Portolese Joana, Gomes Catarina Santos, Daguano Gastaldi Vinicius, Paula Cristiane Silvestre, Caetano Sheila C, Bordini Daniela, Brunoni Décio, Mari Jair de Jesus, Vêncio Ricardo Z N, Brentani Helena
Laboratório de Psicopatologia e Terapêutica Psiquiátrica (LIM23), Faculdade de Medicina FMUSP, Universidade de São Paulo, Sao Paulo 05403-010, SP, Brazil.
Departamento & Instituto de Psiquiatria, Faculdade de Medicina FMUSP, Universidade de São Paulo, Sao Paulo 05403-010, SP, Brazil.
Brain Sci. 2024 Dec 14;14(12):1254. doi: 10.3390/brainsci14121254.
Currently, there is a need for approaches to understand and manage the multidimensional autism spectrum and quantify its heterogeneity. The diagnosis is based on behaviors observed in two key dimensions, social communication and repetitive, restricted behaviors, alongside the identification of required support levels. However, it is now recognized that additional modifiers, such as language abilities, IQ, and comorbidities, are essential for a more comprehensive assessment of the complex clinical presentations and clinical trajectories in autistic individuals. Different approaches have been used to identify autism subgroups based on the genetic and clinical heterogeneity, recognizing the importance of autistic behaviors and the assessment of modifiers. While valuable, these methods are limited in their ability to evaluate a specific individual in relation to a normative reference sample of autistic individuals. A quantitative score based on axes of phenotypic variability could be useful to compare individuals, evaluate the homogeneity of subgroups, and follow trajectories of an individual or a specific group. Here we propose an approach by (i) combining measures of phenotype variability that contribute to clinical presentation and could impact different trajectories in autistic persons and (ii) using it with normative modeling to assess the clinical heterogeneity of a specific individual.
Using phenotypic data available in a comprehensive reference sample, the Simons Simplex Collection ( = 2744 individuals), we performed principal component analysis (PCA) to find components of phenotypic variability. Features that contribute to clinical heterogeneity and could impact trajectories in autistic people were assessed by the Autism Diagnostic Interview-Revised (ADI-R), Vineland Adaptive Behavior Scales (VABS) and the Child Behavior Checklist (CBCL). Cognitive assessment was estimated by the Total Intelligence Quotient (IQ).
Three PCs embedded 72% of the normative sample variance. PCA-projected dimensions supported normative modeling where a multivariate normal distribution was used to calculate percentiles. A Multidimensional General Functionality Score (MGFS) to evaluate new prospective single subjects was developed based on percentiles.
Our approach proposes a basis for comparing individuals, or one individual at two or more times and evaluating homogeneity in phenotypic clinical presentation and possibly guides research sample selection for clinical trials.
目前，需要有方法来理解和管理多维自闭症谱系并量化其异质性。自闭症的诊断基于在两个关键维度（社交沟通和重复、受限行为）中观察到的行为，以及所需支持水平的确定。然而，现在人们认识到，其他修饰因素，如语言能力、智商和共病情况，对于更全面地评估自闭症个体复杂的临床表现和临床病程至关重要。基于遗传和临床异质性，人们采用了不同方法来识别自闭症亚组，认识到自闭症行为及修饰因素评估的重要性。虽然这些方法很有价值，但它们在根据自闭症个体的规范参考样本评估特定个体方面能力有限。基于表型变异性轴的定量评分可能有助于比较个体、评估亚组的同质性以及追踪个体或特定群体的病程。在此，我们提出一种方法，即（i）结合有助于临床表现且可能影响自闭症患者不同病程的表型变异性测量方法，以及（ii）将其用于规范建模以评估特定个体的临床异质性。
利用综合参考样本西蒙斯简易病例集（n = 2744 名个体）中可用的表型数据，我们进行主成分分析（PCA）以找出表型变异性的成分。通过修订版自闭症诊断访谈量表（ADI-R）、文兰适应行为量表（VABS）和儿童行为清单（CBCL）评估有助于临床异质性且可能影响自闭症患者病程的特征。通过总智商（IQ）估计认知评估。
三个主成分包含了规范样本方差的 72%。主成分分析投影维度支持规范建模，其中使用多元正态分布来计算百分位数。基于百分位数开发了一个多维总体功能评分（MGFS）以评估新的前瞻性单一受试者。
我们的方法为比较个体或同一个体在两个或更多时间点的情况、评估表型临床表现的同质性提供了基础，并可能为临床试验的研究样本选择提供指导。
