Current diagnosis and prognosis for Parkinson's disease (PD) face formidable challenges due to the heterogeneous nature of the disease course, including that (i) the impairment severity varies hugely between patients, (ii) whether a symptom occur independently or co-occurs with related symptoms differs significantly, and (iii) repeated symptom measurements exhibit substantial temporal dependence. To tackle these challenges, we propose a novel blockwise mixed membership model (BM3) to systematically unveil between-patient, between-symptom, and between-time clinical heterogeneity within PD. The key idea behind BM3 is to partition multivariate longitudinal measurements into distinct blocks, enabling measurements within each block to share a common latent membership while allowing latent memberships to vary across blocks. Consequently, the heterogeneous PD-related measurements across time are divided into clinically homogeneous blocks consisting of correlated symptoms and consecutive time. From the analysis of Parkinson's Progression Markers Initiative data (n=1,531), we discover three typical disease profiles (stages), four symptom groups (i.e., autonomic function, tremor, left-side and right-side motor function), and two periods, advancing the comprehension of PD heterogeneity. Moreover, we identify several clinically meaningful PD subtypes by summarizing the blockwise latent memberships, paving the way for developing more precise and targeted therapies to benefit patients. Our findings are validated using external variables, successfully reproduced in validation datasets, and compared with existing methods. Theoretical results of model identifiability further ensures the reliability and reproducibility of latent structure discovery in PD.

翻译：当前帕金森病（PD）的诊断与预后面临严峻挑战，这主要源于疾病进程的高度异质性，具体表现为：（i）患者间的功能障碍严重程度差异巨大；（ii）症状是否独立出现或与相关症状共现存在显著差异；（iii）重复的症状测量结果展现出显著的时序依赖性。为应对这些挑战，我们提出了一种新颖的分块混合隶属度模型（BM3），以系统性地揭示帕金森病内在的患者间、症状间及时间点间的临床异质性。BM3的核心思想是将多变量纵向测量数据划分为不同的数据块，使得每个块内的测量共享一个共同的潜在隶属度，同时允许潜在隶属度在不同块之间变化。由此，随时间变化的异质性帕金森病相关测量被划分为由相关症状和连续时间点构成的临床同质性数据块。通过对帕金森病进展标志物倡议数据（n=1,531）的分析，我们发现了三种典型的疾病特征（阶段）、四个症状群组（即自主神经功能、震颤、左侧及右侧运动功能）以及两个时期，从而推进了对帕金森病异质性的理解。此外，我们通过汇总分块潜在隶属度，识别出若干具有临床意义的帕金森病亚型，为开发更精准、更具针对性的疗法以惠及患者铺平了道路。我们的发现通过外部变量得到验证，在验证数据集中成功复现，并与现有方法进行了比较。模型可识别性的理论结果进一步确保了帕金森病潜在结构发现的可靠性与可复现性。