Assessing Time to Menopause Using Daily Hormone Patterns with Emphasis on PDG

Overview

This study demonstrates that pregnanediol glucuronide (PDG), analyzed through novel entropy and dynamic time warping metrics, is a powerful predictor of time to final menstrual period (FMP). PDG's trajectory patterns uniquely identify risk groups for menopause timing, independent of traditional hormonal and demographic factors.

Background

Menopause, defined by 12 months without menstruation, marks a critical health transition linked to cardiovascular, bone, and mortality outcomes. Traditional predictors of time to FMP include follicle-stimulating hormone (FSH), estrogen conjugates (E1C), and antimüllerian hormone (AMH), but these rely on cross-sectional or linear analyses. This study introduces a novel approach using daily hormone measurements over a single cycle, characterizing hormone trajectories by entropy (Fuzzy entropy) and deviation from a premenopausal gold standard (dynamic time warping).

Data Highlights

Key Findings

• PDG trajectories mapped by entropy and dynamic time warping effectively stratify women into groups with statistically different times to FMP.
• PDG outperforms traditional hormones (FSH, E1C, LH) in predicting ovarian aging when analyzed via the DTW/FuzzEn framework.
• Cluster groups based on PDG remain significant predictors of FMP timing after adjusting for age, BMI, smoking, financial hardship, AMH, and cycle length.
• PDG levels and trajectory complexity decrease as women approach menopause, consistent with diminished luteal-phase activity.
• The combined use of entropy and dynamic time warping provides a novel, robust analytical framework for assessing ovarian aging from daily hormone data.

Clinical Implications

Incorporating PDG trajectory analysis into clinical assessments could improve prediction of menopause timing, enabling earlier identification of women at risk for adverse health outcomes related to early or late menopause. This approach supports personalized preventive care by integrating dynamic hormone pattern analysis beyond static hormone levels.

Conclusion

This study validates a novel analytical framework using PDG daily hormone patterns to predict time to menopause, highlighting PDG as a valuable biomarker for ovarian aging. The findings encourage broader use of dynamic hormone trajectory analyses in menopausal research and clinical practice.

References

1. Study of Women's Health Across the Nation (SWAN) -- Longitudinal multisite study on menopausal transition
2. Santoro et al. -- Characterization of early luteal activity menstrual cycles