Translational Study of using FOCM/TS Metabolites for Supporting Autism Spectrum Disorder Diagnosis

Abstract Purpose Several clinical studies have shown correlations between certain physiological measurements and an autism spectrum disorder (ASD) diagnosis. Such findings, however, have generally not resulted in tangible progress towards practical translation. In fact, most studies have been retrospective in nature and compare biomarker profiles in children with an ASD diagnosis to those who are typically developing. A clinically meaningful test, however, requires an ASD diagnostic center to distinguish children from those with a developmental disorder pre-diagnosis. Methods This paper presents, for the first time, a double-blind case/control trial design in which metabolic profiles, collected at two developmental pediatric clinics, were collected from children on a diagnostic waitlist for the purpose of developing a blood-based test for ASD. Besides obtaining blood samples, the children underwent gold-standard clinical evaluations, including the Autism Diagnostic Observation Schedule (ADOS), Mullens Scale of Early Learning (MSEL), and Vineland Adaptive Behavior Scale (VABS). The analysis, together with a complete medical history and physical exam, allowed to confirm or rule-out suspected ASD using DSM-V criteria. The study was based on a cohort of 140 children between the ages 18-60 months, that were referred to a developmental pediatrician because of concerns in their development. Results 114 of these children received an ASD diagnosis, while 26 were diagnosed with non-ASD related developmental delays. Based on the measured metabolites, artificial intelligence-based classification algorithms allowed for an over 80% accuracy in predicting whether a sample came from a child diagnosed with ASD or not. Conclusion While these results need to be replicated in a larger study, especially involving more children with non-ASD related developmental delays, this is the first work using physiological measurements, coupled with AI, to support ASD diagnoses in a clinically relevant setting.