University of Illinois at Urbana-Champaign, 901 S. Sixth St, Champaign, IL, 61820, USA.
Emory University, 615 Michael St, Atlanta, GA, 30322, USA.
J Neurodev Disord. 2021 Apr 19;13(1):16. doi: 10.1186/s11689-021-09365-2.
There is a high degree of inter- and intra-individual variability observed within the phenotype of Down syndrome. The Down Syndrome Cognition Project was formed to capture this variability by developing a large nationwide database of cognitive, behavioral, health, and genetic information on individuals with Down syndrome, ages 6-25 years. The current study used the Down Syndrome Cognition Project database to characterize cognitive and behavioral variability among individuals with Down syndrome.
Latent profile analysis was used to identify classes across a sample of 314 participants based on their cognition (IQ and executive functioning), adaptive and maladaptive behavior, and autism spectrum disorder symptomatology. A multivariate multinomial regression model simultaneously examined demographic correlates of class.
Results supported a 3-class model. Each class demonstrated a unique profile across the subdomains of cognition and behavior. The "normative" class was the largest (n = 153, 48%) and displayed a relatively consistent profile of cognition and adaptive behavior, with low rates of maladaptive behavior and autism symptomatology. The "cognitive" class (n = 109, 35%) displayed low cognitive scores and adaptive behavior and more autism symptomatology, but with low rates of maladaptive behavior. The "behavioral" class, the smallest group (n = 52, 17%), demonstrated higher rates of maladaptive behavior and autism symptomatology, but with cognition levels similar to the "normative" class; their adaptive behavior scores fell in between the other two classes. Household income and sex were the only demographic variables to differ among classes.
These findings highlight the importance of subtyping the cognitive and behavioral phenotype among individuals with Down syndrome to identify more homogeneous classes for future intervention and etiologic studies. Results also demonstrate the feasibility of using latent profile analysis to distinguish subtypes in this population. Limitations and future directions are discussed.
唐氏综合征患者的表型存在高度的个体内和个体间变异性。唐氏综合征认知项目的成立是为了通过开发一个关于唐氏综合征患者的认知、行为、健康和遗传信息的大型全国性数据库来捕捉这种变异性，这些患者的年龄在 6-25 岁之间。本研究使用唐氏综合征认知项目数据库来描述唐氏综合征患者的认知和行为变异性。
基于 314 名参与者的认知（智商和执行功能）、适应和适应不良行为以及自闭症谱系障碍症状，使用潜在剖面分析来识别样本中的类别。多元多项回归模型同时检查了类别的人口统计学相关性。
结果支持 3 类模型。每个类别在认知和行为的子领域都表现出独特的特征。“正常”类是最大的（n=153，48%），表现出相对一致的认知和适应行为特征，适应不良行为和自闭症症状的发生率较低。“认知”类（n=109，35%）表现出较低的认知分数和适应行为，以及更多的自闭症症状，但适应不良行为的发生率较低。“行为”类是最小的群体（n=52，17%），表现出更高的适应不良行为和自闭症症状发生率，但认知水平与“正常”类相似；他们的适应行为分数介于其他两个类之间。家庭收入和性别是类之间唯一不同的人口统计学变量。
这些发现强调了对唐氏综合征患者的认知和行为表型进行亚分型的重要性，以识别更同质的类别，用于未来的干预和病因研究。结果还表明，使用潜在剖面分析来区分该人群中的亚型是可行的。讨论了限制和未来方向。
