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)的研究已转向关注生物标志物轨迹及其在理解AD相关病理过程中的潜在应用。现代AD研究提出一个概念框架，假设生物标志物级联是潜在AD病理的结果。本文利用生物标志物级联思想，开发了使用非线性混合效应模型描述AD生物标志物轨迹作为潜在AD疾病进展函数的方法。我们针对BIOCARD和ADNI研究中存在的多个真实数据挑战对方法进行了定制化调整。通过模拟研究以及对BIOCARD和ADNI数据中来自脑脊液(CSF)、磁共振成像(MRI)和认知领域的多种生物标志物排序分析结果，验证了所提出方法的有效性。我们研究了这些数据集中AD生物标志物的级联模式，并呈现了个体水平随时间变化的预测结果。这些发现凸显了概念性生物标志物级联框架在诊断与监测中的应用潜力。