Venkatraghavan Vikram, Vinke Elisabeth J, Bron Esther E, Niessen Wiro J, Ikram M Arfan, Klein Stefan, Vernooij Meike W
Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands.
Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, The Netherlands.
Neuroimage. 2021 Sep;238:118233. doi: 10.1016/j.neuroimage.2021.118233. Epub 2021 Jun 4.
Data-driven disease progression models have provided important insight into the timeline of brain changes in AD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a population-based setting has not yet been investigated. In this study, we investigated if the disease timelines constructed in a case-controlled setting, with subjects stratified according to APOE status, are generalizable to a population-based cohort, and if progression along these disease timelines is predictive of AD. Seven volumetric biomarkers derived from structural MRI were considered. We estimated APOE-specific disease timelines of changes in these biomarkers using a recently proposed method called co-initialized discriminative event-based modeling (co-init DEBM). This method can also estimate a disease stage for new subjects by calculating their position along the disease timelines. The model was trained and cross-validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and tested on the population-based Rotterdam Study (RS) cohort. We compared the diagnostic and prognostic value of the disease stage in the two cohorts. Furthermore, we investigated if the rate of change of disease stage in RS participants with longitudinal MRI data was predictive of AD. In ADNI, the estimated disease timeslines for ϵ4 non-carriers and carriers were found to be significantly different from one another (p<0.001). The estimate disease stage along the respective timelines distinguished AD subjects from controls with an AUC of 0.83 in both APOEϵ4 non-carriers and carriers. In the RS cohort, we obtained an AUC of 0.83 and 0.85 in ϵ4 non-carriers and carriers, respectively. Progression along the disease timelines as estimated by the rate of change of disease stage showed a significant difference (p<0.005) for subjects with pre-symptomatic AD as compared to the general aging population in RS. It distinguished pre-symptomatic AD subjects with an AUC of 0.81 in APOEϵ4 non-carriers and 0.88 in carriers, which was better than any individual volumetric biomarker, or its rate of change, could achieve. Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a population-based cohort setting and that progression along the disease timelines is predictive of the development of AD in the general population. We expect that this approach can help to identify at-risk individuals from the general population for targeted clinical trials as well as to provide biomarker based objective assessment in such trials.
数据驱动的疾病进展模型为深入了解阿尔茨海默病（AD）表型的大脑变化时间线提供了重要见解。然而，其在基于人群的环境中预测症状前AD进展的效用尚未得到研究。在本研究中，我们调查了在病例对照环境中构建的、根据载脂蛋白E（APOE）状态分层的疾病时间线是否可推广到基于人群的队列，以及沿着这些疾病时间线的进展是否能预测AD。我们考虑了从结构磁共振成像（MRI）得出的七种体积生物标志物。我们使用一种最近提出的称为共同初始化判别事件建模（co-init DEBM）的方法，估计了这些生物标志物变化的APOE特异性疾病时间线。该方法还可以通过计算新受试者在疾病时间线上的位置来估计其疾病阶段。该模型在阿尔茨海默病神经成像倡议（ADNI）数据集上进行训练和交叉验证，并在基于人群的鹿特丹研究（RS）队列中进行测试。我们比较了两个队列中疾病阶段的诊断和预后价值。此外，我们调查了具有纵向MRI数据的RS参与者中疾病阶段的变化率是否能预测AD。在ADNI中，发现ε4非携带者和携带者的估计疾病时间线存在显著差异（p<0.001）。在APOEε4非携带者和携带者中，沿着各自时间线估计的疾病阶段在区分AD受试者和对照方面的曲线下面积（AUC）均为0.83。在RS队列中，我们在ε4非携带者和携带者中分别获得了0.83和0.85的AUC。与RS中的一般老年人群相比，根据疾病阶段变化率估计的沿着疾病时间线的进展在症状前AD受试者中显示出显著差异（p<0.005）。它在APOEε4非携带者中以0.81的AUC、在携带者中以0.88的AUC区分症状前AD受试者，这比任何单个体积生物标志物或其变化率所能达到的效果都要好。我们的结果表明，在病例对照数据上训练的co-init DEBM可推广到基于人群的队列环境，并且沿着疾病时间线的进展能预测一般人群中AD的发生。我们期望这种方法能够帮助从一般人群中识别出有风险的个体以进行有针对性的临床试验，并在此类试验中提供基于生物标志物的客观评估。
