Dynamics of menopause from deconvolution of millions of lab tests

Menopause reshapes female physiology, yet its full temporal footprint is obscured by uncertainty in the age of the final menstrual period (FMP). Here we analyse cross-sectional data on 300 million laboratory tests from more than a million women in two population-scale cohorts (Israel-Clalit and US-NHANES). We apply a deconvolution algorithm inspired by astronomical image"de-blurring"to align each test to time-from-FMP rather than chronological age. Nearly every assay - spanning endocrine, bone, hepatic, lipid, osmolality, inflammatory and muscular systems - exhibits a jump at FMP that is absent in males and highly concordant between cohorts. Jumps were largest in the sex hormones, followed by bone, toxins, red blood cells, liver, iron, lipids, kidney, and muscle. Changes are mostly detrimental except iron indices and anemia that improve post-menopause, and depression scores that spike only transiently. Hormone-replacement therapy attenuates many of the step-like changes. Sex hormone dysregulation occurs more than 10 years prior to FMP. These findings reveal the step-like dysregulation across physiology caused by loss of sex hormones and establish deconvolution as a general strategy for disentangling age-related transitions in large, noisy datasets.