Why Inclusion Matters for Alzheimer's Disease Biomarker Discovery in Plasma.
Why Inclusion Matters for Alzheimer's Disease Biomarker Discovery in Plasma.
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DOI：
10.3233/jad-201318
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发表时间：
2021
影响因子：
4
通讯作者：
Robinson, Rena A. S.
中科院分区：
文献类型：
--
作者：
Khan, Mostafa J.;Desaire, Heather;Lopez, Oscar L.;Kamboh, M. Ilyas;Robinson, Rena A. S.
关键词：
非裔美国人/黑人成年人阿尔茨海默病 (AD) 的发病率不成比例，并且在生物标志物发现工作中代表性不足。本研究旨在结合蛋白质组学和机器学习方法，在包括非裔美国人/黑人成年人的队列中识别 AD 的潜在诊断生物标志物。我们对从临床诊断的 AD 和自我报告的非裔美国人/黑人或非西班牙裔白人的认知正常成年人获得的血浆样本 (N = 113) 进行了一项基于发现的血浆蛋白质组学研究。然后使用支持向量机 (SVM) 对差异表达的蛋白质组进行分类，以识别候选生物标志物。总共鉴定了 740 种蛋白质，其中 25 种 AD 中差异表达的蛋白质来自于单一种族和民族背景组内的比较。无论种族和民族背景如何，六种蛋白质在 AD 中都有差异表达。当使用非西班牙裔白人成年人特有的差异表达蛋白进行训练时，SVM 监督分类的曲线下面积 (AUC) 为 0.91，区分 AD 的准确度为 86%。然而，同一模型在区分非裔美国人/黑人成年人样本中的 AD 时，AUC 为 0.49，准确度为 47%。其他协变量，如年龄、APOE4 状态、性别和受教育年限，主要在非西班牙裔白人成人样本中被发现可以改善模型，用于对 AD 进行分类。这些结果证明了研究设计在 AD 生物标志物发现中的重要性，其中必须包括不同的种族和民族群体，例如非裔美国人/黑人成年人，以开发有效的生物标志物。
African American/Black adults have a disproportionate incidence of Alzheimer’s disease (AD) and are underrepresented in biomarker discovery efforts. This study aimed to identify potential diagnostic biomarkers for AD using a combination of proteomics and machine learning approaches in a cohort that included African American/Black adults. We conducted a discovery-based plasma proteomics study on plasma samples (N = 113) obtained from clinically diagnosed AD and cognitively normal adults that were self-reported African American/Black or non-Hispanic White. Sets of differentially-expressed proteins were then classified using a support vector machine (SVM) to identify biomarker candidates. In total, 740 proteins were identified of which, 25 differentially-expressed proteins in AD came from comparisons within a single racial and ethnic background group. Six proteins were differentially-expressed in AD regardless of racial and ethnic background. Supervised classification by SVM yielded an area under the curve (AUC) of 0.91 and accuracy of 86% for differentiating AD in samples from non-Hispanic White adults when trained with differentially-expressed proteins unique to that group. However, the same model yielded an AUC of 0.49 and accuracy of 47% for differentiating AD in samples from African American/Black adults. Other covariates such as age, APOE4 status, sex, and years of education were found to improve the model mostly in the samples from non-Hispanic White adults for classifying AD. These results demonstrate the importance of study designs in AD biomarker discovery, which must include diverse racial and ethnic groups such as African American/Black adults to develop effective biomarkers.