TY - CONF
T1 - 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
AU - Melson, Eka
AU - Rocha, Thais P.
AU - Veen, Roland J.
AU - Abdi, Lida
AU - Mcdonnell, Tara
AU - Tandl, Veronika
AU - Hawley, James M.
AU - Wittemans, Laura B.L.
AU - Anthony, Amarah V.
AU - Gilligan, Lorna C.
AU - Shaheen, Fozia
AU - Kempegowda, Punith
AU - Gillett, Caroline D.T
AU - Cussen, Leanne
AU - Missbrenner, Cornelia
AU - Lajeunesse-Trempe, Fannie
AU - Gleeson, Helena
AU - Aled, Rees D.
AU - Robinson, Lynne
AU - Jayasena, Channa
AU - Randeva, Harpal S.
AU - Dimitriadis, Georgios K.
AU - Gomes, Larissa
AU - Sitch, Alice J.
AU - Vradi, Eleni
AU - Taylor, Angela E.
AU - O'Reilly, Michael W.
AU - Obermayer-Pietsch, Barbara
AU - Biehl, Michael
AU - Arlt, Wiebke
PY - 2023/2
Y1 - 2023/2
N2 - 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 androgen profiles and compare their cardiometabolic risk parameters. Methods: We cross-sectionally studied 488 treatment-naïve women with PCOS diagnosed according to Rotterdam criteria [median age 28 (IQR 24-32) years; BMI 27.5 (22.4-34.6) kg/m 2 ] prospectively recruited at eight centres in the UK & Ireland (n=208), Austria (n=242) and Brazil (n=38). All participants underwent a standardised assessment including clinical history, anthropometric measurements, fasting bloods and a 2-hour oral glucose tolerance test. We quantified 11 androgenic serum steroids, including classic and 11-oxygenated androgens, using a validated multi-steroid profiling tandem mass spectrometry assay. We measured serum insulin to calculate HOMA-IR and the Matsuda insulin sensitivity index (ISI). Steroid data were analysed by unsupervised k-means clustering, followed by statistical analysis of differences in clinical phenotype and metabolic parameters. Results: Machine learning analysis identified three stable subgroups of women with PCOS with minimal overlap and distinct steroid metabolomes: a cluster characterised by mainly gonadal-derived androgen excess (testosterone, dihydrotestosterone; GAE cluster; 21.5% of women), a cluster with predominantly adrenal-derived androgen excess (11-oxygenated androgens; AAE cluster; 21.7%), and a cluster with comparably mild androgen excess (MAE cluster; 56.8%). Age and BMI were similar between groups. As 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 values for fasting insulin, 120min insulin and HOMA-IR, and lower ISI than GAE and MAE clusters (all p
AB - 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 androgen profiles and compare their cardiometabolic risk parameters. Methods: We cross-sectionally studied 488 treatment-naïve women with PCOS diagnosed according to Rotterdam criteria [median age 28 (IQR 24-32) years; BMI 27.5 (22.4-34.6) kg/m 2 ] prospectively recruited at eight centres in the UK & Ireland (n=208), Austria (n=242) and Brazil (n=38). All participants underwent a standardised assessment including clinical history, anthropometric measurements, fasting bloods and a 2-hour oral glucose tolerance test. We quantified 11 androgenic serum steroids, including classic and 11-oxygenated androgens, using a validated multi-steroid profiling tandem mass spectrometry assay. We measured serum insulin to calculate HOMA-IR and the Matsuda insulin sensitivity index (ISI). Steroid data were analysed by unsupervised k-means clustering, followed by statistical analysis of differences in clinical phenotype and metabolic parameters. Results: Machine learning analysis identified three stable subgroups of women with PCOS with minimal overlap and distinct steroid metabolomes: a cluster characterised by mainly gonadal-derived androgen excess (testosterone, dihydrotestosterone; GAE cluster; 21.5% of women), a cluster with predominantly adrenal-derived androgen excess (11-oxygenated androgens; AAE cluster; 21.7%), and a cluster with comparably mild androgen excess (MAE cluster; 56.8%). Age and BMI were similar between groups. As 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 values for fasting insulin, 120min insulin and HOMA-IR, and lower ISI than GAE and MAE clusters (all p
UR - https://www.mendeley.com/catalogue/83d919b9-628a-3eb8-b8f2-baeddb75bb46/
U2 - 10.1530/endoabs.90.oc11.2
DO - 10.1530/endoabs.90.oc11.2
M3 - Abstract
SP - OC11.2
T2 - 25th European Congress of Endocrinology
Y2 - 13 May 2023 through 16 May 2023
ER -