Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach
Authors: Tillmann J. , Uljarevic M. , Crawley D. , Dumas G. , Loth E. , Murphy D. , Buitelaar J. , Charman T. ,   the AIMS-2-TRIALS LEAP group, Ahmad Jumana , Ambrosino Sara , Auyeung Bonnie , Baumeister Sarah , Beckmann Christian , Bourgeron Thomas , Bours Carsten , Brammer Michael , Brandeis Daniel , Brogna Claudia , de Bruijn Yvette , Chakrabarti Bhismadev , Cornelissen Ineke , Acqua Flavio Dell’ , Dumas Guillaume , Ecker Christine , Faulkner Jessica , Frouin Vincent , Garcés Pilar , Goyard David , Hayward Hannah , Hipp Joerg , Johnson Mark H. , Jones Emily J. H. , Kundu Prantik , Lai Meng-Chuan , D’ardhuy Xavier Liogier , Lombardo Michael , Lythgoe David J. , Mandl René , Mason Luke , Meyer-Lindenberg Andreas , Moessnang Carolin , Mueller Nico , O’Dwyer Laurence , Oldehinkel Marianne , Oranje Bob , Pandina Gahan , Persico Antonio M. , Ruggeri Barbara , Ruigrok Amber , Sabet Jessica , Sacco Roberto , Toro Roberto , Tost Heike , Waldman Jack , Williams Steve C. R. , Wooldridge Caroline , Zwiers Marcel P. 
Published: August 2020

Background

Heterogeneity in the phenotypic presentation of autism spectrum disorder (ASD) is apparent in the profile and the severity of sensory features. Here, we applied factor mixture modelling (FMM) to test a multidimensional factor model of sensory processing in ASD. We aimed to identify homogeneous sensory subgroups in ASD that differ intrinsically in their severity along continuous factor scores. We also investigated sensory subgroups in relation to clinical variables: sex, age, IQ, social-communication symptoms, restricted and repetitive behaviours, adaptive functioning and symptoms of anxiety and attention-deficit/hyperactivity disorder.

Methods

Three hundred thirty-two children and adults with ASD between the ages of 6 and 30 years with IQs varying between 40 and 148 were included. First, three different confirmatory factor models were fit to the 38 items of the Short Sensory Profile (SSP). Then, latent class models (with two-to-six subgroups) were evaluated. The best performing factor model, the 7-factor structure, was subsequently used in two FMMs that varied in the number of subgroups: a two-subgroup, seven-factor model and a three-subgroup and seven-factor model.

Results

The ‘three-subgroup/seven-factor’ FMM was superior to all other models based on different fit criteria. Identified subgroups differed in sensory severity from severe, moderate to low. Accounting for the potential confounding effects of age and IQ, participants in these sensory subgroups had different levels of social-communicative symptoms, restricted and repetitive behaviours, adaptive functioning skills and symptoms of inattention and anxiety.

Limitations

Results were derived using a single parent-report measure of sensory features, the SSP, which limits the generalisability of findings.

Conclusion

Sensory features can be best described by three homogeneous sensory subgroups that differ in sensory severity gradients along seven continuous factor scores. Identified sensory subgroups were further differentiated by the severity of core and co-occurring symptoms, and level of adaptive functioning, providing novel evidence on the associated clinical correlates of sensory subgroups. These sensory subgroups provide a platform to further interrogate the neurobiological and genetic correlates of altered sensory processing in ASD.