Juergen Hahn, Ph.D

Model developed at Rensselaer predicts diagnosis despite the presence of other medical conditions

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… Continue reading.

Children with autism spectrum disorder (ASD) are often affected by co-occurring conditions, such as epilepsy, immune disorders, gastrointestinal problems, and developmental delays. According to research published today in Autism Research, creating a classification system for ASD based on co-occurring conditions could provide useful insights into the underlying mechanics of ASD and these conditions.

The study was produced by a Rensselaer Polytechnic Institute team, led by Juergen Hahn, a professor of biomedical engineering, which analyzed de-identified administrative claims data from the OptumLabs Data Warehouse for thousands of children with and without ASD over five years. What the team found, Hahn said, were three subgroups within the cohort of 3,278 children with autism… Continue reading.

Researchers at Rensselaer Polytechnic Institute who developed a blood test to help diagnose autism spectrum disorder have now successfully applied their distinctive big data-based approach to evaluating possible treatments.

The findings, recently published in Frontiers in Cellular Neuroscience, have the potential to accelerate the development of successful medical interventions. One of the challenges in assessing the effectiveness of a treatment for autism is how to measure improvement. Currently, diagnosis and evaluating the success of an intervention rely heavily on observations by professionals and caretakers.

“Having some kind of a measure that measures something that’s happening inside the body is really important,” said Juergen Hahn, systems biologist, professor, and head of the Rensselaer Department of Biomedical Engineering… Continue reading.

Researchers at Rensselaer Polytechnic Institute—led by Juergen Hahn, professor and head of biomedical engineering—are continuing to make remarkable progress with their research focused on autism spectrum disorder (ASD). A recent paper authored by Hahn and Jill James from the University of Arkansas for Medical Sciences (UAMS) in the journal Research in Autism Spectrum Disorders discusses their work on predicting with approximately 90 percent accuracy whether a pregnant mother has a 1.7 percent or a tenfold increased risk of having a child diagnosed with ASD.

Currently there is no test for pregnant mothers that can predict the probability of having a child that will be diagnosed with ASD. Recent estimates indicate that if a mother has previously had a child with ASD, the risk of having a second child with ASD is approximately 18.7 percent, whereas the risk of ASD in the general population is approximately 1.7 percent.

“However,” said Hahn, a member of the Rensselaer Center for Biotechnology and Interdisciplinary Studies, “it would be highly desirable if a prediction based upon physiological measurements could be made to determine which risk group a prospective mother falls into… Continue reading.

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… Continue reading.

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 Texas ChE alumnus Juergen Hahn (Ph.D. ’02) and researchers at Rensselaer Polytechnic Institute, is the first physiological test for autism and opens the door to earlier diagnosis and potential future development of therapeutics.

“Instead of looking at individual metabolites, we investigated patterns of several metabolites and found significant differences between metabolites of children with ASD and those that are neurotypical. These differences allow us to categorize whether an individual is on the Autism spectrum,” said Hahn, lead author, systems biologist, professor, and head of the Rensselaer Department of Biomedical Engineering. “By measuring 24 metabolites from a blood sample, this algorithm can tell whether or not an individual is on the Autism spectrum, and even to some degree where on the spectrum they land.”

Big data techniques applied to biomedical data found different patterns in metabolites relevant to two connected cellular pathways (a series of interactions between molecules that control cell function) that have been hypothesized to be linked to ASD: the methionine cycle and the transulfuration pathway. The methionine cycle is linked to several cellular functions, including DNA methylation and epigenetics, and the transulfuration pathway results in the production of the antioxidant glutathione, decreasing oxidative stress.

Troy, N.Y. — With a $2 million grant from the National Institutes of Health (NIH), a team of researchers – including Rensselaer Polytechnic Institute professor Juergen Hahn – will investigate the potential of using transplanted regulatory T cells (Tregs) to reduce inflammation in diseases like inflammatory bowel disease, which currently has no known viable treatment options.

“The challenge is that the transplanted cells are not very ‘stable’ and may end up contributing to inflammation rather than combating inflammation,” said Hahn, professor, head of the Department of Biomedical Engineering, and member of the Rensselaer Center for Biotechnology and Interdisciplinary Studies. “We propose to condition the regulatory T cells by exposing them to various conditions prior to transplantation such that their stability is increased, and we expect that this will make them more potent in combating inflammation. Our goal is to transplant conditioned Tregs into a host for therapeutic inhibition of inflammation.”

The project will combine computational research at Rensselaer with research in vitro and in vivo at two Texas A&M University research laboratories. The team will create a computational model able to predict Tregs induction, function, and stability. That model will be used to develop treatment regimens that use transplanted Tregs to inhibit inflammation, providing new treatment options for a variety of diseases characterized by excessive inflammation.