Identifying menstrual metrics as personal health markers: Age trends and individual footprints in temperature across 5674 cycles.

Abstract

The menstrual cycle is a rich yet underused source of physiological information. To address this, we developed an open-source tool called WAVES (women's health assessment through variability in endocrine-related signals) that leverages physiological signals to extract menstrual cycle metrics and facilitate biomarker discovery. We tested it on basal body temperature data from 5674 nonconceptive cycles from 753 participants aged 18 to 42 years. We identified multiple associations between aging and menstrual metrics changes, including higher average temperatures, shorter cycles, and decrease in regularity across multiple metrics. In addition, values and cycle-to-cycle regularity of several metrics capturing temperature level and temporal structure of the cycle showed moderate to strong within-individual stability (ICC > 0.5). This work suggests that the WAVES algorithm can be used for advancing digital biomarker discovery and highlights the relevance of a personalized approach in the development of next-generation tools for women's health.