J Autism Dev Disord DOI 10.1007/s10803-015-2366-0
ORIGINAL PAPER
Longitudinal comparison between male and female preschool children with autism spectrum disorder Valentina Postorino • Laura Maria Fatta • Lavinia De Peppo • Giulia Giovagnoli • Marco Armando • Stefano Vicari • Luigi Mazzone
Ó Springer Science+Business Media New York 2015
Abstract Epidemiological studies have highlighted a strong male bias in autism spectrum disorder (ASD), however few studies have examined gender differences in autism symptoms, and available findings are inconsistent. The aim of the present study is to investigate the longitudinal gender differences in developmental profiles of 30 female and 30 male age-matched preschool children with ASD. All the children underwent a comprehensive evaluation at T0 and at T1. Our results have shown no significant interaction between time and gender for predicting autism symptoms, developmental quotient, parental stress, children’s adaptive skills and behavior problems. Shedding light on the developmental trajectories in ASD could help clinicians to recognize children with ASD at an earlier age and contribute to the development of appropriate treatments. Keywords Autism spectrum disorder Preschoolers Males Females Longitudinal
V. Postorino L. M. Fatta L. De Peppo G. Giovagnoli M. Armando S. Vicari L. Mazzone (&) Child Neuropsychiatry Unit, Department of Neuroscience, I.R.C.C.S. Children’s Hospital Bambino Gesu`, Piazza S. Onofrio, 4, 00165 Rome, Italy e-mail: [email protected] L. M. Fatta I.R.C.C.S. Centro Neuolesi ‘‘Bonino-Pulejo’’ Via Provinciale Palermo, S.S.113 Contrada Casazza, 98124 Messina, Italy L. De Peppo G. Giovagnoli Psychology Department, Libera Universita’ Maria Ss. Assunta, Rome, Italy
Introduction Autism spectrum disorder (ASD) is a broad category characterized by persistent deficits in social communication and social interaction, and restricted and repetitive patterns of behavior. According to the Diagnostic and Statistical Manual of Mental Disorders—fifth edition (DSM-5), due to the heterogeneity of this diagnostic classification, ASD is better differentiated by severity of autism symptoms, and association with language impairment and intellectual disability (American Psychiatric Association 2013). Recent epidemiological studies on ASD show a significantly higher prevelence of the disorder in males (Fombonne 2009). These studies found a male: female (M:F) ratio ranging from 1.33:1 to 16:1, with a mean of 4:1 (Fombonne 2009; Baird et al. 2006; Nygren et al. 2012a, 2012b; CDC 2014). This M:F ratio rises to 10:1 in Asperger syndrome and ‘‘high functioning autism’’, whereas it drops to 2:1 in subjects with a comorbid intellectual disability (Fombonne 1999, 2009; Nicholas et al. 2008; Bryson et al. 2008). Some studies have observed that this gender disparity could be due to a strong social gender bias that causes parents and clinicians to have different perceptions and expectations for boys compared to girls, with diagnosis of ASD being made at a later age in the latter group, even when symptom severity is constant across genders (Carter et al. 2007; Holtmann et al. 2007; McLennan et al. 1993; Shattuck et al. 2009; Mandell et al. 2009; Russell et al. 2011; Mondschein et al. 2000). Furthermore, it has been hypothesized that these epidemiological data could reflect a bias in diagnostic criteria, probably due to a different behavioral phenotype in males and in females, and it is therefore possible that girls either go undiagnosed or are misdiagnosed (Kopp and Gillberg 1992; Attwood 2007).
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Another possible explanation is the widely cited ‘‘extreme male brain’’ theory of Baron-Cohen et al. (2003), that assumes a neuropsychological difference between genders, with males predominating in the systemizing domain, whereas females are superior in the empathizing domain. According to this theory, in ASD there is a stronger drive to systemize independent of gender compared to normal subjects and this hypothesis seems to be associated to biological and genetic factors (i.e., fetal testosterone, epigenetic effect on X chromosome genes and the malelimited expression of genes on the Y chromosome) (BaronCohen et al. 2014; Baron-Cohen Baron-Cohen 2003b; Baron-Cohen et al. 2003, 2005, 2011; Chapman et al. 2006; Chura et al. 2010; Ingudomnukul et al. 2007). In order to understand the sex bias in ASD the hypothesis of a female protective effect against autistic behavior was empirically tested by Robinson et al. (2013) in a study on two nationally-representative samples of dizygotic twins. The results of this study provide the strongest evidence to date that female sex protects girls from autistic impairments and suggest that girls require a greater etiological load to manifest an autistic phenotype (Werling et al. 2013b). Although gender disparity in prevalence rates has been extensively studied, only a few studies examined gender differences in autistic symptoms, and available findings are inconsistent. Most studies failed to find any differences in the core symptoms of ASD between males and females (Carter et al. 2007; Banach et al. 2009; Andersson et al. 2013; Mayes and Calhoun 2011; Zwaigenbaum et al. 2012; Mandy et al. 2012; Szatmari et al. 2012; Donna et al. 2013). On the other hand, a meta-analysis of 20 studies investigating gender differences in ASD reported few differences in symptom severity between males and females. Specifically, there were no gender differences in social behaviors or communication, but girls showed less restricted, repetitive and stereotyped behaviors (RRB) than boys (Van Wijngaarden-Cremers et al. 2013). It is worth noting that female with ASD and an average or above average intellectual ability may be undiagnosed or misdiagnosed due to a different clinical manifestation, and therefore differences in symptom severity between genders may be affected by this diagnostic bias (Shattuck et al. 2009; Mandell et al. 2009; Baron-Cohen 2003b; Kirkovski et al. 2013). Regarding co-occurring comorbid psychopathology among samples with ASD, the majority of studies failed to identify any gender differences (Hofvander et al. 2009; Lugnegard et al. 2011; Matson and Nebel-Schwalm 2007; Park et al. 2012; Simonoff et al. 2008; Lai et al. 2011; Mazzone et al. 2013). However, some studies reported that girls with ASD appeared to be at greater risk than boys for internalizing psychopathology (Solomon et al. 2012; Mazzone et al. 2012). Indeed, recent studies report that parents of children with ASD experience higher levels of stress than parents of
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typically developing children (Mugno et al. 2007; May et al. 2012; Zaidman-Zait et al. 2011; Bauminger et al. 2010). Moreover, parental stress was found to be positively correlated with autism symptoms and severity, as well as with internalizing and externalizing behavior problems (Bauminger et al. 2010; Herring et al. 2006). However, none of these studies has focused on gender differences in parenting stress reported by parents of children with ASD. Longitudinal studies investigating the differences between genders with regard to symptom severity, developmental changes in symptoms presentation and course may have significant implications for the development of adequate assessment and treatment strategies for patients with ASD. Shedding light on gender differences in developmental trajectories is crucial for both clinical and research purposes with regard to diagnosis and treatment. However, to our knowledge, no previous study has addressed this issue. Therefore, the aim of the present study was to investigate the longitudinal gender differences in developmental profiles in a sample of preschool children suffering from ASD with particular focus on the relationship between children’s behavior problems and experienced stress in the parenting role reported by parents.
Participants and methods Participants and procedure A total of 60 preschool children suffering from ASD were enrolled in this study. Of these, 30 females were age-matched with 30 males (aged 2–5.4 years; Mean age ± SD: 3.55 ± 0.9). All the children were referred to the Child Neuropsychiatry Unit of the Bambino Gesu’ Children’s Hospital in Rome (Italy) between December 2010 and December 2012. All the participants’ parents provided a written informed consent. Exclusion criteria included the presence of specific genetic disorders, other medical disorders or epilepsy. On entry into the study, autism had to be diagnosed by an expert clinician using DSM-IV-TR criteria (American Psychiatric Association 2000). All the subjects underwent a comprehensive developmental profile evaluation at the time of admission (Time 0, T0) and at follow-up (Time 1, T1). Developmental quotient assessment All the children were assessed using the Griffiths Mental Development Scale-Extend Revised (GMDS-ER) (Griffiths 2006). GMDS-ER I and II assess a child’s strengths and weaknesses in all developmental areas, and can be used to measure the rate of development of children from birth to 8 years of age. The six areas of development measured by the scales are: (A) Locomotor, measuring the gross motor
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development; (B) Personal-Social, examining the social and daily living skills; (C) Hearing and Speech, measuring receptive and expressive language; (D) Eye-Hand Coordination, focusing on fine motor skills, manual dexterity and visual monitoring skills; (E) Performance, focusing on manipulation of objects; (F) Practical Reasoning, measuring mathematic ability and abstract reasoning. Each subscale provides a different developmental quotient and a diagnostic indication of individual problems in early childhood. In the present study, all the children were unable to perform GMDS-ER practical reasoning scale either at T0 or T1. Griffith’s six subscales are expressed as quotients constituting the general developmental quotient (GDQ). The GDQ is derived from the average of quotients resulting from the six subscales. The test scores are transformed into developmental ages (D.A.) and then into quotients according to the following equation: developmental quotient (DQ) = Developmental age 9 100/chronological age (CA). Developmental quotients rather than mental age are used to make it possible to compare children of different chronological ages and to compare a child’s performance at different time periods. Evaluation of autism symptoms All the children were assessed for the presence of autism symptoms using the Autism Diagnostic Observation Schedule-Generic (ADOS-G) performed by a licensed clinician (Lord et al. 2000). The ADOS-G is a semi-structured, standardized, play-based assessment measure evaluating current autistic behaviors. The ADOS-G is divided into four separate modules. Each module is aimed at a specific level of expressive language ability (ranging from pre-verbal to fluent speech). The choice of modules is based on the subject’s expressive language level. The use of different modules reduces possible biasing effects of differences in language skills. Scoring is done immediately after administration of the ADOS-G. Each item is scored on a 0–3 scale (0 = no evidence of abnormal behavior to 3 = markedly abnormal behavior) and each module has a specific diagnostic algorithm. Items used in the algorithms are divided into four areas: communication, social interaction, play/creativity, and restricted/repetitive behaviors or interests (RRB). The total score for communication and social interaction provides a cutoff for diagnosis at various ‘‘levels of ASD’’. In the present study, at T0 all children performed module 1 (cut-off for autism = 12 and ASD = 7), whereas, at T1, 54 children (28 males and 26 females) performed module 1 and 6 children (2 males and 4 females) performed module 2 (cut-off for autism = 12 and ASD = 8). The ADOS-G has good to excellent psychometric properties, and satisfactory ability to differentiate individuals with and without ASD. However, the ADOSG algorithm does not include RRB and literature studies have suggested that if RRB are included it may help to increase
ADOS-G diagnostic stability (Lord et al. 2006). Therefore, the ADOS-G has been revised (Gotham et al. 2007). In the revised version, module 1 is split into ‘‘No words’’ and ‘‘Some words’’, and module 2 into ‘‘Younger than 5’’ and ‘‘5 or older’’. The revised algorithm is divided into two new domains, social affect (SA) and RRB. To calculate the total cut-off, SA and RRB are combined into one score. An ASD severity scale has been developed basing on 18 narrowly defined age and language cells, and the total raw scores have been divided into a 10-point severity score (1 = fewer problems and 10 = more severe problems) (de Bildt et al. 2011; Gotham et al. 2009). Assessment of adaptive skills, behavior problems and parental stress All the parents of the participating children performed the Vineland Adaptive Behavior Scale-Survey Form (VABSSF) with a trained and experienced clinician, in order to measure the children’s adaptive skills (Sparrow et al. 1984; Balboni and Pedrabissi 2003). Moreover, they completed the Child Behavior Checklist version 1–5 (CBCL) and the Parent Stress Index-Short Form (PSI-SF) to rate children’s emotional and behavior problems and levels of stress experienced in the parenting role (Achenbach and Eofbrock 1983; Achenbach and Rescorla 2000, 2001; Abidin 1990, 1995; Guarino et al. 2008). The VABS-SF is a standardized parent interview of everyday adaptive functioning, designed to measure adaptive behaviors in children from birth to 18 years. It consists of 297 items falling into four general functioning domains: communication skills, daily living skills, social skills and motor skills. The communication domain assesses receptive, expressive, and written skills according to age level. The daily living skills domain taps personal, domestic, and community skills. For the socialization domain, the child is rated on interpersonal relationship skills, socialization during play and leisure time, and coping skills. The motor skills domain includes development of gross and fine motor skills. An adaptive behavior composite score for each of the four domains was attained for all participants and transformed into equivalent ages based on published Italian norms (Balboni and Pedrabissi 2003). The CBCL version 1–5 is an extensively used tool of 99 items on a three point Likert scale, that provides scores for seven syndrome scales, five different DSM-oriented scales and three broad-brand scales (i.e., internalizing symptoms, externalizing symptoms and total behavior problems). Raw scores for each clinical factor were transformed into T-scores based on published norms: T-scores[63 were considered indicative of clinical impairment for the three broad-band scales, whereas T-scores C70 were considered indicative of clinical impairment for
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syndrome and DSM-oriented scales (Achenbach and Eofbrock 1983; Achenbach and Rescorla 2000, 2001). The psychometric properties of the CBCL show good validity and reliability (Achenbach and Eofbrock 1983; Achenbach and Rescorla 2000, 2001). The PSI-SF is a 36-item questionnaire that measures different aspects of experienced stress in the parenting role using three subscales: parental distress (PD), measuring an impaired sense of parental competence and depression, parent–child dysfunctional interaction (P-CDI), intended to measure unsatisfactory parent–child interactions, and difficult child (DC), measuring behavioral characteristics of the child that make him/her easy or difficult to manage. The total PSI-SF score is seen as an indicator of the parent’s overall experience of parenting stress (Abidin 1990, 1995; Guarino et al. 2008). Parents rate each of the 36 items on a 5-point scale. The 90th percentile of the total PSI-SF score represents a ‘‘clinically significant’’ level of parenting stress. The PSI-SF has been shown to be a valid and reliable measure (Abidin 1995; Guarino et al. 2008; Zaidman-Zait et al. 2011). Data Analysis Data analyses were performed using the Statistical Package for Social Sciences (SPSS 20.0 for Windows). Independent samples t-tests, at T0 and T1, were performed to evaluate differences between gender and ADOS severity score, GMDS-ER, VABS-SF, CBCL and PSI-SF. Mixed ANOVA analysis were performed between time (withingroup variable) and gender (between-group variable) on GMDS-ER, VABS-SF, ADOS severity score, CBCL and PSI-SF (dependent variables). The strength of the relationship between independent variables (time and gender) and dependent variables was assessed by calculating the F test and the corresponding P value. Mixed ANOVA analysis showed that the assumption of sphericity had been violated for all the variables tested (ADOS severity score: p \ .001, GMDS-ER: p \ .001, VABS-SF: p = .001, CBCL: p \ .001 and PSI-SF: p \ .001) therefore degrees of freedom were corrected using Greenhouse-Geisser estimates of sphericity. Furthermore, paired samples t-tests were applied to the data in order to evaluate differences within gender between each time assessment and autism symptoms, developmental quotient, parental stress, children’s adaptive skills and behavior problems. An alpha level of 0.05 was set for statistical significance.
Results Comparisons between male and female groups at T0, using independent samples t-tests, showed significantly higher
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scores in the male group on GDQ (t = 2.213, p = .033), eye–hand coordination (t = 2.618, p = .013) and performance (t = 2.901, p = .005) GMDS-ER scales and CBCL anxiety problems subscale (t = 2.640, p = .012; Table 1 and 2). Whereas, comparison between male and female groups at T1, using independent samples t-tests, showed significantly higher scores in the male group on eye-hand coordination (t = 2.016, p = .049) GMDS-ER scale and CBCL sleep problems subscale (t = 2.044, p = .047; Tables 1 and 2). Evaluating differences between time (T0 and T1) and gender on developmental quotient (GMDSER), mixed ANOVA analysis indicated a main effect of time (F:21.702; p \ .001) and this effect was not modified by gender (F:1.627; p for the effect modification = .208). Moreover, mixed ANOVA analysis between time (T0 and T1) and gender on children’s adaptive skills (VABS-SF) indicated a main effect of time (F:46.7; p \ .001) that again was not modified by gender (F:0.585; p for the effect modification = .448). Furthermore, mixed ANOVA analysis between time (T0 and T1) and gender on autism symptoms (ADOS severity score), children’s behavior problems (CBCL) and parental stress (PSI-SF) revealed no statistically significant main effect or interaction. Moreover, within the female group, paired samples t-tests between T0 and T1 showed significantly higher scores at T1 on GDQ (t = -4.271, p \ .001), locomotor (t = -2.805, p = .009), personal–social (t = -4.495, p \ .001) and performance (t = -4.148, p \ .001) GMDS-ER scales, communication skills (t = -3.342, p = .002), daily living skills (t = -5.580, p \ .001), social skills (t = -2.529, p = .018) and motor skills (t = -8.260, p \ .001) VABSSF domains (Fig. 1). On the other hand, within the male group, paired samples t-tests between T0 and T1 indicated higher scores at T1 on GDQ (t = -2.588, p = .016), personal–social (t = -2.881, p = .008) GMDS-ER scale, communication (t = -2.452, p = .023), daily living skills (t = -2.227, p = .037), social skills (t = -3.215, p = .004) and motor skills (t = -2.853, p = .009) VABS-SF domains (Fig. 1).
Discussion Epidemiological data have shown a significantly higher prevalence of ASD in males and recent evidence suggests that the observed gender ratio may be influenced by sex differences in symptomatology of ASD (Fombonne 2009). However, there is a great deal of literature concerning the varying profile of autism as expressed by gender differences and the vast majority of studies have been conducted with predominantly male samples (Bell et al. 2005). Furthermore, the available findings on the differences in clinical profiles between males and females with ASD have
63.04 ± 24.16
40.98 ± 25.79 59.81 ± 22.34
71.58 ± 27.27
Personal-social
Hearing and speech Eye–hand coordination
Performance
1.88 ± 0.45
2.48 ± 0.84
Social skills
Motor skills
f
e
d
c
b
a
2.28 ± 0.55
1.83 ± 0.29
1.99 ± 0.43
1.62 ± 0.39
62.07 ± 20.30
35.86 ± 17.36 52.60 ± 13.06
57.07 ± 17.76
80.87 ± 17.94
58.20 ± 13.78
6.66 ± 1.696
Vineland Adaptive Behavior Scale-Survey Form
Griffiths Mental Development Scale-Extended Revised General Developmental Quotient
Not significant P [ 0.05
Autism Diagnostic Observation Schedule
Standard Deviation
Bold values are statistically significant
1.80 ± 0.81
2.12 ± 0.77
Communication skills
Daily living skills
VABS-SFf
63.81 ± 20.33
83.11 ± 24.01
Locomotor
6.42 ± 1.734
GDQe
GMDS-ER
d
ADOSb Severity score
t
P
2.72 ± 1.07
1.93 ± 0.59
2.27 ± 1.04
2.02 ± 1.09
82.15 ± 30.37
46.48 ± 31.97 67.81 ± 27.53
69.67 ± 28.62
85.59 ± 29.51
70.04 ± 24.53
6.20 ± 1.769
1.917
0.801
1.326
1.833
2.901
1.559 2.618
1.971
0.739
2.213
-1.008
NS
NS
NS
NS
.005
NS .013
NS
NS
.033
NSc
3.17 ± 0.88
2.18 ± 0.55
2.55 ± 0.70
2.25 ± 1.09
84.09 ± 26.58
45.21 ± 27.85 65.00 – 22.84
74.95 ± 26.56
90.56 ± 25.20
72.09 ± 20.98
6.27 ± 1.552
All participants (N = 60) Mean ± SD
Males (N = 30) Mean ± SD
All participants (N = 60) Mean ± SDa
Females (N = 30) Mean ± SD
T1
T0
Table 1 ADOS severity score, GMDS-ER and VABS-SF at T0 and T1
3.25 ± 0.79
2.08 ± 0.49
2.50 ± 0.62
2.05 ± 0.89
82.82 ± 28.90
38.75 ± 25.88 58.96 ± 21.50
72.32 ± 25.46
95.21 ± 25.99
70.11 ± 21.14
6.48 ± 1.37
Females (N = 30) Mean ± SD
3.09 ± 0.97
2.29 ± 0.59
2.61 ± 0.78
2.46 ± 1.24
85.31 ± 24.59
51.45 ± 28.69 70.83 ± 22.93
77.48 ± 29.69
86.07 ± 24.01
74.00 ± 21.01
6.07 ± 1.701
Males (N = 30) Mean ± SD
-0.715
1.454
0.575
1.436
0.351
1.752 2.016
0.703
-1.380
0.697
-1.030
t
NS
NS
NS
NS
NS
NS .049
NS
NS
NS
NS
P
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123 52.30 ± 4.13
52.68 ± 4.67
59.34 ± 7.34
52.81 ± 4.47
Sleep problems
Attention problems
Aggressive behavior
66.74 ± 8.80
54.75 ± 6.81
53.40 ± 5.57
Perv dev problemse
ADHDf problems
Opp def problemsg
72.83 ± 21.41
72.61 ± 23.70
72.74 ± 26.44
P-CDIj percentile
DCk percentile
Total percentile
k
j
i
h
g
f
e
d
c
b
a
73.24 ± 28.73
74.60 ± 24.82
74.40 ± 23.15
59.04 ± 33.15
53.74 ± 6.34
55.07 ± 6.42
66.93 ± 7.38
52.07 ± 3.32
Difficult Child
Parent–Child Dysfunctional Interaction
Parental Distress
Parent Stress Index-Short Form
Attention Deficit/Hyperactivity problems Oppositional defiant problems
Pervasive developmental problems
Internalizing, Externalizing, Total and Other Problems
Not significant P [ 0.05
Child Behavior Checklist version 1–5
Standard Deviation
Bold values are statistically significant
56.13 ± 34.05
PDi percentile
PSI-SFh
56.02 ± 6.65
54.08 ± 5.87
Affective problems
Anxiety problems
55.70 ± 5.07
57.56 ± 10.44
57.78 ± 10.08
DSM-oriented scales
Total problems
51.81 ± 6.46
57.67 ± 8.15
51.98 ± 6.97
Internalizing Problems
57.19 ± 7.98
53.04 ± 4.83
59.96 ± 7.04
53.37 ± 5.23
Externalizing problems
Int, Ext, Tot, Otherd
70.00 ± 10.64
53.68 ± 5.12
70.28 ± 10.06
Somatic complaints
Withdrawn
54.22 ± 5.39 52.85 ± 3.91
55.08 ± 6.43
53.79 ± 5.10
Emotionally reactive
Anxious/depressed
Syndrome scales
CBCL 1–5b
t
P
72.14 ± 24.11
70.24 ± 22.66
70.95 ± 19.53
52.67 ± 35.58
53.04 ± 4.74
54.42 ± 7.31
66.54 ± 10.21
56.15 ± 7.17
56.35 ± 7.61
58.00 ± 9.89
52.15 ± 7.56
58.15 ± 8.44
52.58 ± 4.15
58.69 ± 7.74
53.08 ± 5.23
70.58 ± 9.62
54.00 ± 5.09
54.77 ± 6.02
55.96 ± 7.36
0.139
0.617
-0.540
0.628
0.455
-0.345
-0.159
2.640
0.349
0.160
0.174
0.431
0.371
0.626
0.604
0.207
0.443
1.378
0.984
NS
NS
NS
NS
NS
NS
NS
.012
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NSc
69.00 ± 20.81 68.00 ± 24.37
69.44 ± 23.19
71.17 ± 22.65
54.73 ± 30.95
52.60 ± 5.29
52.83 ± 4.94
63.33 ± 7.76
52.63 ± 4.35
56.00 ± 6.10
57.07 ± 10.28
51.62 ± 7.38
55.76 ± 6.92
52.03 ± 3.82
59.20 ± 7.29
52.14 ± 3.39
68.17 ± 10.11
54.17 ± 5.09
52.33 ± 3.81
54.67 ± 5.42
Females (N = 30) Mean ± SD
66.19 – 28.11
67.24 ± 27.38
56.33 ± 32.50
53.09 ± 5.54
54.33 ± 5.74
65.60 ± 9.23
54.24 ± 6.85
56.76 ± 7.61
56.91 ± 10.02
52.05 ± 8.15
56.95 ± 8.57
52.76 ± 4.87
59.86 ± 7.80
53.49 ± 5.18
67.76 ± 9.88
54.67 ± 5.68
53.74 ± 6.10
56.00 ± 7.54
All participants (N = 60) Mean ± SD
Males (N = 30) Mean ± SD
All participants (N = 60) Mean ± SDa
Females (N = 30) Mean ± SD
T1
T0
Table 2 CBCL and PSI-SF at T0 and T1
63.92 ± 32.60
70.00 ± 26.31
62.33 ± 32.18
58.33 ± 34.91
53.61 ± 5.85
55.93 ± 6.18
68.04 ± 10.16
55.96 ± 8.54
57.57 ± 9.00
56.75 ± 9.93
52.50 ± 8.99
58.18 ± 9.97
53.54 ± 5.77
60.57 ± 8.38
54.89 ± 6.30
67.32 ± 9.81
55.21 ± 6.31
55.25 ± 7.64
57.43 ± 9.18
Males (N = 30) Mean ± SD
-0.527
0.156
-1.182
0.401
0.688
2.112
1.988
1.890
0.783
0.119
0.404
1.067
1.176
0.666
2.044
0.323
0.698
1.857
1.406
t
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
.047
NS
NS
NS
NS
P
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Fig. 1 Trajectories of changes in the male and female groups
shown contrasting results (Carter et al. 2007; Banach et al. 2009; Andersson et al. 2013; Van Wijngaarden-Cremers et al. 2013; Hofvander et al. 2009; Lugnegard et al. 2011; Matson and Nebel-Schwalm 2007; Park et al. 2012; Simonoff et al. 2008; Lai et al. 2011; Mayes and Calhoun 2011; Zwaigenbaum et al. 2012; Mandy et al. 2012; Szatmari et al. 2012; Donna et al. 2013). On the one hand, most studies found no gender differences on social behaviors or communication nor on RRB (Carter et al. 2007; Banach et al. 2009; Andersson et al. 2013; Hofvander et al. 2009; Lugnegard et al. 2011; Matson and Nebel-Schwalm 2007; Park et al. 2012; Simonoff et al. 2008; Lai et al. 2011; Mayes and Calhoun 2011; Zwaigenbaum et al. 2012; Mandy et al. 2012). For instance, Andersson et al. (2013), in a cross-sectional study aimed at investigating gender differences in clinical and developmental profiles in 20 preschool girls and 20 age-matched preschool boys with suspected ASD, found no significant differences on communication and RRB. On the other hand, some literature data have reported girls to be less impaired than boys with regard to RRB: this result does not
suggest that females with ASD report no deficits in this area, but instead that females may not exhibit the same behavioral pattern of RRB as that presented by males, and it is possible therefore that RRB of females can be masked and less clinically identifiable (Van Wijngaarden-Cremers et al. 2013; Szatmari et al. 2012; Donna et al. 2013). In our study, in order to compare children performing different modules of ADOS-G, we used the ADOS-G severity score which combines SA and RRB scores into one total cut-off score. Therefore, we could not detect the gender differences on RRB in our sample. However, in line with the results of other reports, we found no differences in severity of autism symptoms between males and females either across time or at a specific time assessment (nor at T0 or at T1; Carter et al. 2007; Banach et al. 2009; Andersson et al. 2013; Mayes and Calhoun 2011; Zwaigenbaum et al. 2012; Mandy et al. 2012; Szatmari et al. 2012; Donna et al. 2013). Furthermore, our results have shown no significant interaction between time and gender for predicting cognitive ability, parental stress, children’s adaptive skills or behavior problems. Indeed, confirming
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previous studies reporting that a lower cognitive ability is found more frequently in females with ASD than in males, in our population, at admission, females showed significantly lower scores on generalized developmental quotient (Fombonne 1999, 2009; Nicholas et al. 2008; Bryson et al. 2008). However, at follow-up, in our study, this gender difference was not confirmed. This result could be explained taking into account the trajectories of changes in cognitive ability of males and females separately. Specifically, even though both male and female groups showed a statistically significant improvement on cognitive ability over time, gains in cognitive ability made between T0 and T1 assessments in the male group were of small effect size (d = 0.2), whereas females presented considerably developments, of large effect size (d = 0.9), on cognitive ability between time of admission and time of follow-up. These findings could support the hypothesis that clinical symptoms may be masked in higher functioning autism females with the result that they are undiagnosed or misdiagnosed, or that girls, particularly that girls recognized at an early age, require a greater etiological load to manifest an autistic phenotype (Shattuck et al. 2009; Mandell et al. 2009; Baron-Cohen 2003a; Robinson et al. 2013; Werling et al. 2013a; Kirkovski et al. 2013). Besides these results, the present study has some important limitations that should to be taken into account. First, due to the limited number of girls referred for screening, we could only collect a small sample. Second, the sample was clinically referred and not intended to be representative of children with ASD in the general population; we did not include control groups of males and females without ASD, which is important given the increasing evidence for the presence of autistic traits in the general population (Ruta et al. 2012). Although, to our knowledge there are no previous longitudinal studies investigating gender differences regarding symptomatology in preschoolers with ASD, the span of the assessments in our study is not wide. Epidemiological studies have highlighted a strong male bias in ASD and further research is needed on the prevalence estimates of this disorder between genders. Several studies have highlighted the benefits of early identification and intervention for children with ASD (Dawson 2008; Cangialose and Allen 2014). Therefore, shedding light on the developmental trajectories in ASD could increase clinicians’ diagnostic sensitivity and lead to an earlier identification of children with ASD. Furthermore, defining the presentation and course of symptom domains and severity in patients with ASD would contribute to the development of appropriate early intervention and treatment strategies. Indeed, given that sex hormone levels change during a lifetime and may modulate the presentation of the autism phenotype, studies on the mechanism by which these
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factors may be a risk for males and females are crucial. Finally, our findings suggest that male and female preschool children with ASD present similar clinical profiles over time, but, given the lack of longitudinal controlled studies on this issue, further studies are essential. Acknowledgments Authors would like to thank Giovanni Tripepi for his valuable guidance in the statistical analysis of data, interpretation of results and discussion on the themes addressed in this manuscript.
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ORIGINAL PAPER
Longitudinal comparison between male and female preschool children with autism spectrum disorder Valentina Postorino • Laura Maria Fatta • Lavinia De Peppo • Giulia Giovagnoli • Marco Armando • Stefano Vicari • Luigi Mazzone
Ó Springer Science+Business Media New York 2015
Abstract Epidemiological studies have highlighted a strong male bias in autism spectrum disorder (ASD), however few studies have examined gender differences in autism symptoms, and available findings are inconsistent. The aim of the present study is to investigate the longitudinal gender differences in developmental profiles of 30 female and 30 male age-matched preschool children with ASD. All the children underwent a comprehensive evaluation at T0 and at T1. Our results have shown no significant interaction between time and gender for predicting autism symptoms, developmental quotient, parental stress, children’s adaptive skills and behavior problems. Shedding light on the developmental trajectories in ASD could help clinicians to recognize children with ASD at an earlier age and contribute to the development of appropriate treatments. Keywords Autism spectrum disorder Preschoolers Males Females Longitudinal
V. Postorino L. M. Fatta L. De Peppo G. Giovagnoli M. Armando S. Vicari L. Mazzone (&) Child Neuropsychiatry Unit, Department of Neuroscience, I.R.C.C.S. Children’s Hospital Bambino Gesu`, Piazza S. Onofrio, 4, 00165 Rome, Italy e-mail: [email protected] L. M. Fatta I.R.C.C.S. Centro Neuolesi ‘‘Bonino-Pulejo’’ Via Provinciale Palermo, S.S.113 Contrada Casazza, 98124 Messina, Italy L. De Peppo G. Giovagnoli Psychology Department, Libera Universita’ Maria Ss. Assunta, Rome, Italy
Introduction Autism spectrum disorder (ASD) is a broad category characterized by persistent deficits in social communication and social interaction, and restricted and repetitive patterns of behavior. According to the Diagnostic and Statistical Manual of Mental Disorders—fifth edition (DSM-5), due to the heterogeneity of this diagnostic classification, ASD is better differentiated by severity of autism symptoms, and association with language impairment and intellectual disability (American Psychiatric Association 2013). Recent epidemiological studies on ASD show a significantly higher prevelence of the disorder in males (Fombonne 2009). These studies found a male: female (M:F) ratio ranging from 1.33:1 to 16:1, with a mean of 4:1 (Fombonne 2009; Baird et al. 2006; Nygren et al. 2012a, 2012b; CDC 2014). This M:F ratio rises to 10:1 in Asperger syndrome and ‘‘high functioning autism’’, whereas it drops to 2:1 in subjects with a comorbid intellectual disability (Fombonne 1999, 2009; Nicholas et al. 2008; Bryson et al. 2008). Some studies have observed that this gender disparity could be due to a strong social gender bias that causes parents and clinicians to have different perceptions and expectations for boys compared to girls, with diagnosis of ASD being made at a later age in the latter group, even when symptom severity is constant across genders (Carter et al. 2007; Holtmann et al. 2007; McLennan et al. 1993; Shattuck et al. 2009; Mandell et al. 2009; Russell et al. 2011; Mondschein et al. 2000). Furthermore, it has been hypothesized that these epidemiological data could reflect a bias in diagnostic criteria, probably due to a different behavioral phenotype in males and in females, and it is therefore possible that girls either go undiagnosed or are misdiagnosed (Kopp and Gillberg 1992; Attwood 2007).
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Another possible explanation is the widely cited ‘‘extreme male brain’’ theory of Baron-Cohen et al. (2003), that assumes a neuropsychological difference between genders, with males predominating in the systemizing domain, whereas females are superior in the empathizing domain. According to this theory, in ASD there is a stronger drive to systemize independent of gender compared to normal subjects and this hypothesis seems to be associated to biological and genetic factors (i.e., fetal testosterone, epigenetic effect on X chromosome genes and the malelimited expression of genes on the Y chromosome) (BaronCohen et al. 2014; Baron-Cohen Baron-Cohen 2003b; Baron-Cohen et al. 2003, 2005, 2011; Chapman et al. 2006; Chura et al. 2010; Ingudomnukul et al. 2007). In order to understand the sex bias in ASD the hypothesis of a female protective effect against autistic behavior was empirically tested by Robinson et al. (2013) in a study on two nationally-representative samples of dizygotic twins. The results of this study provide the strongest evidence to date that female sex protects girls from autistic impairments and suggest that girls require a greater etiological load to manifest an autistic phenotype (Werling et al. 2013b). Although gender disparity in prevalence rates has been extensively studied, only a few studies examined gender differences in autistic symptoms, and available findings are inconsistent. Most studies failed to find any differences in the core symptoms of ASD between males and females (Carter et al. 2007; Banach et al. 2009; Andersson et al. 2013; Mayes and Calhoun 2011; Zwaigenbaum et al. 2012; Mandy et al. 2012; Szatmari et al. 2012; Donna et al. 2013). On the other hand, a meta-analysis of 20 studies investigating gender differences in ASD reported few differences in symptom severity between males and females. Specifically, there were no gender differences in social behaviors or communication, but girls showed less restricted, repetitive and stereotyped behaviors (RRB) than boys (Van Wijngaarden-Cremers et al. 2013). It is worth noting that female with ASD and an average or above average intellectual ability may be undiagnosed or misdiagnosed due to a different clinical manifestation, and therefore differences in symptom severity between genders may be affected by this diagnostic bias (Shattuck et al. 2009; Mandell et al. 2009; Baron-Cohen 2003b; Kirkovski et al. 2013). Regarding co-occurring comorbid psychopathology among samples with ASD, the majority of studies failed to identify any gender differences (Hofvander et al. 2009; Lugnegard et al. 2011; Matson and Nebel-Schwalm 2007; Park et al. 2012; Simonoff et al. 2008; Lai et al. 2011; Mazzone et al. 2013). However, some studies reported that girls with ASD appeared to be at greater risk than boys for internalizing psychopathology (Solomon et al. 2012; Mazzone et al. 2012). Indeed, recent studies report that parents of children with ASD experience higher levels of stress than parents of
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typically developing children (Mugno et al. 2007; May et al. 2012; Zaidman-Zait et al. 2011; Bauminger et al. 2010). Moreover, parental stress was found to be positively correlated with autism symptoms and severity, as well as with internalizing and externalizing behavior problems (Bauminger et al. 2010; Herring et al. 2006). However, none of these studies has focused on gender differences in parenting stress reported by parents of children with ASD. Longitudinal studies investigating the differences between genders with regard to symptom severity, developmental changes in symptoms presentation and course may have significant implications for the development of adequate assessment and treatment strategies for patients with ASD. Shedding light on gender differences in developmental trajectories is crucial for both clinical and research purposes with regard to diagnosis and treatment. However, to our knowledge, no previous study has addressed this issue. Therefore, the aim of the present study was to investigate the longitudinal gender differences in developmental profiles in a sample of preschool children suffering from ASD with particular focus on the relationship between children’s behavior problems and experienced stress in the parenting role reported by parents.
Participants and methods Participants and procedure A total of 60 preschool children suffering from ASD were enrolled in this study. Of these, 30 females were age-matched with 30 males (aged 2–5.4 years; Mean age ± SD: 3.55 ± 0.9). All the children were referred to the Child Neuropsychiatry Unit of the Bambino Gesu’ Children’s Hospital in Rome (Italy) between December 2010 and December 2012. All the participants’ parents provided a written informed consent. Exclusion criteria included the presence of specific genetic disorders, other medical disorders or epilepsy. On entry into the study, autism had to be diagnosed by an expert clinician using DSM-IV-TR criteria (American Psychiatric Association 2000). All the subjects underwent a comprehensive developmental profile evaluation at the time of admission (Time 0, T0) and at follow-up (Time 1, T1). Developmental quotient assessment All the children were assessed using the Griffiths Mental Development Scale-Extend Revised (GMDS-ER) (Griffiths 2006). GMDS-ER I and II assess a child’s strengths and weaknesses in all developmental areas, and can be used to measure the rate of development of children from birth to 8 years of age. The six areas of development measured by the scales are: (A) Locomotor, measuring the gross motor
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development; (B) Personal-Social, examining the social and daily living skills; (C) Hearing and Speech, measuring receptive and expressive language; (D) Eye-Hand Coordination, focusing on fine motor skills, manual dexterity and visual monitoring skills; (E) Performance, focusing on manipulation of objects; (F) Practical Reasoning, measuring mathematic ability and abstract reasoning. Each subscale provides a different developmental quotient and a diagnostic indication of individual problems in early childhood. In the present study, all the children were unable to perform GMDS-ER practical reasoning scale either at T0 or T1. Griffith’s six subscales are expressed as quotients constituting the general developmental quotient (GDQ). The GDQ is derived from the average of quotients resulting from the six subscales. The test scores are transformed into developmental ages (D.A.) and then into quotients according to the following equation: developmental quotient (DQ) = Developmental age 9 100/chronological age (CA). Developmental quotients rather than mental age are used to make it possible to compare children of different chronological ages and to compare a child’s performance at different time periods. Evaluation of autism symptoms All the children were assessed for the presence of autism symptoms using the Autism Diagnostic Observation Schedule-Generic (ADOS-G) performed by a licensed clinician (Lord et al. 2000). The ADOS-G is a semi-structured, standardized, play-based assessment measure evaluating current autistic behaviors. The ADOS-G is divided into four separate modules. Each module is aimed at a specific level of expressive language ability (ranging from pre-verbal to fluent speech). The choice of modules is based on the subject’s expressive language level. The use of different modules reduces possible biasing effects of differences in language skills. Scoring is done immediately after administration of the ADOS-G. Each item is scored on a 0–3 scale (0 = no evidence of abnormal behavior to 3 = markedly abnormal behavior) and each module has a specific diagnostic algorithm. Items used in the algorithms are divided into four areas: communication, social interaction, play/creativity, and restricted/repetitive behaviors or interests (RRB). The total score for communication and social interaction provides a cutoff for diagnosis at various ‘‘levels of ASD’’. In the present study, at T0 all children performed module 1 (cut-off for autism = 12 and ASD = 7), whereas, at T1, 54 children (28 males and 26 females) performed module 1 and 6 children (2 males and 4 females) performed module 2 (cut-off for autism = 12 and ASD = 8). The ADOS-G has good to excellent psychometric properties, and satisfactory ability to differentiate individuals with and without ASD. However, the ADOSG algorithm does not include RRB and literature studies have suggested that if RRB are included it may help to increase
ADOS-G diagnostic stability (Lord et al. 2006). Therefore, the ADOS-G has been revised (Gotham et al. 2007). In the revised version, module 1 is split into ‘‘No words’’ and ‘‘Some words’’, and module 2 into ‘‘Younger than 5’’ and ‘‘5 or older’’. The revised algorithm is divided into two new domains, social affect (SA) and RRB. To calculate the total cut-off, SA and RRB are combined into one score. An ASD severity scale has been developed basing on 18 narrowly defined age and language cells, and the total raw scores have been divided into a 10-point severity score (1 = fewer problems and 10 = more severe problems) (de Bildt et al. 2011; Gotham et al. 2009). Assessment of adaptive skills, behavior problems and parental stress All the parents of the participating children performed the Vineland Adaptive Behavior Scale-Survey Form (VABSSF) with a trained and experienced clinician, in order to measure the children’s adaptive skills (Sparrow et al. 1984; Balboni and Pedrabissi 2003). Moreover, they completed the Child Behavior Checklist version 1–5 (CBCL) and the Parent Stress Index-Short Form (PSI-SF) to rate children’s emotional and behavior problems and levels of stress experienced in the parenting role (Achenbach and Eofbrock 1983; Achenbach and Rescorla 2000, 2001; Abidin 1990, 1995; Guarino et al. 2008). The VABS-SF is a standardized parent interview of everyday adaptive functioning, designed to measure adaptive behaviors in children from birth to 18 years. It consists of 297 items falling into four general functioning domains: communication skills, daily living skills, social skills and motor skills. The communication domain assesses receptive, expressive, and written skills according to age level. The daily living skills domain taps personal, domestic, and community skills. For the socialization domain, the child is rated on interpersonal relationship skills, socialization during play and leisure time, and coping skills. The motor skills domain includes development of gross and fine motor skills. An adaptive behavior composite score for each of the four domains was attained for all participants and transformed into equivalent ages based on published Italian norms (Balboni and Pedrabissi 2003). The CBCL version 1–5 is an extensively used tool of 99 items on a three point Likert scale, that provides scores for seven syndrome scales, five different DSM-oriented scales and three broad-brand scales (i.e., internalizing symptoms, externalizing symptoms and total behavior problems). Raw scores for each clinical factor were transformed into T-scores based on published norms: T-scores[63 were considered indicative of clinical impairment for the three broad-band scales, whereas T-scores C70 were considered indicative of clinical impairment for
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syndrome and DSM-oriented scales (Achenbach and Eofbrock 1983; Achenbach and Rescorla 2000, 2001). The psychometric properties of the CBCL show good validity and reliability (Achenbach and Eofbrock 1983; Achenbach and Rescorla 2000, 2001). The PSI-SF is a 36-item questionnaire that measures different aspects of experienced stress in the parenting role using three subscales: parental distress (PD), measuring an impaired sense of parental competence and depression, parent–child dysfunctional interaction (P-CDI), intended to measure unsatisfactory parent–child interactions, and difficult child (DC), measuring behavioral characteristics of the child that make him/her easy or difficult to manage. The total PSI-SF score is seen as an indicator of the parent’s overall experience of parenting stress (Abidin 1990, 1995; Guarino et al. 2008). Parents rate each of the 36 items on a 5-point scale. The 90th percentile of the total PSI-SF score represents a ‘‘clinically significant’’ level of parenting stress. The PSI-SF has been shown to be a valid and reliable measure (Abidin 1995; Guarino et al. 2008; Zaidman-Zait et al. 2011). Data Analysis Data analyses were performed using the Statistical Package for Social Sciences (SPSS 20.0 for Windows). Independent samples t-tests, at T0 and T1, were performed to evaluate differences between gender and ADOS severity score, GMDS-ER, VABS-SF, CBCL and PSI-SF. Mixed ANOVA analysis were performed between time (withingroup variable) and gender (between-group variable) on GMDS-ER, VABS-SF, ADOS severity score, CBCL and PSI-SF (dependent variables). The strength of the relationship between independent variables (time and gender) and dependent variables was assessed by calculating the F test and the corresponding P value. Mixed ANOVA analysis showed that the assumption of sphericity had been violated for all the variables tested (ADOS severity score: p \ .001, GMDS-ER: p \ .001, VABS-SF: p = .001, CBCL: p \ .001 and PSI-SF: p \ .001) therefore degrees of freedom were corrected using Greenhouse-Geisser estimates of sphericity. Furthermore, paired samples t-tests were applied to the data in order to evaluate differences within gender between each time assessment and autism symptoms, developmental quotient, parental stress, children’s adaptive skills and behavior problems. An alpha level of 0.05 was set for statistical significance.
Results Comparisons between male and female groups at T0, using independent samples t-tests, showed significantly higher
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scores in the male group on GDQ (t = 2.213, p = .033), eye–hand coordination (t = 2.618, p = .013) and performance (t = 2.901, p = .005) GMDS-ER scales and CBCL anxiety problems subscale (t = 2.640, p = .012; Table 1 and 2). Whereas, comparison between male and female groups at T1, using independent samples t-tests, showed significantly higher scores in the male group on eye-hand coordination (t = 2.016, p = .049) GMDS-ER scale and CBCL sleep problems subscale (t = 2.044, p = .047; Tables 1 and 2). Evaluating differences between time (T0 and T1) and gender on developmental quotient (GMDSER), mixed ANOVA analysis indicated a main effect of time (F:21.702; p \ .001) and this effect was not modified by gender (F:1.627; p for the effect modification = .208). Moreover, mixed ANOVA analysis between time (T0 and T1) and gender on children’s adaptive skills (VABS-SF) indicated a main effect of time (F:46.7; p \ .001) that again was not modified by gender (F:0.585; p for the effect modification = .448). Furthermore, mixed ANOVA analysis between time (T0 and T1) and gender on autism symptoms (ADOS severity score), children’s behavior problems (CBCL) and parental stress (PSI-SF) revealed no statistically significant main effect or interaction. Moreover, within the female group, paired samples t-tests between T0 and T1 showed significantly higher scores at T1 on GDQ (t = -4.271, p \ .001), locomotor (t = -2.805, p = .009), personal–social (t = -4.495, p \ .001) and performance (t = -4.148, p \ .001) GMDS-ER scales, communication skills (t = -3.342, p = .002), daily living skills (t = -5.580, p \ .001), social skills (t = -2.529, p = .018) and motor skills (t = -8.260, p \ .001) VABSSF domains (Fig. 1). On the other hand, within the male group, paired samples t-tests between T0 and T1 indicated higher scores at T1 on GDQ (t = -2.588, p = .016), personal–social (t = -2.881, p = .008) GMDS-ER scale, communication (t = -2.452, p = .023), daily living skills (t = -2.227, p = .037), social skills (t = -3.215, p = .004) and motor skills (t = -2.853, p = .009) VABS-SF domains (Fig. 1).
Discussion Epidemiological data have shown a significantly higher prevalence of ASD in males and recent evidence suggests that the observed gender ratio may be influenced by sex differences in symptomatology of ASD (Fombonne 2009). However, there is a great deal of literature concerning the varying profile of autism as expressed by gender differences and the vast majority of studies have been conducted with predominantly male samples (Bell et al. 2005). Furthermore, the available findings on the differences in clinical profiles between males and females with ASD have
63.04 ± 24.16
40.98 ± 25.79 59.81 ± 22.34
71.58 ± 27.27
Personal-social
Hearing and speech Eye–hand coordination
Performance
1.88 ± 0.45
2.48 ± 0.84
Social skills
Motor skills
f
e
d
c
b
a
2.28 ± 0.55
1.83 ± 0.29
1.99 ± 0.43
1.62 ± 0.39
62.07 ± 20.30
35.86 ± 17.36 52.60 ± 13.06
57.07 ± 17.76
80.87 ± 17.94
58.20 ± 13.78
6.66 ± 1.696
Vineland Adaptive Behavior Scale-Survey Form
Griffiths Mental Development Scale-Extended Revised General Developmental Quotient
Not significant P [ 0.05
Autism Diagnostic Observation Schedule
Standard Deviation
Bold values are statistically significant
1.80 ± 0.81
2.12 ± 0.77
Communication skills
Daily living skills
VABS-SFf
63.81 ± 20.33
83.11 ± 24.01
Locomotor
6.42 ± 1.734
GDQe
GMDS-ER
d
ADOSb Severity score
t
P
2.72 ± 1.07
1.93 ± 0.59
2.27 ± 1.04
2.02 ± 1.09
82.15 ± 30.37
46.48 ± 31.97 67.81 ± 27.53
69.67 ± 28.62
85.59 ± 29.51
70.04 ± 24.53
6.20 ± 1.769
1.917
0.801
1.326
1.833
2.901
1.559 2.618
1.971
0.739
2.213
-1.008
NS
NS
NS
NS
.005
NS .013
NS
NS
.033
NSc
3.17 ± 0.88
2.18 ± 0.55
2.55 ± 0.70
2.25 ± 1.09
84.09 ± 26.58
45.21 ± 27.85 65.00 – 22.84
74.95 ± 26.56
90.56 ± 25.20
72.09 ± 20.98
6.27 ± 1.552
All participants (N = 60) Mean ± SD
Males (N = 30) Mean ± SD
All participants (N = 60) Mean ± SDa
Females (N = 30) Mean ± SD
T1
T0
Table 1 ADOS severity score, GMDS-ER and VABS-SF at T0 and T1
3.25 ± 0.79
2.08 ± 0.49
2.50 ± 0.62
2.05 ± 0.89
82.82 ± 28.90
38.75 ± 25.88 58.96 ± 21.50
72.32 ± 25.46
95.21 ± 25.99
70.11 ± 21.14
6.48 ± 1.37
Females (N = 30) Mean ± SD
3.09 ± 0.97
2.29 ± 0.59
2.61 ± 0.78
2.46 ± 1.24
85.31 ± 24.59
51.45 ± 28.69 70.83 ± 22.93
77.48 ± 29.69
86.07 ± 24.01
74.00 ± 21.01
6.07 ± 1.701
Males (N = 30) Mean ± SD
-0.715
1.454
0.575
1.436
0.351
1.752 2.016
0.703
-1.380
0.697
-1.030
t
NS
NS
NS
NS
NS
NS .049
NS
NS
NS
NS
P
J Autism Dev Disord
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123 52.30 ± 4.13
52.68 ± 4.67
59.34 ± 7.34
52.81 ± 4.47
Sleep problems
Attention problems
Aggressive behavior
66.74 ± 8.80
54.75 ± 6.81
53.40 ± 5.57
Perv dev problemse
ADHDf problems
Opp def problemsg
72.83 ± 21.41
72.61 ± 23.70
72.74 ± 26.44
P-CDIj percentile
DCk percentile
Total percentile
k
j
i
h
g
f
e
d
c
b
a
73.24 ± 28.73
74.60 ± 24.82
74.40 ± 23.15
59.04 ± 33.15
53.74 ± 6.34
55.07 ± 6.42
66.93 ± 7.38
52.07 ± 3.32
Difficult Child
Parent–Child Dysfunctional Interaction
Parental Distress
Parent Stress Index-Short Form
Attention Deficit/Hyperactivity problems Oppositional defiant problems
Pervasive developmental problems
Internalizing, Externalizing, Total and Other Problems
Not significant P [ 0.05
Child Behavior Checklist version 1–5
Standard Deviation
Bold values are statistically significant
56.13 ± 34.05
PDi percentile
PSI-SFh
56.02 ± 6.65
54.08 ± 5.87
Affective problems
Anxiety problems
55.70 ± 5.07
57.56 ± 10.44
57.78 ± 10.08
DSM-oriented scales
Total problems
51.81 ± 6.46
57.67 ± 8.15
51.98 ± 6.97
Internalizing Problems
57.19 ± 7.98
53.04 ± 4.83
59.96 ± 7.04
53.37 ± 5.23
Externalizing problems
Int, Ext, Tot, Otherd
70.00 ± 10.64
53.68 ± 5.12
70.28 ± 10.06
Somatic complaints
Withdrawn
54.22 ± 5.39 52.85 ± 3.91
55.08 ± 6.43
53.79 ± 5.10
Emotionally reactive
Anxious/depressed
Syndrome scales
CBCL 1–5b
t
P
72.14 ± 24.11
70.24 ± 22.66
70.95 ± 19.53
52.67 ± 35.58
53.04 ± 4.74
54.42 ± 7.31
66.54 ± 10.21
56.15 ± 7.17
56.35 ± 7.61
58.00 ± 9.89
52.15 ± 7.56
58.15 ± 8.44
52.58 ± 4.15
58.69 ± 7.74
53.08 ± 5.23
70.58 ± 9.62
54.00 ± 5.09
54.77 ± 6.02
55.96 ± 7.36
0.139
0.617
-0.540
0.628
0.455
-0.345
-0.159
2.640
0.349
0.160
0.174
0.431
0.371
0.626
0.604
0.207
0.443
1.378
0.984
NS
NS
NS
NS
NS
NS
NS
.012
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NSc
69.00 ± 20.81 68.00 ± 24.37
69.44 ± 23.19
71.17 ± 22.65
54.73 ± 30.95
52.60 ± 5.29
52.83 ± 4.94
63.33 ± 7.76
52.63 ± 4.35
56.00 ± 6.10
57.07 ± 10.28
51.62 ± 7.38
55.76 ± 6.92
52.03 ± 3.82
59.20 ± 7.29
52.14 ± 3.39
68.17 ± 10.11
54.17 ± 5.09
52.33 ± 3.81
54.67 ± 5.42
Females (N = 30) Mean ± SD
66.19 – 28.11
67.24 ± 27.38
56.33 ± 32.50
53.09 ± 5.54
54.33 ± 5.74
65.60 ± 9.23
54.24 ± 6.85
56.76 ± 7.61
56.91 ± 10.02
52.05 ± 8.15
56.95 ± 8.57
52.76 ± 4.87
59.86 ± 7.80
53.49 ± 5.18
67.76 ± 9.88
54.67 ± 5.68
53.74 ± 6.10
56.00 ± 7.54
All participants (N = 60) Mean ± SD
Males (N = 30) Mean ± SD
All participants (N = 60) Mean ± SDa
Females (N = 30) Mean ± SD
T1
T0
Table 2 CBCL and PSI-SF at T0 and T1
63.92 ± 32.60
70.00 ± 26.31
62.33 ± 32.18
58.33 ± 34.91
53.61 ± 5.85
55.93 ± 6.18
68.04 ± 10.16
55.96 ± 8.54
57.57 ± 9.00
56.75 ± 9.93
52.50 ± 8.99
58.18 ± 9.97
53.54 ± 5.77
60.57 ± 8.38
54.89 ± 6.30
67.32 ± 9.81
55.21 ± 6.31
55.25 ± 7.64
57.43 ± 9.18
Males (N = 30) Mean ± SD
-0.527
0.156
-1.182
0.401
0.688
2.112
1.988
1.890
0.783
0.119
0.404
1.067
1.176
0.666
2.044
0.323
0.698
1.857
1.406
t
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
.047
NS
NS
NS
NS
P
J Autism Dev Disord
J Autism Dev Disord
Fig. 1 Trajectories of changes in the male and female groups
shown contrasting results (Carter et al. 2007; Banach et al. 2009; Andersson et al. 2013; Van Wijngaarden-Cremers et al. 2013; Hofvander et al. 2009; Lugnegard et al. 2011; Matson and Nebel-Schwalm 2007; Park et al. 2012; Simonoff et al. 2008; Lai et al. 2011; Mayes and Calhoun 2011; Zwaigenbaum et al. 2012; Mandy et al. 2012; Szatmari et al. 2012; Donna et al. 2013). On the one hand, most studies found no gender differences on social behaviors or communication nor on RRB (Carter et al. 2007; Banach et al. 2009; Andersson et al. 2013; Hofvander et al. 2009; Lugnegard et al. 2011; Matson and Nebel-Schwalm 2007; Park et al. 2012; Simonoff et al. 2008; Lai et al. 2011; Mayes and Calhoun 2011; Zwaigenbaum et al. 2012; Mandy et al. 2012). For instance, Andersson et al. (2013), in a cross-sectional study aimed at investigating gender differences in clinical and developmental profiles in 20 preschool girls and 20 age-matched preschool boys with suspected ASD, found no significant differences on communication and RRB. On the other hand, some literature data have reported girls to be less impaired than boys with regard to RRB: this result does not
suggest that females with ASD report no deficits in this area, but instead that females may not exhibit the same behavioral pattern of RRB as that presented by males, and it is possible therefore that RRB of females can be masked and less clinically identifiable (Van Wijngaarden-Cremers et al. 2013; Szatmari et al. 2012; Donna et al. 2013). In our study, in order to compare children performing different modules of ADOS-G, we used the ADOS-G severity score which combines SA and RRB scores into one total cut-off score. Therefore, we could not detect the gender differences on RRB in our sample. However, in line with the results of other reports, we found no differences in severity of autism symptoms between males and females either across time or at a specific time assessment (nor at T0 or at T1; Carter et al. 2007; Banach et al. 2009; Andersson et al. 2013; Mayes and Calhoun 2011; Zwaigenbaum et al. 2012; Mandy et al. 2012; Szatmari et al. 2012; Donna et al. 2013). Furthermore, our results have shown no significant interaction between time and gender for predicting cognitive ability, parental stress, children’s adaptive skills or behavior problems. Indeed, confirming
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previous studies reporting that a lower cognitive ability is found more frequently in females with ASD than in males, in our population, at admission, females showed significantly lower scores on generalized developmental quotient (Fombonne 1999, 2009; Nicholas et al. 2008; Bryson et al. 2008). However, at follow-up, in our study, this gender difference was not confirmed. This result could be explained taking into account the trajectories of changes in cognitive ability of males and females separately. Specifically, even though both male and female groups showed a statistically significant improvement on cognitive ability over time, gains in cognitive ability made between T0 and T1 assessments in the male group were of small effect size (d = 0.2), whereas females presented considerably developments, of large effect size (d = 0.9), on cognitive ability between time of admission and time of follow-up. These findings could support the hypothesis that clinical symptoms may be masked in higher functioning autism females with the result that they are undiagnosed or misdiagnosed, or that girls, particularly that girls recognized at an early age, require a greater etiological load to manifest an autistic phenotype (Shattuck et al. 2009; Mandell et al. 2009; Baron-Cohen 2003a; Robinson et al. 2013; Werling et al. 2013a; Kirkovski et al. 2013). Besides these results, the present study has some important limitations that should to be taken into account. First, due to the limited number of girls referred for screening, we could only collect a small sample. Second, the sample was clinically referred and not intended to be representative of children with ASD in the general population; we did not include control groups of males and females without ASD, which is important given the increasing evidence for the presence of autistic traits in the general population (Ruta et al. 2012). Although, to our knowledge there are no previous longitudinal studies investigating gender differences regarding symptomatology in preschoolers with ASD, the span of the assessments in our study is not wide. Epidemiological studies have highlighted a strong male bias in ASD and further research is needed on the prevalence estimates of this disorder between genders. Several studies have highlighted the benefits of early identification and intervention for children with ASD (Dawson 2008; Cangialose and Allen 2014). Therefore, shedding light on the developmental trajectories in ASD could increase clinicians’ diagnostic sensitivity and lead to an earlier identification of children with ASD. Furthermore, defining the presentation and course of symptom domains and severity in patients with ASD would contribute to the development of appropriate early intervention and treatment strategies. Indeed, given that sex hormone levels change during a lifetime and may modulate the presentation of the autism phenotype, studies on the mechanism by which these
123
factors may be a risk for males and females are crucial. Finally, our findings suggest that male and female preschool children with ASD present similar clinical profiles over time, but, given the lack of longitudinal controlled studies on this issue, further studies are essential. Acknowledgments Authors would like to thank Giovanni Tripepi for his valuable guidance in the statistical analysis of data, interpretation of results and discussion on the themes addressed in this manuscript.
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