Phenoscreening: a developmental approach to research domain criteria-motivated sampling / C. M. DOYLE in Journal of Child Psychology and Psychiatry, 62-7 (July 2021)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 62-7 (July 2021) . - p.884-894 Titre : Phenoscreening: a developmental approach to research domain criteria-motivated sampling Type de document : Texte imprimé et/ou numérique Auteurs : C. M. DOYLE, Auteur ; C. LASCH, Auteur ; E. P. VOLLMAN, Auteur ; Christopher David DESJARDINS, Auteur ; N. E. HELWIG, Auteur ; S. JACOB, Auteur ; J. J. WOLFF, Auteur ; J. T. ELISON, Auteur Article en page(s) : p.884-894 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnosis  Child, Preschool  Humans  Infant  Phenotype  Development  autism spectrum disorder  communication  infancy  social behavior Index. décimale : PER Périodiques Résumé : BACKGROUND: To advance early identification efforts, we must detect and characterize neurodevelopmental sequelae of risk among population-based samples early in development. However, variability across the typical-to-atypical continuum and heterogeneity within and across early emerging psychiatric/neurodevelopmental disorders represent fundamental challenges to overcome. Identifying multidimensionally determined profiles of risk, agnostic to DSM categories, via data-driven computational approaches represents an avenue to improve early identification of risk. METHODS: Factor mixture modeling (FMM) was used to identify subgroups and characterize phenotypic risk profiles, derived from multiple parent-report measures of typical and atypical behaviors common to autism spectrum disorder, in a community-based sample of 17- to 25-month-old toddlers (n = 1,570). To examine the utility of risk profile classification, a subsample of toddlers (n = 107) was assessed on a distal, independent outcome examining internalizing, externalizing, and dysregulation at approximately 30 months. RESULTS: FMM results identified five asymmetrically sized subgroups. The putative high- and moderate-risk groups comprised 6% of the sample. Follow-up analyses corroborated the utility of the risk profile classification; the high-, moderate-, and low-risk groups were differentially stratified (i.e., HR > moderate-risk > LR) on outcome measures and comparison of high- and low-risk groups revealed large effect sizes for internalizing (d = 0.83), externalizing (d = 1.39), and dysregulation (d = 1.19). CONCLUSIONS: This data-driven approach yielded five subgroups of toddlers, the utility of which was corroborated by later outcomes. Data-driven approaches, leveraging multiple developmentally appropriate dimensional RDoC constructs, hold promise for future efforts aimed toward early identification of at-risk-phenotypes for a variety of early emerging neurodevelopmental disorders. En ligne : http://dx.doi.org/10.1111/jcpp.13341 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=456 [article] Phenoscreening: a developmental approach to research domain criteria-motivated sampling [Texte imprimé et/ou numérique] / C. M. DOYLE, Auteur ; C. LASCH, Auteur ; E. P. VOLLMAN, Auteur ; Christopher David DESJARDINS, Auteur ; N. E. HELWIG, Auteur ; S. JACOB, Auteur ; J. J. WOLFF, Auteur ; J. T. ELISON, Auteur . - p.884-894. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 62-7 (July 2021) . - p.884-894 Mots-clés : Autism Spectrum Disorder/diagnosis  Child, Preschool  Humans  Infant  Phenotype  Development  autism spectrum disorder  communication  infancy  social behavior Index. décimale : PER Périodiques Résumé : BACKGROUND: To advance early identification efforts, we must detect and characterize neurodevelopmental sequelae of risk among population-based samples early in development. However, variability across the typical-to-atypical continuum and heterogeneity within and across early emerging psychiatric/neurodevelopmental disorders represent fundamental challenges to overcome. Identifying multidimensionally determined profiles of risk, agnostic to DSM categories, via data-driven computational approaches represents an avenue to improve early identification of risk. METHODS: Factor mixture modeling (FMM) was used to identify subgroups and characterize phenotypic risk profiles, derived from multiple parent-report measures of typical and atypical behaviors common to autism spectrum disorder, in a community-based sample of 17- to 25-month-old toddlers (n = 1,570). To examine the utility of risk profile classification, a subsample of toddlers (n = 107) was assessed on a distal, independent outcome examining internalizing, externalizing, and dysregulation at approximately 30 months. RESULTS: FMM results identified five asymmetrically sized subgroups. The putative high- and moderate-risk groups comprised 6% of the sample. Follow-up analyses corroborated the utility of the risk profile classification; the high-, moderate-, and low-risk groups were differentially stratified (i.e., HR > moderate-risk > LR) on outcome measures and comparison of high- and low-risk groups revealed large effect sizes for internalizing (d = 0.83), externalizing (d = 1.39), and dysregulation (d = 1.19). CONCLUSIONS: This data-driven approach yielded five subgroups of toddlers, the utility of which was corroborated by later outcomes. Data-driven approaches, leveraging multiple developmentally appropriate dimensional RDoC constructs, hold promise for future efforts aimed toward early identification of at-risk-phenotypes for a variety of early emerging neurodevelopmental disorders. En ligne : http://dx.doi.org/10.1111/jcpp.13341 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=456

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