OR20-01 Machine Learning-based Steroid Metabolome Analysis In Women With Polycystic Ovary Syndrome Reveals Three Distinct Androgen Excess Subtypes With Different Metabolic Risk Profiles.
Authors: Rocha Thaís P , Melson Eka , Veen Roland J , Abdi Lida , McDonnell Tara , Tandl Veronika , Hawley James , Wittemans Laura , Anthony Amarah , Gilligan Lorna , Shaheen Fozia , Kempegowda Punith , Gillett Caroline D T , Cussen Leanne , Missbrenner Cornelia , Lajeunesse-Trempe Fannie , Gleeson Helena , Rees Aled , Robinson Lynne , Jayasenna Channa , Randeva Harpal S , Randeva Harpal Singh , Dimitriadis Georgios K , Gomes Larissa G , Sitch Alice , Vradi Eleni , Taylor Angela E , O'Reilly Michael W , Obermayer-Pietsch Barbara Maria , Biehl Michael , Arlt Wiebke 
Published: October 2023

Abstract
Disclosure: T.P. Rocha: None. E. Melson: None. R.J. Veen: None. L. Abdi: None. T. McDonnell: None. V. Tandl: None. J. Hawley: None. L. Wittemans: None. A. Anthony: None. L. Gilligan: None. F. Shaheen: None. P. Kempegowda: None. C.D. Gillett: None. L. Cussen: None. C. Missbrenner: None. F. Lajeunesse-Trempe: None. H. Gleeson: None. A. Rees: None. L. Robinson: None. C. Jayasenna: None. H.S. Randeva: None. H.S. Randeva: None. G.K. Dimitriadis: None. L.G. Gomes: None. A. Sitch: None. E. Vradi: Employee; Self; Bayer Schering Pharma. A.E. Taylor: None. M.W. O'Reilly: None. B.M. Obermayer-Pietsch: None. M. Biehl: None. W. Arlt: None.
Introduction: Polycystic ovary syndrome affects 10% of women and is associated with an increased risk of type 2 diabetes, hypertension, and fatty liver disease. Androgen excess, a defining feature of PCOS, has been implicated as a driver of metabolic risk. Adrenal-derived 11-oxygenated androgens are a component of PCOS-related androgen excess and are preferentially activated in adipose tissue. Here, we aimed to identify PCOS sub-types with distinct steroid metabolomes and compare their cardiometabolic risk parameters. Methods: We prospectively recruited 488 treatment-naïve women with PCOS fulfilling diagnostic Rotterdam criteria [median age 28 (IQR 24-32) years; BMI 27.5 (22.4-34.6) kg/m2] at eight centres in the UK & Ireland (n=208), Austria (n=242), and Brazil (n=38). Participants underwent a standardised assessment, including insulin sensitivity at baseline (HOMA-IR) and across a 2-h oGTT (Matsuda ISI). We used tandem mass spectrometry to measure 11 androgenic serum steroids, including classic and 11-oxygenated androgens. Results were analysed by unsupervised k-means clustering, followed by a comparison of clinical and metabolic phenotype parameters. Results: Machine learning analysis identified three distinct androgen metabolomes: a cluster with mainly gonadal-derived androgen excess (GAE; testosterone, dihydrotestosterone; 21.5% of women), a cluster with mainly adrenal-derived androgen excess (AAE; 11-oxygenated androgens; 21.7%), and a cluster with comparably mild androgen excess (MAE; 56.8%). Age and BMI were similar between groups. Compared to GAE and MAE, the AAE cluster had the highest rates of hirsutism (76.4% vs 67.6% vs 59.9%) and female pattern hair loss (32.1% vs 14.3% vs 21.7%). The AAE cluster had significantly increased insulin resistance as indicated by higher fasting insulin,120min insulin and HOMA-IR, and lower ISI than GAE and MAE clusters (all p&lt;0.01). The AAE cluster had a 2-3fold higher prevalence of impaired glucose tolerance and newly diagnosed type 2 diabetes. Conclusion: Unsupervised cluster analysis revealed three distinct androgen excess subtypes in PCOS. Women within the adrenal androgen excess cluster had a significantly higher prevalence of insulin resistance, impaired glucose tolerance and type 2 diabetes. These results implicate 11-oxygenated androgens as drivers of metabolic risk in PCOS and provide proof-of-principle for an androgen-based stratification tool to guide preventative and therapeutic strategies in PCOS.
Presentation Date: Saturday, June 17, 2023