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Related Concept Videos

Disorders of the Female Reproductive System01:24

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The female reproductive system can be affected by several disorders, including Premenstrual Syndrome (PMS), Premenstrual Dysphoric Disorder (PMDD), endometriosis, and various forms of cancer. PMS and PMDD are cyclical conditions that cause physical and emotional distress, with symptoms that include edema, mood swings, and food cravings. PMDD is a more severe form of PMS characterized by increased symptom severity that peaks during the luteal phase and tends to improve or resolve shortly after...
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Related Experiment Video

Updated: May 26, 2025

Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants
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Association between maternal overprotection and premenstrual disorder: a machine learning based exploratory study.

Kaori Tsuyuki1, Miho Egawa2, Takuma Ohsuga1

  • 1Department of Gynecology and Obstetrics, Kyoto University Graduate School of Medicine, Kyoto, Japan.

Biopsychosocial Medicine
|February 25, 2025
PubMed
Summary

Maternal overprotection and parental bonding experiences may be linked to premenstrual disorder (PMD). This machine learning study explored novel risk factors for PMD, finding significant associations with early life experiences.

Keywords:
Affective vulnerabilityMachine learning analysisOverprotectionParental bondingPremenstrual disorderPremenstrual dysphoric disorderPremenstrual syndrome

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Area of Science:

  • Psychiatry and psychology
  • Machine learning applications in healthcare
  • Reproductive health

Background:

  • Premenstrual disorder (PMD) pathogenesis is complex, potentially involving emotional cognition and memory.
  • Mechanisms linking these factors to PMD remain unclear.
  • This study investigates non-traditional risk factors for PMD.

Purpose of the Study:

  • To explore novel risk factors for premenstrual disorder (PMD) using machine learning.
  • To identify factors contributing to PMD beyond typical considerations.
  • To investigate the role of early life experiences in PMD development.

Main Methods:

  • A predictive model for PMD was developed using questionnaire and heart rate variability data from 60 participants.
  • PMD status was defined using the Japanese Premenstrual Symptom Screening Tool.
  • Shapley Additive exPlanations (SHAP) assessed feature contributions to the predictive model.

Main Results:

  • The predictive model achieved an AUC of 0.90.
  • Among the top 20 features, six related to maternal bonding were identified.
  • Four of these maternal bonding features were linked to overprotection.

Conclusions:

  • Parental bonding experiences, particularly maternal overprotection, may be associated with premenstrual disorder (PMD).
  • These findings suggest early life factors could play a role in PMD development.