Welcome back to Reading the Research, where I trawl the Internet to find noteworthy research on autism and related subjects, then discuss it in brief with bits from my own life, research, and observations.
Today's article is a glimpse of a possible future for autism diagnostic codes and our understanding of the spectrum.
Autism is what scientists call a heterogeneous condition. Which is to say, there isn't just one cause, and there isn't just one treatment. Symptoms vary incredibly widely, and so do the genetics involved. As often as not, the experiences of any given group of autistic people will have commonalities, but their specific symptoms might only have the smallest amount of overlap.
A great deal of money and time have been poured into finding out what causes autism over the last 40 years or so. The results have not been conclusive. Everything from air pollution, reduced gut diversity, and the genetic history of humanity itself contributes, it seems. There's evidence that autism has produced human specialists over thousands of years, and those specialists have been valued enough to pass their genetics on throughout the generations.
It was hoped, with the advent of the human genome project, that we might finally understand how autism is coded for. That hope proved futile... at least until now.
Machine learning and Big Data may eventually provide these answers, assuming both can be harnessed. This study is a very small example of the idea. Basically, you get enough relevant data points (thousands of autistic people's genetics), and then you use a powerful computer find categories for you from those data points.
The researchers in this study did exactly that, but for one very small subset of autism. If this was done on a grander scale, it's likely we could have actual categories of autism, rather than simply sorting ourselves based on symptoms.
In all honesty, I have mixed feelings about this. Part of the reason the autism community is a community is because we have a lot in common, including the diagnosis code. Splitting the spectrum into dozens of microcategories seems like it might intrude on that unity and de-legitimize autistic experiences.
However, these microcategories might also allow for more targeted treatments for specific issues. The category these researchers found suffers from cholesterol issues. Having that knowledge lets you know what to test for, and what to be careful of overall. In short, having these categories may allow us to more easily and quickly reduce autistic suffering due to related medical conditions.
(Pst! If you like seeing the latest autism-relevant research, visit my Twitter, which has links and brief comments on studies that were interesting, but didn't get a whole Reading the Research article about them.)
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