Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are distinct neurodevelopmental conditions that frequently co-occur.
Research indicates that 30-80% of individuals with ASD also meet criteria for ADHD, while 20-50% of those with ADHD show autistic traits.
This overlap is attributed to shared characteristics such as difficulties with social interaction and attention regulation.
The high rate of co-occurrence may result from shared genetic factors or similar brain structure differences. Understanding this overlap is crucial for accurate diagnosis and effective treatment planning.
Chau, T., Tiego, J., Brown, L. E., Mellahn, O. J., Johnson, B. P., & Bellgrove, M. A. (2024). The distribution of parent‐reported autistic and subclinical ADHD traits in children with and without an autism diagnosis. JCPP Advances, e12259. https://doi.org/10.1002/jcv2.12259
Key Points
- The primary methods of studying the distribution of autistic and subclinical ADHD traits in children include factor mixture modeling (FMM), confirmatory factor analysis (CFA), and latent profile analysis (LPA).
- Factors like autistic trait endorsement, ADHD trait endorsement, and diagnostic status (autistic or neurotypical) significantly affect the distribution of these traits across the population.
- Higher parent-reported autistic traits corresponded to higher levels of parent-reported subclinical ADHD traits in children with and without an autism diagnosis.
- The research, while enlightening, has certain limitations such as potential sample selection bias and the exclusion of children with co-occurring conditions.
- Understanding the distribution of autistic traits and ADHD traits is crucial for improving diagnostic tools, intervention strategies, and overall support for neurodivergent children.
Rationale
Autism spectrum disorder (autism) traits are understood to be continuously distributed in the population and share some neurobiological and clinical overlap with attention-deficit/hyperactivity disorder (ADHD) traits (Constantino, 2003; Hoogman et al., 2022; van der Meer et al., 2017).
The presence of ADHD traits, even at subclinical levels, can significantly impact how autistic children manage social, cognitive, and adaptive demands (Harkins & Mazurek, 2023; Liu et al., 2021; Schachar et al., 2023; Carpenter et al., 2022; Yerys et al., 2019).
However, the distribution of these traits in autistic children has yet to be established.
Understanding this distribution may help clarify relevant symptoms and thresholds that maximize the discriminant and predictive validity of ADHD diagnostic instruments (Antshel & Russo, 2019) and contribute to improving the accessibility and relevance of current assessments and interventions for neurodivergent children.
Previous research has shown that co-occurring ADHD traits are common in autistic individuals (Lee & Ousley, 2006; Leyfer et al., 2006; Simonoff et al., 2008; Sinzig et al., 2009), but only one study has investigated the latent structure of ADHD traits in autistic children (Ghanizadeh, 2012).
The current study aims to expand upon these findings by modeling autistic and ADHD traits in tandem and potentially identifying latent classes of presentations that may inform future interventions.
Method
The study employed a quantitative research design using factor mixture modeling (FMM) to investigate the distribution of parent-reported autistic and subclinical ADHD traits in children with and without an autism diagnosis.
The researchers analyzed data from two independent samples: a discovery sample from the Monash Autism-ADHD Genetics and Neurodevelopment (MAGNET) Project in Australia and a replication sample from the Healthy Brain Network (HBN) project in the United States.
Procedure
Parents completed the Social Responsiveness Scale – 2nd edition (SRS-2) and the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN) while participating children were administered Wechsler intelligence scales.
The researchers then applied factor mixture modeling to the caregiver responses to identify latent classes and factors representing autistic and ADHD traits.
Sample
Discovery sample (MAGNET): 164 children (121 neurotypical, 43 autistic) aged 4-17 years (M = 8.64 years, SD = 2.95 years), 53.7% female.
Replication sample (HBN): 418 children (351 neurotypical, 67 autistic) aged 5-18 years (M = 9.96 years, SD = 3.17 years), 56.5% male.
Measures
- Social Responsiveness Scale – 2nd edition (SRS-2): A standardized parent-report questionnaire screening for social and non-social traits characteristic of autism in children aged 4-18 years.
- Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN): A parent-report questionnaire evaluating inattentive and hyperactive/impulsive behaviors consistent with ADHD.
- Wechsler intelligence scales: Used to obtain Full-scale intelligence (FSIQ) scores.
Statistical measures
The researchers used factor mixture modeling (FMM), confirmatory factor analysis (CFA), and latent profile analysis (LPA).
They employed various fit indices including the Bayesian Information Criterion (BIC), Vuong-Lo-Mendell-Rubin adjusted likelihood ratio test p-value (VLMRp), and parametric bootstrapped likelihood ratio test p-value (BLRTp) to select the best-fitting models.
They also conducted multivariate analysis of variance (MANOVA) and post-hoc tests to investigate class-based differences in SRS-2 and SWAN subscale ratings.
Results
Hypothesis 1: Parent-reported subscale measures of autistic and ADHD traits would load onto distinct (1) autism and (2) ADHD factors.
Result: Confirmed. CFA models in both samples demonstrated that autistic and ADHD traits coalesced around distinct factors as estimated by the SRS-2 and SWAN, respectively.
Hypothesis 2: FMM would produce two discrete classes of participants corresponding to (1) predominantly neurotypical children with low caregiver-reported autistic traits and (2) predominantly autistic children with clinically elevated parent-reported autistic traits.
Result: Partially confirmed. The best-fitting models in both samples produced three classes rather than two, but these classes did capture distinct subgroups of children with different levels of autistic traits.
Hypothesis 3: Caregiver-reported ADHD traits would be lower in the first class compared to the second class, but there would be overlap in the distribution between the classes.
Result: Confirmed. In both samples, mean subclinical ADHD trait endorsement increased in line with autistic trait endorsement, with overlap in the distribution between classes.
Key findings
- A 2-factor, 3-class factor mixture model demonstrated the best fit to the data across both independent samples.
- The factors represented the latent constructs of ‘autism’ and ‘ADHD.’
- The latent classes represented subtypes of children with different levels of autistic traits, with higher levels of ADHD traits as autistic trait endorsement increased.
- The endorsement of subclinical ADHD traits tended to increase alongside autistic trait endorsement across neurotypical and autistic presentations.
Insight
This study provides novel insights into the distribution of autistic and subclinical ADHD traits in children with and without an autism diagnosis.
The findings suggest that autistic and ADHD traits are continuously distributed in the population, rather than being discrete categories.
This supports a dimensional conceptualization of these traits, which has implications for how we understand and diagnose neurodevelopmental conditions.
The study extends previous research by modeling autistic and ADHD traits simultaneously, revealing that higher levels of autistic traits tend to correspond with higher levels of subclinical ADHD traits.
This relationship holds true across both neurotypical and autistic children, suggesting a common underlying mechanism or shared risk factors for these traits.
The identification of three distinct classes of children based on their trait profiles provides a more nuanced understanding of the heterogeneity within autism and ADHD.
This could lead to more personalized approaches to assessment and intervention, as children within each class may have different needs and respond differently to various treatments.
Future research could explore:
- The stability of these trait profiles over time through longitudinal studies.
- The neurobiological underpinnings of the observed relationship between autistic and ADHD traits.
- The impact of these trait profiles on functional outcomes and quality of life.
- The effectiveness of interventions tailored to specific trait profiles.
- The role of environmental factors in shaping the distribution of these traits.
Strengths
The study had many methodological strengths, including:
- Use of two independent samples for discovery and replication, enhancing the reliability of the findings.
- Application of advanced statistical techniques (factor mixture modeling) to capture the complexity of trait distributions.
- Inclusion of both autistic and neurotypical children, allowing for a comprehensive examination of trait distributions across the population.
- Use of well-validated measures (SRS-2 and SWAN) to assess autistic and ADHD traits.
- Consideration of subclinical ADHD traits, which are often overlooked but can significantly impact functioning.
- Rigorous model selection process, considering multiple fit indices and substantive interpretability.
- Sensitivity analyses to account for potential age-related effects.
Limitations
This study also had several methodological strtengths, including:
- Sample selection may have introduced bias, as the exclusion criteria (e.g., excluding children with co-occurring conditions) may limit the generalizability of the findings.
- The study relied solely on parent-reported measures, which may not capture the full complexity of autistic and ADHD traits, particularly internal experiences.
- The cross-sectional nature of the study limits conclusions about the developmental trajectory of these traits.
- The samples were drawn from specific geographical areas (Australia and the United States), which may limit generalizability to other cultural contexts.
- The study did not account for potential sex differences in the presentation and recognition of autistic and ADHD traits.
- The focus on children and adolescents (4-18 years) means the findings may not apply to adults or very young children.
These limitations suggest that caution should be exercised when generalizing the results to broader populations or different age groups. Future research should aim to address these limitations by including more diverse samples, incorporating multiple informant measures, and conducting longitudinal studies.
Implications
The results of this study have significant implications for clinical practice, research, and our understanding of neurodevelopmental conditions:
- Clinical Practice:
- The findings suggest that routine screening for subclinical ADHD traits in children with high levels of autistic traits may be clinically meaningful. This could lead to earlier identification of children who might benefit from additional support or interventions targeting ADHD-related difficulties.
- The dimensional nature of autistic and ADHD traits supports a more nuanced approach to diagnosis and treatment, moving away from strict categorical distinctions.
- The identified classes of trait profiles could inform more personalized intervention strategies, tailored to the specific needs of children with different combinations of autistic and ADHD traits.
- Diagnostic Criteria and Tools:
- The study highlights the need for diagnostic tools that can capture the continuous nature of autistic and ADHD traits, rather than relying solely on categorical cut-offs.
- The observed overlap between autistic and ADHD traits suggests that diagnostic criteria and assessments should consider the co-occurrence of these traits more explicitly.
- Research:
- The study provides a methodological framework for investigating the distribution of neurodevelopmental traits, which could be applied to other conditions or traits.
- The findings underscore the importance of considering subclinical traits in research on neurodevelopmental conditions, as these can significantly impact functioning even in the absence of a formal diagnosis.
- Public Health and Education:
- The continuous distribution of traits suggests that support and interventions could be beneficial for a broader range of children, not just those meeting diagnostic criteria for autism or ADHD.
- Education and healthcare systems may need to adapt to provide more flexible and individualized support based on trait profiles rather than diagnostic categories.
- Theoretical Understanding:
- The study supports a dimensional view of neurodevelopmental traits, challenging traditional categorical approaches to understanding autism and ADHD.
- The relationship between autistic and ADHD traits suggests potential shared underlying mechanisms, which could inform future research into the etiology of these conditions.
Variables that may influence the results include:
- Age and developmental stage
- Cultural context and societal norms
- Parental awareness and recognition of autistic and ADHD traits
- Presence of other co-occurring conditions or traits not captured in this study
- Environmental factors that may shape the expression of these traits
In conclusion, while the study has some limitations, its findings have far-reaching implications for how we conceptualize, assess, and support individuals with autistic and ADHD traits. The results underscore the importance of a more nuanced, dimensional approach to understanding neurodevelopmental diversity.
References
Primary reference
Chau, T., Tiego, J., Brown, L. E., Mellahn, O. J., Johnson, B. P., & Bellgrove, M. A. (2024). The distribution of parent‐reported autistic and subclinical ADHD traits in children with and without an autism diagnosis. JCPP Advances, e12259. https://doi.org/10.1002/jcv2.12259
Other references
Antshel, K. M., & Russo, N. (2019). Autism spectrum disorders and ADHD: Overlapping phenomenology, diagnostic issues, and treatment considerations. Current psychiatry reports, 21, 1-11. https://doi.org/10.1007/s11920-019-1020-5
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Keep Learning
Here are some Socratic questions for a college class to discuss this paper:
- Given the limitations of the study, such as the exclusion of children with co-occurring conditions, how might future research address these gaps? Design a follow-up study that could extend or complement these findings.
- How might the dimensional view of autistic and ADHD traits presented in this study challenge our current diagnostic system? What are the potential benefits and drawbacks of moving towards a more dimensional approach?
- The study found that higher levels of autistic traits corresponded with higher levels of subclinical ADHD traits. What theories could explain this relationship? How might this inform our understanding of the etiology of these conditions?
- How might the identification of distinct classes of trait profiles impact the development of interventions for autistic and ADHD traits? Can you think of specific ways interventions could be tailored to each class?
- The study relied on parent-reported measures. How might the results differ if self-report measures or clinical observations were used instead? What are the strengths and limitations of each approach?
