MedicalResearch.com Interview with:
Juergen Hahn, Professor and Department Head
Department of Biomedical Engineering Department of Chemical & Biological Engineering Center for Biotechnology and Interdisciplinary Studies Rensselaer
MedicalResearch.com: What is the background for this study?
Response: Autism Spectrum Disorder (ASD) encompasses a large group of early‐onset developmental disorders that are collectively characterized by deficits in social interaction and communication as well as the expression of restricted, repetitive behaviors and interests. ASD is currently estimated to affect 1 in 59 children in the US. Despite this high prevalence, relatively little is know about the pathophysiology of ASD. The result of this is that no lab test exists for ASD and the diagnosis is based upon observations of the child. The average age of diagnosis is 4 years of age, but it is generally acknowledged that diagnosis at 2 years of age is possible and desirable.
MedicalResearch.com: What are the main findings?
Response: This work represents a step toward the goal of developing a biochemical diagnostic for ASD by comparing five classification algorithms on existing data of folate‐dependent one carbon metabolism (FOCM) and transsulfuration (TS) pathways metabolites, and also validating the classification results with new data from an ASD cohort.
The comparison results indicate a high sensitivity and specificity for the original data set and up to a 88% correct classification of the ASD cohort at an expected 5% misclassification rate for typically‐developing controls. These results form the foundation for the development of a biochemical test for ASD which promises to aid diagnosis of ASD and provide biochemical understanding of the disease, applicable to at least a subset of the ASD population.
MedicalResearch.com: What should readers take away from your report?
Response: There are several take away messages from this work:
(1) metabolite concentrations of the FOCM/TS pathways are different for children with ASD and their typically developing peers,
(2) markers of oxidative stress and DNA methylation are significant contributors to this difference in metabolites, and
(3) multivariate analysis is an important tool for extracting these differences of the metabolite concentrations.
MedicalResearch.com: What recommendations do you have for future research as a result of this work?
Response: The results look very promising and should lead to a medium to large scale clinical trial in the future with the goal to validate these findings. This clinical trial should include children with ASD, their typically developing peers, and children with a developmental delay. The latter group has not been included in our work, but it is of significant interest as it is important to determine if differences in metabolite concentrations also exist between child diagnosed with ASD and children who have a non-ASD related developmental delay.
MedicalResearch.com: Is there anything else you would like to add?
Response: We are excited about the results and hope that it will lead to the future work described above in a reasonable time frame.
Howsmon, D. P., Vargason, T. , Rubin, R. A., Delhey, L. , Tippett, M. , Rose, S. , Bennuri, S. C., Slattery, J. C., Melnyk, S. , James, S. J., Frye, R. E. and Hahn, J. (2018), Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically‐developing peers: A comparison and validation study. Bioengineering & Translational Medicine. . doi:10.1002/btm2.10095
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