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Facial Structure Analysis Separates Autism Spectrum Disorders into Meaningful Clinical Subgroups

Abstract

Varied cluster analysis were applied to facial surface measurements from 62 prepubertal boys with essential autism to determine whether facial morphology constitutes viable biomarker for delineation of discrete Autism Spectrum Disorders (ASD) subgroups. Earlier study indicated utility of facial morphology for autism subgrouping (Aldridge et al. in Mol Autism 2(1):15, 2011). Geodesic distances between standardized facial landmarks were measured from three-dimensional stereo-photogrammetric images. Subjects were evaluated for autism-related symptoms, neurologic, cognitive, familial, and phenotypic variants. The most compact cluster is clinically characterized by severe ASD, significant cognitive impairment and language regression. This verifies utility of facially-based ASD subtypes and validates Aldridge et al.’s severe ASD subgroup, notwithstanding different techniques. It suggests that language regression may define a unique ASD subgroup with potential etiologic differences.

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Correspondence to Ye Duan.

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Obafemi-Ajayi, T., Miles, J.H., Takahashi, T.N. et al. Facial Structure Analysis Separates Autism Spectrum Disorders into Meaningful Clinical Subgroups. J Autism Dev Disord 45, 1302–1317 (2015). https://doi.org/10.1007/s10803-014-2290-8

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Keywords

  • Autism
  • Cluster analysis
  • Language regression
  • Facial phenotype
  • Biomarker
  • Outcome indicators