A Simple Blood Test for Autism? Study’s Use Of ‘Big Data’ Validates Early Intervention
TROY, N.Y. — A new study conducted by the Center for Biotechnology and Interdisciplinary Studies (CBIS) at the Rensselaer Polytechnic Institute has resulted in the first physiological test for autism, paving the way for earlier diagnoses of the illness as well as more effective intervention and treatment.
The precise causes of autism, a neuro-developmental disorder that afflicts 1.5% of the US population, remain fuzzy at best. Experts in the field suspect a combination of biological and environmental factors, but most children are not formally diagnosed until age of four. By then, autism’s behavioral symptoms are more obvious but the condition is already well-advanced, making treatment more difficult. Many children never get diagnosed until they experience problems in school.
Past studies have pointed to an individual gene or biomarker that might distinguish an autistic brain from a normal or “neurotypical” one. However, those brain differences proved impossible to confirm on repeat trials. As a result, there’s been little progress in developing an early test for autism and a possible medical intervention that might reverse or delay the condition — until now.
Researchers at CBIS, led by Dr. Juergen Hahn, a systems specialist and head of the Rensselaer Department of Biomedical Engineering, decided to test a broader cluster of small molecules in the brain – known as “metabolites” — employing never-before-used “big data” techniques that generate a predictive algorithm to improve the statistical reliability of their findings over past studies.
Hahn’s team examined a total of 149 subjects, about half with diagnosed autism. They were able to isolate the brain metabolites that distinguish autism sufferers from those without the condition. He hopes to repeat the experiment with a new experimental cohort to try to replicate his team’s findings.
“A lot of studies have looked at one biomarker, one metabolite, one gene, and have found some differences, but most of the time those differences weren’t statistically significant or the results could not be reliably replicated,” Hahn says in an Institute news release.
Estimates of the population afflicted with autism continue to increase as more becomes known about the disorder. In 2000, the Centers for Disease Control and Prevention reported that 1 in 80 children suffered from the condition, but in 2010 revised that estimate to 1 in 68 (1 in 42 boys compared to 1 in 189 girls). More recent estimates place the prevalence rate at 1 in 45 overall.
Hahn remains hopeful that his research will aid in developing early intervention options.
“If these pathways [in the autistic brain] are different, what happens if I can manipulate the pathway so that it works similarly to the neurotypical ones? “What do I need to prod? Which molecules do I need to add or take away? Having a model that describes these pathways makes it a lot easier to adjust them,” he says.
Hahn’s research appeared in PLOS Computational Biology, an open access journal published by the Public Library of Science.