https://www.sciencedaily.com/releases/2018/06/180619122434.htm
Success of blood test for autism affirmed
First physiological test for autism proves high accuracy in second trial
Date:
June 19, 2018
Source:
Rensselaer Polytechnic Institute
Summary:
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.
The initial success in 2017 analyzed data from a group of 149 people, about half of whom had been previously diagnosed with ASD. For each member of the group, Hahn obtained data on 24 metabolites related to the two cellular pathways -- the methionine cycle and the transsulfuration pathway. Deliberately omitting data from one individual in the group, Hahn subjected the remaining dataset to advanced analysis techniques and used results to generate a predictive algorithm. The algorithm then made a prediction about the data from the omitted individual. Hahn cross-validated the results, swapping a different individual out of the group and repeating the process for all 149 participants. His method correctly identified 96.1 percent of all typically developing participants and 97.6 percent of the ASD cohort. ...
The new study applies Hahn's approach to an independent dataset....To avoid the lengthy process of gathering new data through clinical trials, Hahn and his team searched for existing datasets that included the metabolites he had analyzed in the original study. ...The data included only 22 of the 24 metabolites he used to create the original predictive algorithm, however Hahn determined the available information would be sufficient for the test. ...
The algorithm was then applied to the new group of 154 children for testing purposes. When the predictive algorithm was applied to each individual, it correctly predicted autism with 88 percent accuracy. ...
Hahn said the difference between the original accuracy rate and that of the new study can likely be attributed to several factors, the most important being that two of the metabolites were unavailable in the second dataset. Each of the two metabolites had been strong indicators in the previous study.