It has been shown that intensive behavior therapy can improve the symptoms of autism and that the earlier such intervention is started, the greater the benefit. However, diagnosing autism at a young age can be tricky, and evaluations can take up to a year due to the large number of preliminary false positives that can occur with traditional testing methods. Recently, Qudrant Biosciences Inc. announced promising results from a study of a saliva-based biomarker panel and associated algorithm that could improve the ability to accurately identify children with autism spectrum disorder in its earliest stages. According to the study, published in Frontiers in Genetics, the panel was able to distinguish children with ASD from non-ASD children with 85% accuracy.
The study included 456 children, including 235 with ASD and 218 without ASD (including 84 with developmental delay and 134 with typical delay). Levels of human and bacterial RNAs were measured in saliva samples using next-generation sequencing. Machine-learning was used to identify the top RNAs.
From these efforts, the researchers came up with a panel of 32 small RNAs could the children with ASD from those without with 85% accuracy. The RNAs extracted from the saliva-based testing methods could provide the means to interrogate genomic, physiologic, microbiome, and environmental factors implicated in ASD in a single analysis, which is important as autism is associated with both environmental and genetic causes.
While further evaluation would be required to accurately diagnose a child with autism, it could be used to prioritize specialist referral and become a component of a wholistic approach to diagnosis.