Food and Behaviour Research

Donate Log In

Physiological test for autism proves effective independent of co-occurring conditions

Torie Wells, Rensselaer Polytechnic Institute

child with backpack

Progress in classification of autism spectrum disorder (ASD) via blood measures - latest results consistent with a potential role for nutrition.


The lack of any objective diagnostic tests for autistic spectrum disorder (ASD) has been a major obstacle to both early identification and the search for more effective treatments.

This study provides further validation of a potential objective method of screening, based on blood metabolite profiling, which correctly classified a very high proportion of ASD children and controls in previous studies.

These latest findings show that its effectiveness was not impaired by co-occuring conditions (such as gastrointestinal disorders, allergies, immune disorders or neurological symptoms). This was important to establish, as the vast majority of children diagnosed with ASD also show one or more of these conditions.

As the authors emphsises, further research is still needed before this kind of measure could be of any clinical use for screening or diagnostic purposes.

Meanwhile, however, it is notable that among the blood metabolites that appear to distinguish ASD children from controls with a high degree of accuracy are various biomarkers related to the metabolism of folate (and other B vitamins), DNA methylation (which helps regulate gene expression), and 'oxidative stress', among others.

These findings are consistent with an increasing body of research indicating that ASD involves metabolic abnormalities, some of which may be open to improvement via dietary and/or nutritional interventions. 

For the related research article please see:

See also: 

28/08/2020 - Medical Xpress

Developing a physiological test for diagnosing autism spectrum disorder (ASD), one that measures certain components in the blood, has the potential to be a paradigm shift for diagnosing ASD. However, the large heterogeneity of how ASD affects individuals has long been viewed as a key obstacle to the development of such a test.

Research conducted at Rensselaer Polytechnic Institute, and published online today in the journal Research in Autism Spectrum Disorders, represents a significant step toward addressing this challenge.

The research, led by Juergen Hahn, the head of the Department of Biomedical Engineering at Rensselaer, builds upon his team's previous discoveries, including the development of a physiological test for autism.

That physiological test relies on an algorithm that analyzes measurements of metabolites in a blood sample to predict whether or not a person has an ASD diagnosis.

Hahn and his team sought to assess the strength of their algorithm even further, by testing it with data collected from children with ASD who also have one or more other condition—so called co-occurring conditions—like allergies or gastrointestinal symptoms.

"We wanted to see if the results from our previous analysis still hold up even in the presence or absence of a number of co-occurring conditions," said Hahn, who is also a member of the Center for Biotechnology & Interdisciplinary Studies at Rensselaer. "We found that, for the conditions that we looked at, the accuracy of the prediction results was only minimally affected by the presence of co-occurring conditions."

The model, according to the research, was able to successfully identify 124 of 131 children with ASD—94.7%—regardless of whether or not the child also had a co-occurring condition. In general, Hahn said, the algorithm actually worked slightly better when a co-occurring condition was present. The reason for that finding, he said, needs to be examined further.

Hahn's big data approach to uncovering new insights about autism has also been used to examine the effectiveness of possible treatments, and the idea that ASD—although extremely heterogonous—may have some subgroups.

He and his team use depersonalized medical data to perform these analyses, and continue to dig deeper into their model and its abilities as more data becomes available.

"This was a question we definitely had to answer as many individuals with ASD have one or more co-occurring condition," Hahn said. "These findings will also guide some of our future research."