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  2. Risk Factors For Menstrual Dysfunction In Junior Athletes.
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Risk Factors for Menstrual Dysfunction in Junior Athletes.

Haruka Morota1, Noriko Motoki, Hotaru Fukushima

  • 1Department of Pediatrics, Shinshu University School of Medicine, Matsumoto, Japan.

Clinical Journal of Sport Medicine : Official Journal of the Canadian Academy of Sport Medicine
|June 14, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study identified key clinical and biochemical factors linked to menstrual dysfunction in junior female athletes. Findings highlight potential risk predictors but require further research for clinical application.

Keywords:
adolescentbone mineral densityfemale athlete triadjunior athleteslow energy availabilitymenstrual dysfunction

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Area of Science:

  • Sports Medicine
  • Endocrinology
  • Adolescent Health

Background:

  • Menstrual dysfunction is a concern in junior female athletes.
  • Identifying risk factors is crucial for athlete health and performance.

Purpose of the Study:

  • To investigate risk factors associated with menstrual dysfunction in junior female athletes.
  • To compare clinical and biochemical parameters between athletes with and without menstrual dysfunction.

Main Methods:

  • Observational case-control study involving 61 junior female athletes.
  • Comparison of anthropometric, exercise, lifestyle, and blood data.
  • Logistic regression analysis to identify risk factors for menstrual dysfunction.

Main Results:

  • White blood cell count, blood urea nitrogen, free triiodothyronine, zinc, and estradiol were significantly associated with menstrual dysfunction.
  • Exercise energy expenditure and sleep time showed weak associations.
  • Overlap in laboratory values between groups was noted.

Conclusions:

  • Several clinical and biochemical variables are associated with menstrual dysfunction in junior female athletes.
  • Findings are exploratory and do not establish clinical screening utility.
  • Further prospective studies are needed to clarify mechanisms and develop risk prediction models.