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

项目总结/摘要 为了加强正在进行的READISCA项目的临床试验准备，本行政补充文件 申请要求批准和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）体积和弥散MRI的附加分析 从携带SCA 1或SCA 3突变的受试者中获得的数据。 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 来自对照组和早期共济失调组的样本。预期后续样本中的NfL水平 提供NfL水平从共济失调前期范围到早期共济失调范围的演变， 血浆NfL水平与诸如标准化反应平均值（SRM）等指标的响应性， 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 目前作为Aim 1的一部分储存在BioSEND储存库中。SIMOA分析 血浆样品将确定NfL水平。血浆NfL水平将与临床相关 结果评估（COA）数据。 2)为了增加结构的基于标准感兴趣区域（ROI）的体积分析的价值， MR数据和READISCA目标2中的简单扩散张量成像（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的分析。 我们的假设是，对于血浆NfL水平和MR参数的变化存在临界阈值， 其中表型转化（即，共济失调疾病发作）发生在携带SCA 1或 SCA 3突变。我们进一步假设，血浆和成像生物标志物一起提供了以下预测 转换和发病年龄强于个体生物标志物。我们将使用SARA总分≥3 作为表型转化指标。对于统计学，我们计划使用生存模型来确定 转换.将联合收割机生存模型和线性混合模型相结合的联合模型将用于寻找 时变协变量的影响。将使用C静校正标准选择最佳模型。

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.