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ea0090oc11.2 | Oral Communications 11: Late Breaking | ECE2023

Machine learning-based steroid metabolome analysis reveals three distinct subtypes of polycystic ovary syndrome and implicates 11-oxygenated androgens as major drivers of metabolic risk

Melson Eka , Rocha Thais P. , Veen Roland J. , Abdi Lida , Mcdonnell Tara , Tandl Veronika , Hawley James M. , Wittemans Laura B.L. , Anthony Amarah V. , Gilligan Lorna C. , Shaheen Fozia , Kempegowda Punith , Gillett Caroline D.T , Cussen Leanne , Missbrenner Cornelia , Lajeunesse-Trempe Fannie , Gleeson Helena , Aled Rees D. , Robinson Lynne , Jayasena Channa , Randeva Harpal S. , Dimitriadis Georgios K. , Gomes Larissa , Sitch Alice J. , Vradi Eleni , Taylor Angela E. , O'Reilly Michael W. , Obermayer-Pietsch Barbara , Biehl Michael , Arlt Wiebke

Introduction: Polycystic ovary syndrome affects 10% of women and comes with a 2-3fold increased risk of type 2 diabetes, hypertension, and fatty liver disease. Androgen excess, a cardinal feature of PCOS, has been implicated as a major contributor to metabolic risk. Adrenal-derived 11-oxygenated androgens represent an important component of PCOS-related androgen excess and are preferentially activated in adipose tissue. We aimed to identify PCOS sub-types with distinct androge...

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Endocrine Abstracts

Machine learning-based steroid metabolome analysis reveals three distinct subtypes of polycystic ovary syndrome and implicates 11-oxygenated androgens as major drivers of metabolic risk

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