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Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach
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 homog...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457751/ https://www.ncbi.nlm.nih.gov/pubmed/32867850 http://dx.doi.org/10.1186/s13229-020-00367-w |
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author | Tillmann, J. Uljarevic, M. Crawley, D. Dumas, G. Loth, E. Murphy, D. Buitelaar, J. Charman, T. |
author_facet | Tillmann, J. Uljarevic, M. Crawley, D. Dumas, G. Loth, E. Murphy, D. Buitelaar, J. Charman, T. |
author_sort | Tillmann, J. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7457751 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74577512020-09-02 Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach Tillmann, J. Uljarevic, M. Crawley, D. Dumas, G. Loth, E. Murphy, D. Buitelaar, J. Charman, T. Mol Autism Research 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. BioMed Central 2020-08-31 /pmc/articles/PMC7457751/ /pubmed/32867850 http://dx.doi.org/10.1186/s13229-020-00367-w Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Tillmann, J. Uljarevic, M. Crawley, D. Dumas, G. Loth, E. Murphy, D. Buitelaar, J. Charman, T. Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach |
title | Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach |
title_full | Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach |
title_fullStr | Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach |
title_full_unstemmed | Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach |
title_short | Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach |
title_sort | dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457751/ https://www.ncbi.nlm.nih.gov/pubmed/32867850 http://dx.doi.org/10.1186/s13229-020-00367-w |
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