Cycle tracking, redefined—accurate insights from a single blood draw.
A Nature Medicine study identified 198 proteins that vary across the menstrual cycle and developed a proteomic score capable of accurately predicting cycle timing. The findings could support more precise cycle tracking, personalized drug dosing, and improved understanding of mood-related changes.
Historically, clinicians have relied on patient-reported cycle dates or hormonal profiling to estimate menstrual cycle phase. While these approaches provide a useful starting point for clinical decision-making, due to the subjective nature of self-reporting and the variability introduced by irregular cycles, their accuracy is limited. There is a growing need for a more objective, robust, and precise measure of cycle timing.
A recent study published in Nature Medicine reveals how the body's molecular profile shifts throughout the menstrual cycle, providing a powerful new window into cycle biology.
As part of the UK Biobank plasma proteomics project, thousands of participants underwent untargeted plasma proteomic profiling using the Olink proximiy extension assay https://olink.com/. Researchers from Denmark, analysed data from female participants under the age of 55 with regular menstrual cycle lenths (21–35 days) to assess associations between circulating protein levels and cycle day.
A total of 2,900 proteins were quantified across the menstrual cycle, of which 198 showed significant associations with cycle timing. Expression patterns of these proteins varied systematically across cycle phases, enabling the identification of four distinct clusters.
Cluster 1 comprised proteins with peak expression on day 1 of menstruation, including MMP10, PAEP, and LEFTY. Cluster 2 showed highest expression during the early follicular phase, including CGA, FSHB, and TNFS10. Cluster 3 peaked in the late follicular phase, with proteins such as STC2, SFRP4, and CLLF1. Finally, Cluster 4 demonstrated peak expression during the mid-to-late luteal phase, including PROK1, RLN1, and CXCL13.
Data from 70% of participants was used as a training set to develop a proteomic score, which was then validated in the remaining cohort. The score showed a strong association with cycle day, enabling a robust molecular readout of menstrual cycle progression.
This powerful approach may pave way for more precise non-invasive cycle-based drug dosing and substantially support understanding of phase-related mood changes.
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Reference: Riishede, I., Rode, L., Lundegaard, P.R. et al. Plasma proteomic signature of the human menstrual cycle. Nat Med (2026). https://doi.org/10.1038/s41591-026-04326-5
