Evaluating ASD Symptomatology in Children with Down Syndrome

评估唐氏综合症儿童的 ASD 症状

• 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
• 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

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，自闭症谱系障碍（ASD）的风险增加十倍。共病自闭症谱系障碍 (ASD) 与 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 整个生命周期的医疗保健。精准医学的最新进展有可能大大提高 改善 DS 和自闭症谱系障碍 (ASD) 等共病患者的长期预后。然而，对于 要实现这一潜力，需要采取可靠和有效的措施。目前尚无科学依据 DS 中 ASD 症状的识别和测量。如果没有精确的测量，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。 因此，临床试验迫切需要可靠、有效的 ASD 筛查、诊断和治疗。 DS 中的症状监控工具。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 ASD 症状概况： 深层表型分析。描述 DS 中 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 症状概况。这些目标与 NIH INCLUDE 项目的两个优先事项相一致：(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 患者并发病症的病程。为了提高效率、通用性， 以及未来临床试验的包容性，拟议的研究将在线进行。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 DS360）进行大规模、 一项针对 500 名 6 至 18 岁 DS 患者的 ASD 症状的全国性研究。我们将检查信度、效度、 三位知名护理人员基于报告的 ASD 筛查和症状测量的变异性。我们将 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。一次 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，以记录 对治疗的反应结果。可行性研究将确定远程评估能够在多大程度上发挥作用 可用于 DS 儿童基于表现的 ASD 评估。所获得的知识将为 在线进行临床试验的领域，这在 COVID-19 大流行时代尤其重要。