Translational Study of using FOCM/TS Metabolites for Supporting Autism Spectrum Disorder Diagnosis
Journal:
medRxiv
Published Date:
Jul 24, 2026
Abstract
Purpose Several clinical studies have shown correlations between certain physiological measure-ments and an ASD diagnosis. Such findings, however, have generally not resulted in tangible progress towards practical translation due to a number of factors which this work seeks to address. Methods This paper presents a double-blind case/control trial design in which metabolic profiles, collected at two developmental pediatric clinics, were collected from children on a diag-nostic waitlist for the purpose of developing a blood-based test for ASD. Besides obtain-ing 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-5 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 con-cerns 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 chil-dren with non-ASD related developmental delays, this is the first work using physiologi-cal measurements, coupled with AI, to support ASD diagnoses in a clinically relevant set-ting. The clinical trial that was part of this work was registered on clinicaltrials.gov as NCT04672967 and was entitled the Metabolic Autism Prediction (MAP) Study. The study was IRB approved by the Biomedical Research Alliance of New York (BRANY) IRB on August 12, 2021 (approval number: A21-10-282-888). Keywords: Autism spectrum disorder, Folate-dependent one-carbon metabolism path-way, Transsulfuration pathway, Translational study, Machine learning, Clinical study
