Elemental dynamics in hair accurately predict future autism spectrum disorder diagnosis: an international multi-center study

Abstract Autism spectrum disorder (ASD) is a neurodevelopmental condition diagnosed in approximately 2% of children. Reliance on the emergence of clinically observable behavioral patterns only delays mean age of diagnosis to approximately 4 years. However, neural pathways critical to language and social functions develop during infancy and current diagnostic protocols miss the age when therapy would be most effective3,4. We developed non-invasive ASD biomarkers using mass spectrometry analysis of elemental metabolism in single hair strands, coupled with machine learning. We undertook a national prospective study in Japan where hair samples were collected at 1 month and clinical diagnosis was undertaken at 4 years. Next, we analyzed a national sample of Swedish twins and, in our third study, participants from a specialist ASD center in the US. In blinded analysis, a predictive algorithm detected ASD risk as early as 1 month with 96.4% sensitivity, 75.4% specificity, and 81.4% accuracy (n=486; 175 cases).