Alzheimer disease identification using amyloid imaging and reserve variables Proof of concept
Alzheimer disease identification using amyloid imaging and reserve variables Proof of concept
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DOI：
10.1212/wnl.0b013e3181e620f4
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发表时间：
2010-07-06
期刊：
影响因子：
9.9
通讯作者：
Morris, J. C.
中科院分区：
文献类型：
--
作者：
Roe, C. M.;Mintun, M. A.;Morris, J. C.
目的：多种因素可能影响阿尔茨海默病（AD）病变与痴呆表达的关系，包括与脑和认知储备相关的因素。其他因素可能混淆阿尔茨海默病病理与痴呆之间的联系。当考虑淀粉样蛋白成像时，我们测试了被认为影响AD病理和痴呆关联的因素是否有助于准确识别阿尔茨海默氏型痴呆（DAT）。方法：认知正常（180例）和DAT患者（25例），年龄≥50岁，参加临床、神经学和心理测量学评估。PET与匹兹堡化合物B （PiB）示踪剂用于测量脑淀粉样蛋白，得到反映PiB摄取的平均皮质结合电位（MCBP）。使用Logistic回归生成受试者工作特征曲线及其曲线下面积（AUC），以比较单独使用MCBP与MCBP联合使用其他变量的预测准确性，使用逐步选择程序识别DAT与正常认知的参与者。结果：单独MCBP导致的AUC为0.84（95%可信区间[CI] = 0.73-0.94；交叉验证的AUC = 0.80, 95% CI = 0.68-0.92）。包括教育程度、标准化全脑容量、身体健康等级、性别和可能干扰认知的药物使用的逐步模型生成的预测方程的AUC为0.94 (95% CI = 0.90-0.98；交叉验证的AUC = 0.91, 95% CI = 0.85-0.96)，比单独使用MCBP产生的预测方程改善（p = 0.025）。结论：研究结果表明，影响阿尔茨海默病病理与痴呆之间关联的因素可以提高淀粉样蛋白成像对症状性阿尔茨海默病的预测准确性。神经病学(R) 2010；75: 42-48
Objective: Several factors may influence the relationship between Alzheimer disease (AD) lesions and the expression of dementia, including those related to brain and cognitive reserve. Other factors may confound the association between AD pathology and dementia. We tested whether factors thought to influence the association of AD pathology and dementia help to accurately identify dementia of the Alzheimer type (DAT) when considered together with amyloid imaging.Methods: Participants with normal cognition (n = 180) and with DAT (n = 25), aged 50 years or older, took part in clinical, neurologic, and psychometric assessments. PET with the Pittsburgh compound B (PiB) tracer was used to measure brain amyloid, yielding a mean cortical binding potential (MCBP) reflecting PiB uptake. Logistic regression was used to generate receiver operating characteristic curves, and the areas under those curves (AUC), to compare the predictive accuracy of using MCBP alone vs MCBP together with other variables selected using a stepwise selection procedure to identify participants with DAT vs normal cognition.Results: The AUC resulting from MCBP alone was 0.84 (95% confidence interval [CI] = 0.73-0.94; cross-validated AUC = 0.80, 95% CI = 0.68-0.92). The AUC for the predictive equation generated by a stepwise model including education, normalized whole brain volume, physical health rating, gender, and use of medications that may interfere with cognition was 0.94 (95% CI = 0.90-0.98; cross-validated AUC = 0.91, 95% CI = 0.85-0.96), an improvement (p = 0.025) over that yielded using MCBP alone.Conclusion: Results suggest that factors reported to influence associations between AD pathology and dementia can improve the predictive accuracy of amyloid imaging for the identification of symptomatic AD. Neurology (R) 2010; 75: 42-48