一种基于数据驱动的方法，使用 CSF 生物标志物来补充 A/T/(N) 分类系统。
A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers.
机构信息
Department of Neurology, San Carlos Research Institute (IdSSC), Hospital Clínico San Carlos, Madrid, Spain.
Department of Computer Architecture and Automation, Computer Science Faculty, Complutense University of Madrid, Madrid, Spain.
出版信息
CNS Neurosci Ther. 2024 Feb;30(2):e14382. doi: 10.1111/cns.14382. Epub 2023 Jul 27.
AIMS
The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values.
METHODS
We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1-42), Aβ(1-42)/Aβ(1-40) ratio, tTau, and pTau.
RESULTS
The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia.
CONCLUSION
We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.
目的
AT（N）分类系统不仅改善了阿尔茨海默病（AD）的生物学特征，也为其临床应用带来了挑战。无偏倚的数据驱动技术，如聚类，可以帮助优化该系统，对生物标志物值进行信息分类。
方法
我们比较了脑脊液生物标志物聚类结果与 AT（N）分类的诊断和预后能力。我们研究了临床（来自我们中心的患者）和研究队列（阿尔茨海默病神经影像学倡议）。研究的脑脊液生物标志物包括 Aβ（1-42）、Aβ（1-42）/Aβ（1-40）比值、tTau 和 pTau。
结果
在两个队列中，最优解决方案产生了三个聚类，在诊断、AT（N）分类、值分布和生存方面有显著差异。我们将这三个 CSF 组定义为：（i）与 AD 无关或不相关，（ii）早期阶段和/或更延迟向痴呆转化的风险，以及（iii）认知障碍更严重、向痴呆进展更快的受试者。
结论
我们提出了这种基于数据驱动的三分组分类方法，作为评估向痴呆转化风险的一种有意义且简单的方法，与 AT（N）系统分类互补。
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