Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are distinct neurodevelopmental conditionsthat 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.
Key Points
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-occurringADHD traitsare 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).
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 anautism 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
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.
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
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.
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:
Strengths
The study had many methodological strengths, including:
Limitations
This study also had several methodological strtengths, including:
Implications
The results of this study have significant implications for clinical practice, research, and our understanding of neurodevelopmental conditions:
Variables that may influence the results include:
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
Carpenter, K. L. H., Davis, N. O., Spanos, M., Sabatos-Devito, M., Aiello, R., Baranek, G. T., Compton, S. N., Egger, H. L., Franz, L., Kim, S. J., King, B. H., Kolevzon, A., Mcdougle, C. J., Sanders, K., Veenstra-Vanderweele, J., Sikich, L., Kollins, S. H., & Dawson, G. (2022). Adaptive behavior in young autistic children: Associations with irritability and ADHD symptoms.Journal of Autism and Developmental Disorders.https://doi.org/10.1007/s10803-022-05753-2
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
Ghanizadeh, A. (2012). Co-morbidity and factor analysis on attention deficit hyperactivity disorder and autism spectrum disorder DSM-IV-derived items.Journal of Research in Medical Sciences, 17, 368–372.
Harkins, C., & Mazurek, M. O. (2023). The impact of Co-occurring ADHD on social competence intervention outcomes in youth with autism spectrum disorder.Journal of Autism and Developmental Disorders.https://doi.org/10.1007/s10803-023-05987-8
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Saul McLeod, PhD
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Olivia Guy-Evans, MSc
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
