The combination of apolipoprotein E4, age and Alzheimer's Disease Assessment Scale - Cognitive Subscale improves the prediction of amyloid positron emission tomography status in clinically diagnosed mild cognitive impairment
The combination of apolipoprotein E4, age and Alzheimer's Disease Assessment Scale - Cognitive Subscale improves the prediction of amyloid positron emission tomography status in clinically diagnosed mild cognitive impairment
复制标题
DOI：
10.1111/ene.13881
复制
发表时间：
2019-05-01
影响因子：
5.1
通讯作者：
Yu, L.
中科院分区：
文献类型：
--
作者：
Ba, M.;Ng, K. P.;Yu, L.
背景和目的涉及抗淀粉样蛋白干预措施的随机临床试验侧重于使用淀粉样蛋白正电子发射断层扫描（淀粉样蛋白-PET）成像或脑脊液分析，研究具有已证实淀粉样蛋白病理学的阿尔茨海默病（AD）的早期阶段。然而，这些调查要么昂贵，要么具有侵入性，并且在资源有限的中心不容易进行。因此，非常需要找到淀粉样蛋白 PET 的具有成本效益的临床替代方案。本研究旨在探讨联合临床标志物预测轻度认知障碍 (MCI) 个体淀粉样蛋白 PET 状态的准确性。方法 总共，来自阿尔茨海默氏病神经影像倡议数据库的 406 名 MCI 参与者被分为淀粉样蛋白-PET(+) 和淀粉样蛋白-PET(-)，使用 >1.11 的截止值。使用受试者工作特征曲线分析评估单一临床标志物 [载脂蛋白 E4 (ApoE4) 基因型、人口统计学、认知测量和脑脊液分析] 预测淀粉样蛋白 PET 状态的准确性。然后使用逻辑回归模型来确定结合临床标志物的最佳模型来预测淀粉样蛋白-PET 状态。结果 脑脊液淀粉样蛋白-β (Aβ) 显示淀粉样蛋白-PET 状态的最佳预测准确性 [曲线下面积 (AUC) = 0.927]。虽然 ApoE4 基因型 (AUC = 0.737) 和阿尔茨海默病评估量表 - 认知分量表 (ADAS-Cog) 13 (AUC = 0.724) 独立区分淀粉样蛋白-PET(+) 和淀粉样蛋白-PET(-) MCI 个体，但临床标志物的组合（ApoE4 携带者、年龄 > 60 岁和 ADAS-Cog 13 > 13.5）改善了预测淀粉样蛋白-PET 状态的准确性（AUC = 0.827，P < 0.001）。结论 脑脊液 Aβ 是一种侵入性检查，对于预测 MCI 个体的淀粉样蛋白-PET 状态最为准确。 ApoE4、年龄和 ADAS-Cog 13 的组合也可以准确预测淀粉样蛋白-PET 状态。由于这种临床标志物组合价格便宜、非侵入性且易于获得，因此它为资源有限环境中 MCI 个体的淀粉样蛋白状态提供了有吸引力的替代评估。
Background and purpose Randomized clinical trials involving anti-amyloid interventions focus on the early stages of Alzheimer's disease (AD) with proven amyloid pathology, using amyloid positron emission tomography (amyloid-PET) imaging or cerebrospinal fluid analysis. However, these investigations are either expensive or invasive and are not readily available in resource-limited centres. Hence, the identification of cost-effective clinical alternatives to amyloid-PET is highly desirable. This study aimed to investigate the accuracy of combined clinical markers in predicting amyloid-PET status in mild cognitive impairment (MCI) individuals. Methods In all, 406 MCI participants from the Alzheimer's Disease Neuroimaging Initiative database were dichotomized into amyloid-PET(+) and amyloid-PET(-) using a cut-off of >1.11. The accuracies of single clinical markers [apolipoprotein E4 (ApoE4) genotype, demographics, cognitive measures and cerebrospinal fluid analysis] in predicting amyloid-PET status were evaluated using receiver operating characteristic curve analysis. A logistic regression model was then used to determine the optimal model with combined clinical markers to predict amyloid-PET status. Results Cerebrospinal fluid amyloid-beta (A beta) showed the best predictive accuracy of amyloid-PET status [area under the curve (AUC) = 0.927]. Whilst ApoE4 genotype (AUC = 0.737) and Alzheimer's Disease Assessment Scale - Cognitive Subscale (ADAS-Cog) 13 (AUC = 0.724) independently discriminated amyloid-PET(+) and amyloid-PET(-) MCI individuals, the combination of clinical markers (ApoE4 carrier, age >60 years and ADAS-Cog 13 > 13.5) improved the predictive accuracy of amyloid-PET status (AUC = 0.827, P < 0.001). Conclusions Cerebrospinal fluid A beta, which is an invasive procedure, is most accurate in predicting amyloid-PET status in MCI individuals. The combination of ApoE4, age and ADAS-Cog 13 also accurately predicts amyloid-PET status. As this combination of clinical markers is cheap, non-invasive and readily available, it offers an attractive surrogate assessment for amyloid status amongst MCI individuals in resource-limited settings.