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Clustering of co-occurring conditions in autism spectrum disorder during early childhood: A retrospective analysis of medical claims data / T. VARGASON in Autism Research, 12-8 (August 2019)
[article]
Titre : Clustering of co-occurring conditions in autism spectrum disorder during early childhood: A retrospective analysis of medical claims data Type de document : Texte imprimé et/ou numérique Auteurs : T. VARGASON, Auteur ; R. E. FRYE, Auteur ; D. L. MCGUINNESS, Auteur ; J. HAHN, Auteur Article en page(s) : p.1272-1285 Langues : Anglais (eng) Mots-clés : k-means clustering autism spectrum disorder co-occurring condition comorbidity medical claims retrospective analysis Index. décimale : PER Périodiques Résumé : Individuals with autism spectrum disorder (ASD) are frequently affected by co-occurring medical conditions (COCs), which vary in severity, age of onset, and pathophysiological characteristics. The presence of COCs contributes to significant heterogeneity in the clinical presentation of ASD between individuals and a better understanding of COCs may offer greater insight into the etiology of ASD in specific subgroups while also providing guidance for diagnostic and treatment protocols. This study retrospectively analyzed medical claims data from a private United States health plan between years 2000 and 2015 to investigate patterns of COC diagnoses in a cohort of 3,278 children with ASD throughout their first 5 years of enrollment compared to 279,693 children from the general population without ASD diagnoses (POP cohort). Three subgroups of children with ASD were identified by k-means clustering using these COC patterns. The first cluster was characterized by generally high rates of COC diagnosis and comprised 23.7% (n = 776) of the cohort. Diagnoses of developmental delays were dominant in the second cluster containing 26.5% (n = 870) of the cohort. Children in the third cluster, making up 49.8% (n = 1,632) of the cohort, had the lowest rates of COC diagnosis, which were slightly higher than rates observed in the POP cohort. A secondary analysis using these data found that gastrointestinal and immune disorders showed similar longitudinal patterns of prevalence, as did seizure and sleep disorders. These findings may help to better inform the development of diagnostic workup and treatment protocols for COCs in children with ASD. Autism Res 2019, 12: 1272-1285. (c) 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Medical conditions that co-occur with autism spectrum disorder (ASD) vary significantly from person to person. This study analyzed patterns in diagnosis of co-occurring conditions from medical claims data and observed three subtypes of children with ASD. These results may aid with screening for co-occurring conditions in children with ASD and with understanding ASD subtypes. En ligne : http://dx.doi.org/10.1002/aur.2128 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=405
in Autism Research > 12-8 (August 2019) . - p.1272-1285[article] Clustering of co-occurring conditions in autism spectrum disorder during early childhood: A retrospective analysis of medical claims data [Texte imprimé et/ou numérique] / T. VARGASON, Auteur ; R. E. FRYE, Auteur ; D. L. MCGUINNESS, Auteur ; J. HAHN, Auteur . - p.1272-1285.
Langues : Anglais (eng)
in Autism Research > 12-8 (August 2019) . - p.1272-1285
Mots-clés : k-means clustering autism spectrum disorder co-occurring condition comorbidity medical claims retrospective analysis Index. décimale : PER Périodiques Résumé : Individuals with autism spectrum disorder (ASD) are frequently affected by co-occurring medical conditions (COCs), which vary in severity, age of onset, and pathophysiological characteristics. The presence of COCs contributes to significant heterogeneity in the clinical presentation of ASD between individuals and a better understanding of COCs may offer greater insight into the etiology of ASD in specific subgroups while also providing guidance for diagnostic and treatment protocols. This study retrospectively analyzed medical claims data from a private United States health plan between years 2000 and 2015 to investigate patterns of COC diagnoses in a cohort of 3,278 children with ASD throughout their first 5 years of enrollment compared to 279,693 children from the general population without ASD diagnoses (POP cohort). Three subgroups of children with ASD were identified by k-means clustering using these COC patterns. The first cluster was characterized by generally high rates of COC diagnosis and comprised 23.7% (n = 776) of the cohort. Diagnoses of developmental delays were dominant in the second cluster containing 26.5% (n = 870) of the cohort. Children in the third cluster, making up 49.8% (n = 1,632) of the cohort, had the lowest rates of COC diagnosis, which were slightly higher than rates observed in the POP cohort. A secondary analysis using these data found that gastrointestinal and immune disorders showed similar longitudinal patterns of prevalence, as did seizure and sleep disorders. These findings may help to better inform the development of diagnostic workup and treatment protocols for COCs in children with ASD. Autism Res 2019, 12: 1272-1285. (c) 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Medical conditions that co-occur with autism spectrum disorder (ASD) vary significantly from person to person. This study analyzed patterns in diagnosis of co-occurring conditions from medical claims data and observed three subtypes of children with ASD. These results may aid with screening for co-occurring conditions in children with ASD and with understanding ASD subtypes. En ligne : http://dx.doi.org/10.1002/aur.2128 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=405 Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder / Xiaonan GUO in Molecular Autism, 13 (2022)
[article]
Titre : Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder Type de document : Texte imprimé et/ou numérique Auteurs : Xiaonan GUO, Auteur ; Guangjin ZHAI, Auteur ; Junfeng LIU, Auteur ; Yabo CAO, Auteur ; Xia ZHANG, Auteur ; Dong CUI, Auteur ; Le GAO, Auteur Article en page(s) : 52 p. Langues : Anglais (eng) Mots-clés : Humans Male Child Autism Spectrum Disorder/diagnostic imaging Brain Mapping/methods Magnetic Resonance Imaging/methods Brain/diagnostic imaging Autistic Disorder Neural Pathways/diagnostic imaging Autism spectrum disorder Functional connectivity Functional magnetic resonance imaging Subtype k-means clustering Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks. METHODS: Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model. RESULTS: Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively. LIMITATIONS: Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity. CONCLUSIONS: These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD. En ligne : http://dx.doi.org/10.1186/s13229-022-00535-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=491
in Molecular Autism > 13 (2022) . - 52 p.[article] Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder [Texte imprimé et/ou numérique] / Xiaonan GUO, Auteur ; Guangjin ZHAI, Auteur ; Junfeng LIU, Auteur ; Yabo CAO, Auteur ; Xia ZHANG, Auteur ; Dong CUI, Auteur ; Le GAO, Auteur . - 52 p.
Langues : Anglais (eng)
in Molecular Autism > 13 (2022) . - 52 p.
Mots-clés : Humans Male Child Autism Spectrum Disorder/diagnostic imaging Brain Mapping/methods Magnetic Resonance Imaging/methods Brain/diagnostic imaging Autistic Disorder Neural Pathways/diagnostic imaging Autism spectrum disorder Functional connectivity Functional magnetic resonance imaging Subtype k-means clustering Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks. METHODS: Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model. RESULTS: Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively. LIMITATIONS: Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity. CONCLUSIONS: These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD. En ligne : http://dx.doi.org/10.1186/s13229-022-00535-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=491