Understanding Women’s Health Through the Lens of Hormonal Sensitivity: Genetics, Mechanisms and Health Trajectories

Description du projet

Cartographier le spectre de sensibilité hormonale chez les femmes

Les troubles de la sensibilité hormonale, tels que le syndrome prémenstruel, les effets secondaires des traitements hormonaux et les symptômes de la ménopause, affectent des milliards de femmes dans le monde. Ces conditions reflètent la diversité des réactions des femmes aux fluctuations naturelles des œstrogènes et de la progestérone. Le projet UNDERwHER, financé par le CER, entend redéfinir le concept de spectre de sensibilité hormonale en combinant les données génétiques, les dossiers médicaux et les symptômes déclarés par les patientes dans la biobanque d’Estonie. L’objectif est d’identifier des modèles distincts de sensibilité aux hormones, de découvrir les déterminants génétiques et les voies biologiques qui les sous-tendent, et d’explorer les liens avec les effets à long terme sur la santé. Les résultats devraient améliorer notre compréhension de la santé hormonale des femmes et permettre des traitements hormonaux plus personnalisés et plus efficaces.

Objectif

Hormonal sensitivity conditions such as premenstrual syndrome, hormonal medication side effects, and (peri)menopausal symptoms affect billions of women but remain poorly understood. These conditions share a common factor: fluctuations in estrogen and progesterone levels that interact with endocrine, neurological, and metabolic systems, creating a continuum of individual hormone sensitivity. UNDERwHER aims to redefine the hormone sensitivity spectrum (HSS) by addressing its biological underpinnings and health impact.

UNDERwHER builds on the applicant's recent work on 42 female reproductive health phenotypes, which demonstrated the value of genetics in understanding women’s health. By leveraging the data and opportunities in Estonian Biobank (N=140K women), we will combine self-reported HSS phenotypes with longitudinal healthcare data and app-based menstrual tracking. This unparalleled dataset will be used to characterize HSS static and dynamic subtypes through advanced clustering and modeling. Genome-wide association studies and functional genomic tools will identify genetic variants and biological pathways underlying HSS, revealing shared genetic links with other health domains. To evaluate the wider health impact, we will map the associations between HSS and long-term health outcomes, and by integrating genetic risk with observational data we will inform personalized hormonal medication recommendations. Finally, genetics-informed cellular models will uncover the transcriptomic effects of hormonal fluctuations, providing mechanistic insights.

This research will help to i) provide a new concept on female hormonal health, ii) map the biological pathways linked to HSS to provide input for drug discovery, iii) propose evidence-based approaches for genetics-informed hormonal drug prescription, and iv) facilitate research on this topic by creating unique data resources and cellular models, with the ultimate goal of improving women's health and wellbeing.