Neurodegenerative diseases are characterized by numerous markers of progression and clinical endpoints. For instance, Multiple System Atrophy (MSA), a rare neurodegenerative synucleinopathy, is characterized by various combinations of progressive autonomic failure and motor dysfunction, and a very poor prognosis. Describing the progression of such complex and multi-dimensional diseases is particularly difficult. One has to simultaneously account for the assessment of multivariate markers over time, the occurrence of clinical endpoints, and a highly suspected heterogeneity between patients. Yet, such description is crucial for understanding the natural history of the disease, staging patients diagnosed with the disease, unravelling subphenotypes, and predicting the prognosis. Through the example of MSA progression, we show how a latent class approach modeling multiple repeated markers and clinical endpoints can help describe complex disease progression and identify subphenotypes for exploring new pathological hypotheses. The proposed joint latent class model includes class-specific multivariate mixed models to handle multivariate repeated biomarkers possibly summarized into latent dimensions and class-and-cause-specific proportional hazard models to handle time-to-event data. Maximum likelihood estimation procedure, validated through simulations is available in the lcmm R package. In the French MSA cohort comprising data of 598 patients during up to 13 years, five subphenotypes of MSA were identified that differ by the sequence and shape of biomarkers degradation, and the associated risk of death. In posterior analyses, the five subphenotypes were used to explore the association between clinical progression and external imaging and fluid biomarkers, while properly accounting for the uncertainty in the subphenotypes membership.

翻译：神经退行性疾病具有多种进展标志物和临床终点的特征。例如多系统萎缩症（MSA）——一种罕见的神经突触核蛋白病，表现为进行性自主神经衰竭与运动功能障碍的不同组合，且预后极差。描述此类复杂多维疾病的进展尤为困难：需同时考虑多变量标志物的纵向评估、临床终点的发生，以及患者间高度可疑的异质性。然而，这种描述对理解疾病自然史、对确诊患者进行分期、揭示亚表型及预测预后至关重要。通过MSA进展的实例，我们展示了利用潜类方法建模多重重复测量标志物与临床终点如何帮助描述复杂疾病进展并识别亚表型，以探索新的病理学假设。所提出的联合潜类模型包含：处理多变量重复生物标志物（可汇总为潜在维度）的类别特异性多变量混合模型，以及处理时间至事件数据的类别-病因特异性比例风险模型。基于模拟验证的最大似然估计程序已在lcmm R包中实现。在包含598名患者长达13年随访数据的法国MSA队列中，我们识别出五种MSA亚表型，其差异体现在生物标志物退化序列模式及相关的死亡风险。在后验分析中，这五种亚表型被用于探索临床进展与外部成像及体液标志物的关联，同时恰当考虑了亚表型归属的不确定性。