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<p>Primary dysmenorrhea (PDM) is a common cyclic menstrual pain that significantly affects the quality of life for women. Several epidemiological studies have suggested a potential association between PDM and mental health traits, including stress, depression, and anxiety. However, there is a lack of systematic investigation into whether a causal relationship exists between PDM and mental health phenotypes compared to other physical phenotypes. In this study, we conducted a large-scale phenome study on a cohort of 7401 young female Chinese college students to explore the association between PDM and various physical and mental health phenotypes. Using a multi-phenotype correlation network model, we discovered that the correlation between the PDM phenotypes and mental health phenotypes was the most dominant among the complex inter-connections across different categories of phenotypes. Furthermore, employing a two-sample Mendelian randomization analysis, we systematically elucidated the genomic-level impact of PDM on the mental health traits of young women. Specifically, we identified an increased risk of depression and anxiety associated with PDM, potentially influenced by several Single-nucleotide polymorphism&#xa0;(SNP) variants such as <i>ZMIZ1, DIO1, GRIK4</i> and <i>RBFOX1</i>. This study offers valuable insights into the genetic mechanism through which dysmenorrhea impacts mental health, which contributes to a better understanding of the comprehensive management of PDM and its associated psychological challenges.</p>

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Association Between Primary Dysmenorrhea and Mental Health Traits: A Study Based on Multi-Phenotype Correlation Network and Mendelian Randomization Analysis in Female College Students

  • 期刊论文

  • 发表日期：

    2025年7月9日

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  • Huiting Jiangzhou,
  • Hanpeng Xu,
  • Yanqin Wen,
  • Zeyi Guan,
  • Yang Zheng,
  • Xuemin Jian,
  • Weichen Song,
  • Aamir Fahira,
  • Jinmai Zhang,
  • Qing Zhang,
  • Ying Zhao,
  • Manfei Zhang,
  • Jianhua Chen,
  • Zhiqiang Li,
  • Zhuo Wang,
  • Yongyong Shi

摘要

Primary dysmenorrhea (PDM) is a common cyclic menstrual pain that significantly affects the quality of life for women. Several epidemiological studies have suggested a potential association between PDM and mental health traits, including stress, depression, and anxiety. However, there is a lack of systematic investigation into whether a causal relationship exists between PDM and mental health phenotypes compared to other physical phenotypes. In this study, we conducted a large-scale phenome study on a cohort of 7401 young female Chinese college students to explore the association between PDM and various physical and mental health phenotypes. Using a multi-phenotype correlation network model, we discovered that the correlation between the PDM phenotypes and mental health phenotypes was the most dominant among the complex inter-connections across different categories of phenotypes. Furthermore, employing a two-sample Mendelian randomization analysis, we systematically elucidated the genomic-level impact of PDM on the mental health traits of young women. Specifically, we identified an increased risk of depression and anxiety associated with PDM, potentially influenced by several Single-nucleotide polymorphism (SNP) variants such as ZMIZ1, DIO1, GRIK4 and RBFOX1. This study offers valuable insights into the genetic mechanism through which dysmenorrhea impacts mental health, which contributes to a better understanding of the comprehensive management of PDM and its associated psychological challenges.