Santos Nadine Correia, Costa Patrício Soares, Cunha Pedro, Cotter Jorge, Sampaio Adriana, Zihl Joseph, Almeida Osborne F X, Cerqueira João J, Palha Joana Almeida, Sousa Nuno
Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Campus Gualtar, 4710-057, Braga, Portugal.
Age (Dordr). 2013 Oct;35(5):1983-93. doi: 10.1007/s11357-012-9482-y. Epub 2012 Oct 6.
Identification of predictors of cognitive trajectories through the establishment of composite or single-parameter dimensional categories of cognition and mood may facilitate development of strategies to improve quality of life in the elderly. Participants (n = 487, aged 50+ years) were representative of the Portuguese population in terms of age, gender, and educational status. Cognitive and mood profiles were established using a battery of neurocognitive and psychological tests. Data were subjected to principal component analysis to identify core dimensions of cognition and mood, encompassing multiple test variables. Dimensions were correlated with age and with respect to gender, education, and occupational status. Cluster analysis was applied to isolate distinct patterns of cognitive performance and binary logistic regression models to explore interrelationships between aging, cognition, mood, and socio-demographic characteristics. Four main dimensions were identified: memory, executive function, global cognitive status, and mood. Based on these, strong and weak cognitive performers were distinguishable. Cluster analysis revealed further distinction within these two main categories into very good, good, poor, and very poor performers. Mood was the principal factor contributing to the separation between very good and good, as well as poor and very poor, performers. Clustering was also influenced by gender and education, albeit to a lesser extent; notably, however, female gender × lower educational background predicted significantly poorer cognitive performance with increasing age. Mood has a significant impact on the rate of cognitive decline in the elderly. Gender and educational level are early determinants of cognitive performance in later life.
通过建立认知和情绪的综合或单参数维度类别来识别认知轨迹的预测因素，可能有助于制定提高老年人生活质量的策略。参与者（n = 487，年龄在50岁及以上）在年龄、性别和教育程度方面代表了葡萄牙人口。使用一系列神经认知和心理测试来建立认知和情绪概况。对数据进行主成分分析，以识别认知和情绪的核心维度，包括多个测试变量。各维度与年龄以及性别、教育程度和职业状况相关。应用聚类分析来分离不同的认知表现模式，并使用二元逻辑回归模型来探索衰老、认知、情绪和社会人口学特征之间的相互关系。确定了四个主要维度：记忆、执行功能、整体认知状态和情绪。基于这些维度，可以区分认知表现强和弱的人。聚类分析揭示了在这两个主要类别中进一步分为表现非常好、好、差和非常差的人。情绪是导致表现非常好与好以及差与非常差的人之间分离的主要因素。聚类也受到性别和教育程度的影响，尽管程度较小；然而，值得注意的是，女性性别×较低的教育背景预示着随着年龄增长认知表现明显更差。情绪对老年人认知衰退的速度有显著影响。性别和教育水平是晚年认知表现的早期决定因素。
