Comparison of CSF markers and semi-quantitative amyloid PET in Alzheimer's disease diagnosis and in cognitive impairment prognosis using the ADNI-2 database.
Comparison of CSF markers and semi-quantitative amyloid PET in Alzheimer's disease diagnosis and in cognitive impairment prognosis using the ADNI-2 database.
复制标题
DOI：
10.1186/s13195-017-0260-z
复制
发表时间：
2017-04-26
期刊：
影响因子：
--
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
中科院分区：
文献类型：
--
作者：
Ben Bouallègue F;Mariano-Goulart D;Payoux P;Alzheimer’s Disease Neuroimaging Initiative (ADNI)
半定量淀粉样蛋白正电子发射断层扫描（PET）和脑脊液（CSF）标记物在诊断阿尔茨海默病（AD）和预测轻度认知障碍（MCI）患者的认知演变方面的相对性能仍有争议。来自阿尔茨海默病神经影像学倡议2的具有完整基线认知评估（简易精神状态检查、临床痴呆评定[CDR]和阿尔茨海默病评估量表-认知子量表[ADAS-cog]评分）、CSF采集（淀粉样蛋白-β1-42 [Aβ]、tau和磷酸化tau）和18 F-florbetapir扫描的受试者被纳入我们的横断面队列。其中，MCI或大量记忆投诉的患者构成了我们的纵向队列，并随访了30 ± 16个月。PET淀粉样蛋白沉积定量使用相对保留指数（标准化摄取值比[SUVr]）相对于脑桥，小脑和复合参考区。基于PET和CSF的诊断和预后性能使用ROC分析、多变量线性回归和生存分析以及考克斯比例风险模型进行评价。横断面研究包括677名参与者，并显示脑桥和复合SUVr值是比CSF标记物（Aβ和tau的AUC分别为0.83和0.85，准确性分别为80%和75%）更好的分类器（AUC 0.88，诊断准确性85%）。在多变量回归中，SUVr是认知的一个强独立决定因素，而Aβ不是; tau也是一个决定因素，但程度较低。在纵向研究的396例患者中，82例（21%）在22 ± 13个月内转化为AD。区分AD转换器的最佳SUVr阈值与横截面研究的阈值非常相似。复合SUVr是最好的AD分类器（AUC 0.86，灵敏度88%，特异性81%）。在多变量回归中，基线认知（CDR和ADAS-cog）是随后认知下降的主要预测因素。脑桥和复合SUVr是最终状态和CDR/ADAS-cog进展率的中度但独立的预测因子，而基线CSF标志物的影响很小。PET曲线、Aβ曲线和tau曲线的AD转化校正HR分别为3.8（p = 0.01）、1.2（p = ns）和1.8（p = 0.03）。半定量淀粉样蛋白PET在认知功能下降和AD转换方面的AD分级和MCI预后方面似乎比CSF标记物更有效。
The relative performance of semi-quantitative amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) markers in diagnosing Alzheimer’s disease (AD) and predicting the cognitive evolution of patients with mild cognitive impairment (MCI) is still debated. Subjects from the Alzheimer’s Disease Neuroimaging Initiative 2 with complete baseline cognitive assessment (Mini Mental State Examination, Clinical Dementia Rating [CDR] and Alzheimer’s Disease Assessment Scale–Cognitive Subscale [ADAS-cog] scores), CSF collection (amyloid-β1–42 [Aβ], tau and phosphorylated tau) and 18F-florbetapir scans were included in our cross-sectional cohort. Among these, patients with MCI or substantial memory complaints constituted our longitudinal cohort and were followed for 30 ± 16 months. PET amyloid deposition was quantified using relative retention indices (standardised uptake value ratio [SUVr]) with respect to pontine, cerebellar and composite reference regions. Diagnostic and prognostic performance based on PET and CSF was evaluated using ROC analysis, multivariate linear regression and survival analysis with the Cox proportional hazards model. The cross-sectional study included 677 participants and revealed that pontine and composite SUVr values were better classifiers (AUC 0.88, diagnostic accuracy 85%) than CSF markers (AUC 0.83 and 0.85, accuracy 80% and 75%, for Aβ and tau, respectively). SUVr was a strong independent determinant of cognition in multivariate regression, whereas Aβ was not; tau was also a determinant, but to a lesser degree. Among the 396 patients from the longitudinal study, 82 (21%) converted to AD within 22 ± 13 months. Optimal SUVr thresholds to differentiate AD converters were quite similar to those of the cross-sectional study. Composite SUVr was the best AD classifier (AUC 0.86, sensitivity 88%, specificity 81%). In multivariate regression, baseline cognition (CDR and ADAS-cog) was the main predictor of subsequent cognitive decline. Pontine and composite SUVr were moderate but independent predictors of final status and CDR/ADAS-cog progression rate, whereas baseline CSF markers had a marginal influence. The adjusted HRs for AD conversion were 3.8 (p = 0.01) for PET profile, 1.2 (p = ns) for Aβ profile and 1.8 (p = 0.03) for tau profile. Semi-quantitative amyloid PET appears more powerful than CSF markers for AD grading and MCI prognosis in terms of cognitive decline and AD conversion.