The trajectories of depression and multimorbidity affect the patterns of cognitive decline: a prospective cohort study

医学 情感(语言学) 萧条(经济学) 多项式logistic回归 前瞻性队列研究 认知 队列 认知功能衰退 队列研究 日常生活活动 潜在类模型 老年学 逻辑回归 纵向研究 精神科 人口学 痴呆 疾病 内科学 心理学 外科 经济 社会学 病理 宏观经济学 机器学习 统计 沟通 计算机科学 数学
作者
Kai Cui,Rui Zhao,Ning Ding,Xiaonuo Xu,Wenjing Gu,Jing He,Xiaoyan Ge
出处
期刊:Age and Ageing [Oxford University Press]
卷期号:54 (2) 被引量:1
标识
DOI:10.1093/ageing/afaf036
摘要

Abstract Background The course of decline of global cognitive function is heterogeneous, with patterns varying among individuals. The influence of depression, functional limitations and multimorbidity on patterns of decline of global cognitive function among community-dwelling older adults remains unknown. This study examines the relationship between these health conditions and the course of decline of global cognitive function. Methods A prospective cohort of 4032 participants aged ≥60 years at baseline was constructed using the Health and Retirement Study (2006–20), through surveys every 2 years. Information on depression, functional limitations and multimorbidity was collected from 2006 to 2012. Data on cognitive function were collected between 2012 and 2020. Latent class trajectory models were used to identify the latent trajectories. Multinomial logistic regression models were employed to analyse the association between the latent trajectories and cognitive function. Results Different trajectories were identified for depression (low-stable, decline-rise and rise-decline), activities of daily living (ADLs; low-stable, decline-rise and rise-decline), instrumental activities of daily living (IADLs; low-stable and rise), and multimorbidity (stable and rise). Three trajectories of cognitive decline were identified: high stable, modest and later steep. The trajectories of depression (for decline-rise: OR: 1.47, 95% CI: 1.11–1.95; for rise-decline: OR: 1.72, 95% CI: 1.24–2.38) and multimorbidity (OR: 1.63, 95% CI: 1.08–2.46) increased the risk of cognitive decline. Conclusion The trajectories of depression and multimorbidity affect cognitive decline patterns. Longitudinal surveillance of health conditions can be used to identify different patterns of cognitive decline and predict faster cognitive decline.
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