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AI Identifies Novel Autism Subtype

1 year, 3 months ago

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Posted on Aug 11, 2020, 4 p.m.

An AI enhanced novel precision medicine approach developed by Northwestern University, Ben Gurion University, Harvard University, and the Massachusetts Institute of Technology has laid the groundwork for what might be the first biomedical screening and intervention tool for a subtype of autism. 

"Previously, autism subtypes have been defined based on symptoms only -- autistic disorder, Asperger syndrome, etc. -- and they can be hard to differentiate as it is really a spectrum of symptoms," said study co-first author Dr. Yuan Luo, associate professor of preventive medicine: health and biomedical informatics at the Northwestern University Feinberg School of Medicine. "The autism subtype characterized by abnormal levels identified in this study is the first multidimensional evidenced-based subtype that has distinct molecular features and an underlying cause."

1 in 54 American children are estimated to be affected by autism according to the CDC; boys are 4 times more likely to be diagnosed than girls, and most children are typically diagnosed after the age of 4 although it is possible to reliably diagnose based on symptoms at as early as the age of 2. 

The report published in Nature Medicine describes the study involving the subtype disorder known as dyslipidemia associated autism, a disorder that represents 6.55% of all diagnosis of autism spectrum disorder within America alone.  

Our study is the first precision medicine approach to overlay an array of research and health care data -- including genetic mutation data, sexually different gene expression patterns, animal model data, electronic health record data and health insurance claims data -- and then use an AI-enhanced precision medicine approach to attempt to define one of the world's most complex inheritable disorders," said Luo.

The approach is similar to digital maps: to get a true representation different layers of information were overlaid on top of one another. Clusters of gene exons that function together during brain development were identified, then an artificial intelligence algorithm graph clustering technique on gene expression data was used to build a magnifier.

"The map and magnifier approach showcases a generalizable way of using multiple data modalities for subtyping autism and it holds the potential for many other genetically complex diseases to inform targeted clinical trials," said Luo.

Using this tool the team was able to identify a strong association of parental dyslipidemia with autism spectrum disorder in their children, and altered blood lipid profiles in infants that were later diagnosed with autism spectrum disorder. Their findings have inspired the team to pursue further studies including clinical trials aiming to promote early screening and early intervention of autism. 

"Today, autism is diagnosed based only on symptoms, and the reality is when a physician identifies it, it's often when early and critical brain developmental windows have passed without appropriate intervention," said Luo. "This discovery could shift that paradigm."

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