A Pragmatic, Data-Driven Method to Determine Cutoffs for CSF Biomarkers of Alzheimer Disease Based on Validation Against PET Imaging.
A Pragmatic, Data-Driven Method to Determine Cutoffs for CSF Biomarkers of Alzheimer Disease Based on Validation Against PET Imaging.
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DOI：
10.1212/wnl.0000000000200735
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发表时间：
2022-08-16
期刊：
影响因子：
9.9
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中科院分区：
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阐述一种新的算法，通过验证PET成像得出的CSF分类算法，建立一种标准化方法来定义阿尔茨海默病（AD）CSF生物标志物的截止值。首先鉴定了低水平和高水平的CSF磷酸化tau，以建立CSF β-淀粉样蛋白（Aβ）肽生物标志物的最佳截止值。然后使用这些Aβ截止值确定CSF tau和磷酸化tau标志物的截止值。我们将该算法与基于tau和淀粉样蛋白PET成像状态（ADNI研究）的参考方法进行了比较，然后将该算法应用于10个大型临床患者队列。共纳入6，922例具有CSF生物标志物数据的患者（平均[SD]年龄：70.6 [8.5]岁，51.0%为女性）。在ADNI研究人群（n = 497）中，基于我们算法的分类与基于淀粉样蛋白/tau PET成像的分类之间的一致性很高，Cohen kappa系数在0.87和0.99之间。将该算法应用于10个大型患者队列（n = 6，425），AD患者的比例范围为25.9%至43.5%。提出的确定AD的CSF生物标志物临界值的新颖、实用方法不需要评估其他生物标志物或关于患者临床诊断的假设。使用这种标准化算法可能会减少AD分类的异质性。
To elaborate a new algorithm to establish a standardized method to define cutoffs for CSF biomarkers of Alzheimer disease (AD) by validating the algorithm against CSF classification derived from PET imaging. Low and high levels of CSF phosphorylated tau were first identified to establish optimal cutoffs for CSF β-amyloid (Aβ) peptide biomarkers. These Aβ cutoffs were then used to determine cutoffs for CSF tau and phosphorylated tau markers. We compared this algorithm to a reference method, based on tau and amyloid PET imaging status (ADNI study), and then applied the algorithm to 10 large clinical cohorts of patients. A total of 6,922 patients with CSF biomarker data were included (mean [SD] age: 70.6 [8.5] years, 51.0% women). In the ADNI study population (n = 497), the agreement between classification based on our algorithm and the one based on amyloid/tau PET imaging was high, with Cohen's kappa coefficient between 0.87 and 0.99. Applying the algorithm to 10 large cohorts of patients (n = 6,425), the proportion of persons with AD ranged from 25.9% to 43.5%. The proposed novel, pragmatic method to determine CSF biomarker cutoffs for AD does not require assessment of other biomarkers or assumptions concerning the clinical diagnosis of patients. Use of this standardized algorithm is likely to reduce heterogeneity in AD classification.