FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort.
FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort.
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DOI：
10.1016/j.nicl.2018.01.019
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
BIOMARKAPD Project
中科院分区：
文献类型：
--
作者：
Caminiti SP;Ballarini T;Sala A;Cerami C;Presotto L;Santangelo R;Fallanca F;Vanoli EG;Gianolli L;Iannaccone S;Magnani G;Perani D;BIOMARKAPD Project
在这项多中心临床研究中，我们评估了FDG-PET脑代谢和CSF分类优化程序在预测或排除阿尔茨海默病（AD）痴呆和非AD痴呆转化方面的准确性。我们纳入了80名MCI受试者，在入组时进行了神经学和神经心理学评估、FDG-PET扫描和CSF测量，所有这些都进行了临床随访。FDG-PET数据进行了分析与验证的基于体素的SPM方法。由5名影像学专家根据疾病特异性模式将得到的单个受试者SPM图分类为“典型AD”、“痴呆AD”（即后皮质萎缩、不对称性缺语AD变体、额叶AD变体）、“非AD”（即行为变体FTD、皮质基底节变性、语义变体FTD;路易体痴呆）或“阴性”模式。为了进行统计分析，将个体模式分组为“AD痴呆与非AD痴呆（所有疾病）”或“FTD与非FTD（所有疾病）"。使用埃尔兰根评分算法对Aβ42、总Tau CSF水平和磷酸化Tau CSF水平进行二分分类。多变量logistic模型检验FDG-PET-SPM和CSF二分分类的预后准确性。评价埃尔兰根评分和埃尔兰根评分辅助FDG-PET SPM分级的准确性。多变量logistic模型确定FDG-PET“AD”SPM分类（Expβ = 19.35，95% C. I. 4.8-77.8，p < 0.001）和CSF Aβ42（Expβ = 6.5，95% C.I. 1.64-25.43，p < 0.05）作为MCI向AD痴呆转化的最佳预测因子。“FTD”SPM模式可显著预测随访时转化为FTD痴呆（Expβ = 14，95% C.I. 3.1-63，p < 0.001）。总体而言，FDG-PET-SPM分类是最准确的生物标志物，能够正确区分转化为AD或FTD痴呆的MCI受试者和保持稳定或恢复正常认知的受试者（实验β = 17.9，95% C.I. 4.55-70.46，p < 0.001）。我们的研究结果支持FDG-PET-SPM分类在预测MCI前驱期进展为不同痴呆状况方面的相关作用，以及在排除进展方面优于CSF生物标志物。适当的生物标志物措施可改善MCI患者的早期痴呆诊断。FDG-PET-SPM图和CSF Aβ42是AD痴呆转换的最佳预测因子。FDG-PET-SPM图可准确预测向不同痴呆状况的转化。阴性FDG-PET-SPM模式表征稳定或逆转MCI病例。
In this multicentre study in clinical settings, we assessed the accuracy of optimized procedures for FDG-PET brain metabolism and CSF classifications in predicting or excluding the conversion to Alzheimer's disease (AD) dementia and non-AD dementias. We included 80 MCI subjects with neurological and neuropsychological assessments, FDG-PET scan and CSF measures at entry, all with clinical follow-up. FDG-PET data were analysed with a validated voxel-based SPM method. Resulting single-subject SPM maps were classified by five imaging experts according to the disease-specific patterns, as “typical-AD”, “atypical-AD” (i.e. posterior cortical atrophy, asymmetric logopenic AD variant, frontal-AD variant), “non-AD” (i.e. behavioural variant FTD, corticobasal degeneration, semantic variant FTD; dementia with Lewy bodies) or “negative” patterns. To perform the statistical analyses, the individual patterns were grouped either as “AD dementia vs. non-AD dementia (all diseases)” or as “FTD vs. non-FTD (all diseases)”. Aβ42, total and phosphorylated Tau CSF-levels were classified dichotomously, and using the Erlangen Score algorithm. Multivariate logistic models tested the prognostic accuracy of FDG-PET-SPM and CSF dichotomous classifications. Accuracy of Erlangen score and Erlangen Score aided by FDG-PET SPM classification was evaluated. The multivariate logistic model identified FDG-PET “AD” SPM classification (Expβ = 19.35, 95% C.I. 4.8–77.8, p < 0.001) and CSF Aβ42 (Expβ = 6.5, 95% C.I. 1.64–25.43, p < 0.05) as the best predictors of conversion from MCI to AD dementia. The “FTD” SPM pattern significantly predicted conversion to FTD dementias at follow-up (Expβ = 14, 95% C.I. 3.1–63, p < 0.001). Overall, FDG-PET-SPM classification was the most accurate biomarker, able to correctly differentiate either the MCI subjects who converted to AD or FTD dementias, and those who remained stable or reverted to normal cognition (Expβ = 17.9, 95% C.I. 4.55–70.46, p < 0.001). Our results support the relevant role of FDG-PET-SPM classification in predicting progression to different dementia conditions in prodromal MCI phase, and in the exclusion of progression, outperforming CSF biomarkers. Appropriate biomarkers measures improve early dementia diagnosis in MCI. FDG-PET-SPM maps and CSF Aβ42 are the best predictors of AD dementia conversion. FDG-PET-SPM maps accurately predict conversion to different dementia conditions. A negative FDG-PET-SPM pattern characterizes stable or reverter MCI cases.
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DOI：
10.1016/j.jalz.2014.02.004
发表时间：
2015-01
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
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影响因子：
4
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影响因子：
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影响因子：
4
作者：
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通讯作者：
Bertolino, Alessandro
影响因子：
2.3
作者：
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Mueller, Matthias J.