背景:阿尔茨海默病和相关痴呆(ADRD)的特征是多种进行性解剖临床变化,包括大脑中异常蛋白质的积累,脑萎缩和严重的认知障碍。了解这些变化的顺序和时间对于深入了解疾病自然史并最终允许早期诊断至关重要。然而,根据队列数据对疾病病程的变化进行建模是具有挑战性的,因为通常的时间尺度(自纳入以来的时间,实际年龄)是不合适的,并且在诊断前随访时间短的参与者的小亚样本中可以获得临床诊断时间。规避这一挑战的一个解决方案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,该方法将个体轨迹重新调整为潜伏疾病时间以描述疾病进展。与现有文献相比,我们的方法利用临床诊断信息作为部分观察和近似的参考,以指导潜在疾病时间的估计。使用Stan在贝叶斯框架中进行模型估计。我们将该方法应用于MEMENTO研究,法国多中心临床队列2186名参与者,为期5年的密集随访。重复测量源自脑脊液(CSF)的12种ADRD标记,分析脑成像和认知测试。
结果:临床诊断前估计的潜伏疾病时间超过20年。考虑到一名70岁的妇女的特点,她受教育程度较高,并且是APOE4携带者(ADRD的主要遗传危险因素),tau蛋白积累的CSF标志物先于脑萎缩标志物5年,认知能力下降10年。然而,我们观察到个体特征可以实质性地改变这些变化的顺序和时间,特别是对于A的CSF水平[公式:见正文]。
结论:通过利用可用的临床诊断时机信息,我们的疾病进展模型不仅将轨迹重新调整为最均匀的方式。它解释了痴呆进展中固有的残余个体间变异性,以描述根据临床诊断前几年的长期解剖临床退化。并提供有关事件顺序的临床意义信息。
背景:clinicaltrials.gov,NCT01926249。2013年8月16日注册。
Alzheimer\'s disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) 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.
We developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. In contrast with the existing literature, 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 the MEMENTO study, a French multicentric clinic-based cohort of 2186 participants with 5-year intensive follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed.
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. However we observed that individual characteristics could substantially modify the sequence and timing of these changes, in particular for CSF level of A[Formula: see text].
By leveraging the available clinical diagnosis timing information, 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 anatomo-clinical degradations according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events.
clinicaltrials.gov, NCT01926249. Registered on 16 August 2013.