Evaluating ASD Symptomatology in Children with Down Syndrome

项目总结/摘要 大约五分之一的唐氏综合征（DS）患者符合自闭症谱系共病的标准 自闭症（ASD），与普通人群相比，风险增加了10倍。ASD合并症与 语言延迟，行为挑战增加，对护理人员的要求更高，以及更高的 整个生命周期的医疗保健。精准医疗的最新进展有可能大大提高 改善DS和ASD等共病患者的长期结局。但对于 要实现这一潜力，就需要采取可靠和有效的措施。目前还没有科学依据， 在DS中ASD症状的识别和测量。如果没有准确的测量， DS无法正确应用ASD入选标准，在必要时对队列进行分层，或跟踪治疗反应。 因此，迫切需要临床试验来进行可靠、有效的ASD筛查、诊断和治疗。 DS中的症状监测工具。为了满足这一需求，我们建议（1）评估心理测量 DS中ASD症状测量的特征，以及（2）通过以下方式表征DS中的ASD症状谱： 深层表型分析描述ASD症状和DS中的相关发育特征将进一步告知 临床试验，使他们能够通过共病ASD对队列进行分层，并监测对治疗的反应， 症状特征这些目标与NIH INCLUDE项目的两个优先事项相一致：（a）增加 通过临床结果评估措施的测试来确定临床试验的成功率，以及（B）定义 以及DS患者同时发生的疾病过程。为了提高工作效率、普遍性、 和未来临床试验的包容性，拟议的研究将在网上进行。为了实现这些目标， 我们将利用现有资源（NIH的DS-Connect;埃默里大学的DS 360）进行大规模， 在全国范围内对500名6至18岁患有DS的ASD症状进行研究。我们将检验其可靠性，有效性， 和变异性的三个著名的照顾者报告为基础的ASD筛查和症状的措施。我们将 利用来自这些ASD测量的数据，沿着额外的深度表型，来表征 DS中ASD表型的异质性，并确定症状谱。最后，我们提出了一个探索性的目标， 在高或低ASD风险的子样本（n = 25）中检查远程评估方法的可行性， 收集直接的、基于绩效的ASD评估。该项目产生的数据将增强临床试验 通过在DS中提供ASD措施，可以（a）筛查ASD风险，以确定治疗候选人 和（B）通过ASD症状特征对组群进行分层，并在这些特征中监测对治疗的反应。一旦 经过验证，这些ASD措施将为未来的临床试验提供急需的资源， 治疗的结果。可行性研究将确定远程评估能够在多大程度上 用于DS儿童的基于表现的ASD评估。所获得的知识将准备 在线进行临床试验的领域，在COVID-19大流行时期尤为重要。

PROJECT SUMMARY/ABSTRACT Approximately 1 in 5 individuals with Down syndrome (DS) meet criteria for comorbid autism spectrum disorder (ASD), a tenfold increase in risk compared to the general population. Comorbid ASD is associated with delayed language, increased behavioral challenges, greater demands on caregivers, and higher costs of healthcare across the lifespan. Recent advances in precision medicine have the potential to substantially improve long-term outcomes among individuals with DS and comorbid conditions such as ASD. However, for this potential to be realized, reliable and valid measures are required. There is currently little scientific basis for the identification and measurement of ASD symptoms in DS. Without accurate measurement, clinical trials in DS cannot properly apply ASD inclusion criteria, stratify cohorts where necessary, or track response to treatment. Consequently, there is an urgent need for clinical trials to have reliable, valid ASD screening, diagnostic, and symptom monitoring tools in DS. To address this need, we propose to (1) evaluate the psychometric characteristics of ASD symptom measures in DS, and (2) characterize ASD symptom profiles in DS through deep phenotyping. Characterizing ASD symptoms and related developmental features in DS will further inform clinical trials by enabling them to stratify cohorts by comorbid ASD and monitor response to treatment across symptom profiles. These aims align with two priorities of the NIH INCLUDE Project: (a) increase the likelihood of clinical trial success through testing of clinical outcome assessment measures, and (b) define the presentation and course of co-occurring conditions in individuals with DS. In an effort to improve the efficiency, generalizability, and inclusiveness of future clinical trials, the proposed study will be conducted online. To accomplish these aims, we will leverage existing resources (NIH’s DS-Connect; Emory University’s DS360) to conduct a large-scale, nationwide study of ASD symptoms in 500 6- to 18-year-olds with DS. We will examine the reliability, validity, and variability of three well-known caregiver report-based ASD screening and symptom measures. We will leverage data from these ASD measures, along with additional deep phenotyping, to characterize the heterogeneity of the ASD phenotype in DS and identify symptom profiles. Finally, we propose an exploratory aim among a subsample (n = 25) at high or low ASD risk to examine the feasibility of tele-assessment methods for gathering direct, performance-based ASD evaluations. Data generated from this project will enhance clinical trial readiness by providing ASD measures in DS that can (a) screen for ASD risk to identify candidates for treatment and (b) stratify cohorts by ASD symptom profiles and monitor response to treatment across these profiles. Once validated, these ASD measures will provide a much-needed resource for future clinical trials to document outcomes in response to treatment. The feasibility study will determine the extent to which tele-assessments can be used for performance-based ASD evaluations in children with DS. The knowledge gained will prepare the field for conducting clinical trials online, particularly important in the era of the COVID-19 pandemic.