Abstract

Background

Depressive symptoms among middle-aged and older adults are a significant public health concern, with varying symptom trajectories over time. Understanding these trajectories and their predictors can inform targeted interventions.

Objectives

To identify subgroups of depressive symptom trajectories, determine predictors of these subgroups, and explore the core symptoms and their predictive relationships.

Methods

This study analyzed 7,166 participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study across four waves (2011, 2013, 2015, 2018). Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale. Group-based trajectory modeling (GBTM) identified depressive symptom trajectories. Multivariate logistic regression explored influencing factors, while Cross-lagged panel network models (CLPN) were used to identify core symptoms.

Results

Three distinct trajectory groups were identified: “stable low” (66.4%), “decline followed by an increase” (27.8%), and “continuously rising” (5.8%). Females, those with lower education, poor self-reported health, unmarried status and rural residents were associated with worsening symptoms. CLPN analysis revealed “depressive mood” as the core symptom, with “feeling lonely” and “could not get going” predicting “depressive mood.”

Conclusion

This study identifies distinct trajectories of depressive symptoms in older adults and pinpoints “depressive mood” as a core symptom, which is dynamically predicted by loneliness and a lack of behavioral activation. Therefore, an effective public health strategy should involve not only identifying at-risk individuals based on their trajectory profiles but also targeting these specific precursor symptoms to prevent escalation.

Details

Title
Trajectories of depressive symptoms in middle-aged and older Chinese adults: identifying subgroups, core symptoms and predictors
Author
Fang, Jia; Wu, Wenwen; Chen, Yang; Li, Huiyuan; Cheng, Wencan; Zhang, Ni Zhangoyi; Zhang, Ye; Zhang, Meifen
Pages
1-11
Section
Research
Publication year
2025
Publication date
2025
Publisher
BioMed Central
e-ISSN
1471244X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3237006419
Copyright
© 2025. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.