Review of cluster analysis of phenotypic data in Autism Spectrum Disorders: distinct subtypes or a severity gradient...

Background: Individuals with autism spectrum disorder (ASD) form a heterogeneous group, posing a challenge for clinical definition. Additional problems regarding the diverse clinical presentation arise from changes in diagnostic criteria according to the latest Diagnostic and Statistical Manual of Mental Disorders (DSM-5), with exclusion of individuals who met earlier criteria or inclusion of more than previously. Objectives: To investigate studies that have attempted to reduce the heterogeneity of ASD based on cluster analysis of phenotypic data and to clarify whether ASD should be interpreted as 'a unitary spectrum,' with a severity gradient, or defined by distinct subtypes. This will allow better understanding of the disorder with implications for its treatment and prognosis. Methods: A literature search was made through PubMed, Researchgate and Google Scholar for studies of ASD populations. In addition, reference lists from identified studies were reviewed. Results: Only 10 studies were found that dealt with the heterogeneity of ASD and its different subtypes, based on the review prerequisites. Most of the studies appear to support the existence of subtypes within ASD, but it remains unclear whether these are considered as different specific subtypes with characteristic profiles of symptoms or as a part of a severity gradient across symptom domains. Conclusions: Drawing definitive conclusions from the published studies about the nature of ASD is difficult, due to the fundamental methodological differences among the studies and their inconsistent findings. This review shed light on a number of discrepancies regarding the current classification of ASD. However, future research will be necessary to provide a more definite answer on the question of a definition based on separate diagnostic subtypes or on a severity gradient by including larger samples that are followed longitudinal and by applying better diagnostic system and choosing the appropriate variables.