Polycystic ovary syndrome (PCOS) affects 5–10% of all women, yet no recent breakthroughs have improved how doctors predict or treat its metabolic complications. The condition is often seen as a reproductive disorder, but it also sharply raises the risk of type 2 diabetes, heart disease, and fatty liver disease—driven, the researchers suspect, by excess androgens. This project will test whether controlling androgen levels can improve metabolic function, and which specific androgen pathways matter most. The team will combine advanced physiology experiments, metabolomics, and machine learning to predict metabolic risk from a woman’s individual profile. If successful, the work could lead to personalised treatment strategies—identifying which PCOS patients are most at risk and targeting therapies to the right androgen pathway. That would shift PCOS management from a one-size-fits-all reproductive focus toward tailored metabolic care, potentially preventing long-term disease in millions of women.
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Polycystic ovary syndrome (PCOS) affects 5-10% of all women; androgen excess is one of its major diagnostic features. While often perceived as a reproductive disorder, PCOS is now emerging as a lifelong, complex metabolic disorder, with increased risk of type 2 diabetes, hypertension, cardiovascular disease, and, as recently documented, non-alcoholic fatty liver disease (NAFLD). However, there has been no recent breakthrough regarding risk stratification or therapeutic intervention in PCOS. Our work has provided evidence for a key role of androgens in the development of PCOS-related metabolic complications. We will combine cutting edge in vivo physiology techniques, state-of-the-art metabolomics and machine learning-based computational approaches to address our overarching hypothesis that androgens are major drivers of metabolic risk in PCOS. We will use an integrated set of in vitro, ex vivo and in vivo experimental medicine studies to test this hypothesis and answer our specific questions: Does the control of androgen excess improve metabolic function? What is the role of different androgen pathways in conveying metabolic risk? Can we integrate phenome and metabolome data by machine learning to predict metabolic risk in PCOS? Our overall aim is the identification of novel personalized approaches and therapeutic targets for patients with PCOS.
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