[article] 
inMolecular Autism > 13 (2022) . - 11 p.

Titre :	Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder
Type de document :	Texte imprimé et/ou numérique
Auteurs :	Sanna STROTH, Auteur ; Johannes TAUSCHER, Auteur ; Nicole WOLFF, Auteur ; Charlotte KÜPPER, Auteur ; Luise POUSTKA, Auteur ; Stefan ROEPKE, Auteur ; Veit ROESSNER, Auteur ; Dominik HEIDER, Auteur ; Inge KAMP-BECKER, Auteur
Article en page(s) :	11 p.
Langues :	Anglais (eng)
Mots-clés :	Affect  Autism Spectrum Disorder/diagnosis  Autistic Disorder  Female  Humans  Intellectual Disability/diagnosis  Male  Adi-r  Ados  Asd  Diagnostics  Female autism  Phenotype  Sex
Index. décimale :	PER Périodiques
Résumé :	BACKGROUND: Although autism spectrum disorder (ASD) is a common developmental disorder, our knowledge about a behavioral and neurobiological female phenotype is still scarce. As the conceptualization and understanding of ASD are mainly based on the investigation of male individuals, females with ASD may not be adequately identified by routine clinical diagnostics. The present machine learning approach aimed to identify diagnostic information from the Autism Diagnostic Observation Schedule (ADOS) that discriminates best between ASD and non-ASD in females and males. METHODS: Random forests (RF) were used to discover patterns of symptoms in diagnostic data from the ADOS (modules 3 and 4) in 1057 participants with ASD (18.1% female) and 1230 participants with non-ASD (17.9% % female). Predictive performances of reduced feature models were explored and compared between females and males without intellectual disabilities. RESULTS: Reduced feature models relied on considerably fewer features from the ADOS in females compared to males, while still yielding similar classification performance (e.g., sensitivity, specificity). LIMITATIONS: As in previous studies, the current sample of females with ASD is smaller than the male sample and thus, females may still be underrepresented, limiting the statistical power to detect small to moderate effects. CONCLUSION: Our results do not suggest the need for new or altered diagnostic algorithms for females with ASD. Although we identified some phenotypic differences between females and males, the existing diagnostic tools seem to sufficiently capture the core autistic features in both groups.
En ligne :	http://dx.doi.org/10.1186/s13229-022-00491-9
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477

[article] Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder [Texte imprimé et/ou numérique] / Sanna STROTH, Auteur ; Johannes TAUSCHER, Auteur ; Nicole WOLFF, Auteur ; Charlotte KÜPPER, Auteur ; Luise POUSTKA, Auteur ; Stefan ROEPKE, Auteur ; Veit ROESSNER, Auteur ; Dominik HEIDER, Auteur ; Inge KAMP-BECKER, Auteur . - 11 p.
Langues : Anglais (eng)
in Molecular Autism > 13 (2022) . - 11 p.

Mots-clés :	Affect  Autism Spectrum Disorder/diagnosis  Autistic Disorder  Female  Humans  Intellectual Disability/diagnosis  Male  Adi-r  Ados  Asd  Diagnostics  Female autism  Phenotype  Sex
Index. décimale :	PER Périodiques
Résumé :	BACKGROUND: Although autism spectrum disorder (ASD) is a common developmental disorder, our knowledge about a behavioral and neurobiological female phenotype is still scarce. As the conceptualization and understanding of ASD are mainly based on the investigation of male individuals, females with ASD may not be adequately identified by routine clinical diagnostics. The present machine learning approach aimed to identify diagnostic information from the Autism Diagnostic Observation Schedule (ADOS) that discriminates best between ASD and non-ASD in females and males. METHODS: Random forests (RF) were used to discover patterns of symptoms in diagnostic data from the ADOS (modules 3 and 4) in 1057 participants with ASD (18.1% female) and 1230 participants with non-ASD (17.9% % female). Predictive performances of reduced feature models were explored and compared between females and males without intellectual disabilities. RESULTS: Reduced feature models relied on considerably fewer features from the ADOS in females compared to males, while still yielding similar classification performance (e.g., sensitivity, specificity). LIMITATIONS: As in previous studies, the current sample of females with ASD is smaller than the male sample and thus, females may still be underrepresented, limiting the statistical power to detect small to moderate effects. CONCLUSION: Our results do not suggest the need for new or altered diagnostic algorithms for females with ASD. Although we identified some phenotypic differences between females and males, the existing diagnostic tools seem to sufficiently capture the core autistic features in both groups.
En ligne :	http://dx.doi.org/10.1186/s13229-022-00491-9
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477