Facial Structure Analysis Separates Autism Spectrum Disorders into Meaningful Clinical Subgroups
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.
Autism, Biomarker, Cluster analysis, Facial phenotype, Language regression, Outcome indicators
Obafemi-Ajayi, Tayo, Judith H. Miles, T. Nicole Takahashi, Wenchuan Qi, Kristina Aldridge, Minqi Zhang, Shi-Qing Xin, Ying He, and Ye Duan. "Facial structure analysis separates autism spectrum disorders into meaningful clinical subgroups." Journal of autism and developmental disorders 45, no. 5 (2015): 1302-1317.
Journal of Autism and Developmental Disorders