Statistical disease modeling and clinimetrics to prepare for preventive trials in huntington disease

项目摘要 亨廷顿病(HD)是一种致命的、代价高昂的常染色体显性遗传性神经退行性疾病，没有可用的 疾病修正疗法。虽然基因检测可以确定突变的HTT基因的存在，但在 当采取预防性干预措施时，是否没有标记来记录病变的发生或划分界限？ 是最大限度地提高生活质量的最佳选择。而延缓病情发展、延迟或预防发病的临床试验 一开始，缺乏评估其有效性的方法。拟议的研究将填补我们能力上的这些空白 设计HD的预防性临床试验。首先，我们将使用遗留的预测数据来开发统计 发病、进展率和表型异质性的模型，然后我们将招募250名参与者 完成针对预显性HD的业界最佳临床结果评估(COA)，并对他们进行 可靠性、有效性和响应性的临床测试，以便未来的预防试验人员可以利用这些信息 设计有效的试验以延缓HD的发病或进展

Project Summary Huntington disease (HD) is a fatal, costly, autosomal dominant neurodegenerative disease with no available disease-modifying treatments. While genetic testing can identify the presence of the mutant HTT gene, there are no markers to document the initiation of pathological changes or demarcate when preventive interventions are optimal to maximize quality of life. While clinical trials to slow progression and delay or prevent onset are beginning, methods to assess their efficacy are lacking. The proposed research will fill these gaps in our ability to design preventive clinical trials for HD. First, we will use the legacy PREDICT data to develop statistical models of onset, progression rate and phenotypic heterogeneity and then we will recruit 250 participants to complete the field's best Clinical Outcome Assessments (COAs) for premanifest HD and subject them to clinimetric tests of reliability, validity and responsiveness so future preventive trialists can utilize the information to design well-powered and efficient trials to delay the onset or slow the progression of HD