Evidence map›Paper›PMID 42222014›Full record

ArticlePsychology research and behavior management2026

Dissecting Depression in Cardiovascular Disease in Middle-Aged and Older Adults: Evidence That Comorbidity is Associated with Higher Depression Network Strength.

Rouyi Lin, Yinshi Xiong, Lin Wang, Yuchan Chen, Jiaci Lin, Na Du

Abstract read
In one paragraph

Article in Psychology research and behavior management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Rouyi Lin *Heart Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, People's Republic of China.
Yinshi Xiong *Heart Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, People's Republic of China.
Lin WangHeart Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, People's Republic of China.
Yuchan ChenHeart Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, People's Republic of China.
Jiaci LinSchool of Social and Behavioral Science, Nanjing University, Nanjing, People's Republic of China.ORCID 0000-0001-8659-0395
Na DuHeart Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Depression is prevalent among older adults with cardiovascular disease (CVD). This study utilized network analysis to examine depressive symptom structures in older adults with and without CVD and explored the impact of comorbidities. Methods: Based on 15,234 participants from the 2018 CHARLS data, propensity score matching (PSM) was used to construct a matched cohort of CVD and non-CVD individuals. Depressive symptoms were assessed via the CESD-10. Network analysis identified central symptoms and compared network connectivity and strength. Results: The CVD group exhibited significantly higher depression prevalence and symptom severity than the non-CVD group. In the CVD network, "depressed", "could not get going", and "bothered" were the most central symptoms. Notably, CVD patients with comorbidities showed significantly higher global network strength (S=0.44, Conclusion: Older adults with CVD demonstrate distinct depression symptom interactions. The increased network density in patients with comorbidities suggests a higher vulnerability to self-sustaining depressive states. Targeting central symptoms like "feeling depressed" and "inability to get going" may optimize psychological interventions and clinical management for this population.

Indexed as

cardiovascular diseasecomorbiditydepressionmiddle-aged and older adultsnetwork analysis

Identifiers

PMID42222014
PMCPMC13220825

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LicenceCC BY-NC
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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.