Elemental Dynamics in Hair Accurately Predict Future Autism Spectrum Disorder Diagnosis: An International
Christine Austin1,2, Paul Curtin1,2, Manish Arora1,2
1Linus Biotechnology Inc., New York, NY 10013, USA.
Insights
Early autism spectrum disorder (ASD) detection is possible using non-invasive biomarkers in infant hair. This method analyzes elemental metabolism, enabling risk prediction as early as one month of age.
Area of Science:
- Biochemistry
- Neuroscience
- Pediatrics
Background:
- Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting ~2% of children.
- Current diagnostic methods relying on observable behaviors lead to delayed diagnosis around 4 years, missing critical early intervention windows.
- Early development of neural pathways for language and social functions occurs in infancy.
Purpose of the Study:
- To develop non-invasive biomarkers for early autism spectrum disorder (ASD) detection.
- To identify elemental metabolism signatures in infant hair for predicting ASD risk.
- To enable diagnosis in the first month of life for timely therapeutic intervention.
Main Methods:
- Utilized mass spectrometry to analyze elemental metabolism in single human hair strands.
- Employed machine learning algorithms to develop a predictive model for ASD risk.
- Conducted prospective national studies in Japan, analyzed Swedish twins, and included participants from a US ASD center.
Main Results:
- A blinded analysis of a predictive algorithm demonstrated high performance in detecting ASD risk.
- The algorithm achieved 96.4% sensitivity, 75.4% specificity, and 81.4% accuracy in identifying ASD risk in 1-month-old infants (n=486; 175 cases).
- Systemic dysregulation in elemental metabolism dynamics was identified as a key indicator.
Conclusions:
- Elemental metabolism signatures in infant hair can predict the emergence of autism spectrum disorder (ASD).
- This non-invasive approach allows for ASD risk detection as early as one month of age.
- Early detection via hair-based biomarkers can facilitate timely therapeutic interventions during critical developmental periods.
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 the 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 effective. We developed non-invasive ASD biomarkers using mass spectrometry analyses 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 a 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). These findings emphasize that the dynamics in elemental metabolism are systemically dysregulated in autism, and these signatures can be detected and leveraged in hair samples to predict the emergence of ASD as early as 1 month of age.
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