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

    May 2023 in “ Endocrine Abstracts ”
    Eka Melson, Thais P. Rocha, Roland J. Veen, Lida Abdi, Tara McDonnell, Veronika Tandl, James Hawley, Laura B. L. Wittemans, Amarah V. Anthony, Lorna C Gilligan, Fozia Shaheen, Punith Kempegowda, Caroline D. T Gillett, Leanne Cussen, Cornelia Missbrenner, Fannie Lajeunesse‐Trempe, Helena Gleeson, Rees D. Aled, Lynne Robinson, Channa Jayasena, Harpal Randeva, Georgios K. Dimitriadis, Larissa Garcia Gomes, Alice Sitch, Eleni Vradi, Angela E. Taylor, Michael O’Reilly, Barbara Obermayer-Pietsch, Michael Biehl, Wiebke Arlt
    Studysummary This study identified three subgroups of women with PCOS with distinct androgen profiles, finding that the subgroup with adrenal-derived androgen excess had the highest insulin resistance and rates of hirsutism and hair loss.
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    The study analyzed 488 women with polycystic ovary syndrome (PCOS) to identify subtypes based on androgen profiles and their associated metabolic risks. Using machine learning, researchers identified three distinct subgroups: one with gonadal-derived androgen excess (21.5%), another with adrenal-derived androgen excess (21.7%), and a third with mild androgen excess (56.8%). The adrenal androgen excess group exhibited the highest rates of hirsutism, female pattern hair loss, insulin resistance, impaired glucose tolerance, and type 2 diabetes. The findings suggested that 11-oxygenated androgens were significant contributors to metabolic risk in PCOS, highlighting the potential for an androgen-based stratification tool to inform prevention and treatment strategies.
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