纵向队列中以临床诊断为锚点的疾病进展模型：以阿尔茨海默病及相关痴呆为例
Disease progression model anchored around clinical diagnosis in longitudinal cohorts: example of Alzheimer's disease and related dementia
AI总结：
针对ADRD队列研究中临床诊断时间数据有限、时间尺度不适配的问题，本研究提出以临床诊断为部分参考的贝叶斯多变量混合潜疾病时间模型，在MEMENTO队列中验证其可刻画二十余年疾病进展及个体异质性。
摘要：
研究背景：阿尔茨海默病及相关痴呆（ADRD）的特征是多方面的进行性解剖-临床变化。然而，从队列数据中模拟疾病进程中的变化颇具挑战性，因为常用的时间尺度并不适用，且仅在少量参与者子样本中可获得临床诊断时间数据，这些受试者诊断前的随访时长较短。规避这一挑战的一种方案是将疾病时间定义为潜变量。\n研究方法：我们开发了一种多变量混合模型方法，可将个体轨迹重新对齐至潜疾病时间维度，以此描述疾病进展。我们的方法利用临床诊断信息作为部分可观测的近似参考，指导潜疾病时间的估计。模型估计在贝叶斯框架下借助Stan工具完成。我们将该方法应用于MEMENTO研究的2186名参与者，这些受试者接受了5年随访。研究分析了来自脑脊液（CSF）、脑成像和认知测试的12项ADRD生物标志物的重复测量数据。\n研究结果：估计的潜疾病时间跨度覆盖临床诊断前的二十余年。针对一名70岁、受教育程度较高且为APOE4携带者（ADRD的主要遗传风险因素）的女性的特征分析显示，tau蛋白积累的CSF标志物比脑萎缩标志物早出现5年，比认知衰退早出现10年。我们观察到个体特征可显著改变这些变化的发生顺序和时间节点。\n研究结论：我们的疾病进展模型不仅能以最均匀的方式重新对齐轨迹，还考虑了痴呆进展中固有的残余个体间异质性，可根据临床诊断前的年份描述长期变化，并提供关于疾病事件发生顺序的具有临床意义的信息。
英文摘要：
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.
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