Sex differences in the association between infant markers and later autistic traits

Abstract

Background:
Although it is well established that the prevalence of autism spectrum disorder (ASD) is higher in males than females, there is relatively little understanding of the underlying mechanisms and their developmental time course. Sex-specific protective or risk factors have often been invoked to explain these differences, but such factors are yet to be identified.

Methods:
We take a developmental approach, using a prospective sample of 104 infants at high and low familial risk for ASD, to characterise sex differences in infant markers known to predict emerging autism symptoms. We examine three markers previously shown to be associated with later autistic social-communication symptoms: the Autism Observation Scale for Infants (AOSI) total score, attention disengagement speed and gaze following behaviour. Our aim was to test whether sex differences were already present in these markers at 1 year of age, which would suggest sex-specific mechanisms of risk or protection.

Results:
While no sex differences were found in any of the three markers investigated, we found sex differences in their relationship to 3-year autism traits; all three markers significantly predicted later autism traits only in the boys.

Conclusions:
Previously identified 'early autism markers' were associated with later autism symptoms only in boys. This suggests that there may be additional moderating risk or protective factors which remain to be identified. Our findings have important implications for prospective studies in terms of directly testing for the moderating effect of sex on emerging autistic traits.

Full text

R E S E A R C H Open Access
Sex differences in the association between
infant markers and later autistic traits
Rachael Bedford
1
, Emily J. H. Jones
2
, Mark H. Johnson
2
, Andrew Pickles
1
, Tony Charman
3
and Teodora Gliga
2*
Abstract
Background: Although it is well established that the prevalence of autism spectrum disorder (ASD) is higher in
males than females, there is relatively little understanding of the underlying mechanisms and their developmental
time course. Sex-specific protective or risk factors have often been invoked to explain these differences, but such
factors are yet to be identified.
Methods: We take a developmental approach, using a prospective sample of 104 infants at high and low familial
risk for ASD, to characterise sex differences in infant markers known to predict emerging autism symptoms. We
examine three markers previously shown to be associated with later autistic social-communication symptoms: the
Autism Observation Scale for Infants (AOSI) total score, attention disengagement speed and gaze following
behaviour. Our aim was to test whether sex differences were already present in these markers at 1 year of age,
which would suggest sex-specific mechanisms of risk or protection.
Results: While no sex differences were found in any of the three markers investigated, we found sex differences in
their relationship to 3-year autism traits; all three markers significantly predicted later autism traits only in the boys.
Conclusions: Previously identified ‘early autism markers’were associated with later autism symptoms only in boys.
This suggests that there may be additional moderating risk or protective factors which remain to be identified. Our
findings have important implications for prospective studies in terms of directly testing for the moderating effect of
sex on emerging autistic traits.
Keywords: Sex difference, Infants, Autism, High risk, Differential liability
Background
It is well established that autism spectrum disorder
(ASD) is more prevalent in males than in females, with
1:42 boys and 1:189 girls meeting ASD criteria [1]. Des-
pite this being a topic of great interest (see recent special
issue in Molecular Autism, vol. 6), the mechanisms
underlying sex differences in the prevalence of autism,
as well as in other childhood-onset psychiatric condi-
tions, are still poorly understood [2]. In order to charac-
terise sex differences in infant markers previously shown
to predict emerging symptoms of autism, we take a de-
velopmental approach using a prospective sample of in-
fants at high risk for ASD. The aim of the current paper
is to assess whether sex differences are apparent in
known early autism markers or in the relationships be-
tween early markers and later autistic traits.
According to a prominent hypothesis, the differential
liability model [3], boys are more susceptible than girls
because they are subject to boy-specific risk factors or
because girls benefit from protective factors. To under-
stand the underlying mechanisms, we can examine two
types of evidence [2]: direct evidence for sex differences in
the risk/protective factors themselves (Fig. 1a), or indirect
evidence, in the form of differential relationships between
risk/protective factors and the disorder, suggesting the
presence of additional sex-specific moderating factors
(Fig. 1b). Indirect evidence for girl-specific protective
factors comes from studies showing that a higher load of
genetic/phenotypic risk is needed for girls to eventually ex-
press autistic traits [3]. As a consequence of this, siblings
of girls with autism show a higher incidence of autism than
siblings of boys with autism [4], although see [5, 6] for no
difference in incidence between these groups.
* Correspondence: t.gliga@bbk.ac.uk
2
Centre for Brain and Cognitive Development, Birkbeck College, University of
London, London, UK
Full list of author information is available at the end of the article
© 2016 Bedford et al. 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 made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Bedford et al. Molecular Autism (2016) 7:21
DOI 10.1186/s13229-016-0081-0
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Phenotypic differences between males and females
with autism may also reflect the moderating role of sex-
specific factors, although evidence for sex differences is
mixed. One commonly reported finding is that females
with autism have lower IQ than males ([7, 8], although
see [9]), which is consistent with the idea of sex differ-
ences in the liability threshold. Language has often been
suggested as a protective factor, since girls lead in lan-
guage skills from early in development (e.g. [10]). How-
ever, evidence suggests that autistic girls with better
language also have better cognitive and adaptive skills,
which is not consistent with the idea of protection (e.g.
[11, 12]). Evidence from earlier in development is less
clear still, with Carter et al. [13] showing significantly
better language and fine motor skills in autistic toddler
boys but greater visual reception abilities in girls on the
Mullen Scales of Early Learning (MSEL; [14]). Other
studies find no sex difference in the adaptive and behav-
ioural functioning of toddlers with autism [15, 16]. Dif-
ferences between studies in the age groups tested, in
functional level and in potential diagnostic and sampling
biases may cloud the evidence for the differential liability
model in autism.
Longitudinal prospective designs of high-risk popula-
tions deal with some of these methodological issues,
avoiding ascertainment bias and including a typically de-
veloping control group. In addition, they allow us to
look for sex differences early on in development. Given
the evidence for differential genetic liability (e.g. [17])
and the fact that sex differences in prenatal hormones
like testosterone have been linked to later autism symp-
tomatology [2], it is plausible that sex-specific risk fac-
tors act even before birth, in which case their effects
would be apparent in infancy. Messinger et al. [5] and
Zwaigenbaum et al. [18] have utilised a prospective longitu-
dinal design with infants at high risk for autism (owing to
an older sibling with a diagnosis) to investigate sex differ-
ences in the MSEL. Both studies found increased MSEL
scores in girls compared to boys across both the high- and
low-risk groups. Similarly, Charwarska et al. [19] found that
high-risk girls (as compared to both high-risk boys and all
low-risk controls) show increased attention to social stim-
uli. This suggests that autism risk may be differentially
expressed in males and females, conferring protection in
the girls. As well as allowing the investigation of sex-
specific effects very early in development, these studies also
have the advantage of a typically developing comparison
group, which enables the specificity of sex differences in
the development of autism to be directly tested.
Among familial high-risk infants, approximately 20 %
go on to an autism diagnosis [20] with a further 20 %
showing sub-threshold symptoms [21] and a variety of
early ‘markers’for autism outcome have been described
from 6 months of age (see [22]). We define an early
marker as a particular aspect of cognition, behaviour or
neural response, measured in infancy, which is associ-
ated with later traits of autism. The current study exam-
ines the impact of sex on three early markers, which
span the domains of social and non-social attention and
are known to relate to emerging autistic symptomatol-
ogy: the Autism Observation Scale for Infants (AOSI;
[23, 24]), gaze following behaviour [25] and disengage-
ment latency in the gap-overlap task [26, 27].
The AOSI is an observational assessment of emerging
autism-like atypicality measuring, for example, anticipa-
tion of social interaction, imitation and motor skills. The
second early marker, looking time to the gazed-at object
during a gaze following task, is a measure of social at-
tention. Gaze following behaviour can be decomposed
into first look (did the infant correctly follow gaze) and
looking time (how long did they then spend looking at
the object). Looking time is thought to index a more in-
depth understanding of the referential meaning of eye-
gaze (e.g. [28]) and is the measure which relates to sub-
sequent social-communication abilities [25]. The final
marker, disengagement latency from the gap-overlap
task, measures infants’ability to shift attention from a
centrally presented stimulus to a peripheral one.
If particular early markers are expressed differently
in infant boys and girls, they may act as sex-specific
risk or protective factors (Fig. 1a). For example, high-
risk girls show greater social attention than high-risk
boys—a sex difference in the early marker itself
[25]. Alternatively, if no differences are measured in
expressed early markers, then sex differences may appear
in the relationship between early markers and later
traits (Fig. 1b). Differences in the relationship (i.e. the
Fig. 1 Sex differences can appear in the expression of early autism
markers or in the relationship between marker and outcome. aThe
marker differentiates between the boys and girls, but relates to later
symptoms in the same way in both sexes; here, having lower levels
of this marker is protective in girls. bThe early marker is similarly
expressed in boys and girls but only relates to later symptoms in
boys; this is due to additional moderating factors (which are likely to
be type of marker described in a) decreasing the impact of this
marker in girls. A combination of the two models is also possible
Bedford et al. Molecular Autism (2016) 7:21 Page 2 of 11
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slope) between the marker and autism traits would
point to differential liability effects. A significant rela-
tionship in boys but not in girls would be consistent
with the existence of additional sex-specific factors;
these putative factors may confer protection in girls or
increase risk in boys.
For all three markers (AOSI, gaze following, disen-
gagement), we first ask whether there are sex differences
at 14 months, the age at which these measures have
been previously linked with autistic traits in the same
cohort of children [25, 26, 23]. More particularly, we ask
whether girls show lower AOSI scores, increased atten-
tion to the jointly attended object and faster disengage-
ment latencies compared to boys. Second, we test
whether the relationship between these infant markers
and later autism traits (i.e. Autism Diagnostic Observa-
tional Schedule (ADOS) scores) is similar across males
and females, predicting a stronger magnitude of relation-
ship between risk and outcome in boys than girls. The
ADOS was chosen as the primary outcome because it is
both a gold standard measure of autism traits and a con-
tinuous outcome measure. This is consistent with the
emphasis shift from categorical to more continuous ap-
proaches in characterising psychopathology (Diagnostic
and Statistical Manual 5th edition (DSM-5) [29]).
Methods
Ethical approval was given by the National Health Ser-
vice, National Research Ethics Service London Research
Ethics Committee (08/H0718/76), and parents gave in-
formed consent.
Participants
As part of the British Autism Study of Infant Siblings
(BASIS: http://www.basisnetwork.org/), 104 infants (54
high-risk, 21 males; 50 low-risk, 21 males) took part in a
battery of assessments at approximately 7 months,
14 months, 2 years and 3 years. At the time of enrol-
ment (<5 months of age), none of the infants had been
diagnosed with any medical or developmental condition.
Data presented in the current paper come from the 14-
month infant visit (mean 13.79 months, SD 1.46; males
mean 13.84, SD 1.08; females mean 13.76, SD 1.65) and
3-year outcome visit (mean 37.93 months, SD 3.02;
males mean 38.13, SD 3.36; females mean 37.81, SD
2.82). For the high-risk group, consensus ICD-10 [30]
ASD diagnoses (ASD-sibs; childhood autism; atypical
autism, other pervasive developmental disorder
(PDD)) were achieved using all available information
from all visits by experienced researchers. Seventeen
of the high-risk children met ASD criteria (11 males).
Different numbers of infants contributed data to the
three early autism markers:
Autism Observational Scale for Infants (AOSI) (see [23]):
53 high-risk (21 males) and 48 low-risk (17 males) infants
completed the AOSI assessment (see Table 1).
Gaze following task (see [25]): The same 32 high-risk
(13 males) and 37 low-risk (10 males) siblings from the
Bedford et al. [25] analysis were included in the current
analyses.
Gap-overlap task (see [26]): Data from 52 high-risk
siblings (21 males) and 46 low-risk controls (16 males)
were included from the gap-overlap task.
Autism Diagnostic Observational Schedule-Generic
(ADOS-G); 3-year ADOS assessments were conducted
with 53 high-risk (21 males) and 48 low-risk (17 males)
toddlers.
Social Communication Questionnaire –Lifetime (SCQ-L);
Questionnaires were completed for 52 high-risk (21
males) and 48 low-risk (17 males) toddlers.
Procedure
All the 14-month measures have previously been re-
ported in separate papers—see Gammer et al. [23],
Elsabbagh et al. [26] and Bedford et al. [25] for full de-
scription of the method.
Fourteen-month measures
Autism Observation Scale for Infants
The Autism Observation Scale for Infants (AOSI; [31];
revised version used in this study [32]) is a semi-
structured observational assessment of ASD-related be-
havioural markers in infancy. A 19-item version of the
AOSI was used (see [32]) which gives a total score (sum
of all codes; max score 44), with higher scores indexing
greater atypicality. The majority of assessments were
double coded with excellent reliability (n= 85, intraclass
correlation coefficient = 0.95).
Gaze following task
Infants were seated on their parent’s lap at a distance of
50 cm from the 17-in. flat screen monitor and looking be-
haviour was recorded using a Tobii 1750 eye-tracker.
Stimuli were presented using ClearView software, and
gaze data were recorded at 50 Hz. Each trial began with
two objects on a table and a female model ‘looking down’
(3 s), then looking up—‘direct gaze’(2 s)—and then turn-
ing her head to look at one of the objects—‘shift’(6 s).
The object looked at by the model during ‘shift’is the con-
gruent object, and the other, non-gazed-at object, is the in-
congruent object. There were 12 trials, and six different
pairs of objects counterbalanced across trials.
Trial exclusion criteria were as follows: (1) no looking
to the face during ‘direct gaze’and (2) looking away
from the computer screen for the entire ‘shift’phase.
Three high-risk children (none of whom were diagnosed
with autism at 3 years) had <3 valid trials and were
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excluded. Looking time to the congruent object was calcu-
lated as a proportion (out of total looking time to the
slide) during the ‘shift’phase. A 2*2 ANOVA showed no
significant difference in the number of valid trials by
group (high- versus low-risk infants: F(1, 65) = 1.13, p=
0.29), sex (boys versus girls: F(1, 65) = 0.001, p= 0.98) or
group*sex interaction: F(1, 65) = 0.09, p= 0.77.
Gap-overlap task
Infants were seated on their parent’s lap 60 cm from the
46-in. LCD computer screen. A video camera was used
to record looking behaviour and trial presentation was
controlled by the experimenter. A central stimulus ap-
peared (subtending 13.8° × 18.0°) followed by a periph-
eral target green balloon (subtending 6.3° × 6.3°) which
appeared randomly on the left or right. The target
remained on the screen until either (1) the infant looked
at it or (2) the maximum time of 2.5 s passed. An animal
reward stimulus (elephant, lion, seal, etc.) then appeared
in the place of the green balloon. Up to 70 trials were
presented depending on infants’attentiveness. There were
two trial types analysed in this study: baseline and overlap.
In the baseline condition, the central stimulus disappeared
at the same time as the peripheral target appeared,
whereas in the overlap condition, the central stimulus
remained present while the target stimulus appeared in
the periphery.
Data were video coded frame-by-frame by two raters
(reliability of >0.9 Cohen’s K for trial validity). Invalid
trials were those in which the infant (1) looked away
from the screen; (2) did not look at the central stimulus
immediately before the onset of the peripheral stimulus
and (3) looked away or blinked during the onset of the
peripheral stimulus. A 2*2 ANOVA showed that there
was no significant difference in the number of valid tri-
als by group (high- versus low-risk infants: F(1, 94) =
1.77, p= 0.19), a marginal effect of sex (boys versus girls:
F(1, 65) = 3.97, p= 0.05) and no group*sex interaction:
F(1, 65) = 0.06, p= 0.81. Because the effect of sex ap-
proached significance, with more valid trials for girls, the
number of valid trials was included as a covariate in the
analyses for disengagement. Saccadic reaction times
were analysed from all valid trials in which the infants
oriented towards the peripheral stimulus 100–1200 ms
after its onset. If the infant did not look towards the
stimulus in this time, the trial was called ‘failure to
Table 1 Descriptive statistics split by sex and risk group for the 14-month early markers (AOSI, gaze following, disengagement) and
3-year autistic trait measures (ADOS, SCQ)
AOSI GF Disengagement ADOS SCQ
14 months 14 months 14 months 3 years 3 years
M (SD) M (SD) M (SD) M (SD) M (SD)
Low risk
Overall 3.17 (3.25) 0.31 (0.14) 138.15 (105.81) 5.52 (4.33) 3.00 (2.40)
N=48 N=37 N=46 N=48 N=48
Males 3.59 (3.02) 0.27 (0.08) 148.34 (104.77) 6.41 (5.36) 2.88 (2.12)
N=17 N=10 N=16 N=17 N=17
Females 2.94 (3.40) 0.32 (0.16) 132.71 (107.73) 5.03 (3.65) 3.06 (2.57)
N=31 N=27 N=30 N=31 N=31
High risk
Overall 4.64 (4.47) 0.26 (0.10) 179.55 (152.91) 8.25 (5.34) 6.37 (7.12)
N=53 N=32 N=52 N=53 N=52
Males 5.19 (5.72) 0.25 (0.12) 196.91 (203.30) 9.24 (5.42) 6.00 (5.33)
N=21 N=13 N=21 N=21 N=21
Females 4.28 (3.48) 0.26 (0.08) 167.78 (108.81) 7.59 (5.26) 6.61 (8.20)
N=32 N=19 N=31 N=32 N=31
ANOVA
Risk group F= 3.26 F= 1.86 F= 2.36 F= 7.19** F= 8.75**
Sex F= 0.92 F= 1.11 F= 0.77 F= 2.26 F= 0.12
Risk*sex F= 0.03 F= 0.35 F= 0.05 F= 0.02 F= 0.04
AOSI Autism Observation Scale for Infants, GF gaze following, disengagement overlap–baseline saccadic reaction time in gap-overlap task, ADOS Autism Diagnostic
Observation Schedule total social communication score, SCQ Social Communication Questionnaire
**p< 0.01
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disengage’and reaction time was not analysed. Disen-
gagement was calculated as reaction time in overlap tri-
als–baseline trials.
Three-year measures
Autism Diagnostic Observational Schedule [33]
The ADOS-G is a semi-structured observational assess-
ment used in the diagnosis of ASD, which involves as-
sessment of social interaction with an examiner through
play or conversation (depending on the age of the child),
and a range of different behaviours, including language,
gestures, eye contact, play and creativity, and stereo-
typed behaviours are coded. The codes are 0, 1, and 2
(and in some cases 3) with a higher score indicating a
greater level of autistic-like atypicality. Certain item
scores make up the final ‘algorithm’scores, which are
split into subsections: social, communication, creativity
and repetitive and stereotyped behaviours. Ninety-eight
children did module 2 and three children completed
module 1. At the time the data were collected and first
published, the ADOS-G social-communication algo-
rithm was the standard way of computing ADOS scores,
and thus we used the social and communication algorithm
as a measure of autism traits at 3 years. However, analyses
using the new ADOS-2 calibrated social affective severity
scores are presented in Additional file 1.
Social Communication Questionnaire –Lifetime
The Social Communication Questionnaire (SCQ) is a
parent-completed questionnaire with questions devel-
oped from the Autism Diagnostic Interview-Revised
(ADI-R; [34]).
Statistical analyses
The infant markers were not significantly correlated with
one another in the overall sample (AOSI and gaze follow-
ing: r=−0.17, p= 0.17; AOSI and disengagement: r=0.09,
p= 0.37; gaze following and disengagement: r=−0.12, p=
0.33). To address our first question—are there sex differ-
ences in the early markers, we first ran a 2*2 ANOVA with
sex (boys versus girls), group (high-risk versus low-risk)
and sex*group interaction for each early marker separately
(see Table 1).
To test for sex differences in the relationship between
early markers and later ADOS outcome, we first ex-
plored lowess curves (smoothed lines through the points
on the scatterplot) to check that there was overlap in the
data between boys and girls. Based on visual inspection
of the plots, any extreme non-overlapping values were
trimmed to one point above the next highest score [35].
The relationship between the predictor and outcome
can differ along the predictor’s scale (i.e. a non-linear re-
lationship). Restricting the analysis to the region where
the predictors overlap in boys and girls ensures that any
sex differences in the relationships between early
markers and later autism traits are not due to the groups
spanning different portions of the predictor scale (see
[36] for discussion of this issue). Analyses followed the
same procedure for each early marker: (1) linear regres-
sion with ADOS social and communication algorithm
score as the outcome and early marker, and sex and
early marker*sex interaction as predictors; (2) planned
sex comparisons: linear regression of ADOS score on
predictor split by sex.
Results
Autism Observation Scale for Infants total score
A 2*2 ANOVA showed no significant main effect of sex
on total AOSI score at 14 months: F(1, 97) = 0.92, p=
0.34, ηp
2
= 0.01 (see Table 1). The risk group effect was
marginally significant F(1, 97) = 3.26, p= 0.07, ηp
2
= 0.03,
and there was no group*sex interaction F(1, 97) = 0.03,
p= 0.88, ηp
2
< 0.001.
A smoothed lowess curve indicated good overlap
between males and females across the range of scores
(see Fig. 2). A linear regression showed a significant
relationship between AOSI and ADOS score (β= 0.527,
p< 0.001) and a significant sex*AOSI interaction
(β=−0.44, p= 0.005). When this was broken down by
sex, AOSI was a significant predictor of ADOS in males
(β= 0.57, p< 0.001) but not in females (β=−0.015,
p= 0.905). Results were similar across high- and low-risk
groups with significant effects in males (high risk:
β= 0.588, p= 0.005; low risk: β= 0.542, p= 0.03) but not
in females (high risk: β=−0.29, p= 0.113; low risk:
β= 0.059, p= 0.75). Significance levels remained un-
changed when Mullen Scales of Early Learning (MSEL)
verbal and non-verbal tscores were added as covariates
(see Additional file 1).
Gaze following
Results from a 2*2 ANOVA showed that there was no
significant effect of sex on correct looking time from a
gaze following task: F(1, 65) = 1.11, p= 0.30, ηp
2
= 0.02
(see Table 1). The risk group effect (F(1, 65) = 1.86,
p= 0.18, ηp
2
= 0.03) and group*sex interaction (F(1, 65)
= 0.35, p= 0.56, ηp
2
= 0.005) were also not significant.
Examination of the lowess curves (see Fig. 3a) showed
that at high levels of gaze time there were no data for
males due to an outlier, a female with particularly long
looking time. For the following analysis, this point was
trimmed back to 0.61 (the value one point above the
highest non-outlier score, see [35]).
Gaze time significantly predicted ADOS score (β=
−0.567, p= 0.012), while the effect of sex (β=−0.64, p=
0.053) and the sex*gaze time interaction time (β= 0.689,
p= 0.086) were marginally significant. When we ran sep-
arate simple linear regressions for males and females,
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gaze time predicted later ADOS in males (β=−0.46;
F(1,21) = 5.64, p= 0.027; R
2
= 0.21) but not in females
(β=−0.13, p= 0.39). We did not look at this analysis split
by risk group owing to the very small sample size for the
males (n= 10 low risk, n= 13 high risk). Results remained
substantively similar when MSEL scores were added as
covariates (see Additional file 1).
Disengagement
For disengagement, number of valid trials was included as
a covariate in the analysis, to account for the marginally
significant sex effect. As for the other infant markers, no
significant sex difference in disengagement was found
F(1, 93) = 0.77, p= 0.38, ηp
2
= 0.008 in an ANOVA.
The main effect of risk group F(1, 93) = 2.36, p= 0.13,
ηp
2
= 0.03 and the group*sex interaction were also non-
significant F(1, 93) = 0.05, p=0.82, ηp
2
= 0.001. There was
no effect of the number of valid trials (F(1, 93) = 0.23,
p=0.64, ηp
2
=0.002).
Similarly to the gaze time measure, examination of the
lowess curve suggested a region of no overlap between
male and female disengagement scores at very slow RTs,
owing to a single unmatched point. For the following
analysis, this point was trimmed to a value of 479 ms
(see Fig. 4).
When entered into a regression model, disengagement
reaction time was a significant predictor of subsequent
ADOS score (β= 0.38, p= 0.007). There was no effect of
valid disengagement trials (β=−0.05, p= 0.66). The
interaction between sex and disengagement was
Fig. 2 aThe relationship between infant AOSI and 3-year ADOS outcome with smoothed lowess curves for males and females. bThe relationship
between infant AOSI and 3-year ADOS outcome with linear fit for males and females
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marginally significant (β=−0.36, p= 0.05), and when
this was broken down into two separate regressions,
again the relationship between disengagement and
ADOS score was significant for males (β=0.41, p=
0.013) but not for females (β=−0.02, p=0.86). When
split by risk group, the βvalues remained very similar
with the effect of disengagement on ADOS in males
becoming marginally significant in the high-risk group
(β= 0.43, p= 0.06) and non-significant for the low-risk
group (β=0.39, p=0.18).Neithergroupshowedasig-
nificant effect in females (pvalues >0.66). Again, results
remained similar after controlling for MSEL scores (see
Additional file 1).
Social Communication Questionnaire analysis
To confirm that our results were not due to some spe-
cific measurement issue related to the ADOS, we also
assessed the relationship between risk factors and
parent-reported SCQ score. As the data were skewed, a
square root transformation was also applied to the SCQ
and both transformed and untransformed results are
presented. Results remained similar, with the AOSI
scores significantly predicting SCQ scores in males (β=
0.35, p= 0.04; although this became a trend only for the
transformed scores β= 0.26, p= 0.12) but not in females
(β= 0.07, p= 0.61; transformed β= 0.08, p= 0.53). Gaze
following behaviour was marginally significant in males
Fig. 3 aThe relationship between infant looking time in the gaze following task and 3-year ADOS outcome with smoothed lowess curves for
males and females. For further analysis, the outlier was trimmed back to one point above the next highest value. bThe relationship between
infant looking time in the gaze following task and 3-year ADOS outcome with linear fit for males and females
Bedford et al. Molecular Autism (2016) 7:21 Page 7 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

(β=−0.38, p= 0.08; transformed β=−0.37, p= 0.08) and
not significant in females (β= 0.08, p= 0.59; transformed
β= 0.12, p= 0.44), and disengagement latency signifi-
cantly predicted SCQ in males (β= 0.35, p= 0.04; trans-
formed β= 0.32, p= 0.06) but not in females (β= 0.14,
p= 0.32 transformed β=0.13, p= 0.33).
Discussion
The aim of this paper was to investigate sex differences
in three documented early autism markers, which are
known to associate with the severity of autistic social-
communication symptomatology, at 3 years of age. We
found no evidence for sex differences in the AOSI sever-
ity scores, gaze following and attentional disengagement,
when infants were 14 months of age. However, these
measures appeared to act as early markers in boys only,
with all three significantly relating to later 3-year ADOS
social communication and SCQ scores in males but not
in females.
Our results show that so-called early autism markers,
previously shown to be associated with later autistic
symptomatology [25, 26, 23], are actually only effective
markers in the boys. However, since there are no sex
differences in the expression of these behaviours at
14 months, this suggests the existence of additional
moderating factors. One line of evidence which could
help to determine whether boys have additional risk fac-
tors, or girls additional protective factors, comes from
Fig. 4 aThe relationship between infant disengagement in the gap-overlap task and 3-year ADOS outcome with smoothed lowess curves for
males and females. For further analysis, the outlier was trimmed back to one point above the next highest value. bThe relationship between
infant disengagement in the gap-overlap task and 3-year ADOS outcome with linear fit for males and females
Bedford et al. Molecular Autism (2016) 7:21 Page 8 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

looking at the risk group effects. If the there is a main
effect of risk group (where high-risk boys and girls show
increased early markers compared to low-risk boys and
girls), this would suggest that perhaps the females re-
cover from risk, i.e. they benefit from protective factors.
Alternatively, if the groups show similar levels of these
early markers, this suggests that the boys have additional
risk factors. Our results show no significant risk group
effects for the three markers, although, as there are
trend effects for each measure, this cannot be used as
strong evidence to tease apart increased risk versus pro-
tection. What we can conclude is that there must be
additional sex-specific factors which moderate the devel-
opmental effects of the early markers.
Genetic susceptibility effects have often been described
in psychopathology, where particular gene variants con-
fer more or less susceptibility to the effects of other
genes (e.g. [37]) or particular environments [38]. Given
the variety of the early markers we examined, which
span the social and non-social domains of the early aut-
ism phenotype, any moderating factors that might in-
crease the impact of these traits on emerging pathology
in boys (or that decrease their impact in girls) will need
to have a broad scope. Messinger et al. [5] found sex dif-
ferences in Mullen Scales for Early Learning (MSEL)
scores in both low-risk and high-risk participants. From
18 months (the earliest age included in analysis), girls
outperformed boys, across the subscales (expressive and
receptive language, visual reception, fine motor). Faster
general development could protect girls against the ef-
fect of other risk factors. However, despite replicating
these sex differences in our cohort, covarying for MSEL
scores did not alter the differential relationship between
early markers and outcomes. Other potential moderating
factors will have to be explored in the future. For ex-
ample, one recent hypothesis proposes that sex differ-
ences in synaptic and regional plasticity could explain
differences in autism prevalence rates [39].
It is also possible that interactions occur between the
markers under investigation, i.e. that a combination of
higher AOSI scores, reduced gaze following and slower
disengagement is necessary for girls to manifest autism
symptoms. Indeed, it was shown that gaze following and
attention disengagement act additively in predicting later
autism outcome (e.g. [40]). It could be that girls need
multiple hits across several different factors, consistent
with the existence of additional protective factors in
girls, whereas boys are more susceptible to a single-hit
pathway [41]. Future studies with larger sample sizes will
be important to test for interactive developmental effects.
Given that we found no early sex differences in early
markers at 14 months, i.e. quite late in development,
one interesting possibility is that some sex-specific mod-
erating factors may be environmental. In typical infants,
differences in maternal social interaction with boys and
girls have been documented, with different styles of sen-
sitive responding (e.g. [42]), and increased parent-infant
communication in response to infant girls than boys,
even in the absence of behavioural differences between
the infants themselves [43, 44]. Parent-child interaction
is known to be important in the development of socio-
communication abilities, including in autism [45], al-
though it remains unknown whether the above sex
differences in parental interaction are consequential for
development. However, protracted expression of intrin-
sic moderating factors is also possible. For example,
while sex differences in neural gene expression peak pre-
natally, the effects of certain genes continue on after
birth into early childhood [46].
Our results were similar across high-risk and low-risk
groups indicating that the link with later social-
communication abilities is not specific to those at risk
for autism. This suggests that our markers reflect com-
mon variation as proposed by recent genetic models of
autism [47]. The results are consistent with Messinger
et al.’s [5] study, who found that sex differences in tod-
dlers with autism reflect typically occurring sex differences
also seen in low-risk controls. Other neurodevelopmental
disorders such as ADHD and early onset antisocial behav-
iour disorder also show an increased prevalence in males
[2], and future research will be required to study sex dif-
ferences in the broader context of psychopathology and
development, since it is unlikely that sex-specific mecha-
nisms are disorder-specific. There is evidence, for ex-
ample, of sex differences in the relationship between
sensitive parenting during infancy and the subsequent de-
velopment of callous unemotional (CU) traits (e.g. [48,
49]) with increased maternal sensitivity associated with
lower CU traits most strongly in girls.
A clear strength of the current paper is the use of a
prospective infant sibling design. This allows us to test
for sex differences without the male ascertainment bias
faced by studies with diagnosed individuals [5]. However,
given our modest sample size, there is a clear need for
replication. While we specifically use two clinical tools,
the ADOS and SCQ, to deal with tool-specific lack of
sensitivity, it remains possible that neither of these in-
struments capture autistic traits in girls at 3 years of age,
when they were administered. Future work assessing
symptoms of autism measured later in childhood should
establish whether these sex-specific relationships are
time dependent. In addition, a larger sample would en-
able the relationships with categorical autism diagnostic
outcomes to be assessed.
The results of the current study draw attention to the
need to routinely examine sex differences in the predic-
tion of later autistic symptoms. It is plausible that factors
which have previously been found to be non-significant
Bedford et al. Molecular Autism (2016) 7:21 Page 9 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

in the prediction of autism symptoms may in fact be as-
sociated in the boys, but the overall effect is masked by
the presence of girls. However, although we find that
three different measures previously described as early
markers for autism are only really markers of later autis-
tic traits in boys, this should not encourage research to
selectively study this sex. While investigating only boys
will increase prediction [50], studying girls is crucial not
only for tailoring diagnosis and intervention to this
smaller but by no means negligible group but also for
unveiling potential important protection that could be
used to improve outcomes for both boys and girls.
Conclusions
In conclusion, although we found no evidence for early
sex differences in markers previously shown to relate to
emerging autistic symptoms, we did find sex-specific re-
lationships with later autistic traits, with prediction to
outcome in males but not in females. These data suggest
that so-called early autism markers may only act as
markers in boys. This implies the existence of additional
sex-specific intervening factors, yet to be identified,
which confer risk in boys or protection in girls. Our re-
sults emphasise the importance of testing for a moderat-
ing effect of sex on emerging autism traits. Characterising
sex differences in the developmental trajectory leading to
autism has important potential implications for differen-
tial early identification of autism in boys and girls.
Additional file
Additional file 1: Supplementary information. (DOCX 31 kb)
Abbreviations
ADI-R: Autism Diagnostic Interview-Revised; ADOS: Autism Diagnostic
Observational Schedule; AOSI: Autism Observation Scale for Infants;
ASD: autism spectrum disorder; BASIS: British Autism Study of Infant Siblings;
DSM-5: Diagnostic and Statistical Manual 5th edition; MSEL: Mullen Scales of
Early Learning; SCQ: Social Communication Questionnaire.
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
RB contributed to design of the study, collected and analysed data, wrote
and edited the manuscript; EJ contributed to the design of the study and
helped to draft the manuscript; MJ conceived of the study and helped to
draft the manuscript; AP conceived of the study and helped to draft the
manuscript; TC conceived of the study and helped to draft the manuscript;
TG conceived of the study, collected data, wrote and edited the manuscript.
All authors read and approved the manuscript.
Acknowledgements
We are very grateful for the important contributions BASIS families have
made towards this study. The research was supported by the BASIS funding
consortium led by Autistica (http://www.basisnetwork.org/), a UK Medical
Research Council Programme Grant (G0701484) to M.H. Johnson and a
European Project TRIGGER (Transforming Institutions by Gendering Contents
and Gaining Equality in Research) Grant agreement no. 611034. R. Bedford is
supported by a Sir Henry Wellcome Postdoctoral Fellowship, T. Charman by
the COST Action BM1004 and E. Jones, M. Johnson and T. Charman have
received support from the Innovative Medicines Initiative Joint Undertaking
under grant agreement no. 115300, resources of which are composed of
financial contribution from the European Union’s Seventh Framework
Programme (FP7/2007–2013) and EFPIA companies’in kind contribution.
This work was additionally supported by the National Institute for Health
Research (NIHR) Mental Health Biomedical Research Centre at South London
and Maudsley NHS Foundation Trust and King’s College London. The views
expressed are those of the author(s) and not necessarily those of the NHS,
the NIHR or the Department of Health.
Author details
1
Biostatistics Department, Institute of Psychiatry, Psychology & Neuroscience,
King’s College London, London, UK.
2
Centre for Brain and Cognitive
Development, Birkbeck College, University of London, London, UK.
3
Psychology Department, Institute of Psychiatry, Psychology & Neuroscience,
King’s College London, London, UK.
Received: 13 October 2015 Accepted: 24 February 2016
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