BMC public health · 2025
Cognitive decline and the evolution of depression networks in older adults: evidence from the CHARLS study.
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Wang Jingyi, Yang Lei, Zhou Yu, Zhang Kuo
Аннотация
BACKGROUND: The global trend of population aging poses significant health challenges, particularly in cognitive and mental health. Understanding the heterogeneous patterns of cognitive decline and their relationship with depressive symptoms is crucial for developing targeted interventions. This study explores the complex relationship between cognitive decline trajectories and depressive symptom networks in Chinese middle-aged and older adults using longitudinal data.
METHODS: Data were sourced from the China Health and Retirement Longitudinal Study (CHARLS), involving 5,977 participants aged 45 and above, followed from 2013 to 2020. Cognitive function was assessed through self-reported memory, mathematical ability, and orientation, while depressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10). Growth Mixture Modeling (GMM) identified heterogeneous cognitive development trajectories, and Cross-Lagged Panel Network (CLPN) analysis examined the longitudinal dynamics of depressive symptom networks across different cognitive trajectory subgroups.
RESULTS: GMM analysis identified three distinct cognitive decline trajectories: Class 1 (moderate-baseline moderate-declining, 5.1%), Class 2 (low-baseline moderate-declining, 44.1%), and Class 3 (high-baseline steep-declining, 50.9%). All three classes exhibited substantial cognitive decline over the 7-year follow-up period, with Class 3 showing the largest absolute decline (5.32 points) despite having the highest baseline performance. Depression trajectories varied significantly across cognitive classes, with Class 2 showing the highest baseline depression scores (9.60) and Class 3 maintaining consistently lower depression levels (6.82 at baseline, 8.08 at follow-up). CLPN analysis revealed distinct patterns of depressive symptom network evolution, with key symptoms such as concentration problems, mood disturbance, and lethargy showing differential centrality and connectivity patterns across cognitive trajectory subgroups and time points.
CONCLUSIONS: This study reveals significant heterogeneity in cognitive decline patterns among Chinese middle-aged and older adults, with the majority (50.9%) following a high-baseline but steep-declining trajectory. The complex relationship between cognitive decline and depressive symptoms varies substantially across different cognitive trajectory subgroups, suggesting the need for personalized intervention strategies. The findings highlight the importance of early identification of high-risk cognitive decline patterns and the implementation of targeted mental health interventions. These results provide new insights into the dynamic interplay between cognitive aging and mental health, informing the development of precision medicine approaches for healthy aging.
METHODS: Data were sourced from the China Health and Retirement Longitudinal Study (CHARLS), involving 5,977 participants aged 45 and above, followed from 2013 to 2020. Cognitive function was assessed through self-reported memory, mathematical ability, and orientation, while depressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10). Growth Mixture Modeling (GMM) identified heterogeneous cognitive development trajectories, and Cross-Lagged Panel Network (CLPN) analysis examined the longitudinal dynamics of depressive symptom networks across different cognitive trajectory subgroups.
RESULTS: GMM analysis identified three distinct cognitive decline trajectories: Class 1 (moderate-baseline moderate-declining, 5.1%), Class 2 (low-baseline moderate-declining, 44.1%), and Class 3 (high-baseline steep-declining, 50.9%). All three classes exhibited substantial cognitive decline over the 7-year follow-up period, with Class 3 showing the largest absolute decline (5.32 points) despite having the highest baseline performance. Depression trajectories varied significantly across cognitive classes, with Class 2 showing the highest baseline depression scores (9.60) and Class 3 maintaining consistently lower depression levels (6.82 at baseline, 8.08 at follow-up). CLPN analysis revealed distinct patterns of depressive symptom network evolution, with key symptoms such as concentration problems, mood disturbance, and lethargy showing differential centrality and connectivity patterns across cognitive trajectory subgroups and time points.
CONCLUSIONS: This study reveals significant heterogeneity in cognitive decline patterns among Chinese middle-aged and older adults, with the majority (50.9%) following a high-baseline but steep-declining trajectory. The complex relationship between cognitive decline and depressive symptoms varies substantially across different cognitive trajectory subgroups, suggesting the need for personalized intervention strategies. The findings highlight the importance of early identification of high-risk cognitive decline patterns and the implementation of targeted mental health interventions. These results provide new insights into the dynamic interplay between cognitive aging and mental health, informing the development of precision medicine approaches for healthy aging.