Many mysteries surround autism spectrum disorder. Arguably, one of the largest is how to authoritatively diagnose it in children. However, scientists have developed the first physiological test, based on a big data algorithm. Keep reading to learn more about this advancement and what it might mean for society at large.
Scientists from Rensselaer Polytechnic Institute started by looking at metabolites — the products of metabolic reactions catalyzed by enzymes. After evaluating 24 metabolites within a blood sample, scientists used big data-driven technologies to find similarities between that blood component and cellular pathways with a suspected link to autism.
The ability to do this was significant because big data capabilities allowed scientists to scrutinize the metabolites together instead of separately when looking for patterns. In addition to being able to tell if a person is on the autism spectrum, the blood test can, to a certain degree, identify where on the spectrum that individual falls.
Although other scientists have looked at individual metabolites to pinpoint how they relate to autism, those results have not been conclusive, or easy to replicate in other studies. Because scientists could use big data to look at numerous metabolites simultaneously, their outcomes became statistically stronger.
Members of the medical sector who perform research in laboratories know the tasks they do are very precise, and that seemingly small factors could make big differences in overall results. For example, blood specimen tubes spun in a horizontal centrifuge produce more plasma or serum for analysis, compared to Containers characterized by a fixed-angle centrifuge.
Because big data allows people in the scientific community to look at huge samples of data, it increases scientists’ ability to figure out how smaller factors cause certain results in the overall findings of particular studies. Big data might be instrumental in aiding scientists to improve the aforementioned metabolite test, making it even more reliable than previously thought.
This recent news is far from the first attempt to determine autism risk through blood-work. Doctors are already using genetic tests to help screen for autism, comparing individual DNA with a genome marker contained on a chip. Data says more than 60 percent of parents who received genetic testing results with insight into whether a child might be autistic found them at least somewhat helpful.
Although most parents were glad to know what the test revealed, some were also frustrated because the results were not always informative or immediately helpful. If the big data metabolite test continues to give strong results in the lab, it could be seen as the new standard used when parents want to know more about potential autism risks.
Information from the CDC released before the breaking news about this emerging blood test cautioned there is a two-part screening process for detecting autism. By the time a child reaches his second birthday, the diagnosis of autism is much more reliable than it is earlier in life. However, many kids don’t get final opinions about diagnoses until they are much older, delaying essential treatments that could improve the quality of their lives.
Theoretically, the big data blood test you’ve learned about could cut down on or eliminate those slowed responses. Then, parents have more peace of mind and their sons and daughters have faster access to treatment.
Studies carried out with data collected from the United States and the United Kingdom found that lifetime care costs for a person with autism can be $2 million or more. If this new big data test is consistently more efficient at reaching firm conclusions about whether a patient has autism, some of those expenses might decrease, especially if parents can make well-informed decisions sooner.
Although ongoing experimentation is still necessary for the big data blood test, the results so far are promising. In coming years, we might screen for autism in improved, more dependable ways, because of what this research revealed.
By Kayla Matthews
