Evidence map›Paper›PMID 40665274›Full record

Observational studyBMC psychiatry2025

Trajectories of depressive symptoms in middle-aged and older Chinese adults: identifying subgroups, core symptoms and predictors.

Jia Fang, Wenwen Wu, Chen Yang, Huiyuan Li, Wencan Cheng, Ni Zhang, Baoyi Zhang, Ye Zhang, Meifen Zhang

Abstract readObservational Study
In one paragraph

Observational study in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jia FangSchool of Nursing, Sun Yat-Sen University, Guangzhou, Guangdong, China.ORCID 0000-0003-1602-3445
Wenwen WuSchool of Nursing, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Chen YangSchool of Nursing, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Huiyuan LiSchool of Nursing, Fudan University, Shanghai, China.ORCID 0000-0002-0207-691X
Wencan ChengSchool of Nursing, Sun Yat-Sen University, Guangzhou, Guangdong, China.ORCID 0009-0008-1940-2356
Ni ZhangSchool of Nursing, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Baoyi ZhangSchool of Nursing, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Ye ZhangSchool of Nursing, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Meifen ZhangSchool of Nursing, Sun Yat-Sen University, Guangzhou, Guangdong, China. zhmfen@mail.sysu.edu.cn.ORCID 0000-0001-7931-3285

Funding

The General Program of the National Natural Science Foundation of China 72374232
6 · The paper itself

Abstract

backgroundDepressive 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.

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

methodsThis 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.

resultsThree 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."

conclusionThis 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.

Indexed as

DepressionAgedChinaEast Asian PeopleFemaleHumansLonelinessLongitudinal StudiesMaleMiddle AgedRisk FactorsCross-lagged panel network analysisDepressive symptomsGroup-based trajectory modelingMiddle-aged and older adults

Identifiers

PMID40665274
PMCPMC12265113

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.