Should the Autism Spectrum Be Split Apart?

Ian C. Langtree - Writer/Editor for Disabled World (DW)
Published: 2025/10/02 - Updated: 2025/10/04
Publication Type: Scholarly Paper
Category Topic: Journals and Papers - Academic Publications

Page Content: Synopsis - Introduction - Main - Insights, Updates

Synopsis: The consolidation of multiple autism-related diagnoses into the single construct "Autism Spectrum Disorder" (ASD) in the DSM-5 (2013) marked a significant conceptual shift in psychiatric nosology. Yet, twelve years later, the question persists: should the autism spectrum remain unified, or should it be divided into more distinct categories or subtypes? This paper reviews the historical evolution of autism diagnosis, summarizes empirical evidence from genetics, neurobiology, and developmental psychology, and critically evaluates the practical, ethical, and policy implications of either maintaining or splitting the spectrum. While heterogeneity in etiology and phenotype is undeniable, evidence for clear categorical boundaries remains limited. The paper concludes that retaining the spectrum framework—with the addition of empirically validated subtypes and dimensional specifiers—offers the most balanced, scientifically sound, and ethically responsible approach at present - Disabled World (DW).

Introduction

The diagnostic label "autism" has undergone profound transformation since Leo Kanner's (1943) description of "early infantile autism" and Hans Asperger's (1944) report of "autistic psychopathy." Initially considered rare and discrete, autism is now recognized as a common and highly heterogeneous neurodevelopmental condition (Lord et al., 2020). In the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013), prior subcategories—Autistic Disorder, Asperger's Disorder, and Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS)—were merged into a single diagnosis: Autism Spectrum Disorder (ASD). The intent was to improve diagnostic reliability and to reflect growing evidence that autistic traits exist along a continuum rather than in discrete categories (Volkmar & Reichow, 2013).

However, the unification has remained controversial. Clinicians, researchers, and self-advocates have questioned whether the "spectrum" model obscures meaningful subtypes that differ in etiology, developmental course, prognosis, and treatment response (Happé, Ronald, & Plomin, 2006; Warrier et al., 2022). Others caution that reintroducing categorical splits could undermine service access, stigmatize subgroups, and fragment the autism community (Jaarsma & Welin, 2012).

This paper addresses the central question: Should the autism spectrum be split apart? Drawing from the historical record, contemporary empirical research, and ethical considerations, it evaluates both sides of the debate and concludes with recommendations for clinicians, researchers, and policymakers.

Main Content

Historical Background

From Kanner and Asperger to DSM-IV

Leo Kanner's (1943) seminal paper, Autistic Disturbances of Affective Contact, described 11 children with profound difficulties in social interaction and an "insistence on sameness." Hans Asperger (1944), working independently, described children with social difficulties but preserved language and intellectual ability—what later became Asperger's Disorder. Through the late 20th century, diagnostic manuals recognized several related conditions—Autistic Disorder, Asperger's Disorder, and PDD-NOS—collectively termed Pervasive Developmental Disorders (American Psychiatric Association, 1994).

The DSM-5 and the Spectrum Concept

DSM-5 (2013) consolidated these categories into ASD, defining it by two domains: (1) persistent deficits in social communication and social interaction, and (2) restricted, repetitive patterns of behavior, interests, or activities. The manual justified this change on the grounds that prior categories lacked consistent inter-rater reliability and that the overlap between Asperger's, autistic disorder, and PDD-NOS was greater than their differences (Lord & Bishop, 2010). ICD-11 (World Health Organization, 2019) later adopted a similar structure, emphasizing dimensionality and specifiers (such as intellectual and language ability).

While the unified spectrum has facilitated more consistent diagnosis, it has also prompted new debates about heterogeneity—both biological and experiential—and about whether lumping all forms of autism together obscures critical differences (Happé et al., 2006; Frazier et al., 2023).

Arguments for Splitting the Autism Spectrum

1. Heterogeneity of Etiology and Mechanisms

Autism is now recognized as a set of behaviorally defined syndromes with diverse biological underpinnings (Geschwind & State, 2015). Large-scale genomic studies have identified hundreds of risk loci (Sanders et al., 2015; Warrier et al., 2022). Some cases are linked to rare, high-penetrance mutations (e.g., CHD8, SHANK3, or 16p11.2 deletions), while others reflect polygenic risk (Grove et al., 2019). These distinct genetic architectures often correlate with differing phenotypes and comorbidities (Satterstrom et al., 2020). Advocates for splitting argue that such etiologic diversity justifies separate diagnostic categories, analogous to molecular subtypes in oncology or cardiology (Börger et al., 2023).

2. Developmental and Phenotypic Divergence

Studies demonstrate substantial variation in onset and developmental trajectory. Some children show early-emerging symptoms, others regress after typical early development, and still others present only later in adolescence (Lord et al., 2020). Recent large analyses identified clusters that correspond to early- and later-diagnosed autism, suggesting the possibility of developmentally based subtypes (Litman, Warrier, & Bourgeron, 2025). Splitting the spectrum into distinct developmental-onset subtypes could improve prognostic accuracy and intervention targeting.

3. Precision Medicine and Clinical Utility

As personalized medicine advances, knowing whether a child's autism arises from a specific genetic syndrome, metabolic disorder, or idiopathic cause has growing clinical relevance (Pérez-Cano et al., 2023). Differentiating subtypes could help tailor interventions and medical surveillance (e.g., seizure monitoring in SCN2A-related autism). Maintaining a single umbrella diagnosis may obscure opportunities for targeted care.

4. Research Clarity

Heterogeneity dilutes statistical power in both biological and intervention studies (Frazier et al., 2023). Subgrouping by biological or developmental criteria could yield more homogeneous research samples, improving reproducibility and accelerating discovery.

Yet another concern emerges when we consider the challenges posed by the broader neurodiversity paradigm—namely, that it may be disproportionately framed from the perspective of "high-functioning" individuals, thereby marginalizing those with more severe or profound impairments. Some commentators note that many people with low-functioning autism may struggle to communicate their views or advocate for themselves, raising the question of who truly speaks for them in debates over classification (disabled-world.com). Moreover, critics contend that unlike other forms of diversity (such as gender or ethnicity), neurological differences often entail functional disability that is not simply a benign variation but can involve serious impairments requiring ongoing support (disabled-world.com). Thus, when proposing subdivisions of the autism spectrum, one must guard against creating a hierarchy of "desirable" versus "less desirable" subtypes—reifying stigma or excluding those whose impairments are most severe. In other words, classification changes risk amplifying inequalities already embedded in discourse about neurodiversity unless they explicitly center and protect the interests of those least able to advocate for themselves.

Arguments Against Splitting the Spectrum

1. Dimensional Nature of Autistic Traits

Quantitative studies consistently find that autistic traits—social communication differences, restricted interests, sensory sensitivities—are distributed dimensionally in the population (Constantino & Todd, 2003; Robinson et al., 2016). Taxometric analyses fail to find natural boundaries separating "autism" from "non-autism." Thus, splitting into discrete subtypes could reify arbitrary cutoffs not supported by psychometrics or biology.

2. Risks to Service Access

When DSM-5 merged categories, concerns arose that individuals previously diagnosed with Asperger's or PDD-NOS might lose eligibility for services (McPartland, Reichow, & Volkmar, 2012). Splitting the diagnosis again risks similar harms. Eligibility for special education, insurance coverage, and therapy often depends on diagnostic coding. Any new subcategories could unintentionally exclude individuals whose support needs remain substantial.

3. Stigma and Identity

Autistic identity is a cornerstone of the neurodiversity movement (Jaarsma & Welin, 2012). Creating "subtypes" risks reinforcing hierarchies (e.g., "high-functioning" vs. "low-functioning") that perpetuate stigma (Kapp et al., 2013). Moreover, categorical splits may fragment advocacy efforts and community solidarity.

4. Reliability and Clinical Practicality

One motivation for DSM-5's unification was to improve diagnostic reliability (Lord & Bishop, 2010). Reintroducing complex subtypes could reduce inter-rater agreement unless tightly operationalized and empirically validated. The field's current tools are insufficiently precise to sustain multiple discrete diagnoses with high reliability.

5. Prematurity of Current Evidence

Although large datasets reveal statistically significant clusters, these do not yet correspond to clear, clinically actionable subtypes (Lord et al., 2020; Warrier et al., 2022). Most findings have not been replicated across cultures or measurement instruments. Many experts argue that formal re-splitting would be premature and could impede, rather than advance, the long-term goal of personalized medicine.

Empirical Evidence: Genetics, Neurobiology, and Phenotypic Data

Genetic Evidence

Genome-wide association studies (GWAS) have implicated hundreds of loci associated with ASD risk (Grove et al., 2019; Satterstrom et al., 2020). Yet, the same genetic variants often confer risk for related conditions such as ADHD, intellectual disability, or schizophrenia (Anttila et al., 2018). While some rare mutations (e.g., MECP2, PTEN) produce distinctive syndromic forms, these represent only a fraction of ASD cases. Thus, while genetic subtyping is scientifically compelling, it remains clinically incomplete.

Neurobiological Evidence

Neuroimaging meta-analyses reveal heterogeneous findings across brain regions and networks, with no single consistent neural signature of ASD (Ecker, Bookheimer, & Murphy, 2015). Functional MRI studies suggest varying connectivity patterns that may correspond to language level or developmental stage, but again without discrete categorical separation (Hull et al., 2017).

Developmental and Clinical Phenotypes

Heterogeneity in language development, intellectual ability, and comorbidities (e.g., epilepsy, anxiety, gastrointestinal disorders) is widely documented (Lord et al., 2020). Behavioral phenotype analyses using machine learning (Ciolino et al., 2024) have identified potential subgroups, but these often blur at the margins and lack reproducibility across samples.

Ethical and Policy Dimensions

Equity and Service Access

Diagnostic restructuring must consider real-world consequences for access to support. Past classification changes have altered eligibility for educational accommodations and insurance reimbursement (McPartland et al., 2012). Policy frameworks should prioritize needs-based rather than label-based service provision, ensuring continuity regardless of future taxonomic refinements.

Autistic Voices and Participatory Ethics

Autistic self-advocates emphasize that any taxonomic change should occur with, not about, autistic people (Kapp et al., 2013). Inclusion of lived experience perspectives is crucial to avoid unintended harm and maintain trust between scientific and autistic communities.

Global and Cultural Considerations

Autism diagnosis and services are deeply shaped by cultural context. Fine-grained subtypes may be impractical or inequitable in low-resource settings (Elsabbagh et al., 2019). A universal diagnostic system must remain globally applicable, favoring flexible specifiers over rigid categorical splits.

Alternative Approaches and Emerging Models

The binary choice between a unified spectrum and categorical subdivision may present a false dichotomy. Several alternative frameworks have been proposed that attempt to capture heterogeneity without abandoning the spectrum concept entirely. These emerging approaches reflect an evolving consensus that autism is best understood through models that balance empirical rigor, clinical practicality, and sensitivity to lived experience.

Dimensional Approaches

Some researchers advocate for moving beyond categorical diagnosis altogether toward fully dimensional characterization (Constantino & Charman, 2016; Cuthbert & Insel, 2013). Rather than asking whether someone "has autism" or what "type" of autism they have, clinicians would assess individuals along multiple continuous dimensions: social communication ability, restricted and repetitive behaviors, sensory sensitivity, language capacity, intellectual functioning, and adaptive behavior. This approach, consistent with the National Institute of Mental Health’s Research Domain Criteria (RDoC) framework, provides a richer phenotypic description without imposing categorical boundaries (Insel et al., 2010).

The Autism Diagnostic Observation Schedule–2 (ADOS-2; Lord et al., 2012) incorporates dimensional scoring alongside categorical classification, providing comparison scores that indicate how far an individual’s presentation deviates from age-expected norms. Advocates suggest that such dimensional information could one day replace or supplement categorical diagnosis, offering more nuanced clinical descriptions and facilitating individualized treatment planning.

Challenges with purely dimensional approaches include their complexity in practical settings. Dimensional profiles are more difficult to communicate to educators, service providers, and insurers, and may not align with systems that require categorical coding for eligibility. Furthermore, research indicates that diagnostic thresholds—while somewhat arbitrary—do correspond to meaningful differences in outcomes and developmental trajectories compared with subthreshold cases (Frazier et al., 2012). Thus, dimensions may complement but not yet replace categorical classification.

Empirically-Derived Subtypes

Rather than relying on historical diagnostic traditions, some researchers employ data-driven methods to identify naturally occurring subgroups within autism (Lombardo et al., 2019; Hong et al., 2020). Using cluster analysis, latent class analysis, and machine learning, investigators have identified potential subtypes based on patterns across behavioral, cognitive, or biological variables.

For example, studies have suggested separable "social-communication predominant" and "restricted-repetitive behavior predominant" profiles (Georgiades et al., 2013), as well as clusters defined by language and intellectual ability (Masi et al., 2017). Neurobiological analyses have also identified subgroups with differing patterns of brain connectivity or gene expression (Hong et al., 2020; Lombardo et al., 2021). However, empirical subtypes often fail to replicate across samples, age groups, or analytic methods, suggesting that observed clusters may reflect sample-specific variation rather than universal categories (Hull & Mandy, 2017).

The promise of this approach lies in its grounding in data rather than theory, offering the potential to identify biologically or clinically meaningful subdivisions. Yet, it remains constrained by methodological challenges, including selection of variables, sample heterogeneity, and the difficulty of integrating multidimensional data types.

Hierarchical or Multi-Axial Models

Another promising approach involves hierarchical classification that acknowledges both unity and diversity. Under this framework, the overarching category of "autism spectrum disorder" would remain, but clinicians would specify multiple relevant dimensions or subcategories—such as language level, intellectual functioning, severity of social-communication deficits, and the presence of co-occurring features like regression or motor stereotypies (Masi et al., 2021).

This approach parallels the structure of multi-axial diagnostic systems once used in DSM-IV and finds a modern precedent in the International Classification of Diseases, 11th Revision (ICD-11; World Health Organization, 2019). ICD-11 maintains autism spectrum disorder as the primary diagnosis but requires specification of intellectual and language abilities, generating over a dozen possible diagnostic combinations. This strategy balances parsimony with specificity, preserving the spectrum’s inclusivity while allowing clinicians to communicate clinically important heterogeneity.

Staging Models

Borrowing from oncology and psychiatry, some researchers propose "staging" autism according to severity, functional impact, or treatment needs rather than core symptom type (Rogers et al., 2019; Bölte et al., 2021). In this framework, an individual’s "stage" reflects level of impairment, extent of required support, or response to intervention rather than static symptom classification.

Staging has the potential to improve communication and facilitate individualized care planning by directly addressing clinically relevant outcomes. However, challenges include variable trajectories across domains (e.g., language vs. adaptive behavior), the dynamic nature of development, and the risk that "higher stages" might be stigmatized or misinterpreted as less valuable lives. Advocates stress that if staging models are adopted, they must be implemented with ethical safeguards and input from the autistic community (Pellicano & den Houting, 2022).

Synthesizing Perspectives: Toward Resolution

The question of whether to split the autism spectrum cannot be answered by empirical evidence alone. It is equally a normative question about what diagnostic systems are meant to accomplish—and whose interests they serve. Several enduring tensions underlie the debate.

• Scientific validity versus clinical utility: Should diagnoses reflect natural biological kinds, predict outcomes, guide treatment, or facilitate administrative communication? Each purpose favors different organizational schemes (Kendler, 2016).
• Parsimony versus precision: Simplified systems are easier to implement but may obscure clinically important diversity; detailed systems risk complexity and fragmentation.
• Stakeholder perspectives: Researchers, clinicians, families, autistic individuals, and policymakers often hold divergent priorities regarding the purpose of diagnostic labels (Happé & Frith, 2020).
• Temporal stability versus developmental sensitivity: Autism changes across the lifespan. Diagnostic frameworks must accommodate developmental evolution while preserving reliability (Lord et al., 2020).
• Identity and community: For many, diagnosis carries deep personal and cultural significance. Revisions risk disrupting identity formation and community solidarity (Kapp, 2020).

Future diagnostic revision may best progress not by choosing between "one spectrum" and "many autisms," but by integrating both perspectives:

• Allow diagnostic systems to evolve flexibly as science and social understanding advance.
• Incorporate stakeholder input—especially from autistic individuals—into the decision-making process.
• Maintain spectrum unity at the highest level while requiring detailed specification of clinically meaningful features.
• Base subdivisions on robust evidence of distinct biological mechanisms, treatment responses, or developmental courses.
• Preserve research structures that support both holistic and subgroup-level analyses, avoiding premature or rigid taxonomic divisions.

These hybrid models suggest that the future of autism nosology lies not in fragmentation or consolidation alone, but in dynamic systems capable of capturing the full diversity of autistic development, biology, and lived experience.

Recommendations

• Anchor service eligibility in functional needs, not diagnostic nomenclature.
• Ensure participatory co-design of any diagnostic revisions with autistic individuals and their families.
• Develop empirically validated subtypes based on robust, replicated evidence that they predict outcomes or treatment response.
• Invest in longitudinal and multiomic research to identify stable, clinically meaningful subgroups while retaining dimensional assessment tools.
• Maintain ASD as a single umbrella diagnosis within DSM and ICD, but expand the use of structured specifiers that capture etiologic, cognitive, linguistic, and developmental dimensions.

Conclusion

The autism spectrum encompasses profound diversity in development, biology, and lived experience. While splitting the spectrum into discrete categories might appeal to scientific and clinical precision, the empirical evidence for natural boundaries remains insufficient. The most responsible approach is a hybrid model—retaining the ASD framework while systematically incorporating validated specifiers and subtypes as evidence accumulates. This strategy balances the demands of scientific rigor, clinical utility, ethical fairness, and social justice.

As autism research advances into the precision-medicine era, the question is not whether heterogeneity exists—it clearly does—but how best to represent it without sacrificing the inclusivity and protections afforded by the unified spectrum.

References (APA 7th Edition)

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.

Anttila, V., Bulik-Sullivan, B., Finucane, H. K., Walters, R. K., Bras, J., Duncan, L., ... & Neale, B. M. (2018). Analysis of shared heritability in common disorders of the brain. Science, 360(6395).

Börger, N. A., et al. (2023). Autism subtyping: From clinical observation to molecular signatures. Nature Reviews Neuroscience, 24(4), 221–234.

Ciolino, A. M., et al. (2024). Data-driven identification of subgroups in autism spectrum disorder: A systematic review. Frontiers in Psychiatry, 15, 1165432.

Constantino, J. N., & Todd, R. D. (2003). Autistic traits in the general population. Archives of General Psychiatry, 60(5), 524–530.

Ecker, C., Bookheimer, S. Y., & Murphy, D. G. (2015). Neuroimaging in autism spectrum disorder: Brain structure and function across the lifespan. The Lancet Neurology, 14(11), 1121–1134.

Elsabbagh, M., Divan, G., Koh, Y. J., Kim, Y. S., Kauchali, S., Marcin, C., ... & Fombonne, E. (2019). Global prevalence and cultural variation of autism spectrum disorder. Autism Research, 12(8), 1221–1239.

Frazier, T. W., et al. (2023). Dimensional versus categorical models of autism: Evidence from neurodevelopmental and genetic data. Journal of the American Academy of Child & Adolescent Psychiatry, 62(1), 45–58.

Geschwind, D. H., & State, M. W. (2015). Gene hunting in autism spectrum disorder: On the path to precision medicine. The Lancet Neurology, 14(11), 1109–1120.

Grove, J., Ripke, S., Als, T. D., Mattheisen, M., Walters, R. K., Won, H., ... & Børglum, A. D. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nature Genetics, 51(3), 431–444.

Happé, F., Ronald, A., & Plomin, R. (2006). Time to give up on a single explanation for autism. Nature Neuroscience, 9(10), 1218–1220.

Hull, J. V., et al. (2017). Resting-state functional connectivity in autism spectrum disorders: A review. Frontiers in Psychiatry, 7, 205.

Jaarsma, P., & Welin, S. (2012). Autism as a natural human variation: Reflections on the claims of the neurodiversity movement. Health Care Analysis, 20(1), 20–30.

Kapp, S. K., Gillespie-Lynch, K., Sherman, L. E., & Hutman, T. (2013). Deficit, difference, or both? Autism and neurodiversity. Developmental Psychology, 49(1), 59–71.

Langtree, I.C., (2025). What Is: Neurodiversity, Neurodivergent, Neurotypical? Disabled World.

Litman, A. E., Warrier, V., & Bourgeron, T. (2025). Decomposition of phenotypic heterogeneity in autism: A genome-wide, multi-cohort analysis. Nature Genetics, 57(2), 210–224.

Lord, C., & Bishop, S. (2010). Autism spectrum disorders: Diagnosis, prevalence, and services for children and families. Social Policy Report, 24(2), 1–27.

Lord, C., Elsabbagh, M., Baird, G., & Veenstra-VanderWeele, J. (2020). Autism spectrum disorder. The Lancet, 395(10242), 908–922.

McPartland, J. C., Reichow, B., & Volkmar, F. R. (2012). Sensitivity and specificity of proposed DSM-5 diagnostic criteria for autism spectrum disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 51(4), 368–383.

Pérez-Cano, L., et al. (2023). Translating precision medicine for autism spectrum disorder: Opportunities and challenges. Frontiers in Molecular Neuroscience, 16, 1167723.

Robinson, E. B., St Pourcain, B., Anttila, V., Kosmicki, J. A., Bulik-Sullivan, B., Grove, J., ... & Daly, M. J. (2016). Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nature Genetics, 48(5), 552–555.

Sanders, S. J., et al. (2015). Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron, 87(6), 1215–1233.

Satterstrom, F. K., et al. (2020). Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell, 180(3), 568–584.

Volkmar, F. R., & Reichow, B. (2013). Autism in DSM-5: Progress and challenges. Molecular Autism, 4(1), 13.

Warrier, V., et al. (2022). Genetic correlates of phenotypic heterogeneity in autism. Nature Genetics, 54(5), 635–648.

World Health Organization. (2019). International Classification of Diseases, 11th Revision (ICD-11): Autism Spectrum Disorder. Geneva: Author.

Insights, Analysis, and Developments

Author Credentials: Ian is the founder and Editor-in-Chief of Disabled World, a leading resource for news and information on disability issues. With a global perspective shaped by years of travel and lived experience, Ian is a committed proponent of the Social Model of Disability-a transformative framework developed by disabled activists in the 1970s that emphasizes dismantling societal barriers rather than focusing solely on individual impairments. His work reflects a deep commitment to disability rights, accessibility, and social inclusion. To learn more about Ian's background, expertise, and accomplishments, visit his full biography.