ArticleBriefings in bioinformatics2024
Deciphering the genetic interplay between depression and dysmenorrhea: a Mendelian randomization study.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The genetics of primary dysmenorrhoea: a systematic review.Reproduction & fertility · 2026Pooled it
- Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders.Briefings in bioinformatics · 2026Review
- The association between age at menarche and subsequent risk of chronic pain.Frontiers in global women's health · 2026Review
- Gut microbiota in dysmenorrhea: causal evidence from Mendelian randomization and microbial-targeted intervention validation.Frontiers in microbiology · 2025Article
- Are the Previously Reported Risk Factors for Endometriosis Actually Associated With Pain and Infertility Rather Than Endometriosis Itself?Reproductive medicine and biologyReview
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundThis study aims to explore the link between depression and dysmenorrhea by using an integrated and innovative approach that combines genomic, transcriptomic, and protein interaction data/information from various resources.
methodsA two-sample, bidirectional, and multivariate Mendelian randomization (MR) approach was applied to determine causality between dysmenorrhea and depression. Genome-wide association study (GWAS) data were used to identify genetic variants associated with both dysmenorrhea and depression, followed by colocalization analysis of shared genetic influences. Expression quantitative trait locus (eQTL) data were analyzed from public databases to pinpoint target genes in relevant tissues. Additionally, a protein-protein interaction (PPI) network was constructed using the STRING database to analyze interactions among identified proteins.
resultsMR analysis confirmed a significant causal effect of depression on dysmenorrhea ['odds ratio' (95% confidence interval) = 1.51 (1.19, 1.91), P = 7.26 × 10-4]. Conversely, no evidence was found to support a causal effect of dysmenorrhea on depression (P = .74). Genetic analysis, using GWAS and eQTL data, identified single-nucleotide polymorphisms in several genes, including GRK4, TRAIP, and RNF123, indicating that depression may impact reproductive function through these genetic pathways, with a detailed picture presented by way of analysis in the PPI network. Colocalization analysis highlighted rs34341246(RBMS3) as a potential shared causal variant.
conclusionsThis study suggests that depression significantly affects dysmenorrhea and identifies key genes and proteins involved in this interaction. The findings underline the need for integrated clinical and public health approaches that screen for depression among women presenting with dysmenorrhea and suggest new targeted preventive strategies.
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Registered trials
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