Validating autism subtypes: A crucial but often overlooked step in research

Published by: Spectrum Autism Research News
Written by:  HILDE GEURTS, JOOST AGELINK VAN RENTERGEM

The practice of categorizing autistic people into subtypes based on similarities in their traits and abilities is divisive. Subtypes can have negative connotations, evoking images of stereotyping and marginalization.

For decades, the autism spectrum was, by definition, a collection of subtypes, including Asperger syndrome and pervasive developmental disorder-not otherwise specified. But there was no clear clinical distinction between the subtypes, and they did not fully capture the inherent variation among people on the spectrum. So the fifth and most recent edition of the Diagnostic and Statistical Manual of Mental Disorders, to which clinicians refer to make diagnoses, retired them from use in 2013.

That said, there are often good reasons for subtyping. Identifying subtypes of people who share particular genetic variants may be useful, because these variants may be associated with specific medical issues. Subtyping analysis can also be used to demonstrate the nonexistence of certain subtypes. Or it can help researchers to identify who benefits most from a particular kind of support, without focusing on etiology or ontology.

For these reasons, we should not categorically stop conducting subtyping analyses. But research should focus on the discovery of meaningful subtypes of autism. To seek consensus among scientists on the number and nature of subtypes, we conducted a systematic review of the autism subtyping literature. We limited our search to articles published since 2001 that had used a statistical or machine-learning method to discover subtypes of autistic people. These subtyping methods are data-driven: The researchers did not search for a specific number of subtypes and did not specify in advance what the subtypes would look like; they let the data speak for itself.

We identified 156 articles that met our criteria. Of these, 82 percent found that two to four subtypes described their data well. But these subtypes reflected a highly diverse set of measures, including levels of inflammatory markers, scores on autism trait and sensory sensitivity questionnaires, tests of language skills, hormone levels and patterns of facial features, and this diversity made it difficult to find consensus or draw any firm conclusions. Because the samples included variables that are so heterogeneous across many of these studies, it is impossible to determine whether researchers were looking at the same subdivision from different angles or discovering different subdivisions every time. Click here to read the rest of the story.

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