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A data driven approach in an unbiased sample reveals equivalent sex ratio of autism spectrum disorder associated impairment in early childhood

  • Catherine A. Burrows
    Correspondence
    Corresponding author: Catherine Burrows, PhD,
    Affiliations
    Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
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  • Rebecca L. Grzadzinski
    Affiliations
    Carolina Institute for Developmental Disabilities and Dept. of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
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  • Kevin Donovan
    Affiliations
    Department of Biostatistics, University of Pennsylvania, Philadelphia, PA, USA
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  • Isabella C. Stallworthy
    Affiliations
    Institute of Child Development & Dept. of Pediatrics, University of Minnesota, Minneapolis, MN, USA
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  • Joshua Rutsohn
    Affiliations
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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  • Tanya St. John
    Affiliations
    University of Washington Autism Center, University of Washington, Seattle, WA, USA
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  • Natasha Marrus
    Affiliations
    Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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  • Julia Parish-Morris
    Affiliations
    Center for Autism Research, Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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  • Leigh MacIntyre
    Affiliations
    McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (MNI), McGill University, Montreal, Quebec, Canada
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  • Jacqueline Hampton
    Affiliations
    Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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  • Juhi Pandey
    Affiliations
    Center for Autism Research, Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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  • Mark Shen
    Affiliations
    Carolina Institute for Developmental Disabilities and Dept. of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA

    UNC Neuroscience Center, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
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  • Kelly N. Botteron
    Affiliations
    Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA

    Dept. of Radiology, University of Washington Medical Center, Seattle, WA, USA
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  • Annette M. Estes
    Affiliations
    University of Washington Autism Center, University of Washington, Seattle, WA, USA

    Dept. of Speech and Hearing Science, University of Washington, Seattle, WA, USA
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  • Stephen R. Dager
    Affiliations
    UNC Neuroscience Center, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
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  • Heather C. Hazlett
    Affiliations
    Carolina Institute for Developmental Disabilities and Dept. of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
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  • John R. Pruett Jr.
    Affiliations
    Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
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  • Robert T. Schultz
    Affiliations
    Center for Autism Research, Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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  • Lonnie Zwaigenbaum
    Affiliations
    Dept. of Pediatrics, University of Alberta, Edmonton, AB, Canada
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  • Kinh N. Truong
    Affiliations
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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  • Author Footnotes
    ∗ shared senior author
    Joseph Piven
    Footnotes
    ∗ shared senior author
    Affiliations
    Carolina Institute for Developmental Disabilities and Dept. of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
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  • Author Footnotes
    ∗ shared senior author
    Jed T. Elison
    Footnotes
    ∗ shared senior author
    Affiliations
    Institute of Child Development & Dept. of Pediatrics, University of Minnesota, Minneapolis, MN, USA
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  • for theIBIS Network
    Author Footnotes
    † IBIS Network: The Infant Brain Imaging Study (IBIS) Network is a United States National Institutes of Health funded Autism Center of Excellence (ACE) project and consists of a consortium of 10 universities in the U.S. and Canada. Members and components of the IBIS Network include: J. Piven (IBIS Network PI), Clinical Sites: Children’s Hospital of Philadelphia (CHOP): R.T. Schultz, J. Pandey, J. Parish-Morris, B. Tunç, W. Guthrie; University of Minnesota (UMN): J.T. Elison, J.J. Wolff; University of North Carolina (UNC): J. Piven, H.C. Hazlett, M.D. Shen, J.B. Girault, R. Grzadzinski; University of Washington (UW): S.R. Dager, A.M. Estes, T. St. John, D. Shaw; Washington University School of Medicine in St. Louis (WU): K.N. Botteron, R.C. McKinstry, J.N. Constantino, N. Marrus. Admin Core: WU: Alicia Rocca; UNC: J.C. Chappell. Behavior Core: UW: A.M. Estes, T. St. John; University of Alberta: L. Zwaigenbaum; UMN: J.T. Elison, J.J. Wolff, C. Burrows; University of Texas at Dallas: M.R. Swanson. MRI Core: UNC: M.A. Styner, M.D. Shen; New York University: G. Gerig; WU: J.R. Pruett, Jr., R.C. McKinstry; UMN: J.T. Elison; UW: S.R. Dager. Data Coordinating Center: Montreal Neurological Institute: A.C. Evans, L.C. MacIntyre, S. Torres-Gomez, S. Das (see Das, et al.: The MNI data-sharing and processing ecosystem. Neuroimage 2016; 124:1188–1195). Statistical Analysis Core: UNC: K. Truong. Environmental Risk Core: Johns Hopkins University (JHU): H. Volk. Genetics Core: JHU: M.D. Fallin; UNC: M.D. Shen.
  • Author Footnotes
    ∗ shared senior author
    † IBIS Network: The Infant Brain Imaging Study (IBIS) Network is a United States National Institutes of Health funded Autism Center of Excellence (ACE) project and consists of a consortium of 10 universities in the U.S. and Canada. Members and components of the IBIS Network include: J. Piven (IBIS Network PI), Clinical Sites: Children’s Hospital of Philadelphia (CHOP): R.T. Schultz, J. Pandey, J. Parish-Morris, B. Tunç, W. Guthrie; University of Minnesota (UMN): J.T. Elison, J.J. Wolff; University of North Carolina (UNC): J. Piven, H.C. Hazlett, M.D. Shen, J.B. Girault, R. Grzadzinski; University of Washington (UW): S.R. Dager, A.M. Estes, T. St. John, D. Shaw; Washington University School of Medicine in St. Louis (WU): K.N. Botteron, R.C. McKinstry, J.N. Constantino, N. Marrus. Admin Core: WU: Alicia Rocca; UNC: J.C. Chappell. Behavior Core: UW: A.M. Estes, T. St. John; University of Alberta: L. Zwaigenbaum; UMN: J.T. Elison, J.J. Wolff, C. Burrows; University of Texas at Dallas: M.R. Swanson. MRI Core: UNC: M.A. Styner, M.D. Shen; New York University: G. Gerig; WU: J.R. Pruett, Jr., R.C. McKinstry; UMN: J.T. Elison; UW: S.R. Dager. Data Coordinating Center: Montreal Neurological Institute: A.C. Evans, L.C. MacIntyre, S. Torres-Gomez, S. Das (see Das, et al.: The MNI data-sharing and processing ecosystem. Neuroimage 2016; 124:1188–1195). Statistical Analysis Core: UNC: K. Truong. Environmental Risk Core: Johns Hopkins University (JHU): H. Volk. Genetics Core: JHU: M.D. Fallin; UNC: M.D. Shen.

      ABSTRACT

      Background

      Sex differences in the prevalence of neurodevelopmental disorders are particularly evident in autism spectrum disorder (ASD). Heterogeneous symptom presentation and the potential of measurement bias hinder early ASD detection in females, and may contribute to discrepant prevalence estimates. We examined trajectories of social communication (SC) and restricted and repetitive behaviors (RRBs) in a sample of infant siblings of autistic children, adjusting for age- and sex-based measurement bias. We hypothesized that leveraging a prospective elevated familial likelihood sample, deriving data-driven behavioral constructs, and accounting for measurement bias would reveal less discrepant sex ratios than are typically seen in ASD.

      Methods

      We conducted direct assessments of ASD symptoms at 6-9, 12-15, 24, and 36-60 months of age (total Nobservations=1254) with infant siblings of autistic children (N=377) and a lower ASD-familial-likelihood comparison group (N=168; Nobservations=527). We established measurement invariance across age and sex for separate models of SC and RRB. We then conducted latent class growth mixture modeling with the longitudinal data and evaluated for sex differences in trajectory membership.

      Results

      We identified two latent classes in the SC and RRB models with equal sex ratios in the high-concern cluster for both SC and RRB. Sex differences were also observed in the SC high-concern cluster, indicating that girls classified as “elevated social concerns” demonstrate milder symptoms than boys in this group.

      Conclusions

      This novel approach for characterizing ASD symptom progression highlights the utility of assessing and adjusting for sex-related measurement bias and identifying sex-specific patterns of symptom emergence.

      Keywords

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