New blood test that can detect autism in children with nearly 90 percent of. accuracy

Researchers at Rensselaer Polytechnic Institute have developed a blood test that can detect autism in children with nearly 90 percent of the. accuracy. Already last year, they provided evidence that their method of diagnosing autism has great potential for. Now further studies have confirmed the effectiveness of this technique.

A key component of the test is an algorithm that takes into account the presence and concentration of decimalsoIn roFULL TIESoIn chemicals in the blood, whichore previously been linked to autism. The algorithm was successfully tested on a group of 150 adults, with ktohalf of whom had previously been diagnosed with autism. In a follow-up to their study, the researchers confirmed the test’s effectiveness in diagnosing autism in children.

Although scientists don’t know exactly what contributes to causing autism, it seems to leave traces in the form of metabolitesoin the blood. In a previous study, researchers found that by mapping the level ofoin the blood of 24 compoundsoin chemicals associated with two biochemical pathways linked in turn to autism, they were able to identify those thatoers in the group had autism with very high accuracy.

Physiological test, whichory supports the diagnostic process, detects features associated with the autism spectrum with 88 percent of. accuracy. It will make it possible to start treatment of the disease earlier. The results of the study have been published in „Bioengineering & Translational Medicine”.

– We studied a group of children on the autism spectrum, different from those whoora has participated in previous studies and we have had similar success. We are able to predict with 88 percent accuracy whether children have autism – said Juergen Hahn of the Rensselaer Polytechnic Institute, head of theow author of the publication.

We still don’t know what causes the onset of ASD (autism spectrum disorder). About 30-35 percent. casesoin ASD is associated with rare genetic variants. In addition, environmental factors and rodifferent mutations also play a role.

ASD cases are characterized by a wide range of symptomsow, ktore can include mild behavioral problems or debilitating compulsive behavior, anxiety, cognitive impairment and more. Because the symptoms of ASD are so zrognarly, and the causes are not yet fully understood, diagnosis and treatment are very difficult. Nonetheless, the researchers believe that early diagnosis leads to better outcomeoin therapy.

Instead of looking for a specific indicator of ASD, the approach developed by Hahn uses algorithms to find patternsoIn metabolites relevant to dwoch connected pathwayow comorkowe. This involves a series of interactions between molecules that areore control the function of the comorki.

To avoid the lengthy process of collecting new data in clinical trials, Hahn and his teamoł looked for an existing set ofoin the data, whichore included metabolites of interest. In this wayob selected 154 individuals between the ages of 2 and 17, whoore have previously participated in other studies at Arkansas Children’s Research Institute.

The data included only 22 of 24 metabolitesow, ktorych Hahn used to create the original predictive algorithm, but as it turned out, this was enough to run the testow. The algorithm correctly predicted autism with 88 percent accuracy. In the original tests, the result was even higher, at 97.6 percent. accuracy. According to Hahn, roThe difference between the accuracy can be attributed to several factors, with ktohe most important is that two of the metabolitesow were unavailable in the second dataset.

– We hope that our method will make its way permanently into clinical trials and those available on the commercial market – noted Hahn.