Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease
Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease
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DOI：
10.1212/wnl.0b013e31820af900
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发表时间：
2011-02-08
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.;Fagan, A. M.;Morris, J. C.
目的：研究包括认知和脑储备在内的因素，这些因素可以独立预测阿尔茨海默型痴呆（DAT）的流行和偶发，并确定纳入已鉴定的因素是否会增加CSF生物标志物A β（42）、tau、ptau（181）、tau/A β（42）和ptau（181）/A β（42）的预测准确性。逻辑回归确定了预测流行DAT的变量，当与201名认知正常的参与者和46名DAT参与者的横断面样本中的每种CSF生物标志物一起考虑时。与单独使用生物标志物产生的AUC进行比较。在第二个样本中，基线认知正常，纵向数据可用（n = 213），考克斯比例风险模型确定了预测事件DAT与每个生物标志物的变量，以及模型的一致性概率估计（CPE），将其与单独使用生物标志物产生的CPE进行比较。当与每种生物标志物一起考虑时，APOE基因型（包括一个等位基因）、男性和较小的标准化全脑体积（nWBV）与DAT在横断面上相关。在纵向样本中（平均随访= 3.2年），14名参与者（6.6%）开发DAT。在每个模型中，年龄越大，DAT的时间越快，教育程度越高，5个模型中有4个模型的时间越慢。包括辅助变量导致所有生物标志物的DAT的更好的横截面预测（p < 0.0021），以及5种生物标志物中的4种的更好的纵向预测（p < 0.0022）。结论：通过在分析中包括年龄、教育和nWBV，CSF生物标志物的预测准确性得到提高。神经病学（R）2011; 76：501-510
Objective: To investigate factors, including cognitive and brain reserve, which may independently predict prevalent and incident dementia of the Alzheimer type (DAT) and to determine whether inclusion of identified factors increases the predictive accuracy of the CSF biomarkers A beta(42), tau, ptau(181), tau/A beta(42), and ptau(181)/A beta(42).Methods: Logistic regression identified variables that predicted prevalent DAT when considered together with each CSF biomarker in a cross-sectional sample of 201 participants with normal cognition and 46 with DAT. The area under the receiver operating characteristic curve (AUC) from the resulting model was compared with the AUC generated using the biomarker alone. In a second sample with normal cognition at baseline and longitudinal data available (n = 213), Cox proportional hazards models identified variables that predicted incident DAT together with each biomarker, and the models' concordance probability estimate (CPE), which was compared to the CPE generated using the biomarker alone.Results: APOE genotype including an epsilon 4 allele, male gender, and smaller normalized whole brain volumes (nWBV) were cross-sectionally associated with DAT when considered together with every biomarker. In the longitudinal sample (mean follow-up = 3.2 years), 14 participants (6.6%) developed DAT. Older age predicted a faster time to DAT in every model, and greater education predicted a slower time in 4 of 5 models. Inclusion of ancillary variables resulted in better cross-sectional prediction of DAT for all biomarkers (p < 0.0021), and better longitudinal prediction for 4 of 5 biomarkers (p < 0.0022).Conclusions: The predictive accuracy of CSF biomarkers is improved by including age, education, and nWBV in analyses. Neurology (R) 2011; 76: 501-510