Measuring associations between hormonal entropy, the prevalence of vasomotor symptoms, and menstrual dynamics.

Abstract

This study investigates whether deviations in the regularity/complexity of urinary sex hormones relative to textbook 'gold standard' (GS) menstrual cycle patterns are associated with vasomotor symptom (VMS) occurrence and how these relationships might relate to differences in hormonal profiles. 549 midlife women provided daily urine-based measurements of follicle-stimulating hormone (FSH), estrogen conjugates (E1C), pregnanediol glucuronide (PDG), and luteinizing hormone (LH) over a complete menstrual cycle. Distribution and fuzzy entropy (DistEn, FuzzEn) were used to gauge hormone regularity/complexity, emphasizing structural complexity and temporal unpredictability respectively. Entropy metrics were classified as being elevated or lowered relative to the GS and then evaluated in relation to VMS prevalence. These same entropy classifications were used to evaluate hormone profiles by referencing 11 dynamics indicative of normal or reproductively aging cycles. Elevated entropy was positively associated with the likelihood of VMS for PDG-DistEn and E1C-DistEn and negatively associated for PDG-FuzzEn, E1C-FuzzEn, and LH-FuzzEn. Lowered entropy was negatively associated with VMS likelihood for LH-FuzzEn and PDG-FuzzEn and positively associated for FSH-FuzzEn and E1C-DistEn. Entropy analysis provides useful insight into menstrual cycle dynamics and their associations with VMS. Specifically, entropy can identify different underlying states of hormonal dysregulation associated with increased VMS occurrence, potentially providing insights into VMS causes and treatments. Furthermore, entropy metrics for PDG show potential in gauging degrees of reproductive aging, which could help in addressing health risks associated with late/early menopause. Finally, entropy may contribute towards efforts in understanding how a woman's VMS experience will progress through the menopause transition.