Characterizing daily urinary hormone profiles for women at midlife using functional data analysis

Area of Science:

• Reproductive endocrinology
• Women's health research
• Biostatistics

Background:

• The menopausal transition involves complex hormonal changes.
• Previous analyses often relied on single hormone values, limiting insight into dynamic patterns.
• The Study of Women's Health Across the Nation (SWAN) collected extensive longitudinal data.

Purpose of the Study:

• To apply novel analytic techniques to daily hormone data from the SWAN cohort.
• To confirm existing knowledge and uncover new patterns in hormone profiles during the menopausal transition.
• To investigate the relationship between menstrual cycle length and hormonal fluctuations.

Main Methods:

• Utilized functional data analysis on urinary hormone levels (estrogen, progesterone, FSH, LH) from 572 pre- and early-perimenopausal women.
• Analyzed hormone patterns across entire menstrual cycles or up to 50 days.
• Compared hormone variability using functional data analysis versus single-value comparisons.

Main Results:

• Functional data analysis revealed asymmetries in hormone patterns related to menstrual cycle length.
• Longer cycles were associated with increased dyssynchrony between follicle-stimulating hormone (FSH) and luteinizing hormone (LH) during the luteal phase.
• Identified hormonal pattern variations not detectable with traditional single-point measurements.

Conclusions:

• Functional data analysis is a powerful tool for understanding dynamic hormonal changes during the menopausal transition.
• Menstrual cycle length is linked to specific patterns of hormonal dyssynchrony, particularly between FSH and LH.
• These findings enhance our understanding of reproductive aging and hormonal regulation in women.