Wang Guoqiao, Xiong Chengjie, McDade Eric M, Hassenstab Jason, Aschenbrenner Andrew J, Fagan Anne M, Benzinger Tammie L S, Gordon Brian A, Morris John C, Li Yan, Bateman Randall J
Division of Biostatistics, Washington University School of Medicine, Saint Louis, MO, USA.
Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA.
Alzheimers Dement (N Y). 2018 Dec 7;4:669-676. doi: 10.1016/j.trci.2018.10.009. eCollection 2018.
As the role of biomarkers is increasing in Alzheimer's disease (AD) clinical trials, it is critical to use a comprehensive temporal biomarker profile that reflects both baseline and longitudinal assessments to establish a more precise association between the change in biomarkers and change in cognition. Because age of onset of dementia symptoms is highly predictable, and there are relatively few age-related comorbidities, the Dominantly Inherited Alzheimer Network autosomal dominant AD population affords a unique opportunity to investigate these relationships in a well-characterized population.
A novel joint statistical model was used to simultaneously evaluate how a comprehensive AD biomarker profile predicts change in cognition using amyloid positron emission tomography (PET), CSF Aβ, CSF total tau and Ptau, cortical metabolism using [F-18] fluorodeoxyglucose-PET, and hippocampal volume from participants enrolled in the Dominantly Inherited Alzheimer Network (n = 262) with mean (SD) duration of follow-up of 2.7 (1.2) years.
Baseline amyloid PET levels and CSF biomarkers were associated with change in cognition in contrast to the rate of change of brain metabolism and hippocampal volume, which predicted change in cognition.
This study suggests that the baseline value of amyloid PET and CSF Aβ measures may be useful for screening participants for AD trials; however, brain hippocampus atrophy and hypometabolism are only useful as repeated longitudinal assessments for tracking cognition and disease progression. This suggests that measures of amyloid plaques predict future cognitive decline, but only longitudinal measures of neurodegeneration correlate with cognitive decline. The novel statistical model used in this study can be easily applied to any pair of outcomes and has potential to be widely used by the AD research community.
随着生物标志物在阿尔茨海默病（AD）临床试验中的作用日益增强，使用反映基线和纵向评估的综合时间生物标志物谱至关重要，以便在生物标志物变化与认知变化之间建立更精确的关联。由于痴呆症状的发病年龄具有高度可预测性，且与年龄相关的合并症相对较少，显性遗传阿尔茨海默病网络的常染色体显性AD人群为在特征明确的人群中研究这些关系提供了独特的机会。
使用一种新颖的联合统计模型，同时评估综合AD生物标志物谱如何利用淀粉样蛋白正电子发射断层扫描（PET）、脑脊液Aβ、脑脊液总tau蛋白和磷酸化tau蛋白、使用[F-18]氟脱氧葡萄糖-PET的皮质代谢以及来自显性遗传阿尔茨海默病网络（n = 262）参与者的海马体积来预测认知变化，这些参与者的平均（标准差）随访时间为2.7（1.2）年。
与预测认知变化的脑代谢率和海马体积变化相反，基线淀粉样蛋白PET水平和脑脊液生物标志物与认知变化相关。
本研究表明，淀粉样蛋白PET和脑脊液Aβ测量的基线值可能有助于筛选AD试验的参与者；然而，脑海马萎缩和代谢减退仅作为跟踪认知和疾病进展的重复纵向评估才有用。这表明淀粉样斑块测量可预测未来的认知衰退，但只有神经退行性变的纵向测量与认知衰退相关。本研究中使用的新颖统计模型可轻松应用于任何一对结果，并有潜力被AD研究界广泛使用。
