Technology-based therapy-response and prognostic biomarkers in a prospective study of a de novo Parkinson's disease cohort

This database includes the raw data linked with the paper “ Technology-based therapy-response and prognostic biomarkers in a prospective study of a de novo Parkinson’s disease cohort” published on “NPJ Parkinson’s disease”. This work aims at identifying prognostic biomarkers in newly diagnosed PD patients and quantifying therapy-response. Forty de novo PD patients underwent clinical and technology-based kinematic assessments performing motor tasks (MDS-UPDRS part III) to assess tremor, bradykinesia, gait, and postural stability (T0). A visit after 6 months (T1) and a clinical and kinematic assessment after 12 months (T2) where scheduled. A clinical follow-up was provided between 30 and 36 months after the diagnosis (T3). We performed an ANOVA for repeated measures to compare patients’ kinematic features at baseline and at T2 to assess therapy response. Pearson correlation test was run between baseline kinematic features and UPDRS III score variation between T0 and T3, to select candidate kinematic prognostic biomarkers. A multiple linear regression model was created to predict the long-term motor outcome using T0 kinematic measures. All motor tasks significantly improved after the dopamine replacement therapy. A significant correlation was found between UPDRS scores variation and some baseline bradykinesia (toe tapping amplitude decrement, p = 0.009) and gait features (velocity of arms and legs, sit-to-stand time, p = 0.007; p = 0.009; p = 0.01, respectively). A linear regression model including four baseline kinematic features could significantly predict the motor outcome (p = 0.000214). Technology-based objective measures represent possible early and reproducible therapy-response and prognostic biomarkers.本数据库收录了发表于《NPJ帕金森病》（NPJ Parkinson’s disease）的论文《新发帕金森病队列前瞻性研究中基于技术的治疗反应与预后生物标志物》（Technology-based therapy-response and prognostic biomarkers in a prospective study of a de novo Parkinson’s disease cohort）所关联的原始数据。本研究旨在明确新发帕金森病（de novo Parkinson’s disease, PD）患者的预后生物标志物，并量化其治疗反应。 40例新发PD患者于基线（T0时间点）接受了基于临床与技术的运动任务运动学评估，采用运动障碍学会统一帕金森病评定量表第三部分（MDS-UPDRS part III）评估患者的震颤、运动迟缓、步态与姿势稳定性。研究安排了6个月随访（T1）、12个月随访的临床与运动学评估（T2），并在确诊后30~36个月开展临床随访（T3）。 本研究采用重复测量方差分析（Analysis of Variance, ANOVA）对比患者基线与T2时间点的运动学特征，以评估治疗反应；通过Pearson相关分析，探究基线运动学特征与T0至T3期间UPDRS III评分变化的相关性，以筛选候选运动学预后生物标志物；并构建多元线性回归模型，基于T0时间点的运动学指标预测长期运动结局。 多巴胺替代治疗后，所有运动任务表现均得到显著改善。研究发现UPDRS评分变化与部分基线运动迟缓特征（足叩击幅度衰减，p=0.009）及步态特征（上肢与下肢运动速度、坐站转换时间，分别对应p=0.007、p=0.009、p=0.01）存在显著相关性。包含4项基线运动学特征的线性回归模型可显著预测运动结局（p=0.000214）。 基于技术的客观测量指标有望成为兼具早期性与可重复性的治疗反应与预后生物标志物。