Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal ; ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.
PLoS One. 2013 Aug 20;8(8):e71940. doi: 10.1371/journal.pone.0071940. eCollection 2013.
The main focus of this study is to illustrate the applicability of latent class analysis in the assessment of cognitive performance profiles during ageing. Principal component analysis (PCA) was used to detect main cognitive dimensions (based on the neurocognitive test variables) and Bayesian latent class analysis (LCA) models (without constraints) were used to explore patterns of cognitive performance among community-dwelling older individuals. Gender, age and number of school years were explored as variables. Three cognitive dimensions were identified: general cognition (MMSE), memory (MEM) and executive (EXEC) function. Based on these, three latent classes of cognitive performance profiles (LC1 to LC3) were identified among the older adults. These classes corresponded to stronger to weaker performance patterns (LC1>LC2>LC3) across all dimensions; each latent class denoted the same hierarchy in the proportion of males, age and number of school years. Bayesian LCA provided a powerful tool to explore cognitive typologies among healthy cognitive agers.
本研究的主要重点是说明潜在类别分析在评估老年人认知表现特征中的适用性。主成分分析（PCA）用于检测主要认知维度（基于神经认知测试变量），贝叶斯潜在类别分析（LCA）模型（无约束）用于探索社区居住的老年人的认知表现模式。性别、年龄和受教育年限作为变量进行了探讨。确定了三个认知维度：一般认知（MMSE）、记忆（MEM）和执行（EXEC）功能。基于这些，在老年人中确定了三个认知表现特征的潜在类别（LC1 到 LC3）。这些类别对应于所有维度上更强到更弱的表现模式（LC1>LC2>LC3）；每个潜在类别在男性、年龄和受教育年限的比例上表示相同的层次结构。贝叶斯 LCA 为探索健康认知老年人的认知类型学提供了有力工具。
