Montgomery Alicia, Masi Anne, Whitehouse Andrew, Veenstra-VanderWeele Jeremy, Shuffrey Lauren, Shen Mark D, Karlov Lisa, Uljarevic Mirko, Alvares Gail, Woolfenden Sue, Silove Natalie, Eapen Valsamma
University of New South Wales, Sydney, Australia.
University of Western Australia, Perth, Australia.
Child Adolesc Psychiatry Ment Health. 2023 Feb 20;17(1):27. doi: 10.1186/s13034-023-00565-3.
The identification of reproducible subtypes within autistic populations is a priority research area in the context of neurodevelopment, to pave the way for identification of biomarkers and targeted treatment recommendations. Few previous studies have considered medical comorbidity alongside behavioural, cognitive, and psychiatric data in subgrouping analyses. This study sought to determine whether differing behavioural, cognitive, medical, and psychiatric profiles could be used to distinguish subgroups of children on the autism spectrum in the Australian Autism Biobank (AAB).
Latent profile analysis was used to identify subgroups of children on the autism spectrum within the AAB (n = 1151), utilising data on social communication profiles and restricted, repetitive, and stereotyped behaviours (RRBs), in addition to their cognitive, medical, and psychiatric profiles.
Our study identified four subgroups of children on the autism spectrum with differing profiles of autism traits and associated comorbidities. Two subgroups had more severe clinical and cognitive phenotype, suggesting higher support needs. For the 'Higher Support Needs with Prominent Language and Cognitive Challenges' subgroup, social communication, language and cognitive challenges were prominent, with prominent sensory seeking behaviours. The 'Higher Support Needs with Prominent Medical and Psychiatric and Comorbidity' subgroup had the highest mean scores of challenges relating to social communication and RRBs, with the highest probability of medical and psychiatric comorbidity, and cognitive scores similar to the overall group mean. Individuals within the 'Moderate Support Needs with Emotional Challenges' subgroup, had moderate mean scores of core traits of autism, and the highest probability of depression and/or suicidality. A fourth subgroup contained individuals with fewer challenges across domains (the 'Fewer Support Needs Group').
Data utilised to identify subgroups within this study was cross-sectional as longitudinal data was not available.
Our findings support the holistic appraisal of support needs for children on the autism spectrum, with assessment of the impact of co-occurring medical and psychiatric conditions in addition to core autism traits, adaptive functioning, and cognitive functioning. Replication of our analysis in other cohorts of children on the autism spectrum is warranted, to assess whether the subgroup structure we identified is applicable in a broader context beyond our specific dataset.
在神经发育背景下，识别自闭症群体中可重复的亚型是一个优先研究领域，为生物标志物的识别和靶向治疗建议铺平道路。以前很少有研究在亚组分析中同时考虑医学合并症以及行为、认知和精神数据。本研究旨在确定不同的行为、认知、医学和精神特征是否可用于区分澳大利亚自闭症生物样本库（AAB）中自闭症谱系儿童的亚组。
使用潜在剖面分析来识别AAB中自闭症谱系儿童的亚组（n = 1151），利用社会沟通特征以及受限、重复和刻板行为（RRB）的数据，以及他们的认知、医学和精神特征。
我们的研究识别出了自闭症谱系儿童的四个亚组，其自闭症特征和相关合并症各不相同。两个亚组具有更严重的临床和认知表型，表明需要更高水平的支持。对于“有突出语言和认知挑战的高支持需求”亚组，社会沟通、语言和认知挑战突出，有突出的感觉寻求行为。“有突出医学和精神合并症的高支持需求”亚组在社会沟通和RRB相关挑战方面的平均得分最高，医学和精神合并症的可能性最高，认知得分与总体组平均水平相似。“有情绪挑战的中度支持需求”亚组中的个体，自闭症核心特征的平均得分中等，抑郁和/或自杀倾向的可能性最高。第四个亚组包含各领域挑战较少的个体（“较少支持需求组”）。
本研究中用于识别亚组的数据为横断面数据，因为没有纵向数据。
我们的研究结果支持对自闭症谱系儿童的支持需求进行全面评估，除了核心自闭症特征、适应性功能和认知功能外，还需评估同时存在的医学和精神状况的影响。有必要在其他自闭症谱系儿童队列中重复我们的分析，以评估我们识别出的亚组结构是否适用于我们特定数据集之外的更广泛背景。
