Novel Alzheimer's disease subtypes identified using a data and knowledge driven strategy
Novel Alzheimer's disease subtypes identified using a data and knowledge driven strategy
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DOI：
10.1038/s41598-020-57785-2
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发表时间：
2020-01-28
影响因子：
4.6
通讯作者：
Benjamini, Yoav
中科院分区：
文献类型：
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作者：
Mitelpunkt, Alexis;Galili, Tal;Benjamini, Yoav
患有阿尔茨海默病 (AD) 的成年人群体的需求和结果各不相同。当前 AD 诊断亚组的异质性阻碍了数据分析在临床试验设计中的使用以及将研究结果转化为改进的护理。该项目的目的是定义更多临床同质的 AD 患者群体，并将临床特征与生物标志物联系起来。我们采用了创新的大数据分析策略，即3C策略，将医学知识融入到数据分析过程中。使用 3C 分析了大量经过预处理的 AD 神经影像计划 (ADNI) 数据。数据分析产生了 6 种新的疾病亚型，它们与指定的诊断类型不同，并呈现出不同的临床测量模式和潜在的生物标志物。其中两种亚型“失认痴呆”和“有洞察力的痴呆”根据临床特征和生物标志物来区分严重的参与者。 “无代偿性轻度认知障碍 (MCI)”亚型与“情感性 MCI”亚型在临床、人口统计学和影像学方面存在差异。在“担心的”和“健康的”群体之间也观察到了差异。使用数据驱动的分析产生了亚表型临床簇，其超出了当前的诊断范围并与生物标志物相关。这种同质亚组有可能成为加强脑医学研究的基础。
The population of adults with Alzheimer's disease (AD) varies in needs and outcomes. The heterogeneity of current AD diagnostic subgroups impedes the use of data analytics in clinical trial design and translation of findings into improved care. The purpose of this project was to define more clinically-homogeneous groups of AD patients and link clinical characteristics with biological markers. We used an innovative big data analysis strategy, the 3C strategy, that incorporates medical knowledge into the data analysis process. A large set of preprocessed AD Neuroimaging Initiative (ADNI) data was analyzed with 3C. The data analysis yielded 6 new disease subtypes, which differ from the assigned diagnosis types and present different patterns of clinical measures and potential biomarkers. Two of the subtypes, "Anosognosia dementia" and "Insightful dementia", differentiate between severe participants based on clinical characteristics and biomarkers. The "Uncompensated mild cognitive impairment (MCI)" subtype, demonstrates clinical, demographic and imaging differences from the "Affective MCI" subtype. Differences were also observed between the "Worried Well" and "Healthy" clusters. The use of data-driven analysis yielded sub-phenotypic clinical clusters that go beyond current diagnoses and are associated with biomarkers. Such homogenous sub-groups can potentially form the basis for enhancement of brain medicine research.