Evidence map›Paper›PMID 40069881›Full record

ArticleJournal of ovarian research2025

Causal association between metabolic syndrome and ovarian dysfunction: a bidirectional two-sample mendelian randomization.

Ying He, Yanling Wei, Haixia Liang, Yi Wan, Ying Zhang, Jianfang Zhang

Abstract read
In one paragraph

Article in Journal of ovarian research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

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4 · The record

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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.

Ying He *Department of Obstetrics and Gynecology, Xijing 986 Hospital Department, Air force Medical University, No. 6 Jianshe West Road, Xi'an, 710054, Shaanxi, China.
Yanling Wei *Department of Obstetrics and Gynecology, Xijing Hospital, Air force Medical University, No. 15 Changle West Road, Xi'an, 710033, Shaanxi, China.
Haixia LiangDepartment of Obstetrics and Gynecology, Xijing 986 Hospital Department, Air force Medical University, No. 6 Jianshe West Road, Xi'an, 710054, Shaanxi, China.
Yi WanDepartment of Health Service, Air force Medical University, Xi'an, 710032, Shaanxi, China.
Ying ZhangDepartment of Obstetrics and Gynecology, Xijing 986 Hospital Department, Air force Medical University, No. 6 Jianshe West Road, Xi'an, 710054, Shaanxi, China. anran206@yeah.net.
Jianfang ZhangDepartment of Obstetrics and Gynecology, Xijing Hospital, Air force Medical University, No. 15 Changle West Road, Xi'an, 710033, Shaanxi, China. zhzhhao@163.com.

Funding

Fourth Military Medical University clinical research program 2021LC228the key research and development plan project of Shaanxi Province 2023-YBSF-485
6 · The paper itself

Abstract

backgroundThe relationship between Metabolic Syndrome (MetS) and ovarian dysfunction has been widely reported in observational studies, yet it remains not fully understood. This study employs genetic prediction methods and utilizes summary data from genome-wide association studies (GWAS) to investigate this causal link.

methodsWe employed a bidirectional two-sample Mendelian Randomization (MR) analysis utilizing MetS and ovarian dysfunction summary data from GWAS. Inverse variance weighted (IVW) was employed as the primary MR method, supplemented by Weighted Median, Weighted Mode, and MR-Egger methods. The robustness of the results was further assessed through sensitivity analyses including MR-Egger regression, MR-PRESSO, Cochran's Q, and leave-one-out test.

resultsOur MR analysis identified a causal relationship between genetically determined insulin resistance (OR = 0.26, 95% CI: 0.08-0.89, P = 0.03), waist circumference (OR = 2.14, 95% CI: 1.45-3.15, P < 0.001), BMI (OR = 2.1, 95% CI: 1.56-2.83, P < 0.001) and ovarian dysfunction. Conversely, reverse MR analysis confirmed causal effects of ovarian dysfunction on metabolic syndrome (OR = 0.98, 95% CI: 0.97-0.99, P < 0.001) and waist circumference (OR = 0.99, 95% CI: 0.98-0.99, P = 0.02). The results of MR-Egger regression test indicated that the whole analysis was not affected by horizontal pleiotropy. Additionally, the MR-PRESSO test identified outliers in SNPs, but after removal of outliers, results remained unchanged.

conclusionThis study reveals a bidirectional causal connection between metabolic syndrome and ovarian dysfunction via genetic prediction methods. These findings are crucial for advancing our understanding of the interactions between these conditions and developing strategies for prevention and treatment.

Indexed as

Mendelian Randomization AnalysisMetabolic SyndromeOvarian DiseasesFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansInsulin ResistancePolymorphism, Single NucleotideCausal AssociationGenetic analysisMendelian randomizationMetabolic syndromeOvarian dysfunction

Identifiers

PMID40069881
PMCPMC11895234

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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.