Dynamics of menopause from deconvolution of millions of lab tests

TL;DR

This study uses a novel deconvolution method on millions of lab tests to map the physiological changes during menopause, revealing widespread step-like shifts across multiple systems and early hormone dysregulation.

Contribution

Introduces a deconvolution algorithm to align lab test data to time-from-FMP, uncovering detailed physiological dynamics of menopause across large populations.

Findings

Widespread physiological jumps at FMP across multiple systems.

Hormone dysregulation occurs over a decade before FMP.

Hormone-replacement therapy mitigates many menopausal changes.

Abstract

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.