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

Descrizione del progetto

Mappatura dello spettro di sensibilità ormonale femminile

Le condizioni di sensibilità ormonale, quali la sindrome premestruale, gli effetti collaterali dei trattamenti ormonali e i sintomi della menopausa interessano miliardi di donne in tutto il mondo. Queste condizioni riflettono la diversa risposta individuale alle fluttuazioni naturali degli estrogeni e del progesterone. Il progetto UNDERwHER, finanziato dal CER, si propone di ridefinire il concetto di spettro di sensibilità ormonale (HSS) combinando i dati genetici, le cartelle cliniche e i dati sui sintomi auto-riferiti provenienti dalla Biobanca Estone. L’obiettivo è individuare i distinti schemi di sensibilità ormonale, scoprire i fattori genetici determinanti e le vie biologiche alla base, nonché definire i collegamenti con i risultati sanitari a lungo termine. I risultati mirano a far progredire la comprensione della salute ormonale femminile e a consentire trattamenti ormonali più personalizzati ed efficaci.

Obiettivo

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.