Sato Kenichiro, Ihara Ryoko, Suzuki Kazushi, Niimi Yoshiki, Toda Tatsushi, Jimenez-Maggiora Gustavo, Langford Oliver, Donohue Michael C, Raman Rema, Aisen Paul S, Sperling Reisa A, Iwata Atsushi, Iwatsubo Takeshi
Department of Neurology Graduate School of Medicine The University of Tokyo Tokyo Japan.
Department of Neuropathology Graduate School of Medicine The University of Tokyo Tokyo Japan.
Alzheimers Dement (N Y). 2021 Mar 24;7(1):e12135. doi: 10.1002/trc2.12135. eCollection 2021.
Selecting cognitively normal elderly individuals with higher risk of brain amyloid deposition is critical to the success of prevention trials for Alzheimer's disease (AD).
Based on the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease study data, we built machine-learning models and applied them to our ongoing Japanese Trial-Ready Cohort (J-TRC) webstudy participants registered within the first 9 months ( = 3081) of launch to predict standard uptake value ratio (SUVr) of amyloid positron emission tomography.
Age, family history, online Cognitive Function Instrument and CogState scores were important predictors. In a subgroup of J-TRC webstudy participants with known amyloid status ( = 37), the predicted SUVr corresponded well with the self-reported amyloid test results (area under the curve = 0.806 [0.619-0.992]).
Our algorithms may be usable for automatic prioritization of candidate participants with higher amyloid risks to be preferentially recruited from the J-TRC webstudy to in-person study, maximizing efficiency for the identification of preclinical AD participants.
选择脑淀粉样蛋白沉积风险较高的认知正常老年人对于阿尔茨海默病（AD）预防试验的成功至关重要。
基于无症状阿尔茨海默病抗淀粉样蛋白治疗研究数据，我们构建了机器学习模型，并将其应用于我们正在进行的日本试验就绪队列（J-TRC）网络研究参与者，这些参与者在启动后的前9个月内注册（n = 3081），以预测淀粉样蛋白正电子发射断层扫描的标准摄取值比率（SUVr）。
年龄、家族史、在线认知功能仪器和CogState评分是重要的预测因素。在J-TRC网络研究中已知淀粉样蛋白状态的参与者亚组（n = 37）中，预测的SUVr与自我报告的淀粉样蛋白测试结果高度吻合（曲线下面积 = 0.806 [0.619 - 0.992]）。
我们的算法可用于自动优先选择淀粉样蛋白风险较高的候选参与者，以便从J-TRC网络研究中优先招募到面对面研究中，从而最大限度地提高识别临床前AD参与者的效率。
