Clinical Scorecard: Assessing Time to Menopause Through a Composite Analysis of Daily Sex Hormones: Emphasis on Pregnanediol Glucuronide (PDG)
At a Glance
Category
Detail
Condition
Menopausal transition and timing of final menstrual period (FMP)
Key Mechanisms
Daily hormone trajectories characterized by entropy (Fuzzy entropy) and deviation from stable cycle patterns (dynamic time warping distance), focusing on PDG as a predictor
Target Population
Mid-life women aged 42-52 years, premenopausal with at least one ovary, not using hormone therapy
Care Setting
Longitudinal clinical research and preventive care settings focusing on menopausal transition
Key Highlights
Timing of FMP is a critical indicator of overall health, linked to cardiovascular, bone, reproductive, and mortality outcomes.
PDG, analyzed via entropy and dynamic time warping metrics, uniquely predicts time to FMP beyond traditional hormones like FSH and AMH.
The novel DTW/FuzzEn analytical framework enables identification of latent risk groups for ovarian aging and menopause timing.
Guideline-Based Recommendations
Diagnosis
Use daily urinary hormone measurements, including PDG, LH, FSH, and estrogen conjugates, to assess ovarian aging.
Characterize hormone trajectories using Fuzzy entropy and dynamic time warping distance to identify deviations from stable premenopausal cycles.
Management
Incorporate PDG trajectory analysis into preventive care to better predict timing of menopause and associated health risks.
Consider known covariates such as age, BMI, smoking status, and financial hardship when evaluating menopausal risk profiles.
Monitoring & Follow-up
Perform longitudinal daily hormone sampling over menstrual cycles to monitor changes in PDG and other hormones.
Use cluster analysis of PDG trajectories to stratify patients by risk of imminent FMP.
Risks
Early menopause is associated with increased cardiovascular disease, bone density loss, and early mortality.
Late menopause increases risk for breast, ovarian, and uterine cancers.
Patient & Prescribing Data
Mid-life women undergoing menopausal transition, diverse racial/ethnic backgrounds
PDG trajectory analysis provides additional predictive power for timing of menopause, potentially guiding personalized preventive interventions.
Clinical Best Practices
Collect first-voided daily urine samples over a full menstrual cycle for accurate hormone trajectory assessment.
Apply combined entropy and dynamic time warping metrics to hormone data for comprehensive ovarian aging evaluation.
Integrate PDG analysis with traditional hormonal and demographic factors to improve prediction of final menstrual period timing.
