School of Psychology, Liverpool John Moores University, UK.
School of Psychology, Liverpool John Moores University, UK.
J Neurol Sci. 2024 Sep 15;464:123148. doi: 10.1016/j.jns.2024.123148. Epub 2024 Jul 31.
Early detection of Alzheimer's disease (AD) is one of the critical components of the global response to the growing dementia crisis. Analysis of serial position performance in story recall tests has yielded sensitive metrics for the prediction of AD at low cost. In this study, we examined whether serial position markers in two story recall tests (the logical memory test, LMT, and the Craft Story 21 test, CST) were sensitive to cross-sectional biomarker-based assessment of in vivo neuropathology.
Participants were selected from the Wisconsin Registry of Alzheimer's Prevention (n = 288; WRAP) and the Alzheimer's Disease Research Center (n = 156; ADRC), both from the University of Wisconsin-Madison. Average age at PET was 68.9 (6.7) and 67.0 (8.0), respectively. Data included tau and PiB PET, and LMT for WRAP participants and CST for ADRC participants. Two sets of Bayesian analyses (logistic regressions and ANCOVAs) were conducted within each cohort, separately.
Results indicated that the A+T+ classification was best predicted, cross-sectionally, by the recency ratio (Rr), indexing how much of the end of the story was forgotten between initial learning and delayed assessment. Rr outperformed traditional scores and discriminated between A+T+ and A+T-/A-T-, in both cohorts.
Overall, this study confirms that serial position analysis of LMT and CST data, and particularly Rr as an index of recency loss, is a valuable tool for the identification of in vivo tau pathology in individuals free of dementia. Diagnostic considerations are discussed.
阿尔茨海默病（AD）的早期检测是应对日益严重的痴呆危机的全球对策的关键组成部分之一。在故事回忆测试中分析序列位置表现已经产生了用于低成本预测 AD 的敏感指标。在这项研究中，我们研究了两个故事回忆测试（逻辑记忆测试，LMT 和 Craft Story 21 测试，CST）中的序列位置标记是否对基于生物标志物的体内神经病理学的横断面评估敏感。
参与者从威斯康星州阿尔茨海默病预防登记处（WRAP；n=288）和威斯康星大学麦迪逊分校的阿尔茨海默病研究中心（ADRC；n=156）中选择。平均年龄在 PET 为 68.9（6.7）和 67.0（8.0）。数据包括 tau 和 PiB PET，以及 WRAP 参与者的 LMT 和 ADRC 参与者的 CST。在每个队列中，分别进行了两组贝叶斯分析（逻辑回归和方差分析）。
结果表明，在横截面上，遗忘比（Rr）最能预测 A+T+分类，该指数衡量初始学习和延迟评估之间故事结尾部分的遗忘量。Rr 优于传统得分，并在两个队列中区分了 A+T+和 A+T-/A-T-。
总体而言，这项研究证实，LMT 和 CST 数据的序列位置分析，特别是作为最近损失指数的 Rr，是识别无痴呆个体体内 tau 病理学的有价值的工具。讨论了诊断注意事项。
