Accurate Prediction of Conversion to Alzheimer's Disease using Imaging, Genetic, and Neuropsychological Biomarkers
Accurate Prediction of Conversion to Alzheimer's Disease using Imaging, Genetic, and Neuropsychological Biomarkers
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DOI：
10.3233/jad-150570
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发表时间：
2016-01-01
影响因子：
4
通讯作者：
Bertolino, Alessandro
中科院分区：
文献类型：
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作者：
Dukart, Juergen;Sambataro, Fabio;Bertolino, Alessandro
多种影像学、神经心理学和遗传学生物标志物已被建议作为潜在的生物标志物，用于识别后来发展为阿尔茨海默病（AD）的患者中的轻度认知障碍（MCI）。在这里，我们系统地评估了这些生物标志物的最有前途的组合，关于稳定和转换器MCI之间的区分和疾病分期的反映。AD（n = 144）、对照组（n = 112）、稳定组（n = 265）和转换组（n = 177）MCI的阿尔茨海默病神经影像学研究数据（载脂蛋白E状态、神经心理学评价、结构、葡萄糖和淀粉样蛋白成像可用）均纳入本研究。朴素贝叶斯分类器是建立在AD和对照数据的所有可能的组合，这些生物标志物，有和没有分层的淀粉样蛋白状态。然后将所有分类器应用于MCI队列。我们获得了76%的准确性转换和稳定MCI与葡萄糖正电子发射断层扫描作为一个单一的生物标志物之间的歧视。当包括进一步的成像方式和遗传信息时，该准确性增加到约87%。我们还确定了几种生物标志物组合作为转换时间的强预测因子。当淀粉样蛋白作为生物标志物而不是其他分类器组合时，使用淀粉样蛋白验证的训练数据导致稳定和转换器MCI之间的区分的灵敏度增加和特异性降低。我们的研究结果表明，完全独立的分类器只建立在AD和控制数据，并结合成像，遗传和/或神经心理学生物标志物可以更可靠地区分稳定和转换器MCI比单一模态分类器。几种生物标志物组合被鉴定为强烈预测转化为AD的时间。
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. Naive 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.