Alzheimer's Disease (AD) research has shifted to focus on biomarker trajectories and their potential use in understanding the underlying AD-related pathological process. A conceptual framework was proposed in such modern AD research that hypothesized biomarker cascades as a result of underlying AD pathology. In this paper, we leverage the idea of biomarker cascades and develop methods that use a non-linear mixed effect model to depict AD biomarker trajectories as a function of the latent AD disease progression. We tailored our methods to address a number of real-data challenges present in BIOCARD and ADNI studies. We illustrate the proposed methods with simulation studies as well as analysis results on the BIOCARD and ADNI data showing the ordering of various biomarkers from the CSF, MRI, and cognitive domains. We investigated cascading patterns of AD biomarkers in these datasets and presented prediction results for individual-level profiles over time. These findings highlight the potential of the conceptual biomarker cascade framework to be leveraged for diagnoses and monitoring.
翻译:阿尔茨海默病(AD)研究已经转向重点研究生物标志物轨迹以及它们在理解潜在的AD相关病理过程中的潜在用途。在这样的现代AD研究中提出了一个概念框架,认为生物标志物级联是潜在的AD病理的结果。在本文中,我们利用生物标志物级联的想法,并开发使用非线性混合效应模型来描述AD生物标志物轨迹的方法,其与潜在的AD疾病进展有关。我们定制了我们的方法,以解决BIOCARD和ADNI研究中存在的许多实际数据挑战。我们用模拟研究以及BIOCARD和ADNI数据的分析结果来阐述所提出的方法,展示了来自CSF、MRI和认知领域的各种生物标志物的排序。我们调查了这些数据集中AD生物标志物的级联模式,并呈现了个体在时间上的预测结果。这些发现突出了概念性生物标志物级联框架为诊断和监测提供潜力的可能性。