Two different Alzheimer diseases in men and women: clues from advanced neural networks and artificial intelligence.
Two different Alzheimer diseases in men and women: clues from advanced neural networks and artificial intelligence.
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DOI：
10.1016/s1550-8579(05)80017-8
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发表时间：
2005-06-01
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影响因子：
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通讯作者：
Maurelli, Guido
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文献类型：
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作者：
Grossi, Enzo;Massini, Giulia;Maurelli, Guido
背景技术背景：阿尔茨海默病（AD）的临床表现的性别相关的差异的研究集中在疾病的具体方面（例如，循环代谢产物，认知能力，或流行病学趋势）。目的：本研究占几个描述符的疾病同时，提供了一个多维分析的一个队列的患者AD。方法：我们的分析进行了自组织地图（SOM）。在研究患者中观察到的大量（60）独立变量（临床、人口统计学、生物化学和神经心理学）定义了一个复杂且高维的输入空间，可以由SOM处理。在没有监督的情况下，SOM检查变量和聚类观测之间的非线性关系，使变量之间的拓扑关系对应于它们分布的相似性。通过这种非线性自聚类，可以识别出集中了重要信息的观察子集（即受试者集群）。每个主题是由特定值的变量（记录），和一组特定的变量值（码本）定义了一个独特的class.RESULTS：研究样本包括211例轻度至中度AD（143名女性，68名男性，平均[SD]年龄，71.9 [7.2]岁）。所有患者被分配到3个宏类-称为A，B和C-的基础上的矩阵码本邻居。在码本之间的矢量距离方面，类A和B非常相似，而类C与类A和B之间的分离是明显的。SOM分布的输出矩阵的变量值没有显示任何特定的模式，大多数考虑的特点。然而，我们在C类中仅发现男性患者。当性别从数据库中删除时，这一类别的区别并没有实质性改变。男性和女性患者痴呆的严重程度，人口统计学特征，精神和行为症状，身体残疾的指标，和一般的健康状况。CONCLUSIONS：SOM表示非线性多因素的相互作用之间的描述符的AD的功能，似乎与性别和传统的统计分析会错过。这一发现可能为研究男性和女性AD的不同致病机制提供了一种新的流行病学依据。
BACKGROUND: Studies of the gender-related differences in the clinical presentation of Alzheimer's disease (AD) have focused on specific aspects of the disease (eg, circulating metabolites, cognitive capacity, or epidemiologic trends).OBJECTIVE: This study accounts for several descriptors of the disease simultaneously, providing a multidimensional analysis of a cohort of patients with AD.METHODS: Our analysis was conducted using self-organizing maps (SOMs). The high number (60) of independent variables (clinical, demographic, biochemical, and neuropsychological) observed in the study patients defines a complex and high-dimensional input space that can be processed by SOMs. Without supervision, SOMs examine nonlinear relations among the variables and cluster observations so that topologic relationships between variables correspond to the similarity of their distribution. Through such nonlinear autoclustering, subsets of observations (ie, clusters of subjects) can be identified in which essential information is concentrated. Each subject is identified by particular values of the variables (the record), and a specific set of variable values (the codebook) defines a distinct class.RESULTS: The study sample included 211 patients with mild to moderate AD (143 women, 68 men; mean [SD] age, 71.9 [7.2] years). All patients were assigned to 3 macroclasses-called A, B, and C-on the basis of matrix codebook neighborhoods. In terms of vectorial distance between codebooks, class A and B were quite similar, whereas the separation between class C and classes A and B was evident. The SOM distribution of values of variables across the output matrix did not show any specific pattern for most of the considered characteristics. However, we found only male patients in class C. This class distinction was not substantially changed when sex was removed from the database. Male and female patients were comparable with respect to dementia severity, demographic characteristics, psychiatric and behavioral symptoms, indicators of physical disability, and general health status.CONCLUSIONS: SOMs indicate nonlinear multifactorial interactions among the descriptors of the features of AD that seem to be linked to sex and would have been missed by traditional statistical analysis. This finding may offer a novel epidemiologic rationale for research into different pathogenic mechanisms in men and women with AD.