distinguishing features of autism in boys with fragile x syndrome

Distinguishing features of autism in boys with fragileX syndromejir_1315 894..905

M. Brock & D. Hatton

FPG Child Development Institute, University of North Carolina, Chapel Hill, NC, USA

Abstract

Background Males with fragile X syndrome andautism (FXS/autism) represent a distinct subgroupof males with FXS at risk for markedly poorer out-comes. Early identification and intervention canimprove outcomes for males with autism spectrumdisorder.Method To advance the development of a special-ised autism screening tool for young males withFXS that could assist in early identification, back-ward regression was used to identify the combina-tion of parent-report questionnaire items that bestpredicted autism symptoms in a sample of 60 maleswith FXS, ages 4–18 years old.Results Both social and repetitive behaviours dis-tinguished males with FXS/autism, with repetitivebehaviours playing a more prominent role than pre-viously documented in the literature.Conclusions Healthcare workers and early interven-tionists may be able to interview parents about afew key behaviours to determine if young child withFXS should be formally evaluated for autism.Evidence-based practices identified for childrenwith autism spectrum disorder can be implementedas early as possible.

Keywords autism, autism screening, earlyidentification, fragile X syndrome

Introduction

Males with fragile X syndrome (FXS) are at higherrisk for autism compared with typically developingchildren (Rogers et al. 2001; Demark et al. 2003;Hagerman 2006; Hatton et al. 2006; Lewis et al.2006; Clifford et al. 2007) and children with othercommon (>1/10 000) developmental disabilities(Collacott et al. 1992; Kent et al. 1999; Abrahams &Geschwind 2008). Males with comorbid FXS andautism (FXS/autism) are likely to have poorer out-comes than males with only FXS (Bailey et al.2000, 2001; Rogers et al. 2001; Hatton et al. 2002,2006; Kau et al. 2004; Lewis et al. 2006) or onlyautism (Bailey et al. 2000; Rogers et al. 2001).Because research demonstrates that early interven-tion improves outcomes for young children withautism (National Research Council 2001), it isimperative that males with FXS/autism be identifiedas early as possible.

The purpose of this study was to review researchliterature to identify behaviours that appear to dis-tinguish males with FXS/autism from those withFXS only and to determine if these behavioursactually identify a subgroup with FXS/autism in asample of 60 males with FXS. Based on a literature

Correspondence: Mr Matthew Brock, University of North Caro-lina, Campus Box 8040, Chapel Hill, NC 27599-8040, USA(e-mail: [email protected]).

Journal of Intellectual Disability Research doi: 10.1111/j.1365-2788.2010.01315.x

volume 54 part 10 pp 894–905 october 2010894

© 2010 The Authors. Journal of Intellectual Disability Research © 2010 Blackwell Publishing Ltd

review of specific behavioural features that appearedto distinguish males with comorbid FXS/autismfrom males with only FXS, specific items frombehavioural questionnaires were selected as poten-tial predictors of autism, and backwards regressionanalysis was used to identify a combination of itemsthat best predicted autistic status.

Fragile X syndrome, the most common inheritablegenetic cause of intellectual disability (ID), affectsapproximately 1 in 2500 males (Crawford 2001).FXS is caused by an abnormal nucleotide repetitionat Xq27.3 on the X chromosome that impairs theproduction of fragile X mental retardation protein,resulting in abnormalities in brain development andfunction. Physical characteristics in males with FXSmay include unusually high foreheads, asymmetricalfaces, large jaws, long protruding ears and large tes-ticles after puberty. Males, having only one X chro-mosome, are more frequently affected by FXS andtend to have more severe impairments (Crawford).Because of its known genetic underpinnings, FXScan be detected through DNA tests using bloodsamples, or prenatally through chorionic villus sam-pling or amniocentesis (Crawford).

Unlike FXS, a definitive genetic marker forautism has not been identified; current researchsuggests that autism most likely involves multiplegenes (Happé et al. 2006). Without a known aetiol-ogy, autism is defined behaviourally by impairmentsin social interaction and communication, and byrestricted interests and/or repetitive behaviours(APA 2000).

The reported prevalence of autism in males withFXS ranges from 18% to 47%, depending on thediagnostic tool and sample size (Rogers et al. 2001;Demark et al. 2003; Hagerman et al. 2006; Hattonet al. 2006; Lewis et al. 2006; Clifford et al. 2007).When the category is broadened to include autismspectrum disorders (ASDs), estimates are as high as67% (Clifford et al.). Ninety percent of males withFXS display at least one behaviour that is charac-teristic of autism (Hagerman 2002), suggesting thatmost boys with FXS, even those who do not meetcriteria for autism, display some autistic behaviourssuch as gaze aversion (Cohen et al. 1989; Hesslet al. 2006).

Males who meet diagnostic criteria for autismrepresent a distinct subgroup at risk for markedlypoorer outcomes (Bailey et al. 2000, 2001; Rogers

et al. 2001; Hatton et al. 2002, 2006; Kau et al.2004; Lewis et al. 2006). Males with FXS/autismhad poorer social and communication skills andgreater cognitive impairment (Bailey et al. 2000;Rogers et al. 2001), lower adaptive behaviour scores(Bailey et al. 2001; Rogers et al. 2001; Hatton et al.2003; Kau et al. 2004) and more problem behav-iours (Hatton et al. 2002; Kau et al. 2004) thanmales with FXS alone.

Identification of autism in young males with FXS

There is consensus that early identification andintervention lead to improved outcomes for childrenwith autism (National Research Council 2001).Even though FXS is a known risk factor for autism,there is no simple screening instrument specific toFXS that can be used to efficiently aid in the earlyidentification process. Because 90% of males withFXS display at least one autistic behaviour (Hager-man 2002), and 67% eventually meet criteria forautistic spectrum disorder (Clifford et al. 2007),those who will later display the most severe autisticbehaviour and meet criteria for autism disorder maybe difficult to distinguish at a young age. Infantswho are later diagnosed with autism may moreclosely resemble infants with developmental delaysthan infants with idiopathic autism (Baranek et al.2005).

To date, the only published study to use anautism screening tool with a group of males withFXS involved the Checklist for Autism in Toddlers(CHAT; Baron-Cohen et al. 1992), a screeningtool with 14 yes/no items intended to be used bygeneral practitioners to screen for autism during18-month developmental check-ups. The CHATonly correctly flagged 50% of children who metDSM-IV autism criteria (Scambler et al. 2007).Although sensitivity was poor, the CHAT washighly specific (100%). Scambler and his col-leagues used a relatively small sample of childrenwith FXS (n = 17), so generalisation of their find-ings may be limited. Studies of males with idio-pathic autism demonstrated a similarly high levelof specificity (98%) and an even lower level ofsensitivity (38%) (Baron-Cohen et al.). The CHATdoes not provide an ideal level of sensitivity forscreening males who already have a known riskfactor for autism, such as FXS.

895Journal of Intellectual Disability Research volume 54 part 10 october 2010

M. Brock & D. Hatton • Distinguishing features


Selecting screening items: features unique tomales with FXS/Autism

In sum, there is a body of evidence demonstratingthat males with comorbid FXS/autism are at riskfor markedly poorer outcomes than males withFXS alone, and there is reason to believe thatearly identification and intervention would improvethese outcomes. However, accurately screeningmales with FXS at an early age for autismremains a challenge, as current screening toolsmay not be adequate. To advance understandingof young males with FXS who will be later diag-nosed with autism, a systematic review of theresearch was used to identify the specific behav-iours and risk factors that differentiate this groupby area of impairment: communication, socialbehaviour, and repetitive behaviour and restrictedinterests. Studies were included that had samplesof 15 or more young males with FXS, directlycompared a group of males with comorbid FXS/autism with those with only FXS, demonstratedsignificant differences between groups usingaccepted statistical methods, and were publishedafter the year 2000. Most of the reviewed studiescompared individuals with FXS with and withoutautistic disorder, although some use the broaderclassification of ASD, and are explicitly noted.

Communication

To date, no factors relating to communication thatconsistently predict autism status in males withFXS across studies have been reported. Someresearchers have found that while most boys withFXS score higher on receptive than expressive lan-guage measures, boys with FXS/autism lack thisrelative strength in receptive language (Philofskyet al. 2004; Lewis et al. 2006). However, otherresearchers using the same or similar measures didnot find significant differences between receptiveand expressive language skills (Roberts et al. 2001;Price et al. 2007). Even if findings were similaracross studies, most of the researchers describedbroad findings detected only after administeringentire diagnostic batteries; closer examination ofsub-scales and individual items did not reveal anyspecific features that were strong predictors ofautism.

Social behaviour

A number of research groups suggest that socialbehaviour may predict autism status in males withFXS (Kau et al. 2004; Kaufmann et al. 2004;Budimirovic et al. 2006; Roberts et al. 2007). Adap-tive socialisation and social withdrawal have beenidentified as independent predictors of ASD in FXS(Budimirovic et al. 2006), with the most predictiveitems relating to recognising and responding toemotions, and social avoidance (Kau et al. 2004;Budimirovic et al. 2006). Roberts et al. (2007) theo-rised that while most males with FXS are sociallyanxious and withdraw in novel situations, maleswith FXS/autism are more likely to display socialwithdrawal regardless of whether the situation isnovel or familiar. When Kaufmann et al. (2004)analysed items from the Autism DiagnosticInterview-Revised items reflecting imaginative playand peer interaction were most predictive of autismdiagnosis (Kaufman et al.).

Repetitive behaviour and restricted interests

No studies meeting the criteria for this literaturereview were found that suggest repetitive behavioursand restricted interests predict autism status inmales with FXS. Although Baranek et al. (2005) didnot compare subgroups of males with FXS withand without autism, their comparison of very youngmales with FXS, idiopathic autism and other devel-opmental disabilities yielded some unique distin-guishing features. Using retrospective video analysis,they found that 9- to 12-month-old males with FXSwere best distinguished from males with otherdevelopmental disabilities by unusual object play(including spinning objects) and unusual motor pat-terns (including repetitive leg movements). Becausethese features relate to repetitive behaviours andrestricted interests, a defining category of autisticbehaviour, it is possible that they might distinguishmales with FXS/autism from males with only FXS.

Research aims

If males with FXS/autism are to achieve optimaloutcomes, early identification and intervention areparamount. Current autism screening tools are notsensitive enough to identify the majority of maleswith FXS who will later be diagnosed with autism




(Scambler et al. 2007). A review of the literaturedemonstrates that behavioural features may distin-guish males with FXS/autism. The purpose of thisstudy was to identify parent questionnaire itemsrelated to these distinguishing traits and to deter-mine the extent to which they predict autismsymptom severity in males with FXS. Parent ques-tionnaires have the potential to provide a quick andefficient method for identifying males with FXSwho appear to be at risk for autism, leading toearlier referrals for formal autism assessment. Basedon the most promising findings in the literature,three major categories of behaviour were identifiedas being most likely to distinguish males with FXS/autism: (1) recognising emotions in self andothers (Kau et al. 2004; Kaufmann et al. 2004;Budimirovic et al. 2006); (2) social anxiety, with-drawal and avoidance (Roberts et al. 2001; Kauet al. 2004; Kaufmann et al. 2004; Budimirovic et al.2006); and (3) imaginative play, social play andobject play (Kaufmann et al. 2004; Baranek et al.2005).

Method

Participants

The participants were a subgroup from the Caro-lina Fragile X Project who recently participated ina pilot study of the genetics of ASD in males withFXS, for which informed consent had beenobtained. De-identified data were used for thisstudy. Participants included 60 boys with fullmutation FXS between 4 and 18 years old(M = 11.9; SD = 4.4) who were of EuropeanAmerican (n = 53) and African American (n = 7)descent. The process by which participants wererecruited and identified as having full mutationFXS has been described elsewhere (Hatton et al.2006). Three parent-report measures were col-lected for each participant: the Social Responsive-ness Scale (SRS; Constantino & Gruber 2005),the Social Communication Questionnaire (SCQ;Rutter et al. 2003) and the Repetitive BehaviorScale (RBS; Bodfish et al. 1999). In addition,scores from the Childhood Autism Rating Scale(CARS; Schopler et al. 1988) were used todescribe autism symptom severity.

Measures

The SRS (Constantino & Gruber 2005) is a65-item parent-report questionnaire designed toscreen for autism, Aspergers, PDD-NOS and schiz-oid personality disorder of childhood. Items arerated on a 5-point Likert scale and summed to yieldfive sub-scale scores (social awareness, social cogni-tion, social communication, social motivation andautistic mannerisms) and a total score.

A companion screening tool for the AutismDiagnostic Interview-Revised (Lord et al. 1994),the SCQ (Rutter et al. 2003) is a 40-item parent-report questionnaire designed to provide a dimen-sional measure of autism symptoms, compareoverall levels of autism symptoms across samples,and to approximate severity of autism symptoms.Parents respond yes or no to each item, and itemsare summed to yield three sub-scale scores (recipro-cal social interaction; communication; andrestricted, repetitive and stereotyped patterns ofbehaviour) and a total score.

The RBS (Bodfish et al. 1999) assesses the pres-ence and severity of abnormal repetitive behavioursand restricted interests associated with disorderssuch as ASDs. Items are rated on a 5-point Likertscale and summed to yield five sub-scale scores(stereotypic behaviour, self-injurious behaviour,compulsive behaviour, ritualistic/sameness behav-iour and restricted interests) and a total score usinga revised scoring algorithm. The RBS is an experi-mental instrument, and its psychometric propertieshave been assessed in an independent validationstudy (Lam & Aman 2007). RBS sub-scales showedhigh internal consistency (0.78–0.91) and fair togood inter-rater reliability (0.57–0.73; Lam & Aman2007).

On the CARS (Schopler et al. 1988), profession-als rate 15 items on a scale from 1 (within normallimits) to 4 (severely abnormal) to measure autismsymptom severity, with individuals scoring 30 orabove being considered autistic (Schopler et al.).One of the advantages of the CARS is that it yieldsa single score that represents a continuous measureof autistic behaviour (Bailey et al. 2001; Hattonet al. 2009). Multiple research groups have used theCARS effectively as a measure of autism symptomseverity in subjects with FXS (Levitas et al. 1983;Bailey et al. 2001; Demark et al. 2003; Hatton et al.




2009). Because the participants participated in theCarolina Fragile X Project, a longitudinal study, theCARS had been administered on multiple occa-sions. For this analysis, CARS scores were averagedto yield a single score for each participant. Eachsubject had been assessed with the CARS betweenone and eight times (M = 3.5; SD = 1.5), with scoresranging from 17.5 to 50.5 (n = 209; M = 27.2;SD = 5.7). Other research groups have also usedaverage CARS scores for the purpose of statisticalanalysis (Stone & Caro-Martinez 1990). The proce-dure by which CARS scores were obtained and therationale for using the CARS have been describedelsewhere (Hatton et al. 2006).

Procedure

Variables

A systematic literature review identified three majorcategories of behaviour that may distinguish boyswith FXS who have autism: (1) recognising emo-tions in self and others (Kau et al. 2004; Kaufmannet al. 2004; Budimirovic et al. 2006); (2) socialanxiety, withdrawal and avoidance (Roberts et al.2001; Kau et al. 2004; Kaufmann et al. 2004;Budimirovic et al. 2006); and (3) imaginative play,social play and object play (Kaufmann et al. 2004;Baranek et al. 2005). The three parent-reportedquestionnaires (SRS, SCQ and RBS) were exam-ined, and items that related to one of the threeidentified categories of behaviour were identified, assummarised in Tables 1–3.

Statistical analysis

Predictors identified in Table 1 were entered asindependent variables into a regression model withthe CARS total score as the dependent variable,and items with significant unique contributionswere identified using backward regression. Thisprocess was repeated for predictor variables inTables 2 and 3, so that three groups of optimal pre-dictors were identified from each of the threerespective categories. These three groups of predic-tors were entered into a regression model, andbackward regression again was used to find thecombination of predictor items that best explainedthe variance in CARS scores.

In backward regression, a type of stepwise regres-sion, all independent variables are initially includedin the model. The independent variable with thesmallest partial correlation coefficient is identifiedand removed from the model if it fails to meet theselection criterion (P < 0.10). Then a new model isgenerated, and the process is repeated until thevariable with the smallest partial correlation coeffi-cient meets the minimum criterion (Cohen et al.2003). Backward regression was selected for thisanalysis, as opposed to other types of stepwiseregression, because the elimination process effec-tively ranks the independent variables in order ofunique contribution to the model, aiding in theinterpretation of findings.

Regression models were generated for each of thescreening questionnaires (the SCQ and SRS), withthe total score the sole predictor, and the CARStotal score the dependent variable. An R2 statisticwas generated for each regression equation, repre-senting how well each screening tool’s total scorepredicted the variance in CARS scores. Then theprediction ability of the identified combination ofitems was compared with that of the SCQ and SRStotal scores, because the goal is for a specialised

Table 1 Items that relate to recognising emotions in self andothers*†‡

Test/itemnumber Item

SRS7 Is aware of what others are thinking or feelingSRS12 Is able to communicate his or her feelings to

othersSRS15 Is able to understand the meaning of other

people’s tone of voice and facial expressionsSRS26 Offers comfort to others when they are sadSRS38 Responds appropriately to mood changes in

othersSRS60 Is emotionally distant, doesn’t show his or her

feelingsSCQ27 When she/he was 4 to 5, did she/he smile back

if someone smiled at him/her?SCQ31 When she/he was 4 to 5, did she/he ever try to

comfort you when you were sad or hurt?

* Budimirovic et al. 2006.† Kau et al. 2004.‡ Kaufmann et al. 2004.




screening tool to outperform existing screeninginstruments.

Because the wide age range of the sample couldpossibly confound interpretation of the results, thebackward regression analysis was replicated withtwo subgroups sorted by age, to determine if theresults of the analysis would differ for younger andolder males with FXS. The younger group (n = 30)ranged from 4 to 12 years old (M = 7.5; SD = 2.8),while the older group (n = 30) ranged from 13 to 18

years old (M = 15.5; SD = 1.8).

Results

Phase I: regression analysis within categories

First, backward regression was used to identify theoptimal combination of predictors relating to recog-nising emotions in self and others (see Table 1).The identified predictors, SRS12 (does notcommunicate feelings) and SRS26 (does not offercomfort), were somewhat predictive of CARSscores (R2 = 0.28). When backward regression wasused to identify an optimal combination of predic-

Table 2 Items that relate to social anxiety*, withdrawal† andavoidance†‡§


SRS1 Seems much more fidgety in social situations thanwhen alone

SRS3 Seems self-confident when interacting with othersSRS6 Would rather be alone than with othersSRS16 Avoids eye contact or has unusual eye contactSRS23 Does not join group activities unless told to do soSRS27 Avoids starting social interactions with peers or

adultsSRS34 Avoids people who want to be emotionally close

to him or herSRS45 Focuses his or her attention to where others are

looking or listeningSRS64 Is too tense in social settingsSCQ19 Does she/he have any particular friends or best

friend?SCQ20 When she/he was 4 to 5, did she/he ever talk with

you just to be friendly (rather than to getsomething)?

SCQ26 When she/he was 4 or 5, did she/he usually lookat you directly in the face when doing thingswith you or talking with you?

SCQ28 When she/he was 4 to 5, did she/he ever showyou things that interested her/him to engageyour attention?

SCQ36 When she/he was 4 to 5, did she/he seeminterested in other children of approximatelythe same age whom she/he did not know?

SCQ37 When she/he was 4 to 5, did she/he respondpositively when another child approachedher/him?

* Roberts et al. 2001.† Budimirovic et al. 2006.‡ Kau et al. 2004.§ Kaufmann et al. 2004.

Table 3 Items that relate to imaginative play*, social play* and objectplay†


SRS20 Shows unusual sensory interests or strange waysof playing with toys

SRS22 Plays appropriately with children his or her ownage

SRS40 Is imaginative, good at pretendingSCQ12 Has she/he ever seemed more interested in parts

of a toy or an object (e.g. spinning the wheelsof a car), rather than using the object as it wasintended?

SCQ29 When she/he was 4 to 5, did she/he ever offer toshare things other than food with you?

SCQ34 When she/he was 4 to 5, did she/he everspontaneously join in and try to copy theactions of social games, such as The MulberryBush or London Bridge Is Falling Down?

SCQ35 When she/he was 4 to 5, did she/he play pretendor make-believe games?

SCQ39 When she/he was 4 to 5, did she/he ever playimaginative games with another child in a waythat you could tell that they each understoodwhat the other was doing?

SCQ40 When she/he was 4 to 5, did she/he playcooperatively in games that required joining inwith a group of other children such ashide-and-seek or ball games?

RBS5 Object usage (spins or twirls objects, twiddles orslaps or throws objects, lets objects fall out ofhands)

RBS42 Preoccupation with part(s) of object rather thanthe whole object

RBS43 Fascination, preoccupation with movement/thingsthat move (e.g. fans, clocks)

* Kaufmann et al. 2004.† Baranek et al. 2005




tors that relate to social anxiety, withdrawal andavoidance (see Table 2), three items were identified:SRS23 (avoids group activities), SRS45 (lack ofresponse to joint attention) and SCQ19 (lack ofbest friend). This combination of items was moder-ately predictive of CARS scores (R2 = 0.44). Finally,three items were identified that relate to imaginativeplay, social play and object play (see Table 3):SCQ34 (lack of participation in social games),RBS5 (stereotypical object usage) and SRS20

(unusual sensory interests or object play). Thiscombination of three items was moderately predic-tive of CARS scores (R2 = 0.48).

Phase II: regression analysis among categories

The eight items identified from the three categorieswere entered into a backward regression model with

CARS scores as the dependent variable, as sum-marised in Table 4. Four items were identified as anoptimal combination of predictors: SRS23 (avoidsgroup activities), SRS45 (lack of response to jointattention), RBS5 (stereotypical object usage) andSRS26 (does not offer comfort). These items werehighly intercorrelated (see Table 5) but representedall three categories. This optimal combination ofpredictors explained more variance in the CARSscores (R2 = 0.59) than the SCQ (R2 = 0.24) or SRS(R2 = 0.27) for this sample of boys with FXS.

Although the results from the backward regres-sion with younger and older subgroups were notidentical to the findings for the larger group, theywere similar in that they both identified items thatrelate to social impairments and repetitive behav-iours. For the younger subgroup, the optimal com-

Table 4 Phase II backward regression statistics

Model Item b SE t P

1 CARS ~ SRS23 + SRS45 + SCQ19 + SRS20 + SRS34 + RBS5 + SRS12* + SRS26 SRS23 1.308 0.489 2.677 0.010SRS45 -1.435 0.588 -2.441 0.018SCQ19 1.449 0.991 1.463 0.150SRS20 0.744 0.559 1.332 0.189SRS34 0.433 0.566 0.765 0.448RBS5 1.260 0.685 1.838 0.072SRS12* 0.114 0.648 0.177 0.860SRS26 -1.466 0.525 -2.795 0.007

2 CARS ~ SRS23 + SRS45 + SCQ19 + SRS20 + SRS34* + RBS5 + SRS26 SRS23 1.293 0.476 2.714 0.009SRS45 -1.423 0.578 -2.460 0.017SCQ19 1.458 0.980 1.488 0.143SRS20 0.729 0.547 1.334 0.188SRS34* 0.459 0.542 0.848 0.400RBS5 1.251 0.677 1.848 0.070SRS26 -1.464 0.519 -2.819 0.007

3 CARS ~ SRS23 + SRS45 + SCQ19 + SRS20* + RBS5 + SRS26 SRS23 1.334 0.473 2.823 0.007SRS45 -1.422 0.577 -2.465 0.017SCQ19 1.345 0.968 1.389 0.171SRS20* 0.586 0.519 1.131 0.263RBS5 1.288 0.674 1.911 0.062SRS26 -1.342 0.498 -2.696 0.009

4 CARS ~ SRS23 + SRS45 + SCQ19* + RBS5 + SRS26 SRS23 1.453 0.462 3.146 0.003SRS45 -1.542 0.569 -2.713 0.009SCQ19* 1.536 0.956 1.606 0.114RBS5 1.638 0.601 2.727 0.009SRS26 -1.364 0.499 -2.734 0.008

5 CARS ~ SRS23 + SRS45 + RBS5 + SRS26 SRS23 1.572 0.462 3.400 0.001SRS45 -1.717 0.566 -3.033 0.004RBS5 1.719 0.607 2.832 0.006SRS26 -1.455 0.503 -2.895 0.005

* Least significant predictor in model with P > 0.10; removed from subsequent models.




bination of predictors included items representingincreased repetitive behaviour (RBS5 – stereotypicalobject usage) and social impairments (SCQ19 –lack of best friend, SCQ34 – lack of participation insocial games). In the older subgroup, optimal pre-dictors included items that related to repetitivebehaviour and restricted interests (SRS20 – unusualsensory interests or object play, RBS43 – preoccu-pation with movement), social anxiety (SRS23 –avoids group activities) and social avoidance(SRS45 – lack of response to joint attention). Theassociations between CARS scores and items repre-senting repetitive play and social anxiety were stron-ger in the older subgroup.

Sensitivity and specificity

When the analysis was originally planned, comput-ing composite scores and analysing sensitivity andspecificity were not included, as differences inscaling between the SCQ (2-point yes/no scale) andthe SRS and RBS (5-points Likert scales) posedmethodological problems. However, the final back-ward regression analysis resulted in items from onlythe equivalently scaled SRS and RBS question-naires. Items were summed to compute a compositescore that could range from 0 to 16.

Composite scores were calculated for all subjects.Then, subjects were classified into two groups(‘Autism’ and ‘No Autism’) based on CARS scores.Subjects with CARS scores of 30 or greater wereclassified into the ‘Autism’ group, while subjectswith CARS scores less than 30 were classified intothe ‘No Autism’ group. This resulted in 17 subjects(28%) in the ‘Autism’ group, and 43 subjects (72%)in the ‘No Autism’ group. Then, the composite

scores were analysed as predictors of autism classifi-cation. Different cut-off scores were considered bycalculating how many subjects would be correctlysorted into each group (‘Autism’ or ‘No Autism’)as a function of the cut-off score (see Table 6). Toidentify the optimal cut-off score, the sensitivity(percentage of subjects correctly identified in the‘Autism’ group) and specificity (percentage of sub-jects correctly identified in the ‘No Autism’ group)were summed for each cut-off score. The optimalcut-off score with the greatest sum, 6, was bothhighly sensitive (83%) and specific (79%).

Discussion

The items identified in the final phase of analysis,SRS23 (avoids group activities), SRS45 (lack ofresponse to joint attention), RBS5 (stereotypicalobject usage) and SRS26 (does not offer comfort),represent the combination of items that best predictautism symptom severity as measured by theCARS. This combination of items was highly pre-dictive (R2 = 0.59), markedly superior to the abilityof the SCQ (R2 = 0.24) or SRS (R2 = 0.27) in pre-dicting CARS scores in this sample of males withFXS. As a screening tool, these four items achievehigh sensitivity (82%) and specificity (79%) asshown in Table 6. However, these items were identi-fied because of their unique prediction abilities withthis particular sample, and it is not clear whetherthey would perform similarly in a different sampleof males with FXS. Despite this limitation, theresults of this study suggest that specific types ofparent-report questionnaire items may be useful aspart of a screening tool and provide evidence that

Table 5 Intercorrelations between items identified in Phase II analysis

Item SRS23 SRS45 RBS5 SRS26

SRS23 – Does not join group activities unless told to do so – -0.104 0.252 -0.145SRS45 – Focuses his or her attention to where others are looking or listening – -0.352** 0.205RBS5 – Object usage (spins or twirls objects, twiddles or slaps or throws objects, lets

objects fall out of hands)– -0.443**

SRS26 – Offers comfort to others when they are sad –

* P < 0.05,** P < 0.01.




repetitive behaviours may play a surprisingly promi-nent role in distinguishing males with FXS whohave autism.

An item representing repetitive behaviour, RBS5

(stereotypical object usage) had the highest partialcorrelation coefficient (b = 1.719) and was the mostpowerful single predictor in the final model.Although one research group (Baranek et al. 2005)found that early object play may differentiateinfants with FXS from infants with other develop-mental disabilities, their analysis did not includesubgroups of FXS subjects with and withoutautism. This study may be the first to supportrepetitive behaviours as a key determinant of autismin FXS. This finding may be extremely valuable, asrepetitive behaviours are observable at a young ageand could play an important role in earlyidentification.

The results of the regression analysis for olderand younger subgroups did not markedly differfrom the results of the analysis with the full sample.Repetitive behaviours and restricted interests wereassociated with increased CARS scores for bothsubgroups, suggesting that repetitive behaviours dis-tinguish males with FXS/autism regardless of age.The types of items in the two subgroups differedqualitatively; while the younger subgroup was dis-tinguished by repetitive motor behaviours, the oldersubgroup was distinguished by restricted interestsand sensory behaviours. Also, the associationbetween CARS scores and items representing socialanxiety was stronger in the older subgroup, similarto previous findings that social withdrawal

(Budimirovic et al. 2006) and overall autism symp-toms (Hatton et al. 2006) increase with age in maleswith FXS/autism. These qualitative differences inthe types of repetitive behaviours, restricted inter-ests and social behaviours that distinguished thetwo subgroups suggests that a specialised screeninginstrument would be most effective if it were spe-cific to a certain age group.

While identification of repetitive behaviours as akey determinant of autism in FXS is an emergentfinding, differences in social behaviours have beenestablished as a distinguishing feature of males withFXS/autism (Kau et al. 2004; Kaufmann et al. 2004;Budimirovic et al. 2006; Roberts et al. 2007). Inter-related social impairments and repetitive behavioursdistinguished males with more severe autism symp-toms. Increased repetitive behaviour (RBS5 –stereotypical object usage) was associated withincreased social impairment, specifically lack ofjoint attention (SRS45 – lack of response to jointattention) and lack of empathy (SRS26 – does notoffer comfort). The association between repetitivebehaviour and joint attention may stem fromimpairments in imitation skills. Children with FXS/autism tend to have more deficits in imitation skills,and these deficits have been linked to impairmentsin object play and joint attention (Rogers et al.2003). Impaired joint attention skills likely result infewer opportunities to practice imitation skills. Defi-cits in imitation may decrease a child’s ability toacquire object play skills, resulting in an increase inrepetitive behaviours. The association betweenrepetitive behaviour and lack of empathy may stem

Table 6 Sensitivity and specificity of summed score of identified items (n = 60)

Cut-off

Subjects correctlyidentified in ‘Autism’Group

Subjects correctlyidentified in ‘No Autism’Group Sensitivity Specificity

11 1 43 6% 100%10 5 43 29% 100%9 6 43 35% 100%8 9 40 53% 93%7 11 38 65% 88%6* 14 34 82% 79%5 17 24 100% 56%

* Optimal cut-off score (yielded highest sum of sensitivity + specificity).




from impairments in theory of mind. Impairmentsin theory of mind, or ‘the ability to impute mentalstates to oneself and to others’ (Baron-Cohen et al.1985, p. 39), have been found to discriminate maleswith FXS who do and do not have autism, evenafter controlling for cognitive functioning (Lewiset al. 2006). Clearly, empathy, which requiresunderstanding and reacting to someone else’smental state, is closely tied to theory of mind.Theory of mind may be indirectly tied to repetitivebehaviour; males without theory of mind may notrespond to social stimuli because it is not motivat-ing, but instead engage in more motivating self-stimulating repetitive behaviours.

Limitations and directions for future research

Several limitations should be considered before gen-eralising the findings of this study. First, candidatequestionnaire items were chosen based on researchreports from multiple groups. Ideally, a factoranalysis of all the questionnaire items would havebeen conducted to aid in this process, but the rela-tively small sample size (n = 60) did not permit thistype of analysis. Next, when exploring sensitivityand specificity, CARS scores of 30 or greater wereused to assign autism status, rather than a con-firmed formal diagnosis from a team with extensiveexperience in diagnosing autism. In addition, theCARS is not considered a gold standard tool fordiagnosing autism; it is better established as a con-tinuous measure of autism symptom severity (Baileyet al. 2001; Demark et al. 2003; Hatton et al. 2009).Also, while the combination of identified question-naire items is highly predictive of both autism statusand symptom severity in this particular sample,these findings should be replicated with a differentsample before being generalised to the FXSpopulation.

Conclusion

Both social behaviour (avoiding group activities,lack of response to joint attention, lack of empathy)and repetitive behaviours distinguish males withFXS/autism from males with only FXS. Repetitivebehaviours related to stereotyped object play maybe the best single predictor of autism in males with

FXS. Healthcare workers and early interventionistsmay be able to interview parents about a few keybehaviours to determine if young children withFXS should be formally evaluated for autism.Better identification of males with FXS/autism willallow for increased implementation of evidence-based practices that have been demonstrated toimprove outcomes for individuals who have autism.

Acknowledgements

We would like to thank the families that partici-pated in the Carolina Fragile X Project, along withfunding agencies, the National Institute of ChildHealth and Human Development, the NationalInstitute of Health (HD0031110-35SI), and AutismSpeaks. In addition, we would like to thank SamuelOdom, PhD, Harriet Boone, PhD and Patrick Sulli-van, MD, for their contributions to this project.

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Accepted 20 July 2010




distinguishing features of autism in boys with fragile x syndrome

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