Introduction
Research into the possible link between Autism Spectrum Disorders (ASD) and Anorexia Nervosa (AN) has grown considerably, with studies showing potential links both in specific characteristics of the two disorders (Oldershaw et al.
2011) and in prevalence rates of co-morbidity or elevated levels of ASD traits within AN populations (for a systematic review see Huke et al.
2013).
AN is a severe eating disorder characterised by body weight significantly lower than the normal range relative to height, fear of gaining weight and undue influence of weight and shape on self-evaluation (APA
2013). It predominantly affects females, with an estimated gender ratio of 10:1 females to males, though epidemiological studies report higher variation of 3:1–12:1 (Raevuori et al.
2014). In contrast, ASD is a pervasive developmental disorder that tends to affect more males than females, with a gender ratio of 3.3:1 males to females being reported in a UK national prevalence study (Mills and Kenyon
2013). Unlike AN, which tends to have its onset in adolescence to early adulthood (Micali et al.
2013), for ASD to be diagnosed, symptoms must be present during the early developmental period. These include persistent difficulties with social communication and interaction and restricted, repetitive behaviours and interests (APA
2013).
Despite these two disorders seeming different, several traits associated with ASD have also been found in AN populations including: difficulties with set-shifting (Tchanturia et al.
2012), the capacity to shift a course of thought or action according to situational demands (Lezak
1995); weak central coherence, the lack of an effect of context or difficulty in taking a global approach (Frith
1989; Lang et al.
2014); difficulties with Theory of Mind (ToM), the ability to infer the mental states of others (Baron-Cohen et al.
1985; Tchanturia et al.
2004); and difficulties with emotional processing (Davies et al.
2011; Russell et al.
2009).
Although there is a male bias in the diagnosis of ASD, recent evidence suggests that it may affect more females than previously thought, with diagnostic criteria and research practices leading to an over-stated gender gap (Goldman
2013; Lai et al.
2015). ASD may present differently in females than in males so that females are often under-diagnosed or mislabelled as having other impairments (Mandy and Tchanturia
2015). For example, females with ASD have been found to have better social skills than their male counterparts (Head et al.
2014) and females with ASD out-perform males on tests of executive functioning (Bolte et al.
2011). Within ASD, typical sex differences between males and females on empathising and systemising are attenuated, with both sexes shifting towards the extreme male brain (Baron-Cohen et al.
2014). However, this study found that significant sex differences within ASD still exist, in the same direction as Healthy Controls (HCs), highlighting the need to consider sex-differences in the diagnosis of ASD.
Females may also display less stereotyped behaviours or restricted interests (Mandy et al.
2012) or their interests may be more subtle or in line with gender stereotypes, making them more difficult to detect (Hiller et al.
2015). While AN tends to develop during adolescence, it may be that the presence of undetected ASD traits in early life interact with socio-cultural pressures to leave females in particular, susceptible to the development of eating disorders. Difficulties with set-shifting and central coherence are consistently reported in adults with AN (for a review, see Roberts et al.
2007) but the evidence in children and adolescents is more mixed (Lang et al.
2014a,
b). However, this may be due to several studies using different experimental paradigms to measure central coherence and set-shifting and under-powered studies, which make the data difficult to interpret or compare. It is therefore still unclear whether children with AN, who are exposed to the effects of starvation for less time, have the same cognitive inefficiencies as adults. Findings on ToM are also mixed, with research not supporting a specific link between ToM and AN (Tchanturia et al.
2004). Research on emotional ToM in AN suggests that problems in this area resolve with weight gain and recovery, supporting the notion that this difficulty is at least exacerbated by the ill state of the disorder (Oldershaw et al.
2010).
The marked overlap in specific characteristics present in both ASD and AN has led to attempts to examine the prevalence of co-morbid ASD or elevated levels of autistic traits within AN. As ASD is considered a dimensional disorder (Bolton et al.
1994; Wiggins et al.
2012) a distinction can be made between ASD as a fully diagnosed clinical disorder and the presence of sub-clinical traits, intermediate between typical functioning and ASD, i.e. elevated levels of ASD traits found in the general population. Studies with AN populations have examined the presence of both co-morbid ASD and elevated levels of autistic traits using several instruments. Studies assessing the prevalence of ASD have tended to use diagnostic criteria (Gillberg et al.
1995; Nilsson et al.
1998,
1999; Rastam
1992; Rastam et al.
2003) and The Asperger Syndrome Diagnostic Interview (Anckarsater et al.
2012; Nilsson et al.
1999; Rastam et al.
2003). A meta-analysis of these prevalence studies found an average ASD prevalence rate of 22.9 % (Huke et al.
2013) in eating disorder populations. The majority of these studies, however, involved the same Swedish community sample and the variety of diagnostic tools used makes it difficult to compare the outcomes. The review only contained studies with adults with AN whereas a prevalence study examining ASD in adolescents with early onset-AN found that a diagnosis of ASD was no more common than in healthy controls (Pooni et al.
2012), although the AN sample did have elevated levels of ASD traits.
A widely used measure of autistic traits is the Autism-Spectrum Quotient (AQ; Baron-Cohen et al.
2001) which was developed to provide a brief, self-report measure of autistic traits for use with adults, rather than for use as a diagnostic tool. When the AQ was initially developed, individuals with high functioning autism (HFA) were found to have a mean score of 35.8 (SD = 6.5) which was significantly higher than the control group who had a mean score of 16.4 (SD = 6.3). Among the controls, but not in the HFA group, men were found to score significantly higher than women. For the original validation study, a cut-off score of 32 was chosen as 80 % of those with HFA scored above this level, while only 2 % of controls did. Test–retest and inter-rater reliability of the AQ were good, and a cut-off score of 32 has acceptably high sensitivity (0.77) and specificity (0.74) (Austin
2005; Woodbury-Smith et al.
2005).
The AQ is a 50-item questionnaire consisting of five domains: social skills; attention switching; attention to detail; communication and imagination. The AQ has reasonable face validity as only 2 % of control participants scored above the clinical cut-off during the original validation study (Baron-Cohen et al.
2001). Items comprising each of the five domains showed moderate to high alpha coefficients, indicating reasonable construct validity. Additional analysis also found the AQ to have moderate accuracy in diagnosis of Asperger’s Syndrome in clinical settings, i.e. correctly identifying a ‘true positive’ case as scoring higher than a ‘true negative’ case (Woodbury-Smith et al.
2005).
In addition to the AQ being used as a screening tool for ASD in the general population, it has been used to a examine ASD traits within clinical groups. For example, in schizophrenia (Mealey et al.
2014; Spek and Wouters
2010; Wouters and Spek
2011) and obsessive compulsive disorder (Cath et al.
2008; Mito et al.
2014). The criterion validity of the AQ has been found to be good, with patients with HFA scoring considerably higher than those with social anxiety disorder or obsessive compulsive disorder (Hoekstra et al.
2008). This demonstrates that the AQ can differentiate between individuals with ASD and other disorders.
To examine whether individuals with AN have elevated levels of ASD traits, relative to the general population, several studies have utilised the AQ or the abbreviated ten-item version, the short Autism Spectrum Quotient (AQ-10; Allison et al.
2012). The AQ-10 has similar sensitivity and specificity to the full version and performed well as a screen for ASD (Booth et al.
2013), with a clinical cut-off of 6 being indicative of ASD. If validated, self-report measures such as the AQ or AQ-10 have the benefit of being cost-effective and time efficient and thus could provide a useful addition to more robust but time-consuming clinical assessments.
The presence of ASD in AN represents a significant treatment challenge (Nielsen et al.
2015) and thus identifying ASD traits within this population is important. However, disentangling the relationship between AN and ASD has proved problematic. The presence of ASD traits in AN appear to differ across different stages of the illness, suggesting that these traits are epiphenomena, arising as a result of the ED and only superficially resembling ASD (Mandy and Tchanturia, Additionally, ASD traits in females are difficult to detect and the presence of a severe eating disorder further complicates the identification and diagnosis of ASD in this group. The presence of eating disorders in individuals with established ASD diagnoses has received less attention than ASD in AN populations. Eating disturbances, including selective eating, are known to be overrepresented in ASD (Rastam
2008) and may serve as a risk factor for the development of a clinical eating disorder, such as AN. Karlsson et al. (
2013) note that eating disturbances in ASD are clinically acknowledged but under-researched. Thus, disentangling the relationship between the two disorders is difficult and it is important to establish the most robust method of identifying ASD traits within AN populations.
The aims of this systematic review and meta-analysis were to: (1) examine whether patients with AN have elevated levels of self-reported autistic traits relative to HCs and (2) compare the use of the AQ and AQ-10 in studies with AN populations (3) Examine whether the AQ or AQ-10 are robust measures of ASD traits in AN populations, given the presence of other co-morbidities, the impact of the eating disorder on these traits and the stability of ASD traits during the course of AN.
Discussion
The aim of this review was to provide a synthesis of the existing literature on the presence of autistic traits in AN, as measured by the self-report AQ or AQ-10. It also aimed to establish whether factors such as age or illness severity predict AQ scores and thus whether the AQ is a robust measure of ASD traits in AN populations. A number of methodological differences across studies were highlighted, including the differing use of either the full AQ or shortened AQ-10, differences in sample size and the way in which confounding factors such as BMI, illness duration, medication and co-morbidities were controlled for within the studies. Data on BMI, gender and illness duration were not sufficient to allow for meta-regression to explore whether these factors predict AQ scores. However, generally, the studies all reported enough information to allow for synthesis and analysis of the data.
The meta-analysis indicates a significant difference between AN patients and HCs on the scores on both the AQ and AQ-10, with AN patients scoring significantly higher, suggesting significant difficulties with social skills, communication and flexibility that present in a manner characteristic of autistic traits. This finding is in line with previous prevalence research in this area, for example the systematic review by Huke et al. (
2013) which reported a mean ASD prevalence rate of 22.9 % in AN, higher than that prevalence of ASD within the general population which is estimated to be 1.1 % in the UK (Mills and Kenyon
2013). However, the findings of this meta-analysis should be interpreted with some caution.
All patients included in the studies were within the acute, ill phase of AN with BMIs considerably lower than the normal range. The highest mean BMI for the clinical group was 16.4, recorded by Courty et al. (
2013); this is considered to be moderate in terms of severity (APA
2013). The lowest mean BMI was reported by Calderoni et al. (
2015) at 14.63, which is considered to be extreme AN (APA). Thus, although in both of these studies patients were recruited from inpatient settings, the severity of the AN may have been different and thus severity of AN may have account for the heterogeneous samples included in this review. Countries differ on the treatments offered to patients at different severities. Even in the UK, where five of the studies were based, there is large variation across services in the treatment patients receive, even within the moderate to severe range of AN (Goddard et al.
2013).
Severity of the eating disorder is an important consideration due to the possibility of ASD traits being exacerbated by starvation or the acute state of the illness (Pellicano and Hiller
2013). With the mean duration of illness across the included studies also varying from 4 to 11 years, the possibility of illness chronicity affecting the results of the AQ cannot be ruled out. As the AQ focuses on current behavioural symptoms, rather than on ASD traits earlier on in life, it is possible that individuals with AN develop autistic-like traits as a result of their eating disorder. Lack of data meant that exploring the impact of BMI or illness duration on AQ scores was not possible within this review. While none of the included studies make claims about the aetiology of elevated autistic traits in AN, this is an important treatment consideration and warrants further investigation.
A study comparing the AQ in ASD and schizophrenia (Lugnegård et al.
2015) found that while individuals with either ASD or schizophrenia score high on the AQ, nine individuals with schizophrenia who also met childhood criteria for ASD did not show significantly higher scores on the AQ than the individuals without ASD. This suggests that the AQ may not be sensitive in differentiating between individuals with ASD and those with other psychiatric disorders. Additionally, it may suggest that AQ scores are not stable over time in psychiatric populations. Research on the stability of autistic traits during development (Whitehouse et al.
2011) found that while these traits appeared to be stable in males, with scores in childhood being correlated with those in adulthood, this was not the case in females. Again, this indicates that autistic traits may present and develop differently in females, making it difficult to interpret whether the elevated AQ scores seen in AN females are a true reflection of elevated autistic traits in this population.
Although age was used within the meta-regression in an attempt to explain heterogeneity between the studies, this was not found to be significant. Despite this, only three studies (Baron-Cohen et al.
2013; Calderoni et al.
2015; Lang et al.
2015) included participants below the age of 18 and when child/adolescent studies were analysed separately, the effect size was smaller. There is a lack of consensus about whether difficulties such as those present in ASD, including social communication and flexibility, are present prior to the onset of the AN or whether they are state dependent, resulting from starvation and an enduring illness and thus not truly autistic in origin (Pellicano and Hiller
2013). Therefore, although the results of this meta-analysis suggest elevated levels of autistic traits in AN, the aetiology of these apparent traits remains obscure. Smaller effect sizes in studies with children and adolescents with AN may indicate less difficulties associated with ASD. This would be concordant with other research in this area, which suggests that the similarities between AN and ASD may be less pronounced in children (Lang et al.
2014a,
b; Pooni et al.
2012; Rhind et al.
2014). However, research in this area is not robust enough to confirm this.
Despite the self-report version of the AQ not being validated with children under the age of 16, only one of the child studies included in this review (Baron-Cohen et al.
2013) used the parental AQ version for younger participants. It is therefore possible that the smaller effect found in child/adolescent studies are due to methodological differences between studies. Given the limitations of current data, interpretation of any differences in findings between adults and young people needs to be cautious. Adult samples tend to consist of more chronically ill individuals and the differences in ASD findings could therefore indicate an illness effect, but equally could be maintaining factors of the eating disorder in those in whom ASD traits are observed. Illness effect could not be explored in the context of this review but could account for the heterogeneity of the included samples, or indeed whether longer illness duration elicits ASD traits. Therefore, further research is needed to determine whether the differences on the AQ and AQ-10 found between AN and HC samples extend to young people and if so, how this should be interpreted.
Analysis of the difference in sub-scale scores on the AQ between the AN and HC groups included only one child study (Calderoni et al.
2015) so it is not possible to draw conclusions about the difference between children and adults on sub-scale scores. The sub-scale analysis did suggest, however, that there was no significant difference in Calderoni et al.’s (
2015) study between AN and HC groups on social skills, attention to detail, communication or imagination, again highlighting the need for further studies with both adult and child samples. Whilst analysis of the AQ sub-scales was limited by fewer studies and large heterogeneity, it indicated that that there was no significant difference between patients with AN and HCs on self-report attention to detail. This is contrary to consistent experimental, performance-based findings that individuals exhibit weaker central coherence, i.e. difficulties with bigger picture thinking relative to controls (For a review, see Lopez et al.
2008). However, Lopez et al. (
2008) concluded that while those with AN have global processing difficulties, the findings on local processing were less clear. Thus, while people with AN may struggle with bigger picture thinking, they may not necessarily favour a detail focused approach. Of the seven items assessing attention to detail on the AQ, six referred to detail focused behaviour and only one referred to bigger picture thinking (Baron-Cohen et al.
2001). This may therefore indicate subtle differences between those with AN and individuals with elevated autistic traits or ASD in this domain.
Another key finding of this meta-analysis is that although AN patients were found to score significantly higher than HCs on the AQ and AQ-10, the mean scores were not high enough to meet the indicated cut-off for ASD, or as high as those with a diagnosis of ASD. For studies using the full version of the AQ, the mean scores ranged from 20.3 to 23.2, whereas 80 % of males and 92.3 % of females with ASD scored above 32 in the originally validation study (Baron-Cohen et al.
2001). Similarly, the studies using the AQ-10 reported mean scores of 3.89 to 4.05 which are below the cut-off of 6 (Allison et al.
2012). It therefore appears that the profile seen in AN represents an intermediate state between HCs and those with clinical ASD. This is not to say that scoring below 32 is not clinically significant. It is known that females with ASD may present differently to males. For example, Lai et al. (
2011) found behavioural sex differences in ASD, as measured by the Autism Diagnostic Observation Schedule (ADOS), a standard clinical assessment tool for ASD, despite males and females not differing on childhood severity of core autistic symptoms. While the AQ was originally validated with just 13 females with Asperger’s/HFA (Baron-Cohen et al.
2001) and the scores of males and females in this group did not differ significantly, evidence suggests that the diagnostic profile of ASD may differ by gender, with females with HFA being less impaired in early social development (McLennan et al.
1993) and certain diagnostic items appear to be much more typical of girls than boys (Kopp and Gillberg
2011).
A more recent, large-scale study of 811 adults with ASD, 454 of whom were female (Baron-Cohen et al.
2014), found that AQ scores were higher in the ASD group than in HCs, with both sexes scoring above the suggested cut-off of 32. Males with ASD scored significantly higher than their female counterparts, indicating the presence of normative sex differences within ASD, adding strength to the notion that sex differences need to be considered in the clinical diagnosis of ASD. Lai et al. (
2011) found that females with ASD had higher levels of self-report autistic traits as measured by the AQ, again suggesting a gender difference in the opposite direction of Baron-Cohen et al.’s (
2014) large scale study.
One possibility is that gender differences in self-focused attention may influence how autistic traits are reported. For example, as females are more self-focused (Ingram et al.
1988) they may be more introspective than males, reported higher levels of autistic traits despite males having equal or more symptoms. This theory has not been tested empirically and is therefore an area which may warrant further research. Differences in self-focused attention may also not account for the differences in reported ASD traits between AN and HC groups as recent research suggests that individuals with AN have lower levels of self-focused attention than HCs, possibly accounted for by differences in executive function (Zucker et al.
2015). However, findings on gender differences in symptoms of ASD are not conclusive, and other research has found no gender difference in the symptoms of ASD, once IQ is controlled for (Holtmann et al.
2007). Further studies are therefore needed to address the differing presentation of ASD in males and females.
Regardless of whether the profiles of males and females with ASD are comparable, the presence of elevated levels of autistic traits in AN, as measured by the AQ or AQ-10, could still indicate the presence of a neurodevelopmental disorder prior to the onset of the eating disorder. Many of the domains measured by the AQ have been found to be impaired in both children and adults with AN including social skills and communication (Doris et al.
2014; Krug et al.
2013) and despite mixed findings, there is evidence of difficulties with attention switching and attention to detail in young people with AN (Lang et al.
2014a,
b). Whilst these difficulties may not be severe enough to warrant exploration or diagnosis of ASD, they could still leave an individual vulnerable to the development of AN (Treasure and Schmidt
2013) and may impact on treatment outcome, making them clinically relevant (Nielsen et al.
2015).
Whilst five of the published studies used the full version of the AQ, only two used the brief AQ-10 so separate analysis of the studies using this version could not be performed. Although the effect size of the AQ-10 studies was still significant, it is not known whether either the AQ-10, or indeed the full version, are specific enough to differentiate between symptoms associated with ASD and those caused by co-morbidities such as obsessive compulsive disorder or social anxiety disorder. Fifty-six percent of inpatients with eating disorders have been found to also have an anxiety disorder (Blinder et al.
2006) and certain items on the AQ may tap into symptoms of anxiety rather than ASD per se. Similarly, self-report measures such as the AQ may not be sensitive enough to detect some of the subtle traits associated with female ASD, for example because of their ability to mask the social difficulties they may experience (Lai et al.
2011).
Relying on self-report measures to estimate the prevalence of ASD with AN may therefore lead to a false impression of the true incidence rate. A recent pilot study (Mandy and Tchanturia
2015) aimed to establish whether females with eating disorders who have social and flexibility problems meet ASD criteria. After assessing ten women using the ADOS 2nd Edition (ADOS-2; Lord et al.
2012), the standard assessment tool of ASD diagnosis, three received an ADOS-2 autism classification, a further two met criteria for ASD and two appeared to have ASD based on clinical reports but did not score above clinical cut-off on the ADOS. Thus, whilst there is evidence that even when using thorough, clinical assessment tools such as the ADOS-2 that a subset of women with AN also have elevated levels of ASD traits, it is not known whether the AQ or AQ-10 can accurately capture this. As the AQ and AQ-10 were designed as screening instruments, rather than diagnostic tools (Baron-Cohen et al.
2001), it may still be beneficial to use them in such a way within AN populations to identify individuals whom would benefit from a full ASD assessment. It would, however, benefit from further validity and reliability for use with this specific population. Future studies looking to address the issue of trait or state differences in levels of autistic traits in AN would benefit from using thorough clinical tools such as the ADOS along with developmental history in order to ascertain whether any difficulties associated with ASD were present prior to the onset of the eating disorder and whether they can be detected by clinical diagnostic tools.
The presence of elevated levels of autistic traits in AN may have important clinical and treatment implications. There is evidence that treatments such as Cognitive Remediation Therapy (CRT) effectively address some of the traits associated with ASD in AN including difficulties with set-shifting and central coherence (For a review see Tchanturia et al.
2014). More recently, Cognitive Remediation and Emotion Skills Training (CREST) has been developed to also target the socio-emotion difficulties found in ASD and AN (Tchanturia et al.
2015; Tchanturia et al.
2014). In the ASD field, work is also underway to tailor existing cognitive behavioural treatments to meet the needs of individuals with ASD and other psychiatric co-morbidities (Spain et al.
2015) and building on this work, there is scope to investigate the effectiveness of existing behavioural treatments for individuals with AN and elevated ASD traits. Additionally, the finding that a high proportion of females with AN also have high levels of autistic traits highlights the importance of considering gender differences in the diagnosis and treatment of ASD. ASD traits have been found to affect outcome in teenage-onset AN (Nielsen et al.
2015) and thus identifying these traits in an accurate and timely manner is of clinical importance.