Success of blood test for autism affirmed

June 19, 2018 by Mary L. Martialay, Rensselaer Polytechnic Institute
One year after researchers published their work on a physiological test for autism, a follow-up study confirms its exceptional success in assessing whether a child is on the autism spectrum. Credit: Rensselaer

One year after researchers published their work on a physiological test for autism, a follow-up study confirms its exceptional success in assessing whether a child is on the autism spectrum. A physiological test that supports a clinician's diagnostic process has the potential to lower the age at which children are diagnosed, leading to earlier treatment. Results of the study, which uses an algorithm to predict if a child has autism spectrum disorder (ASD) based on metabolites in a blood sample, published online today, appear in the June edition of Bioengineering & Translational Medicine.

"We looked at groups of children with ASD independent from our previous study and had similar success. We are able to predict with 88 percent accuracy whether children have autism," said Juergen Hahn, lead author, systems biologist, professor, head of the Rensselaer Polytechnic Institute Department of Biomedical Engineering, and member of the Rensselaer Center for Biotechnology and Interdisciplinary Studies (CBIS). "This is extremely promising."

It is estimated that approximately 1.7 percent of all children are diagnosed with ASD, characterized as "a developmental disability caused by differences in the brain," according to the Centers for Disease Control and Prevention. Earlier diagnosis is generally acknowledged to lead to better outcomes as children engage in early intervention services, and an ASD diagnosis is possible at 18-24 months of age. However, because diagnosis depends solely on clinical observations, most children are not diagnosed with ASD until after 4 years of age.

Rather than search for a sole indicator of ASD, the approach Hahn developed uses big techniques to search for patterns in metabolites relevant to two connected cellular pathways (a series of interactions between molecules that control cell function) with suspected links to ASD.

"Juergen's work in developing a physiological test for autism is an example of how the interdisciplinary life science-engineering interface at Rensselaer brings new perspectives and solutions to improve human health," said Deepak Vashishth, CBIS director. "This is a great result from the larger emphasis on Alzheimer's and neurodegenerative diseases at CBIS, where our work joins multiple approaches to develop better diagnostic tools and biomanufacture new therapeutics."

The initial success in 2017 analyzed data from a group of 149 people, about half of whom had been previously diagnosed with ASD. For each member of the group, Hahn obtained data on 24 metabolites related to the two cellular pathways—the methionine cycle and the transsulfuration pathway. Deliberately omitting data from one individual in the group, Hahn subjected the remaining dataset to advanced analysis techniques and used results to generate a predictive algorithm. The algorithm then made a prediction about the data from the omitted individual. Hahn cross-validated the results, swapping a different individual out of the group and repeating the process for all 149 participants. His method correctly identified 96.1 percent of all typically developing participants and 97.6 percent of the ASD cohort.

The results were impressive and created, said Hahn, a new goal: "Can we replicate this?"

The new study applies Hahn's approach to an independent dataset. To avoid the lengthy process of gathering new data through clinical trials, Hahn and his team searched for existing datasets that included the metabolites he had analyzed in the original study. The researchers identified appropriate data from three different studies that included a total of 154 children with autism conducted by researchers at the Arkansas Children's Research Institute. The data included only 22 of the 24 metabolites he used to create the original predictive algorithm, however Hahn determined the available information would be sufficient for the test.

The team used their approach to recreate the predictive algorithm, this time using data of the 22 metabolites from the original group of 149 children. The algorithm was then applied to the new group of 154 for testing purposes. When the predictive algorithm was applied to each individual, it correctly predicted autism with 88 percent accuracy.

Hahn said the difference between the original accuracy rate and that of the new study can likely be attributed to several factors, the most important being that two of the metabolites were unavailable in the second dataset. Each of the two metabolites had been strong indicators in the previous study.

Overall, the second study validates the original results, and provides insights into several variants on the approach.

"The most meaningful result is the high degree of accuracy we are able to obtain using this approach on data collected years apart from the original dataset," said Hahn. "This is an approach that we would like to see move forward into clinical trials and ultimately into a commercially available test."

Explore further: A blood test for autism: Big Data techniques find biomarkers for Autism Spectrum Disorder

More information: Daniel P. Howsmon et al. Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically-developing peers: A comparison and validation study, Bioengineering & Translational Medicine (2018). DOI: 10.1002/btm2.10095

Related Stories

A blood test for autism: Big Data techniques find biomarkers for Autism Spectrum Disorder

March 16, 2017
An algorithm based on levels of metabolites found in a blood sample can accurately predict whether a child is on the Autism spectrum of disorder (ASD), based upon a recent study. The algorithm, developed by researchers at ...

Study shows details of brain networks in autism

May 14, 2018
A CAMH study analyzing more than 1,000 brain scans reveals surprising new insights into brain networks in people with autism, after applying a new personalized approach to brain mapping.

New study links strong pupillary light reflex in infancy to later autism diagnosis

May 7, 2018
A new study published in Nature Communications shows that infants who are later diagnosed with autism react more strongly to sudden changes in light. This finding provides support for the view that sensory processing plays ...

Autism may be overdiagnosed in the United States

October 27, 2015
(HealthDay)—As many as 9 percent of American children diagnosed with autism may not have the disorder, according to a federal government study published online Oct. 20 in Autism.

Children with autism and their younger siblings less likely to be fully vaccinated

March 26, 2018
Children with autism and their younger siblings are significantly less likely to be fully vaccinated than the general population, according to new Kaiser Permanente research published today in JAMA Pediatrics.

Genetic targets for autism spectrum disorder identified

February 20, 2018
Autism is a spectrum of closely related symptoms involving behavioral, social and cognitive deficits. Early detection of autism in children is key to producing the best outcomes; however, searching for the genetic causes ...

Recommended for you

Scientists reveal drumming helps schoolchildren diagnosed with autism

September 14, 2018
Drumming for 60 minutes a week can benefit children diagnosed with autism and supports learning at school, according to a new scientific study.

Overlapping copy number variations underlie autism and schizophrenia in Japanese patients

September 11, 2018
Common genetic variants may underlie autism spectrum disorder and schizophrenia across human populations, according to a study appearing September 11th in the journal Cell Reports. In line with previous studies in Caucasians, ...

New biomarker panel could accelerate autism diagnoses

September 6, 2018
Investigators at the UC Davis MIND Institute and NeuroPointDX, a division of Stemina Biomarker Discovery, have identified a group of blood metabolites that could help detect some children with autism spectrum disorder (ASD). ...

Depression strikes nearly one in five young adults with autism: study

August 31, 2018
(HealthDay)—Depression affects almost 20 percent of young adults with autism, new research shows, a rate that's more than triple that seen in the general population.

Kids with autism learn, grow with the 'social robot'

August 22, 2018
Robots may hold the keys to social success for kids with autism.

First biomarker evidence of DDT-autism link

August 16, 2018
A study of more than 1 million pregnancies in Finland reports that elevated levels of a metabolite of the banned insecticide DDT in the blood of pregnant women are linked to increased risk for autism in the offspring. An ...

4 comments

Adjust slider to filter visible comments by rank

Display comments: newest first

Squirrel
not rated yet Jun 20, 2018
1.7% individuals have ASD (identified with 97.6% accuracy). The test identifies 3.9% of individuals without ASD as having ASD--it's only 96.1% correct. Therefore in a random sample 5.6% (3.9% + 1.7%) individuals will be identified as having ASD, of whom 70% will not have ASD. The test is swamped with false positives.
Anonym846181
not rated yet Jun 20, 2018
Your math sounds good assuming that testing would be done on all individuals. What's the modified false positive rate if you only test symptomatic individuals?
postfuture
not rated yet Jun 28, 2018
"We are able to predict with 88 percent accuracy whether children have autism," said Juergen Hahn, lead author....
@ Squirrel -
Absolutely agree with you about false positives but where did you get 96%? They said - just 88% which means much more false positives?
postfuture
not rated yet Jun 28, 2018
OK, I found - 96%, but then again later - 88%? I do not have time to read it through and figure it out!

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.