Evidence map›Paper›PMID 34903730›Full record

ArticleTranslational psychiatry2021

The association between depression and metabolic syndrome and its components: a bidirectional two-sample Mendelian randomization study.

Min Zhang, Jing Chen, Zhiqun Yin, Lanbing Wang, Lihua Peng

Abstract read
In one paragraph

Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
90citing papers in PubMed, 1 pooled it
–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

90 citing papers in PubMed, 1 synthesis or guideline pooled it.

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30 more citing papers are in PubMed but not listed here.

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

5 authors.

Min ZhangSchool of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China.
Jing ChenDepartment of Anesthesia and Pain Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Zhiqun YinDepartment of Psychiatry and Psychology, No.964 Hospital of People's Liberation Army, Changchun City, 130026, Jilin Province, China.
Lanbing WangDivision of medical affairs, The First Affiliated Hospital of Army Military Medical University, Chongqing, 400038, China.
Lihua PengDepartment of Anesthesia and Pain Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China. plhcqmu@163.com.ORCID http://orcid.org/0000-0002-5202-910X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Observational studies suggested a bidirectional correlation between depression and metabolic syndrome (MetS) and its components. However, the causal associations between them remained unclear. We aimed to investigate whether genetically predicted depression is related to the risk of MetS and its components, and vice versa. We performed a bidirectional two-sample Mendelian randomization (MR) study using summary-level data from the most comprehensive genome-wide association studies (GWAS) of depression (n = 2,113,907), MetS (n = 291,107), waist circumference (n = 462,166), hypertension (n = 463,010) fasting blood glucose (FBG, n = 281,416), triglycerides (n = 441,016), high-density lipoprotein cholesterol (HDL-C, n = 403,943). The random-effects inverse-variance weighted (IVW) method was applied as the primary method. The results identified that genetically predicted depression was significantly positive associated with risk of MetS (OR: 1.224, 95% CI: 1.091-1.374, p = 5.58 × 10

Indexed as

Metabolic SyndromeCholesterol, HDLDepressionGenome-Wide Association StudyHumansMendelian Randomization AnalysisRisk FactorsTriglyceridesCholesterol, HDLTriglycerides

Identifiers

PMID34903730
PMCPMC8668963

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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.