Correlations and network analysis between food intake and depression among Chinese rural elderly based on CLHLS 2018

萧条(经济学) 食物摄入量 心理学 环境卫生 老年学 医学 精神科 内科学 经济 宏观经济学
作者
Junjie Jiang,Xiao Huang,Wenbin Liu
出处
期刊:Journal of Affective Disorders [Elsevier BV]
卷期号:379: 49-57
标识
DOI:10.1016/j.jad.2025.03.019
摘要

Implementing effective interventions for specific depressive symptoms is of vital importance to reduce the disease burden of depression. Previous studies have identified links between various dietary patterns and depression among elderly individuals. However, associations between food consumption and specific depressive symptoms remained largely unknown. We included 5171 individuals living in the rural and aged above 65 from Chinese Longitudinal Health and Longevity Survey (CLHLS 2017-2018). We used the 10-item short form of the Center for Epidemiologic Studies Depression Scale (CESD-10) to assess depressive symptoms and selected 4 common foods to assess food intake. Univariate analysis and multifactorial analysis were used to identify influencing factors of depression. Network analysis was used to identify central symptoms and bridge symptoms between food consumption and depression network. Finally, network stability was examined by a case-dropping bootstrap procedure. Regression model showed vegetable intake, fish intake, and egg intake were associated with depression. Network analysis revealed that nodes "Feeling sad or depressed" (A3) and "Feeling nervous or fearful" (A6) were central symptoms and "Vegetables consumption" (B2), "Eggs consumption" (B4), and "Sleep quality" (A10) were bridge symptoms of the food consumption and depression network. Recall bias introduced by the self-report questionnaire and the use of cross-sectional data. Central symptoms, as well as bridge symptoms, played a critical role in the food consumption and depression network. Timely, systematic, multi-level interventions targeting on central symptoms and bridge symptoms may benefit in alleviating depressive symptoms of Chinese rural elderly.
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