Evidence map›Paper›PMID 35620713›Full record

SynthesisFrontiers in medicine2022

A Systematic Review and Meta-Analysis of the Prevalence and Risk Factors of Depression in Type 2 Diabetes Patients in China.

Xiaobo Liu, Yuxi Li, Li Guan, Xia He, Huiling Zhang, Jun Zhang, Juan Li, Dongling Zhong, Rongjiang Jin

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 3 pooled it
4.9field-weighted citation impact, top 4% of its field
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

23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.

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  15. In Vitro Hypoglycemic Activities ofFoods (Basel, Switzerland) · 2024
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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 at 1 institution in 1 country.

Xiaobo LiuSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Yuxi LiSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Li GuanDepartment of Rehabilitation, Fushun County People's Hospital, Zigong, China.
Xia HeAffiliated Rehabilitation Hospital of Chengdu University of Traditional Chinese Medicine /Sichuan Province Rehabilitation Hospital, Chengdu, China.
Huiling ZhangSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Jun ZhangSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Juan LiSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Dongling ZhongSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Rongjiang JinSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Chengdu University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The prevalence of type 2 diabetes mellitus (T2DM) is increasing in China. Depression in patients with T2DM interferes with blood glucose management, leads to poor treatment outcomes, and has a high risk of dementia and cardiovascular event. We conducted this systematic review and meta-analysis to evaluate the prevalence of depression in patients with T2DM in China and explore potential risk factors associated with depression in T2DM. Methods: We conducted a literature search in MEDLINE/PubMed, EMBASE, the Cochrane Library, the Chinese Biomedical Literature Database (CBM), the China National Knowledge Infrastructure (CNKI), the Chinese Science and Technology Periodical Database (VIP), and the Wanfang Database from their inception to February 25, 2022 to include population-based, cross-sectional surveys that investigated the prevalence of depression in Chinese T2DM patients and studied possible risk factors. Gray literature and reference lists were also manually searched. We used the Agency for Healthcare Research and Quality methodology checklist to assess the risk of bias in the included studies. Two reviewers screened studies, extracted data, and evaluated the risk of bias independently. The primary outcome was the pooled prevalence of depression in Chinese T2DM patients, and the secondary outcomes included potential risk factors for depression in T2DM patients. R (version 3.6.1) and Stata (version 12.0) software were used for data synthesis. Results: We included 48 reports that identified 108,678 subjects. Among the included reports, 4 were rated as low risk of bias, 40 moderate risks of bias, and 4 high risks of bias. The prevalence of depression in T2DM patients in China was 25.9% (95% CI 20.6%-31.6%). The prevalence of depression was higher in women (OR = 1.36, 95% CI 1.19-1.54), subjects ≥60 years (OR = 1.56, 95% CI 1.14-2.14), with a primary school or lower education ( Conclusion: The prevalence of depression in T2DM patients is remarkable in China. Potential risk factors of depression in T2DM patients included women, age ≥ 60 years, low educational level, complications, duration of diabetes ≥ 10 years, insulin use, and living alone. High-quality epidemiological investigations on the prevalence of depression in Chinese T2DM patients are needed to better understand the status of depression in T2DM. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42020182979.

Indexed as

Chinadepressionprevalencerisk factorstype 2 diabetes mellitus

Identifiers

PMID35620713
PMCPMC9127805
OpenAlexW4229455190

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.