Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Alzheimers Res Ther. 2019 Apr 16;11(1):33. doi: 10.1186/s13195-019-0487-y.
Biomarkers such as cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI) have predictive value for progression to dementia in patients with mild cognitive impairment (MCI). The pre-dementia stage takes far longer, and the interpretation of biomarker findings is particular relevant for individuals who present at a memory clinic, but are deemed cognitively normal. The objective of the current study is to construct biomarker-based prognostic models for personalized risk of clinical progression in cognitively normal individuals presenting at a memory clinic.
We included 481 individuals with subjective cognitive decline (SCD) from the Amsterdam Dementia Cohort. Prognostic models were developed by Cox regression with patient characteristics, MRI, and/or CSF biomarkers to predict clinical progression to MCI or dementia. We estimated 5- and 3-year individualized risks based on patient-specific values. External validation was performed on Alzheimer's Disease Neuroimaging Initiative (ADNI) and an European dataset.
Based on demographics only (Harrell's C = 0.70), 5- and 3-year progression risks varied from 6% [3-11] and 4% [2-8] (age 55, MMSE 30) to 38% [29-49] and 28% [21-37] (age 70, MMSE 27). Normal CSF biomarkers strongly decreased progression probabilities (Harrell's C = 0.82). By contrast, abnormal CSF markedly increased risk (5 years, 96% [56-100]; 3 years, 89% [44-99]). The CSF model could reclassify 58% of the individuals with an "intermediate" risk (35-65%) based on the demographic model. MRI measures were not retained in the models.
The current study takes the first steps in a personalized approach for cognitively normal individuals by providing biomarker-based prognostic models.
生物标志物，如脑脊液（CSF）和磁共振成像（MRI），对轻度认知障碍（MCI）患者向痴呆进展具有预测价值。在痴呆前期阶段，时间要长得多，对于在记忆诊所就诊但认知正常的个体，生物标志物检测结果的解释尤其重要。本研究的目的是构建基于生物标志物的预测模型，以预测认知正常的个体在记忆诊所就诊时的临床进展风险。
我们纳入了来自阿姆斯特丹痴呆队列的 481 名有主观认知下降（SCD）的个体。使用 Cox 回归对患者特征、MRI 和/或 CSF 生物标志物进行预后模型构建，以预测向 MCI 或痴呆的临床进展。我们根据患者的特定值来估计 5 年和 3 年的个体化风险。在阿尔茨海默病神经影像学倡议（ADNI）和欧洲数据集上进行了外部验证。
仅基于人口统计学特征（Harrell's C = 0.70），5 年和 3 年的进展风险从 6% [3-11] 和 4% [2-8]（年龄 55 岁，MMSE 30 分）到 38% [29-49] 和 28% [21-37]（年龄 70 岁，MMSE 27 分）不等。正常的 CSF 生物标志物显著降低了进展的可能性（Harrell's C = 0.82）。相比之下，异常的 CSF 大大增加了风险（5 年，96% [56-100]；3 年，89% [44-99]）。CSF 模型可以根据人口统计学模型重新分类 58%的“中等”风险（35-65%）个体。MRI 测量值未被纳入模型中。
本研究通过提供基于生物标志物的预后模型，为认知正常的个体提供了一种个性化方法的初步步骤。
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