McNealis Maya, Kent John, Paskov Kelley, Dunlap Kaitlyn, Lane Jordan, Phillips Brittani, Armstrong-Brine Melissa, Kralovic Shanna, Dimitropoulos Anastasia, Abbeduto Leonard, Wall Dennis P
Departments of Pediatrics (Clinical Informatics), Biomedical Data Science, and Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA.
Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA.
BMC Psychol. 2025 May 26;13(1):561. doi: 10.1186/s40359-025-02739-4.
Autism Spectrum Disorder (ASD), a neurodevelopmental condition marked by restricted, repetitive behaviors and social communication difficulties, is one of the fastest-growing pediatric behavioral health concerns in the United States. Long-term outcomes significantly improve with early intervention, but diagnosis and treatment are complicated by the large range of phenotypic presentations that can be moderated by identity factors like gender and culture. Many physical and behavioral characteristics associated with the autism phenotype are not included in the screening and diagnostic instruments used in research.
We have built a multi-site registry of diverse families with children with autism to collect longitudinal data on their physical and behavioral attributes to study the heterogeneous autism phenotype. Our KidsFirst registry contains 6,951 participants (hereafter "children") from 4,120 families, 1,865 of which have more than one child. In addition to collecting standard clinical instruments such as the Social Communication Questionnaire (SCQ), we have collected information on the phenotypic attributes of hearing issues, noise sensitivity, vision challenges, irregular sleep, impaired motor skills, metabolic disorders, gastrointestinal (GI) problems, infections, seizures, and premature birth for both ASD and non-ASD children. After validating parent-reported diagnoses against SCQ scores, we analyzed the association of each attribute with the ASD diagnosis and the other attributes using a logistic regression model and permutation tests.
Noise sensitivity, impaired motor skills, irregular sleep, GI problems, infections, and seizures attributes were significantly associated with autism diagnosis. These attributes also share correlation structures amongst themselves, suggesting that groupings of attributes may help to define subtypes of autism.
The attributes analyzed in this study are not a comprehensive list of suspected traits of autism. Parent-reported diagnoses may not always be accurate, although we validated diagnoses. Despite accounting for family structure in our experiments, the relationships between attributes and diagnosis are likely stronger in the general population because our "control" sample is comprised of biological siblings who may still possess subclinical autistic traits, given the heritability of autism.
A more expansive conceptualization of the autism phenotype is likely to be useful to both researchers and families for identifying a more targeted approach to intervention.
自闭症谱系障碍(ASD)是一种神经发育疾病,其特征为行为受限、重复,以及社交沟通困难,是美国儿科行为健康领域中增长最快的问题之一。早期干预能显著改善长期预后,但由于性别和文化等身份因素会影响自闭症的表型呈现范围,导致诊断和治疗变得复杂。许多与自闭症表型相关的身体和行为特征并未纳入研究中使用的筛查和诊断工具。
我们建立了一个针对有自闭症儿童的不同家庭的多中心登记处,以收集他们身体和行为特征的纵向数据,研究自闭症的异质表型。我们的“儿童优先”登记处包含来自4120个家庭的6951名参与者(以下简称“儿童”),其中1865个家庭有不止一个孩子。除了收集社会沟通问卷(SCQ)等标准临床工具外,我们还收集了自闭症和非自闭症儿童的听力问题、噪音敏感、视力挑战、睡眠不规律、运动技能受损、代谢紊乱、胃肠道(GI)问题、感染、癫痫发作和早产等表型特征信息。在根据SCQ分数验证家长报告的诊断后,我们使用逻辑回归模型和排列检验分析了每个特征与自闭症诊断以及其他特征之间的关联。
噪音敏感、运动技能受损、睡眠不规律、胃肠道问题、感染和癫痫发作等特征与自闭症诊断显著相关。这些特征之间也存在相关结构,表明特征分组可能有助于定义自闭症的亚型。
本研究分析的特征并非自闭症疑似特征的完整列表。尽管我们验证了诊断,但家长报告的诊断可能并不总是准确的。尽管我们在实验中考虑了家庭结构,但由于自闭症具有遗传性,我们的“对照”样本由可能仍具有亚临床自闭症特征的亲生兄弟姐妹组成,因此在一般人群中,特征与诊断之间的关系可能更强。
对自闭症表型进行更广泛的概念化,可能对研究人员和家庭都有用,有助于确定更有针对性的干预方法。