Supplementary funding for U01NS104326 Clinical Trial Readiness for SCA1 and SCA3 (“READISCA”)

项目概要/摘要 为了加强正在进行的 READISCA 项目的临床试验准备，本行政补充 申请请求批准并资助两项纵向生物标志物研究：1) 确定 神经丝轻链 (NfL) 的血浆水平，以及 2) 体积和扩散 MRI 的附加分析 从携带 SCA1 或 SCA3 突变的受试者获得的数据。 1) 基线访视时获得的血浆样本显示 NfL 水平可以区分共济失调前期 来自对照和早期共济失调样本的样本。后续样本中的 NfL 水平预计将 提供 NfL 水平从共济失调前期范围到共济失调早期范围的演变， 血浆 NfL 水平的反应性与标准化反应平均值 (SRM) 等指标， 以及血浆 NfL 水平在预测共济失调前期受试者发病年龄中的效用。所有样品至 待分析的数据目前作为 Aim1 的一部分存储在 BioSEND 存储库中。 SIMOA 分析 血浆样本将确定 NfL 水平。血浆 NfL 水平将与临床相关 结果评估（COA）数据。 2) 增强基于标准感兴趣区域 (ROI) 的结构体积分析的价值 READISCA 目标 2 中的 MR 数据和简单扩散张量成像 (DTI) 分析，我们将执行 为结构 MR 数据添加基于体素的分析，并使用高阶模型和基于固定素的分析。 我们的假设是，血浆 NfL 水平和 MR 参数的演变存在临界阈值 表型转变（即共济失调疾病发作）发生在携带 SCA1 或 SCA3突变。我们进一步假设血浆和成像生物标志物一起提供了预测 转化率和发病年龄强于单个生物标志物。我们将使用SARA总分≥3 作为表型转化指标。对于统计，我们计划使用生存模型来识别预测因素 转换。将使用结合生存模型和线性混合模型的联合模型来寻找 随时间变化的协变量的影响。将使用 C 静力学标准选择最佳模型。

Project Summary/Abstract To enhance the clinical trial readiness of the ongoing READISCA project, this Administrative Supplement application requests approval and funding for two longitudinal biomarker studies: 1) determination of the plasma level of neurofilament light chain (NfL), and 2) additional analyses of the volumetric and diffusion MRI data obtained from subjects who carry a mutation of SCA1 or SCA3. 1) Plasma samples obtained at baseline visits showed that the NfL level can distinguish pre-ataxia samples from control and early-ataxia samples. NfL levels in the follow-up samples are expected to provide the evolution of the NfL level from the pre-ataxia range to the early ataxia range, the resoponsiveness of the plasma NfL level with indices such as the standardized response mean (SRM), and the utility of plasma NfL level in predicting the age at onset in pre-ataxia subjects. All samples to be analyzed are currently stored at the BioSEND repository as a part of Aim1. The SIMOA analysis of the plasma samples will determine the NfL level. The plasma NfL level will be correlated with clinical outcome assessment (COA) data. 2) To augment the value of standard region-of-interest (ROI) based volumetric analyses of the structural MR data and the simple diffusion tensor imaging (DTI) analyses in Aim 2 of READISCA, we will perform add voxel-based analyses for structural MR data and use higher-order models and fixel-based analysis. Our hypothesis is that there are critical thresholds for the evolving plasma NfL level and MR parameters at which the phenoconversion (i.e., ataxia disease onset) occurs in pre-ataxia subjects who carry the SCA1 or SCA3 mutation. We further postulate that plasma and imaging biomarkers together provide the prediction of the conversion and the age at onset stronger than individual biomarkers. We will use the SARA total score ≥3 as the phenoconversion indicator. For statistics, we plan to use survival models to identify predictors of conversion. Joint models that combine survival models and linear mixed models will be used to look for the influence of time varying covariates. The best model will be selected using C statics criteria.