Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 884-894 |
| Number of pages | 11 |
| Journal | Journal of Child Psychology and Psychiatry and Allied Disciplines |
| Volume | 62 |
| Issue number | 7 |
| Early online date | Nov 2 2020 |
| DOIs | |
| State | Published - Nov 2 2020 |
Bibliographical note
Funding Information:C.D. and C.L. were funded by NSF‐GSF awards. J.J.W. was funded by NIMH K01 MH101653. This study was made possible by an NIMH BRAINS award (R01 MH104324) to J.T.E. The authors thank the families who participated, Dustin Gibson for his consultation, and our research team including Kristen Gault and Emma Shankland. The authors have declared that they have no competing or potential conflicts of interest. Key points
Funding Information:
C.D. and C.L. were funded by NSF-GSF awards. J.J.W. was funded by NIMH K01 MH101653. This study was made possible by an NIMH BRAINS award (R01 MH104324) to J.T.E. The authors thank the families who participated, Dustin Gibson for his consultation, and our research team including Kristen Gault and Emma Shankland. The authors have declared that they have no competing or potential conflicts of interest. Key points Early identification and intervention efforts require the ability to detect risk profiles for psychiatric/neurodevelopmental disorders in early childhood. However, variability across the typical-to-atypical continuum and heterogeneity within and across early emerging disorders represent fundamental challenges. We used a data-driven approach (factor mixture modeling) to overcome these challenges. We identified and characterized phenotypic profiles for five risk groups in a large community-based sample of toddlers. Independent, distal outcomes demonstrated predictive utility of the risk classification with large effect sizes between high-, moderate-, and low-risk groups. Data-driven approaches, leveraging multiple developmentally appropriate dimensional RDoC constructs, holds promise for future efforts aimed toward early identification of at-risk-phenotypes for a variety of early emerging neurodevelopmental disorders. Early identification and intervention efforts require the ability to detect risk profiles for psychiatric/neurodevelopmental disorders in early childhood. However, variability across the typical-to-atypical continuum and heterogeneity within and across early emerging disorders represent fundamental challenges. We used a data-driven approach (factor mixture modeling) to overcome these challenges. We identified and characterized phenotypic profiles for five risk groups in a large community-based sample of toddlers. Independent, distal outcomes demonstrated predictive utility of the risk classification with large effect sizes between high-, moderate-, and low-risk groups. Data-driven approaches, leveraging multiple developmentally appropriate dimensional RDoC constructs, holds promise for future efforts aimed toward early identification of at-risk-phenotypes for a variety of early emerging neurodevelopmental disorders.
Publisher Copyright:
© 2020 Association for Child and Adolescent Mental Health.
Keywords
- Development
- autism spectrum disorder
- communication
- infancy
- social behavior