Li Shanshan, Okonkwo Ozioma, Albert Marilyn, Wang Mei-Cheng
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA.
Am J Alzheimers Dis (Columbia). 2013;2(1):12-28. doi: 10.7726/ajad.2013.1002.
The purpose of this paper is to investigate the relative utility of using neuroimaging, genetic, cerebrospinal fluid (CSF), and cognitive measures to predict progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia over a follow-up period. The studied subjects were 139 persons with MCI enrolled in the Alzheimer's Disease Neuroimaging Initiative. Predictors of progression to AD included brain volume, ventricular volume, hippocampal volume, APOE ε4 two alleles, Aβ, p-tau, p-tau/Aβ, memory, language, and executive function. We employ a combination of Cox regression analyses and time-dependent receiver operating characteristic (ROC) methods to assess the prognostic utility and performance stability of candidate biomarkers. In a demographic-adjusted multivariable Cox model, seven measures- brain volume, hippocampal volume, ventricular volume, APOE ε4 two alleles, Aβ, Memory composite, Executive function composite - predicted progression to AD. Time-dependent ROC revealed that this multivariable model had an area under the curve of 0.832, 0.788, 0.794, and 0.757 at 12, 18, 24, and 36 months respectively. Supplemental Cox models with time of origin set differentially at 12, 18, 24 and 36 months showed that six measures were significant predictors at 12 months whereas only memory and executive function predicted progression to AD at 18 and 24 months. The authors concluded that baseline volumetric MRI and cognitive measures selectively predict progression from MCI to AD, with cognitive measures remaining predictive even late in the follow-up period. These findings may inform case selection for AD clinical trials.
本文旨在研究使用神经影像学、遗传学、脑脊液（CSF）和认知测量方法预测在随访期间从轻度认知障碍（MCI）进展为阿尔茨海默病（AD）痴呆的相对效用。研究对象为139名参与阿尔茨海默病神经影像学倡议的MCI患者。进展为AD的预测因素包括脑容量、脑室容量、海马体容量、APOE ε4两个等位基因、Aβ、p-tau、p-tau/Aβ、记忆、语言和执行功能。我们采用Cox回归分析和时间依赖性受试者工作特征（ROC）方法相结合，以评估候选生物标志物的预后效用和性能稳定性。在人口统计学调整的多变量Cox模型中，七种测量方法——脑容量、海马体容量、脑室容量、APOE ε4两个等位基因、Aβ、记忆综合评分、执行功能综合评分——预测了进展为AD的情况。时间依赖性ROC显示，该多变量模型在12、18、24和36个月时的曲线下面积分别为0.832、0.788、0.794和0.757。将起始时间分别设定为12、18、24和36个月的补充Cox模型显示，六种测量方法在12个月时是显著的预测因素，而在18和24个月时只有记忆和执行功能预测了进展为AD的情况。作者得出结论，基线容积MRI和认知测量方法能够选择性地预测从MCI进展为AD的情况，即使在随访后期认知测量方法仍具有预测性。这些发现可能为AD临床试验的病例选择提供参考。
