Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia.
Biostatistics Unit, Faculty of Health, Deakin University, Geelong, VIC, Australia.
BMC Public Health. 2022 Nov 29;22(1):1990. doi: 10.1186/s12889-022-14379-z.
BACKGROUND: Mounting evidence highlights the importance of combined modifiable lifestyle factors in reducing risk of cognitive decline and dementia. Several a priori additive scoring approaches have been established; however, limited research has employed advanced data-driven approaches to explore this association. This study aimed to examine the association between data-driven lifestyle profiles and cognitive function in community-dwelling Australian adults. METHODS: A cross-sectional study of 4561 Australian adults (55.3% female, mean age 60.9 ± 11.3 years) was conducted. Questionnaires were used to collect self-reported data on diet, physical activity, sedentary time, smoking status, and alcohol consumption. Cognitive testing was undertaken to assess memory, processing speed, and vocabulary and verbal knowledge. Latent Profile Analysis (LPA) was conducted to identify subgroups characterised by similar patterns of lifestyle behaviours. The resultant subgroups, or profiles, were then used to further explore associations with cognitive function using linear regression models and an automatic Bolck, Croon & Hagenaars (BCH) approach. RESULTS: Three profiles were identified: (1) "Inactive, poor diet" (76.3%); (2) "Moderate activity, non-smokers" (18.7%); and (3) "Highly active, unhealthy drinkers" (5.0%). Profile 2 "Moderate activity, non-smokers" exhibited better processing speed than Profile 1 "Inactive, poor diet". There was also some evidence to suggest Profile 3 "Highly active, unhealthy drinkers" exhibited poorer vocabulary and verbal knowledge compared to Profile 1 and poorer processing speed and memory scores compared to Profile 2. CONCLUSION: In this population of community-dwelling Australian adults, a sub-group characterised by moderate activity levels and higher rates of non-smoking had better cognitive function compared to two other identified sub-groups. This study demonstrates how LPA can be used to highlight sub-groups of a population that may be at increased risk of dementia and benefit most from lifestyle-based multidomain intervention strategies.
背景:越来越多的证据强调了可改变的生活方式因素综合作用对于降低认知能力下降和痴呆风险的重要性。已经建立了几种预先设定的可加评分方法;然而,有限的研究采用了先进的数据驱动方法来探索这种关联。本研究旨在研究数据驱动的生活方式特征与社区居住的澳大利亚成年人认知功能之间的关联。
方法:对 4561 名澳大利亚成年人(55.3%为女性,平均年龄 60.9 ± 11.3 岁)进行了横断面研究。使用问卷收集关于饮食、身体活动、久坐时间、吸烟状况和饮酒的自我报告数据。进行认知测试以评估记忆、处理速度以及词汇和语言知识。采用潜在剖面分析(LPA)确定以相似生活方式行为模式为特征的亚组。然后,使用线性回归模型和自动 Bolck、Croon 和 Hagenaars(BCH)方法,使用所得亚组(或剖面)进一步探讨与认知功能的关联。
结果:确定了三个特征:(1)“不活跃,饮食不良”(76.3%);(2)“适度活动,不吸烟”(18.7%);和(3)“高度活跃,饮酒不健康”(5.0%)。特征 2“适度活动,不吸烟”的处理速度优于特征 1“不活跃,饮食不良”。还有一些证据表明,与特征 1 和特征 2 相比,特征 3“高度活跃,饮酒不健康”的词汇和语言知识较差,并且处理速度和记忆得分较差。
结论:在这群社区居住的澳大利亚成年人中,与其他两个确定的特征组相比,具有中度活动水平和更高非吸烟率的亚组认知功能更好。本研究表明,潜在剖面分析可以用于突出人群中的亚组,这些亚组可能处于痴呆风险增加的风险中,并且最受益于基于生活方式的多领域干预策略。
BMC Public Health. 2022-11-29
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