Facial structure analysis separates autism spectrum disorders into meaningful clinical subgroups.
Kids with the flattest, widest faces plus autism often lose language and show low IQ—so snap a photo and plan intensive support.
01Research in Context
What this study did
Obafemi-Ajayi et al. (2015) took 3-D photos of children's faces. All kids had autism spectrum disorder.
Computer software measured tiny shape differences. The team let the numbers form their own groups.
What they found
One cluster stood out. These kids had flatter faces and wider eyes.
They also lost words they once had and scored low on IQ tests. The face pattern flagged a severe subgroup.
How this fits with other research
Boutrus et al. (2019) saw the same thing: autistic kids have more lopsided faces. Their asymmetry rose with repetitive behaviors.
Lifshitz et al. (2014) looked at Chinese preschoolers. More dysmorphic looks went hand-in-hand with lower language and motor scores.
Angkustsiri et al. (2011) used simple photos, not 3-D scans. They still found a dysmorphic group who later had more seizures.
Tan et al. (2021) pushed the idea further: even the parents of autistic kids show extra facial asymmetry, hinting the trait runs in families.
Why it matters
You can spot risk without a blood test. A quick photo or simple checklist for minor anomalies can tell you which children need deeper medical work-ups and tighter therapy plans. Add head-circumference and dysmorphology notes to your intake form today.
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02At a glance
03Original 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.
Journal of autism and developmental disorders, 2015 · doi:10.1007/s10803-014-2290-8