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AI identifies new autism subtype

 


A new AI-enhanced precision medicine approach, developed by Northwestern University, Ben Gurion University, Harvard University, and Massachusetts Institute of Technology, laid the foundation for what would be the first biomedical screening and intervention tool for the autism subtype. It was.

“Previously, autism subtypes were defined based only on symptoms-autism, Asperger’s syndrome, etc.-and it’s actually a spectrum of symptoms, which can be difficult to distinguish.” Dr. Yuan Luo, co-lead author of the study and associate professor of preventive medicine: said a health and biomedical informologist at the Feinberg School of Medicine at Northwestern University. “The autism subtypes characterized by abnormal levels identified in this study are the first multidimensional evidence-based subtypes with different molecular features and underlying causes.

It is estimated by the CDC that 1 in 54 American children have autism. Boys are four times more likely to be diagnosed than girls, and most children are usually diagnosed symptomatically at age 2 years, but they are diagnosed after age 4.

A report published in Nature Medicine describes a study involving a subtype of disorder known as dyslipidemia-related autism. This is a disorder that represents 6.55% of all diagnoses of autism spectrum disorder in the United States alone.

Our research overlays a range of research and healthcare data, including gene mutation data, gene expression patterns of different genders, animal model data, electronic health record data, health claims data, and more AI-enhanced precision medicine approach to define one of the diseases” Luo said.

This approach is similar to digital maps. The various layers of information were overlaid on top of each other to get the true representation. Clusters of gene exons that work together during brain development were identified and a magnifying glass constructed using an artificial intelligence algorithm graph clustering approach to gene expression data.

“The map and magnifying glass approach shows a generalizable method of using multiple data modalities to subtype autism, and is targeted at many other genetically complex diseases in clinical trials. Has the potential to provide information to Luo said.

The team uses this tool to identify a strong association between parental dyslipidemia and autism spectrum disorders in children, and changes in blood lipid profiles in infants subsequently diagnosed with autism spectrum disorders I was able to. Their findings inspired the team to pursue further research, including clinical trials aimed at promoting early screening and early intervention for autism.

“Today, autism is diagnosed only on the basis of symptoms, and the reality is when doctors identify it. Often, without proper intervention, the early critical brain development windows have passed.“Lu said,”This discovery could change that paradigm

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