Dukart Juergen, Sambataro Fabio, Bertolino Alessandro
F. Hoffmann-La Roche, Roche Innovation Centre Basel, Basel, Switzerland.
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
J Alzheimers Dis. 2016;49(4):1143-59. doi: 10.3233/JAD-150570.
A variety of imaging, neuropsychological, and genetic biomarkers have been suggested as potential biomarkers for the identification of mild cognitive impairment (MCI) in patients who later develop Alzheimer's disease (AD). Here, we systematically evaluated the most promising combinations of these biomarkers regarding discrimination between stable and converter MCI and reflection of disease staging. Alzheimer's Disease Neuroimaging Initiative data of AD (n = 144), controls (n = 112), stable (n = 265) and converter (n = 177) MCI, for which apolipoprotein E status, neuropsychological evaluation, and structural, glucose, and amyloid imaging were available, were included in this study. Naïve Bayes classifiers were built on AD and controls data for all possible combinations of these biomarkers, with and without stratification by amyloid status. All classifiers were then applied to the MCI cohorts. We obtained an accuracy of 76% for discrimination between converter and stable MCI with glucose positron emission tomography as a single biomarker. This accuracy increased to about 87% when including further imaging modalities and genetic information. We also identified several biomarker combinations as strong predictors of time to conversion. Use of amyloid validated training data resulted in increased sensitivities and decreased specificities for differentiation between stable and converter MCI when amyloid was included as a biomarker but not for other classifier combinations. Our results indicate that fully independent classifiers built only on AD and controls data and combining imaging, genetic, and/or neuropsychological biomarkers can more reliably discriminate between stable and converter MCI than single modality classifiers. Several biomarker combinations are identified as strongly predictive for the time to conversion to AD.
多种影像学、神经心理学和基因生物标志物已被提出作为在随后发展为阿尔茨海默病（AD）的患者中识别轻度认知障碍（MCI）的潜在生物标志物。在此，我们系统地评估了这些生物标志物最有前景的组合在区分稳定型和转化型MCI以及反映疾病分期方面的情况。本研究纳入了阿尔茨海默病神经影像学计划中AD患者（n = 144）、对照者（n = 112）、稳定型（n = 265）和转化型（n = 177）MCI患者的数据，这些患者均有载脂蛋白E状态、神经心理学评估以及结构、葡萄糖和淀粉样蛋白成像数据。基于这些生物标志物的所有可能组合，在有和没有按淀粉样蛋白状态分层的情况下，利用AD患者和对照者的数据构建朴素贝叶斯分类器。然后将所有分类器应用于MCI队列。以葡萄糖正电子发射断层扫描作为单一生物标志物时，区分转化型和稳定型MCI的准确率为76%。当纳入更多影像学检查方式和基因信息时，这一准确率提高到约87%。我们还确定了几种生物标志物组合可作为转化时间的强预测指标。当将淀粉样蛋白作为生物标志物纳入时，使用经淀粉样蛋白验证的训练数据会提高区分稳定型和转化型MCI的敏感性并降低特异性，但对其他分类器组合则不然。我们的结果表明，仅基于AD患者和对照者数据构建的、结合影像学、基因和/或神经心理学生物标志物的完全独立分类器，比单模态分类器能更可靠地区分稳定型和转化型MCI。几种生物标志物组合被确定为向AD转化时间的强预测指标。
