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.

通过根据 PET 成像得出的 CSF 分类验证该算法，详细阐述一种新算法，以建立标准化方法来定义阿尔茨海默病 (AD) 的 CSF 生物标志物的临界值。

首先确定了低水平和高水平的 CSF 磷酸化 tau 蛋白，以建立 CSF β-淀粉样蛋白 (Aβ) 肽生物标志物的最佳临界值。然后使用这些 Aβ 截止值来确定 CSF tau 和磷酸化 tau 标记物的截止值。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。

总共纳入了 6,922 名具有 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，基于我们的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 的 kappa 系数在 0.87 到 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%。

所提出的确定 AD 的 CSF 生物标志物截止值的新颖、实用的方法不需要评估其他生物标志物或有关患者临床诊断的假设。使用这种标准化算法可能会减少 AD 分类中的异质性。