diagnostic datasets only
cIndicators in row relate to ADI-R 4–5 current datasets only
dThe ratio of between-dataset variance to total variance
eThe highest possible score (i.e. ceiling) on the instrument
only four items, the CSS-RRB encompasses a more limited
range of values (i.e. 1 and 5–10).
The Autism Diagnostic Interview—Revised
Rutter et al. 2003)
was completed with parents or careers
of individuals with ASD. The ADI-R is a standardised
structured interview based on ICD-10 and DSM-IV
diagnostic concepts of ASD and explores across 93 items an
individual’s early development, language acquisition and/or
loss of language, functioning of language and
communication, social development and play as well as interests and
behaviours, general behaviour and behavioural concerns.
The interview focuses on three behavioural domains (i.e.,
reciprocal social interactions, language/communication,
and restricted, repetitive, and stereotyped behaviours and
interests), for which standard algorithm scores are derived
to compute current (where available) and/or historical
(4-to5-years/ever algorithm scores) symptom scores (Table 3).
General Intellectual Ability
Across datasets, the general level of intellectual abilities was
assessed using a range of different
developmentally-appropriate scales and instruments. The majority of individuals
were either administered the Wechsler Intelligence Scale
(WISC-III/IV; Wechsler 1991, 2003)
designed for children aged 6–16 years, the Wechsler
Preschool and Primary Scale of Intelligence for Children-III/IV
(WPPSI-III/IV; Wechsler 2002, 2012)
intended for children
aged 4–6 1/2 years or the Wechsler Adult Intelligence Scale
(WAIS-III/IV; Wechsler 1997, 2008)
adults were also assessed using the Wechsler Abbreviated
Scale of Intelligence (WASI; Wechsler 1995). Other
measures included the Griffiths Mental Development Scales
Extended Revised for children aged 2–8 years
2–8; Luiz et al. 2006)
and the Leiter International
(Leiter–R; Roid and Miller 2011)
individuals aged 2–20 years. For each measure, estimates of
standard nonverbal IQ scores (NVIQ) were derived from the
appropriate subtests and index scores with exception of the
B-L-R, where NVIQ were derived from mean age equivalent
scores of all non-verbal subscales divided by the
chronological age in months * 100. This was done to maximise IQ data
availability across sites.
Infants and toddlers (intended for use from age 0–69
months) received either the Brunet-Lézine Revised
(B-LR, Brunet et al. 1997)
, the Mullen Scales of Early
(MSEL; Mullen 1995)
, the Merrill-Palmer-Revised
(M-P-R; Roid and Sampers 2004)
or the PEP-R
et al. 1990)
. For the MSEL, NVIQ were derived from age
equivalent scores on the on fine motor (FM) and visual
reception (VR) subscale: NVIQ= (mean age equivalent on
FM and VR/chronological age in months) * 100. NVIQ on
the Merrill-Palmer was calculated as (mean age equivalent
on cognitive and fine motor/chronological age in months)
* 100, while for the PEP-R NVIQ was based on (mean
developmental age in months on all subscales except for the
verbal scale/chronological age in months) * 100. IQ scores
lower than 20 (n = 26) were discarded due to difficulties in
establishing a reliable IQ estimate in profound intellectual
Linear mixed-effects models were fit using a maximum
likelihood estimation method and were executed using STATA
. To take into
consideration the multi-level nature of the data, as well as to account
for heterogeneity across datasets in outcome measures, a
random effect for dataset was included in all models. This
affords to estimate differences between datasets in the
specific populations enrolled, the differing IQ tests used, and
other factors that may increase variability due to pooling
individual-level data from many sources. Intraclass
correlation coefficients (ICCs) reflecting the ratio of
betweendataset variance to total variance are reported to provide an
estimate of the amount of shared variance among individuals
from the same dataset that is due to the higher-level unit
only (i.e. belonging to the same dataset; see Table 3). The
linear mixed-effects models yield Chi square coefficients
and p value for categorical predictor variables (i.e. sex) and
standard errors, t-statistics and confidence intervals for slope
coefficients of continuous variables (i.e. chronological age
in years, non-verbal IQ scores). To account for multiple
comparisons for analyses in each measure, Bonferroni
corrections were applied (corrected α-level: p < .016).
Analyses are reported with/without NVIQ as a continuous
predictor (Tables 4, 5, respectively) to (1) capitalise on the
full sample size and (2) test these effects in a sub-sample of
individuals where NVIQ data was available. ADI-R 4-to-5/
ever scores were analysed using a fixed effect for sex, while
ADI-R current scores and ADOS CSS included fixed effects
for sex and chronological age. For categorical predictors,
effect sizes were calculated according to
dividing the difference in marginal means by the square root
of the variance at the within-subject level. This measure of
effect size is equivalent to Cohen’s d or standardised difference
, where an effect size of 0.20–0.30 is taken to be
a small effect, 0.50 a medium effect and greater than 0.80 a
large effect. Prior to analysis, ADOS RRB CSS and both 4–5
ever/diagnostic and current scores on the ADI-R RRB domain
were log-transformed to meet normality assumptions.
Eighteen sites contributed 28 previously collected datasets
on a total of 2,684 individuals, with contributions per site
ranging from 23 to 373 participants (see Table 1). Data
on the ADI-R was available for 2139 individuals (80% of
the total sample), while data on the ADOS was available
for 1,420 individuals (53% of the total sample). On 1030
individuals (38% of the total sample), both ADI-R and
ADOS data was available—a separate analysis including
only those individuals can be found in the supplementary
materials. Given the limited number of individuals with
both ADI-R and ADOS data, demographic information
is reported for all datasets and for ADOS/ADI-R datasets
separately (Table 2).
In the total sample, the mean chronological age was 10.3
(SD = 9.1) years, with males being on average slightly, but
not significantly, younger than females overall (MMale = 10.1,
SDMale = 9.0; MFemale = 11.2, SDFemale = 9.5, x2(1) = 1.05,
p = .306, d = .03). The mean level of non-verbal
intellectual abilities (NVIQ) was 80.9 (SD = 27.3; interquartile
range (IQR) = 38), ranged from 25 to 154 and was available
for 1283 subjects (ADOS datasets: N = 846, 60%, ADI-R
diagnostic datasets: N = 1114, 52%; ADI-R current
datasets: N = 705, 68%). NVIQ scores were on average
significantly higher for males compared to females overall (MMale
= 81.9, SDMale = 27.1; MFemale = 76.1, SDFemale = 27.91,
x2(1) = 19.56, p < .0001, d = .33). Separate analyses for
ADOS/ADI-R diagnostic/current datasets-only can be found
in the Supplementary Materials.
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Marked variation in age and NVIQ across datasets
(and for ADOS and ADI-R datasets separately) was
evident alongside a large predominance of male subjects
(Table 3). This is also reflected in the significant random
effect for dataset included in all models for most of the key
demographic and diagnostic measures. The Intra
Correlation Coefficients (ICCs) indicate that whilst the effect of
dataset was large for age (75–87%), reflecting the variable
recruitment pattern across sites, it was moderate for NVIQ
(32–38%) and 1–5% for sex ratio. On the diagnostic
measures, ICCs were generally low to moderate between 7 and
12% for ADOS scores and between 8 and 25% for ADI-R
scores. Figure 1 highlights the variation between sites by
pooling demographic and clinical information across
datasets within a site.
ASD Measures—Effects of Sex and Age
Excluding NVIQ as a predictor in the model and using the
whole sample, sex-related analyses revealed that ADI-R 4–5
diagnostic/ever scores (Total N = 2139) were higher in males
compared to females on the RRB domain (MMale = 5.05;
SDMale = 3.2, MFemale = 4.38; SDFemale = 3.3, x2(1) = 11.80,
p = .0006, d = .21; see Table 4), but not on the ADI-R social
domain (summary statistics can be found in Supplementary
Table 1). A non-significant trend towards higher scores in
males was found on the ADI-R Communication domain
(p = .074, d = .12). No main effect of sex for ADOS CSS
Total, ADOS SA, ADOS RRB (Total N = 1,420, all p > .60)
and ADI-R current domain Social, Communication and
RRB scores (Total N = 1,030, all p > .20) were observed. For
ADOS CSS RRB, there was a significant sex by age
interaction (b = − .02, p = .004), with females but not males
showing significantly lower scores with increasing age. However,
when restricting the analysis to individuals aged 25 or less
(retaining 97% of the initial sample), the sex by age
interaction was not significant (b = − .01, p = .22), suggesting that
these results are likely to be driven by a small number of
older adult male participants with high RRB symptoms.
Age-related analyses showed significant negative effects
of age for ADI-R Social (b = − .41, p < .001, see Table 4;
Fig. 2 left panel) and Communication domain current scores
(b = − .23, p < .001), but not ADI-R RRB current scores
(b = .01, p = .11). There were also significant negative effects
of age for ADOS CSS Total (b = − .04, p = .002; see Fig. 2
right panel), but not ADOS CSS Social Affect (b = − .03,
p = .03) and ADOS CSS Restricted and Repetitive
Behaviours (RRB; b = − .01, p = .19). It is important to highlight
that the vast majority of individuals with either ADOS CSS
(97%) or ADI-R current scores (98%) fell within the 2–25
years’ age range, beyond which data for both measures
was more limited (see Figure S1). This suggests that the
Fig. 1 Data pooling sample characteristics. a Total number of
participants with ASD by sex for each contributing site ordered as a
function of sample size (labelled alphabetically, see Table 1 for label key).
The same site labels are used for (b–f). b–f are ordered by median
sample statistic per site. b Violin plot of chronological age in years
for all individuals per site. c Distribution of nonverbal IQ scores per
site. Short-dashed line NVIQ for ADI-R datasets, long-dashed line
NVIQ for ADOS datasets. Solid black lines indicate median NVIQ
per site. d–f Tukey’s box-whiskers plots overlaid with scatterplots
of individual data points per site for (d) ADOS Calibrated Severity
Scores (CSS) Total, e ADI-R Social scores (ever/diagnostic) and f
ADI-R Social scores (current)
Fig. 2 Whole sample—left panel: ADI-R Social domain current
scores for males and for females, right panel: ADOS CSS Total
scores for males and for females. a Distribution of scores for males
(blue) and females (red), mean scores by sex presented in dashed
lines; b Scatterplots of scores (Males: blue filled; Females: red
holsignificant differences in symptom scores as a function of
age on these measures largely reflect differences across this
particular age range rather than the entire age range of the
To remove variance in the data due to differences between
participants in cognitive abilities which might relate to
scores on the ADOS or ADI-R, linear mixed-effects models
were re-fitted using NVIQ as an additional predictor in a
sub-sample of participants for whom NVIQ was available
(see Table 5 for a summary of the results). After
Bonferroni correction for multiple comparisons, sex-related
analyses were approaching significance for ADI-R 4-to-5/ever
scores on the RRB domain with males having higher scores
than females (MMale = 4.83; SDMale = 3.4, MFemale = 4.47;
SDFemale = 3.6, x2(1) = 5.07, p = .024, d = .21). All other
comparisons between the sexes for ADOS CSS (Total, SA,
RRB), ADI-R diagnostic scores (Social and Communication
domain) and ADI-R current scores (Social, Communication,
RRB) remained non-significant when controlling for NVIQ.
As with the previous analysis, a significant sex by age
interaction for ADOS CSS RRB was not found to be robust
to restricting the analysis to individuals younger than
25 years (accounting for a potential bias from limited data
points and therefore wide confidence intervals in the older
age groups). A significant main effect of age was retained
for current scores on the ADI-R Social (b = − .29, p < .001)
and Communication domain (b = − .19, p < .001), with older
individuals having lower symptom scores than younger
individuals, but not ADOS CSS total and CSS social affect.
low) with overlaid regression lines for males (blue dotted) and
females (red dashed) separately; c Distribution of chronological age
by sex. Note that for ease of presentation, only individuals aged up to
30 years are displayed here. (Color figure online)
This study investigated sex- and age-related differences in
core ASD symptomatology as measured by the ADI-R and
ADOS in a large and heterogeneous sample of 2684
individuals with ASD seen across 28 European clinical and research
sites. Consistent with a meta-analysis of small-scale studies
(Van Wijngaarden-Cremers et al. 2014)
and findings from
(Mandy et al. 2012; Szatmari et al. 2012;
Frazier et al. 2014; Supekar and Menon 2015; Wilson et al.
2016; Charman et al. 2017)
, we found evidence of a lesser
reported level of early childhood RRB on the ADI-R in
females compared to males alongside comparable levels of
reciprocal social interaction and communication difficulties
at this age of presentation. In contrast to the present findings,
some studies have also identified differences between girls
and boys in early social symptoms on the ADI-R (Carter
et al. 2007), but these findings are more limited and tended
to report null effects when taking account of IQ
et al. 2009; Lord et al. 1982)
While the overall patterns of results were maintained
when non-verbal intellectual functioning was accounted
for in the analyses, the significant finding of lower RRB in
females relative to males dropped to a trend level after
Bonferroni correcting for multiple comparisons. This makes the
interesting proposition that non-verbal intellectual
functioning can account and may attenuate some of the sex
differences found in RRB in ASD. Alternatively, the lower
significance level may also be related to a loss in statistical power
due to analysing a smaller sample, which is supported by
the observation that effect size estimates of sex comparisons
were equivalent between the analyses. Note that regardless
of whether age was accounted for in the analyses or not, the
findings remained unchanged, suggesting that in this
heterogeneous sample studied here, the presence/absence of sex
differences in ASD severity was independent of age.
On current measures of RRB based on both caregiver
interview and direct observation data, females showed as
severe symptoms as males. This is at odds with some
existing data demonstrating fewer current symptoms of RRB in
females relative to males as measured
by the ADOS (Bölte
et al. 2011
; Lai et al. 2011). One possible reason for
differences in results may be the smaller sample size and
narrower age range of the samples studied, i.e. adolescents
(N = 56;
Bölte et al. 2011
) and adults-only
(N = 83; Lai et al.
, compared to the much larger sample and broader age
range reported in the present study from early childhood to
adulthood. This may suggest that our sample composition
obscured any age-dependent sex differences in RRB in
adolescence and adulthood. While we did observe a significant
sex by age interaction for RRB measured by the ADOS,
supporting this suggestion, the results were not robust and
likely the result of a small proportion of older male
subjects with more severe RRB. Due to limited data points in
this older age group, we were however unable to further test
this hypothesis. It is important to point out that the present
findings of equivalent RRB in females relative to males on
the ADOS are consistent with other large-scale studies with
similar age distributions
(Charman et al. 2017; Frazier et al.
and a recent study in adults with ASD
(Wilson et al.
2016: sample N = 1244 adults with ASD; inter-quartile age
range: 22–39 years)
. This potentially indicates that some
of the previous findings of sex differences in current
symptoms of RRB in adolescence and adulthood may have been
sample- and/or study-specific. No sex differences relating
to current social communication symptoms, as captured
by the ADOS (CSS social affect) and ADI-R (social and
communication domain scores), and overall ASD severity
(ADOS CSS total) were observed. While this contradicts
some reports of greater socio-communication difficulties on
the ADOS in females
(Carter et al. 2007; Hartley and Sikora
2009; Frazier et al. 2014)
, it is in line with others that
identified no differences between the sexes (Holtmann et al. 2007;
Bölte et al. 2011
; Mandy et al. 2012; Reinhardt et al. 2015).
This study adds to the now growing literature that
suggests that girls with ASD tend to show lesser levels of
restricted interests, behaviours and stereotypes during the
most ‘abnormal’ or ‘prototypic age’ of presentation, i.e. ever
and 4-to-5-years, but exhibit a more similar autistic
phenotype to boys in relation to social communication deficits
both at younger and older ages. However, in the absence of
longitudinal data in this study, conclusions about symptom
trajectory or developmental changes should be considered
The current findings therefore indicate the presence of
specific sex-related differences in the early developmental
pattern of repetitive behaviours, routines and/or interests.
What may be the factors that underlie this finding? One
possibility could be etiologic protective factors, such that
females have a higher liability threshold for expressing ASD
symptoms compared to males, particularly for RRB
(Szatmari et al. 2012)
. This is also consistent with behavioural
(Ronald et al. 2006; Robinson et al. 2016)
highlighting the possibility for sex-and domain-specific
(Constantino and Charman 2012, 2016)
In the context of the skewed sex ratio in ASD towards a
greater preponderance of males over females, a higher
liability threshold for expressing RRB, particularly in
higherability females with ASD, may contribute to the commonly
reported widening of the sex ratio particularly at the
intellectually able end of the spectrum.
Aside from a differential liability threshold, it may also be
possible that higher-ability females are being
under-identified as a result of displaying fewer RRB even if they present
with considerable difficulties across other domains. This is in
line with suggestions that clinicians are reluctant to consider
a diagnosis of ASD without the presence of RRB
et al. 2012)
, and is reflected by the requirement for an ASD
diagnosis in the DSM-5 for the presence of at least two
significant indications of RRB, which is putting females at even
greater risk of
being unnoticed (Mandy et al. 2011
Alternatively, girls may simply exhibit ‘different’ rather than ‘fewer’
RRB than males which are therefore discounted during
clinical and diagnostic assessments
(Lai et al. 2015; see special
issue in Autism; Mandy and Lai 2017)
. Clearly, future
studies of the specific symptom patterns of females and how this
relates to DSM-5 criteria are needed. Furthermore, early
descriptions of ASD tended to be male-focussed (Kanner
1943) and diagnostic instruments including the ADI-R and
ADOS were predominantly developed using male samples,
leading potentially to a male-biased understanding of ASD
and concomitant sex bias in the construct and item-structure
of the instruments themselves. This may suggest that future
revisions of these instruments require additional items to
be included that are more characteristic of the female ASD
phenotype. At least for the ADI-R, there is some evidence to
suggest equivalent scale and item structure of the ASD
phenotype in males and females
(Duku et al. 2013; Frazier and
, but such evidence is missing for the ADOS. A
future goal of research should therefore be continued
exploration of the psychometric properties of these instruments
(including establishing measurement equivalence across
sexes) to evaluate the requirement for sex-specific norms
(Constantino and Charman 2016; Lai et al. 2015)
studies will also benefit from investigating sex differences
using instruments that might be more sensitive to potential
sex differences in presentation of ASD characteristics also
outside of the clinical arena, such as the SRS-2
, a parent, teacher, spouse, and/or self-report
questionnaire measure of autistic—like traits
(Frazier et al. 2014;
Howe et al. 2015; Charman et al. 2017; Ratto et al. 2017)
compared to the ‘gold-standard’ diagnostic instruments the
ADI-R and ADOS used in the current study.
Another possibility for the current results is that rater
reports may have influenced the findings. Mothers are
typically the primary source of information during diagnostic
assessments and sex differences reported on the ADI-R may
be a function of parents reporting symptoms differently for
girls and boys. In the current study however, we were
unable to further assess these possibilities. Lastly, the current
results may also potentially reflect sex differences in RRB
in early typical development. However, while some
studies have found boys to score higher than girls on ratings
of repetitive behaviours and preoccupations with restricted
patterns of interest, but not repetitive movements, sensory
interest, or rigidity
(Leekam et al. 2007)
, others have not
demonstrated sex differences in RRB in early development
(Evans et al. 1997; Øien et al. 2017)
Age-related analyses revealed lower current social and
communication symptoms with age as measured by the
ADI-R, both with and without covarying for NVIQ, with
older subjects reporting lower symptom scores than younger
subjects. Since the majority of participants fell within the
2–25 years’ age range, beyond which data was more limited,
the significant differences in symptom scores as a function
of age largely reflected differences across this particular age
range rather than the entire sample. ADOS CSS total and
CSS social affect displayed a similar albeit attenuated effect
of a negative relationship between symptom scores and age,
which however disappeared when non-verbal intellectual
functioning was accounted for in the analyses. These results
broadly support a range of studies showing reduced ASD
symptoms with increasing age, including those studies that
tracked samples longitudinally since childhood
et al. 2007; Howlin et al. 2013; Shattuck et al. 2007)
cross-sectional samples that have also reported differences
in symptomatology with age are rare, but those that did, did
not find significant age differences on the ADOS when IQ
was included in the model
(e.g. N = 325, Mandy et al. 2012;
N = 437; Charman et al. 2017)
. Given the cross-sectional
nature of the data, it is not clear if the age-related differences
observed reflect true effects or are due to sampling
differences between datasets that recruited participants across
Although the total sample size of the current study was
large, the sample consisted of individual datasets pooled
across many different sites that were not fully matched
for assessment methodologies, diagnostic procedures and
ascertainment strategies. Also, samples were derived across
different research programmes with different purposes (e.g.
early screening studies, intervention programs, high-risk
sibling studies, genetic and imaging studies), and differed
in respect to the distribution and range of ASD symptom
severity, age and intellectual functioning. However,
unfortunately, the individual sample sizes for each dataset were too
small to allow for any additional meaningful comparisons
within individual datasets.
It is also important to acknowledge that for data
relating to the ADOS, participants were not equally distributed
across the different modules, with the majority of subjects
completing Module 1 designed for individuals who are
preverbal or who use single words to communicate. This
somewhat limits the conclusions drawn in relation to age-related
trends in the ADOS data.
Pooling datasets across European clinical and research sites
allowed us to analyse sex and age-related differences in
ADOS and ADI-R in one of the largest ASD samples studied
to-date. The size and heterogeneous nature of the datasets
collected, both in relation to age, IQ and cultural factors,
circumvented previous limitations of low statistical power
due to small samples, narrow age and IQ ranges, which may,
in part, explain some of the inconsistencies found in earlier
studies. We identified some phenotypic differences between
males and females, particularly in relation to early childhood
symptoms of RRB, but found little evidence for sex
differences in social communication deficits both at younger and
older ages. We also observed lower social-communicative
symptoms in older compared to younger individuals with
ASD, consistent with previous longitudinal studies. A
better understanding of sex differences in ASD symptom
presentation is motivated by the need to improve recognition
and diagnosis in females to facilitate support that can
follow from an ASD diagnosis in the form of early
interventions and targeted health care and educational programs for
the child and family. In addition, it may help to elucidate
important basic science questions to better understand the
neurobiological and/or developmental mechanisms that
potentially underlie some of the differences in ASD
Author Contributions JT, KA, MA, SB, FBB, JB, SC, RC, RCB, RC,
ADB, MG, PH, AK, HMC, DM, AN, IO, MPM, AP, OP, HR, NR, RS,
VS, AS, EZ, TC conceived the study, participated in its design, and
interpreted the data. All authors drafted the manuscript and read and
approved the final manuscript.
Funding This work was supported by EU-AIMS (European Autism
Interventions), which receives support from the Innovative Medicines
Initiative Joint Undertaking under Grant agreement no. 115300, the
resources of which are composed of financial contributions from the
European Union’s Seventh Framework Programme (Grant FP7/2007–
2013), from the European Federation of Pharmaceutical Industries
and Associations companies’ in-kind contributions, and from Autism
Speaks. Rosa Calvo’s and Olga Puig’s work was supported by the
Instituto de Salud Carlos III, Fondo Investigaciones Sanitarias (PI09/1588),
European Union European Regional Development Fund (FEDER) and
Fundació La Marató-TV3 (091510). Andrew Stanfield’s work was
supported by the Wellcome Trust (WT802131MF) and Medical Research
Scotland (206FRG). Data were derived from two independent research
studies commissioned by the UK National Institute for Health Research
under the Research for Patient Benefit programme
(PB-PG-040816069, PB-PG-1010-23305) led by Professor Helen McConachie and
Dr Victoria Grahame respectively.
Compliance with Ethical Standards
Conflict of interest Author Annelies De Bildt receives royalties due
to her authorship for the Dutch version of the ADOS, the proceeds of
which go fully to Accare, Child and Adolescent Psychiatry Center in
Groningen, The Netherlands.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://creativeco
mmons.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 made.
Department of Psychology, Institute of Psychiatry,
Psychology & Neuroscience, King’s College London, De
Crespigny Park, Denmark Hill, London SE5 8AF, UK
Department of Forensic and Neurodevelopmental Sciences,
Institute of Psychiatry, Psychology and Neuroscience,
King’s College London, De Crespigny Park, Denmark Hill,
London SE5 8AF, UK
Newcomen Children’s Neurosciences Centre, Evelina
London Children’s Hospital at Guy’s and St Thomas’ NHS
Foundation Trust, London SE1 7EH, UK
Division of Neuropsychiatry, Department of Women’s
and Children’s Health, Center for Neurodevelopmental
Disorders (KIND), Karolinska Institutet Stockholm,
Child and Adolescent Psychiatry, Center of Psychiatry
Research, Stockholm County Council, Stockholm, Sweden
UMR930, INSERM, Université François–Rabelais de Tours,
Department of Cognitive Neuroscience, Radboudumc,
Donders Institute for Brain, Cognition and Behaviour,
Nijmegen, The Netherlands
Department of Developmental Neuroscience, IRCCS Stella
Maris Foundation and University of Pisa, Pisa, Italy
Department of Child and Adolescent Psychiatry
and Psychology, CIBERSAM, Hospital Clínic, Barcelona,
Karakter Child and Adolescent Psychiatry University Centre,
Nijmegen, The Netherlands
School of Medicine, Institute of Mental Health, University
of Belgrade, Belgrade, Serbia
Service for Neurodevelopmental Disorders, University
Campus Bio-Medico, Rome, Italy
University of Edinburgh, Edinburgh, UK
Child and Adolescent Psychiatry Helsingborg, Psychiatry
Skåne, Region Skåne, Sweden
Department of Women and Children’s Health, School of Life
Course Sciences, Faculty of Life Sciences and Medicine,
King’s College London, London, UK
Department of Special Needs Education, University of Oslo,
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10 Instituto Universitario de Integración en la Comunidad (INICO) , Universidad de Salamanca, Salamanca, Spain University Hospital of Siena, Siena, Italy Department of Child and Adolescent Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
1 2 3 4 5 6 7 8 9 11 12 13 16 17 18 20 21 22 23 24 25 26 27 14 Oslo University Hospital, Oslo, Norway 15 Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK
19 Interdepartmental Program “Autism 0 -90”, “Gaetano Martino” University Hospital, University of Messina, Messina, Italy