Background. Alzheimer's disease and related dementia (ADRD) are characterized by multiple and progressive anatomo clinical changes. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable. Methods: We developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. Our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to 2186 participants of the MEMENTO study with 5-year follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed. Result: The estimated latent disease time spanned over twenty years before the clinical diagnosis. Considering the profile of a woman aged 70 with a high level of education and APOE4 carrier (the main genetic risk factor for ADRD), CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5 years and cognitive decline by 10 years. We observed that individual characteristics could substantially modify the sequence and timing of these changes. Conclusion: Our disease progression model does not only realign trajectories into the most homogeneous way. It accounts for the inherent residual inter-individual variability in dementia progression to describe the long-term changes according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events.

翻译：背景：阿尔茨海默病及相关痴呆症 (ADRD) 以多种渐进性解剖临床变化为特征。然而，从队列数据中建模疾病病程变化颇具挑战，因为常规时间尺度并不适用，且仅可在诊断前随访时间较短的小样本亚组中获得临床诊断时间。解决此问题的一种方法是将疾病时间定义为潜在变量。方法：我们开发了一种多变量混合模型方法，将个体轨迹重新对齐至潜在疾病时间以描述疾病进展。该方法利用临床诊断信息作为部分观测且近似的参考，引导潜在疾病时间的估计。模型估计在贝叶斯框架下使用Stan软件执行。我们将此方法应用于MEMENTO研究中2186名参与者的五年随访数据，分析了来自脑脊液 (CSF)、脑影像及认知测试的12种ADRD标志物的重复测量结果。结果：估计的潜在疾病时间跨度长达临床诊断前二十年。以一名70岁、高教育水平且携带APOE4基因（ADRD主要遗传风险因素）的女性为例，tau蛋白积累的CSF标志物早于脑萎缩标志物5年出现，并早于认知衰退10年。我们观察到个体特征可显著改变这些变化的序列与时间。结论：我们的疾病进展模型不仅以最均质化方式重新对齐轨迹，还考虑了痴呆进展中固有的残余个体间变异，从而根据临床诊断前数年描述长期变化，并提供关于事件序列的临床有意义信息。