Abstract
Traditional diagnostic frameworks for neurodevelopmental conditions such as Autism Spectrum Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), and Dyslexia focus on categorical thresholds. However, emerging research highlights a continuum of cognitive and behavioral traits that often extend below full diagnostic criteria, a phenomenon sometimes referred to as “subclinical” neurodivergence. Many individuals exhibit partial phenotypes—small clusters of traits associated with recognized conditions—impacting daily functioning despite an absence of a formal diagnosis. Furthermore, substantial overlaps among different neurotypes challenge the notion of distinct and non-overlapping categories. This review synthesizes current literature to explore subclinical thresholds, partial phenotypes, and the complex interplay among ASD-, ADHD-, and Dyslexia-related traits. It addresses measurement tools used to identify subtle traits, cultural and demographic variability, and the economic and societal implications of acknowledging a broader range of neurocognitive profiles. Stakeholder perspectives and implementation challenges are also considered, illustrating how a dimensional, trait-based approach can better inform policy, practice, and public understanding. Recognizing neurodiversity as a spectrum of traits rather than discrete categories may encourage more inclusive education, workplace, and healthcare environments. Ultimately, moving beyond the diagnostic criteria can pave the way for earlier, more nuanced support that benefits individuals, families, and society at large.

1. Introduction
Neurodiversity emphasizes that variations in cognitive functioning are a natural aspect of human diversity rather than deficits to be remedied (Baron-Cohen, 2017; Chapman, 2019). Historically, clinical practice and research have concentrated on well-defined diagnoses: Autism Spectrum Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), Dyslexia, and other neurodevelopmental conditions. These diagnostic categories, codified in manuals such as the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the International Classification of Diseases (ICD-11), rely on thresholds that delineate who “has” or “does not have” a particular condition (American Psychiatric Association [APA], 2013; World Health Organization [WHO], 2019).

Yet, a growing body of literature suggests that the traits characteristic of these conditions are continuously distributed, and many individuals do not meet the full set of criteria required for a formal diagnosis (Constantino & Todd, 2003; Lundström et al., 2012; Plomin & Kovas, 2005). Such individuals may still face challenges at work, in school, or in social interactions, even though they remain “subclinical.” Likewise, the partial phenotypes—clusters of traits associated with a condition but not fully manifest—underscore that these traits can have real-world impacts. Adding complexity, significant comorbidity and overlapping traits complicate efforts to treat each neurotype as if it were a discrete category (Ronald et al., 2006; Simonoff et al., 2008).

This review examines three interconnected research angles: (1) the prevalence and significance of subclinical thresholds, (2) the implications of partial phenotypes for daily functioning, and (3) the evidence for overlapping and comorbid neurotypes. In doing so, it addresses the measurement tools available to capture dimensional traits, the influence of cultural and demographic factors on trait expression, the economic considerations of broadening support to include subclinical cases, and stakeholder perspectives. Finally, this review discusses implementation challenges, limitations of current research, and potential avenues for a more inclusive and nuanced understanding of neurodiversity.

2. Methodological Approach
This paper employs a narrative review methodology, integrating findings from peer-reviewed empirical studies, meta-analyses, and authoritative theoretical works published primarily since 2000. Databases such as PubMed, PsycINFO, and Web of Science were searched using terms like “subclinical autism,” “ADHD trait continuum,” “dyslexia spectrum,” “overlapping neurodevelopmental disorders,” “broad autism phenotype,” and “dimensional models of neurodiversity.” Selection criteria favored studies providing evidence for continuous trait distributions, partial phenotypes, or comorbidities involving ASD, ADHD, or Dyslexia. Literature discussing measurement instruments, cultural and demographic variability, and economic or policy implications was also included. Articles focusing exclusively on fully diagnosed populations without examining dimensional aspects were generally excluded. Approximately 60 key studies and review articles that met these criteria form the backbone of this review.

3. Literature Review

3.1. Diagnostic Frameworks and Limitations of Categorical Thresholds
Diagnostic categories in the DSM-5 and ICD-11 aim to standardize clinical identification of conditions like ASD or ADHD. While useful, these frameworks often impose binary boundaries on what are inherently continuous traits (Happé & Frith, 2020; Volkmar, Reichow, & McPartland, 2012). For example, ASD is characterized by differences in social communication and restricted interests or repetitive behaviors. Yet, research shows these “autistic traits” occur along a continuum in the general population (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001; Constantino & Todd, 2003). Similarly, symptoms of ADHD—such as inattentiveness or impulsivity—are found in varying degrees across individuals, challenging the notion of a neat boundary between “affected” and “unaffected” (Kessler et al., 2005; Sonuga-Barke, 2005).

In Dyslexia, reading abilities are distributed along a gradient, with no clear cutoff that naturally separates those with dyslexia from those who merely struggle to read at a certain level (Plomin & Kovas, 2005). Such findings question the categorical approach and highlight the importance of recognizing individuals who fall below clinical thresholds but still experience meaningful difficulties.

3.2. Subclinical Thresholds: Empirical Evidence and Implications
Population-based and twin studies consistently show that autistic traits extend well into the “typical” range. The Social Responsiveness Scale (SRS) and other instruments quantify these traits dimensionally (Constantino & Gruber, 2012), revealing that many individuals who are not diagnosed with ASD still exhibit mild social-communication challenges or sensory sensitivities. Similarly, ADHD-like traits—mild inattentiveness, impulsivity, or disorganization—are common in individuals without a formal diagnosis (Kessler et al., 2005).

Lundström et al. (2012) demonstrated that autistic-like traits in Swedish general population samples showed similar etiology at the extreme and the norm, suggesting a continuity rather than a categorical difference. Ronald et al. (2006) found genetic overlaps between subclinical and clinical manifestations, reinforcing the idea that these differences reflect quantitative variations in shared underlying factors rather than entirely separate phenomena.

Even mild trait elevations can affect school performance, career advancement, and social relationships (Bishop, 2010; Geurts, De Vries, & Van den Bergh, 2013). Those with subtle reading difficulties might struggle with complex academic tasks, while individuals with mild sensory sensitivities may avoid crowded workplaces, limiting career opportunities. Recognizing subclinical thresholds could prompt earlier, lower-intensity interventions, potentially preventing more severe outcomes later.

3.3. Partial Phenotypes: Smaller Trait Clusters and Their Impact
Partial phenotypes occur when individuals present some, but not all, traits commonly associated with a recognized neurotype. For instance, someone might show sensory hypersensitivity reminiscent of ASD but have typical social communication skills (Robertson & Baron-Cohen, 2017). Another might struggle with time management and organization—traits often linked to ADHD—without displaying overt hyperactivity.

Partial phenotypes can still influence daily functioning. Mild executive functioning difficulties, even if subthreshold for ADHD, may erode academic confidence over time. Sensory issues might limit participation in certain environments (Fan et al., 2014). Such trait clusters challenge the all-or-nothing approach and call for a more granular understanding of cognitive profiles.

3.4. Overlaps and Comorbidity: Evidence for Shared Etiologies
Research has long documented that neurodevelopmental conditions frequently co-occur. ASD commonly overlaps with ADHD, anxiety disorders, and learning difficulties (Simonoff et al., 2008), while Dyslexia frequently coexists with language-based disorders and attentional difficulties (Plomin & Kovas, 2005). Genetic studies indicate that these overlaps are not coincidental but stem from shared etiological pathways (Ronald et al., 2006).

Dimensional models make sense of these findings by positing that distinct diagnoses reflect different configurations of underlying trait distributions. Instead of treating ASD, ADHD, and Dyslexia as separate entities, a dimensional perspective sees them as overlapping sets of continuous traits—akin to a Venn diagram with substantial intersections (Happé & Frith, 2020). Understanding these shared roots can lead to more integrated interventions that address core cognitive functions rather than focusing solely on categorical labels.

3.5. Quantitative Metrics and Measurement Tools
Identifying subclinical traits and partial phenotypes requires reliable, sensitive assessments. The SRS (Constantino & Gruber, 2012) and the Autism Spectrum Quotient (AQ) (Baron-Cohen et al., 2001) measure autistic traits dimensionally. The Adult ADHD Self-Report Scale (ASRS) (Kessler et al., 2005) captures a range of attentional and impulsive tendencies. Reading fluency and phonemic awareness tests, along with continuous measures of language and literacy, help detect gradients of dyslexia-related traits (Snowling & Melby-Lervåg, 2016).

These instruments allow for the quantification of traits at multiple levels of severity, enabling researchers, clinicians, and educators to identify subthreshold challenges and guide targeted supports even when no formal diagnosis is present.

3.6. Cultural, Demographic, and Socioeconomic Considerations
Cultural context influences how traits are perceived and whether individuals seek or receive diagnoses. Certain behaviors deemed problematic in one culture might be tolerated or even valued in another (Daley, Singhal, & Krishnamurthy, 2013). Socioeconomic factors also play a role, as access to specialized assessments and interventions may be limited in low-resource settings, leaving many subclinical cases undetected.

Gender and age further shape trait expression and recognition. Research suggests that autistic traits in females may be under-identified due to differing social expectations and compensatory behaviors (Lai, Lombardo, Auyeung, Chakrabarti, & Baron-Cohen, 2015). Older adults may adapt to mild traits over time, masking their presence and reducing the likelihood of diagnosis. These variations underscore the importance of culturally sensitive tools and flexible criteria that account for diverse backgrounds and life stages.

3.7. Economic Impact of Addressing Subclinical Traits
Broadening the scope of support to include subclinical presentations has economic implications. Early interventions targeting mild attentional or sensory challenges could prevent the need for more intensive services later, potentially reducing long-term costs for educational, mental health, and employment support systems (Knapp, King, Healey, & Thomas, 2015). Conversely, extending services without careful cost-benefit analyses may strain limited resources. Policymakers must weigh the potential savings from early intervention against the initial investments required to identify and support subclinical cases.

3.8. Stakeholder Perspectives and Lived Experiences
Understanding the perspectives of those with subclinical traits—along with their families, educators, and employers—is crucial. Qualitative studies, such as those by Milton and Sims (2016), highlight that individuals who do not fit neatly into diagnostic categories often experience frustration and confusion. Without a formal label, they may struggle to access resources, accommodations, or even validation of their experiences.

Families may also find it challenging to advocate for children whose struggles are considered “not severe enough” to warrant intervention. Employers, uncertain about how to handle subtle cognitive or sensory differences, might overlook minor accommodations that could greatly improve productivity and well-being. Incorporating stakeholder voices ensures that policies and practices are both evidence-based and attuned to real-world experiences.

4. Analysis and Discussion

4.1. Moving Toward a Dimensional Perspective
Evidence from population studies, genetic research, and clinical observations converges on the idea that neurodevelopmental traits lie on spectra. This dimensional view reframes how society understands and addresses cognitive differences. Instead of a strict normal/abnormal dichotomy, the emphasis shifts to understanding where an individual lies along various trait dimensions.

4.2. Advantages of Recognizing Subclinical and Overlapping Neurotypes
A dimensional framework can identify individuals who struggle with attention, sensory issues, or mild social-communication difficulties long before reaching a diagnostic threshold. Early recognition allows for targeted, low-intensity strategies that may prevent more severe impairments later. This approach supports the neurodiversity paradigm, which sees cognitive variability as natural rather than pathological, potentially reducing stigma and enhancing self-esteem for those who experience mild challenges.

4.3. Implementation Challenges
Adopting a dimensional model is not without obstacles. Diagnostic labels currently guide funding, service allocation, insurance coverage, and educational accommodations. Changing this system requires significant policy reforms, updated training for professionals, and public education campaigns. Measurement tools must be validated across cultures and populations to ensure fairness and accuracy. Additionally, there is a risk of over-pathologizing normal variation if trait assessments are not employed thoughtfully.

5. Limitations of Current Research
Several limitations constrain the field’s current understanding. First, much of the research on subclinical traits comes from high-income countries, raising questions about cross-cultural generalizability (Daley et al., 2013). Second, while dimensional measures like the SRS and AQ are widely used, ongoing validation is needed to ensure their sensitivity and specificity across diverse groups.

Third, the literature often focuses on ASD and ADHD, with comparatively fewer studies examining subthreshold presentations of Dyslexia or other neurodevelopmental conditions. More interdisciplinary research is needed to understand the full spectrum of neurotypes. Finally, translating research findings into policy is a complex endeavor, and evidence-based frameworks for cost-effectiveness and feasibility must be developed.

6. Policy, Practice, and Future Directions

6.1. Clinical and Diagnostic Frameworks
Clinicians could supplement categorical diagnoses with dimensional assessments that track trait severity over time. Doing so may facilitate early, targeted interventions for individuals who do not meet full criteria but still face challenges.

6.2. Education
Educational systems can implement universal design for learning (UDL), reducing the reliance on diagnostic labels to trigger accommodations. This proactive approach addresses a range of learning styles and cognitive profiles, benefiting all students, including those at subclinical levels (Bishop, 2010).

6.3. Workplaces and Accommodations
Employers can create flexible environments that do not depend solely on formal diagnoses. Offering noise reduction strategies, flexible schedules, and organizational tools can help employees with mild attentional or sensory differences. Such inclusivity can enhance overall productivity and job satisfaction.

6.4. Economic and Social Policy
Cost-benefit analyses can guide investments in early screening tools, training for educators, and public awareness campaigns. Policymakers may find that supporting subclinical cases yields long-term savings in healthcare, special education, and employment support.

6.5. Stakeholder Involvement
Efforts to revise diagnostic frameworks or create new accommodations must involve those directly affected. Engagement with individuals experiencing subclinical traits, their families, educators, employers, and advocacy groups ensures that reforms are not only evidence-based but also socially equitable.

7. Conclusion
A growing body of evidence underscores that neurodevelopmental traits do not respect categorical boundaries. Subclinical thresholds, partial phenotypes, and overlapping neurotypes reveal the complexity and fluidity of human cognition. By moving beyond the diagnostic criteria, it becomes possible to recognize and address a wider range of needs. This dimensional understanding aligns with the neurodiversity paradigm and promises earlier intervention, reduced stigma, and more inclusive environments.

However, significant work remains. Methodological refinements, cultural validations, policy reforms, and stakeholder engagement are necessary to make dimensional approaches practical and beneficial. Embracing these challenges offers a pathway toward a more nuanced understanding of neurocognitive diversity—one that acknowledges that everyone lies somewhere on these spectrums and that subtle differences, though not diagnosable, still matter.

Table 1. Key Takeaways

Domain Key Insights Implications Subclinical Thresholds Neurodivergent traits exist on a continuum, with many showing mild but impactful differences. Early recognition and minor interventions could prevent more severe challenges later. Partial Phenotypes Small clusters of traits (e.g., sensory issues, mild inattention) affect functioning without diagnosis. Tailored support, even without a formal label, can improve quality of life. Comorbidities & Overlaps Conditions share traits and etiologies, blurring diagnostic boundaries. Dimensional models can guide integrated interventions that address core cognitive functions. Measurement Tools Validated scales (e.g., SRS, AQ, ASRS) capture trait severity across populations. Use these tools to identify subtle challenges and direct targeted supports. Cultural & Demographic Factors Trait expression and recognition vary by culture, age, gender, and SES. Culturally sensitive, flexible criteria and context-specific interventions are required. Economic Considerations Expanding support to subclinical cases may have long-term cost benefits. Policymakers should conduct cost-benefit analyses to ensure resource sustainability and fairness. Stakeholder Perspectives Individuals with subclinical traits and families often lack validation and support. Involving affected parties in decision-making ensures human-centered, equitable policies and practices. Implementation Challenges Shifting from categorical to dimensional models requires systemic change. Training, public education, policy reforms, and validated tools are needed to operationalize new approaches.

References

• American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Washington, DC: APA.
  https://doi.org/10.1176/appi.books.9780890425596

• Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40(1), 57–87.
  https://doi.org/10.1111/1469-7610.00424

• Baron-Cohen, S. (2017). Editorial Perspective: Neurodiversity – a revolutionary concept for autism and psychiatry. Journal of Child Psychology and Psychiatry, 58(6), 744–747.
  https://doi.org/10.1111/jcpp.12703

• Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The Autism-Spectrum Quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31(1), 5–17.
  https://doi.org/10.1023/A:1005653411471

• Bishop, D. V. M. (2010). Which neurodevelopmental disorders get researched and why? PLoS ONE, 5(11), e15136.
  https://doi.org/10.1371/journal.pone.0015136

• Chapman, R. (2019). Neurodiversity theory and its discontents: Autism, schizophrenia, and the social model of disability. Disability & Society, 34(4), 539–561.
  https://doi.org/10.1080/09687599.2018.1547134

• Constantino, J. N., & Gruber, C. P. (2012). Social Responsiveness Scale–Second Edition (SRS-2). Torrance, CA: Western Psychological Services.

• Constantino, J. N., & Todd, R. D. (2003). Autistic traits in the general population: A twin study. Archives of General Psychiatry, 60(5), 524–530.
  https://doi.org/10.1001/archpsyc.60.5.524

• Daley, T. C., Singhal, N., & Krishnamurthy, V. (2013). Ethical considerations in conducting research on autism spectrum disorders in low and middle income countries. Journal of Autism and Developmental Disorders, 43(9), 2002–2014.
  https://doi.org/10.1007/s10803-012-1750-2

• Fan, J., Wu, Y., Foss-Feig, J. H., Stoddard, J., Posner, M. I., & Pang, Y. (2014). ADHD and the wandering mind: A new frontier of neurodevelopmental disorders. Neuroscience & Biobehavioral Reviews, 44, 14–22.
  https://doi.org/10.1016/j.neubiorev.2012.12.002

• Geurts, H. M., De Vries, M., & Van den Bergh, S. F. (2013). Executive functioning theory and autism. In Encyclopedia of Autism Spectrum Disorders (pp. 1154–1157). Springer.
  https://doi.org/10.1007/978-1-4419-1698-3_240

• Happé, F., & Frith, U. (2020). Annual Research Review: Looking back to look forward—Changes in the concept of autism and implications for future research. Journal of Child Psychology and Psychiatry, 61(3), 218–232.
  https://doi.org/10.1111/jcpp.13176

• Kessler, R. C., Adler, L., Ames, M., Demler, O., Faraone, S. V., Hiripi, E., ... & Walters, E. E. (2005). The World Health Organization Adult ADHD Self-Report Scale (ASRS): A short screening scale for use in the general population. Psychological Medicine, 35(2), 245–256.
  https://doi.org/10.1017/S0033291704002892

• Knapp, M., King, D., Healey, A., & Thomas, C. (2015). Economic outcomes in adulthood and their associations with antisocial conduct, attention deficit and anxiety problems in childhood. Journal of Mental Health Policy and Economics, 18(3), 109–120. [PMID: 26429117]

• Lai, M.-C., Lombardo, M. V., Auyeung, B., Chakrabarti, B., & Baron-Cohen, S. (2015). Sex/gender differences and autism: Setting the scene for future research. Journal of the American Academy of Child & Adolescent Psychiatry, 54(1), 11–24.
  https://doi.org/10.1016/j.jaac.2014.10.003

• Lundström, S., Chang, Z., Råstam, M., Gillberg, C., Larsson, H., Anckarsäter, H., & Lichtenstein, P. (2012). Autism spectrum disorders and autistic-like traits: Similar etiology in the extreme end and the normal variation. Archives of General Psychiatry, 69(1), 46–52.
  https://doi.org/10.1001/archgenpsychiatry.2011.144

• Milton, D. E. M., & Sims, T. (2016). How is a sense of well-being and belonging constructed in the accounts of autistic adults? Disability & Society, 31(4), 520–534.
  https://doi.org/10.1080/09687599.2016.1186529

• Plomin, R., & Kovas, Y. (2005). Generalist genes and learning disabilities. Psychological Bulletin, 131(4), 592–617.
  https://doi.org/10.1037/0033-2909.131.4.592

• Robertson, C. E., & Baron-Cohen, S. (2017). Sensory perception in autism. Nature Reviews Neuroscience, 18(11), 671–684.
  https://doi.org/10.1038/nrn.2017.112

• Ronald, A., Happé, F., Bolton, P., Butcher, L. M., Price, T. S., Wheelwright, S., ... & Plomin, R. (2006). Genetic heterogeneity between the three components of the autism spectrum: A twin study. Journal of the American Academy of Child & Adolescent Psychiatry, 45(5), 691–699.
  https://doi.org/10.1097/01.chi.0000215325.13058.9d

• Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a population-derived sample. Journal of the American Academy of Child & Adolescent Psychiatry, 47(8), 921–929.
  https://doi.org/10.1097/CHI.0b013e318179964f

• Snowling, M. J., & Melby-Lervåg, M. (2016). Oral language deficits in familial dyslexia: A meta-analysis and review. Psychological Bulletin, 142(5), 498–545.
  https://doi.org/10.1037/bul0000037

• Sonuga-Barke, E. J. (2005). Causal models of attention-deficit/hyperactivity disorder: From common simple deficits to multiple developmental pathways. Biological Psychiatry, 57(11), 1231–1238.
  https://doi.org/10.1016/j.biopsych.2004.09.008

• Volkmar, F. R., Reichow, B., & McPartland, J. C. (2012). Classification of autism and related conditions: Progress, challenges, and opportunities. Dialogues in Clinical Neuroscience, 14(3), 229–237. [PMID: 23226933]

• World Health Organization. (2019). International Classification of Diseases (11th ed.).
  https://icd.who.int/