Population-scale reconstruction of menopause dynamics reveals a physiological cliff at the final menstrual period.
Menopause reshapes female physiology, yet current understanding is limited by small longitudinal cohorts that characterize it as a gradual transition. Large-scale biomedical datasets remain underutilized because the final menstrual period (FMP) age is rarely recorded. Here we present a computational framework, inspired by astronomy, that reconstructs FMP-anchored trajectories from cross-sectional data by removing the blurring caused by variation in FMP age across individuals. Applying this to tw
Menopause reshapes female physiology, yet current understanding is limited by small longitudinal cohorts that characterize it as a gradual transition. Large-scale biomedical datasets remain underutilized because the final menstrual period (FMP) age is rarely recorded. Here we present a computational framework, inspired by astronomy, that reconstructs FMP-anchored trajectories from cross-sectional data by removing the blurring caused by variation in FMP age across individuals. Applying this to two independent cohorts totaling 300 million laboratory tests revealed a step-like physiological cliff at the FMP including endocrine, skeletal, hepatic, renal and lipid systems. These discontinuities are absent in male individuals, are concordant across populations and appear markedly attenuated by menopausal hormone therapy. Dysregulation began more than a decade before FMP. These findings support a systemic, quantifiable view of menopause and provide a generalizable strategy for recovering hidden biological trajectories applicable to other transitions such as puberty or disease progression.
