Evaluating ASD Symptomatology in Children with Down Syndrome

评估唐氏综合症儿童的 ASD 症状

基本信息

批准号：
10294431
负责人：
Marie Moore Channell
金额：
$ 44.08万
依托单位：
UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-09 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10294431
关键词：
18 year old Address Area Behavior Behavioral Biometry COVID-19 pandemic Caregivers Characteristics Child Clinical Clinical Trials Complex Conduct Clinical Trials Data Data Collection Development Diagnostic Down Syndrome Epidemiology Evaluation Family Feasibility Studies Future General Population Genetic Health Care Costs Heterogeneity Individual Intellectual functioning disability Intervention Knowledge Language Language Delays Longevity Measurement Measures Methods Monitor Outcome Participant Performance Phenotype Population Positioning Attribute Psychometrics Questionnaires Relative Risks Reporting Research Resources Risk Sampling Symptoms Testing United States National Institutes of Health Universities autism spectrum disorder base behavioral phenotyping clinical outcome assessment clinical practice clinical trial readiness cohort comorbidity disorder risk executive function improved inclusion criteria individualized medicine inter-individual variation neurodevelopment performance based measurement precision medicine recruit screening social social communication sound success symptomatology tool treatment response

项目摘要

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.

项目概要/摘要大约五分之一的唐氏综合症 (DS) 患者符合共病自闭症谱系标准与一般人群相比，患有自闭症谱系障碍 (ASD) 的风险增加十倍。语言迟缓、行为挑战增加、对护理人员的要求更高以及护理成本更高精准医学的最新进展有可能极大地促进整个生命周期的医疗保健。改善 DS 和自闭症谱系障碍 (ASD) 等共病患者的长期预后。要实现这一潜力，需要采取可靠有效的措施，目前尚无科学依据。 DS 中 ASD 症状的识别和测量如果没有准确的测量，则无法进行临床试验。 DS 无法正确应用 ASD 纳入标准、在必要时对队列进行分层或跟踪治疗反应。经过测试，迫切需要临床试验进行可靠、有效的 ASD 筛查、诊断和治疗为了满足这一需求，我们建议 (1) 评估心理测量。 DS 中 ASD 症状测量的特征，以及 (2) 通过以下方式描述 DS 中 ASD 症状概况：表征 DS 的 ASD 症状和相关发育特征将进一步提供信息。临床试验，使他们能够根据共病自闭症谱系障碍（ASD）对队列进行分层，并监测整个群体对治疗的反应这些目标与 NIH INCLUDE 项目的两个优先事项一致：(a) 增加可能性通过测试临床结果评估措施来确定临床试验是否成功，以及 (b) 定义演示文稿以及 DS 患者并发病症的过程，以努力提高效率、普遍性，和未来临床试验的包容性，拟议的研究将在线进行，以实现这些目标，我们将利用现有资源（NIH 的 DS-Connect；埃默里大学的 DS360）进行大规模、对 500 名 6 至 18 岁患有 DS 的 ASD 症状进行的全国性研究我们将检查其可靠性、有效性、我们将根据三位知名护理人员报告的 ASD 筛查和症状测量结果进行分析和变异。利用这些 ASD 测量数据以及额外的深度表型分析来表征 DS 中 ASD 表型的异质性并确定症状概况最后，我们提出了一个探索性目标。在 ASD 高风险或低风险的子样本 (n = 25) 中进行研究，以检验远程评估方法的可行性收集该项目生成的直接、基于绩效的 ASD 评估将加强临床试验。通过在 DS 中提供 ASD 措施来做好准备，这些措施可以 (a) 筛查 ASD 风险以确定治疗候选者 (b) 根据 ASD 症状特征对队列进行分层，并监测这些特征对治疗的反应。经过验证，这些 ASD 测量将为未来的临床试验提供急需的资源来记录可行性研究将确定远程评估的作用范围。用于对患有 DS 的儿童进行基于表现的 ASD 评估。在线进行临床试验的领域，这在 COVID-19 大流行时代尤其重要。