Autism spectrum disorder in Down syndrome: cluster analysis of Aberrant Behaviour Checklist data supports diagnosis.
Standard autism checklists work for kids with Down syndrome and reveal two subtypes, but social deficits look milder than in autism-only peers.
01Research in Context
What this study did
The team fed Aberrant Behaviour Checklist scores into a cluster program.
All participants had Down syndrome and an autism diagnosis.
The math grouped kids to see if DSM criteria still made sense for this dual-diagnosis group.
What they found
The clusters lined up with the DSM-based autism labels.
Two clear sub-groups appeared inside the Down-plus-autism sample.
This tells clinicians the usual autism checklist works even when Down syndrome is present.
How this fits with other research
Johnson et al. (2009) showed the same thing two years earlier with smaller case studies.
Schwichtenberg et al. (2013) later counted that about one in five people with Down syndrome screen positive for autism, backing the overlap this paper validates.
Amaral et al. (2017) adds a twist: kids with Down syndrome who meet autism criteria show milder social-interaction problems than kids with autism alone.
So the diagnosis holds, but expect softer social deficits than in typical autism.
Why it matters
You can trust standard autism tools for kids who also have Down syndrome.
Keep an eye on social skills; they may be stronger than in autism-only peers, so adjust peer-training goals.
If a child shows sudden skill loss, rule out autism first—Udhnani et al. (2025) found it is the top alternative diagnosis in Down regression cases.
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02At a glance
03Original abstract
BACKGROUND: The diagnostic validity of autism spectrum disorder (ASD) based on Diagnostic and Statistical Manual of Mental Disorders (DSM) has been challenged in Down syndrome (DS), because of the high prevalence of cognitive impairments in this population. Therefore, we attempted to validate DSM-based diagnoses via an unbiased categorisation of participants with a DSM-independent behavioural instrument. METHODS: Based on scores on the Aberrant Behaviour Checklist - Community, we performed sequential factor (four DS-relevant factors: Autism-Like Behaviour, Disruptive Behaviour, Hyperactivity, Self-Injury) and cluster analyses on a 293-participant paediatric DS clinic cohort. The four resulting clusters were compared with DSM-delineated groups: DS + ASD, DS + None (no DSM diagnosis), DS + DBD (disruptive behaviour disorder) and DS + SMD (stereotypic movement disorder), the latter two as comparison groups. RESULTS: Two clusters were identified with DS + ASD: Cluster 1 (35.1%) with higher disruptive behaviour and Cluster 4 (48.2%) with more severe autistic behaviour and higher percentage of late onset ASD. The majority of participants in DS + None (71.9%) and DS + DBD (87.5%) were classified into Cluster 2 and 3, respectively, while participants in DS + SMD were relatively evenly distributed throughout the four clusters. CONCLUSIONS: Our unbiased, DSM-independent analyses, using a rating scale specifically designed for individuals with severe intellectual disability, demonstrated that DSM-based criteria of ASD are applicable to DS individuals despite their cognitive impairments. Two DS + ASD clusters were identified and supported the existence of at least two subtypes of ASD in DS, which deserve further characterisation. Despite the prominence of stereotypic behaviour in DS, the SMD diagnosis was not identified by cluster analysis, suggesting that high-level stereotypy is distributed throughout DS. Further supporting DSM diagnoses, typically behaving DS participants were easily distinguished as a group from those with maladaptive behaviours.
Journal of intellectual disability research : JIDR, 2011 · doi:10.1111/j.1365-2788.2011.01465.x