ECE2023 Oral Communications Oral Communications 11: Late Breaking (6 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
Eka Melson 1 , Thais P. Rocha 1,2 , Roland J. Veen 3 , Lida Abdi 1 , Tara Mcdonnell 4 , Veronika Tandl 5 , James M. Hawley 1,6 , Laura B.L. Wittemans 7 , Amarah V. Anthony 1 , Lorna C. Gilligan 1 , Fozia Shaheen 1 , Punith Kempegowda 1,8 , Caroline D.T Gillett 1,9 , Leanne Cussen 4 , Cornelia Missbrenner 5 , Fannie Lajeunesse-Trempe 10 , Helena Gleeson 11 , D. Aled Rees 12 , Lynne Robinson 13 , Channa Jayasena 14 , Harpal S. Randeva 15 , Georgios K. Dimitriadis 10,16 , Larissa Gomes 2 , Alice J. Sitch 8,9 , Eleni Vradi 17 , Angela E. Taylor 1 , Michael W. O’Reilly 4 , Barbara Obermayer-Pietsch 5 , Michael Biehl 18 & Wiebke Arlt 1,9,19,20
1University of Birmingham, Institute of Metabolism and Systems Research, United Kingdom; 2Disciplina de Endocrinologia e Metabologia, Faculdade de Medicina da Universidade de São Paulo, , São Paulo,; 3University of Groningen, Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence; 4Royal College of Surgeons in Ireland, Endocrinology Research Group, Department of Medicine, Dublin,; 5Medical University of Graz, Division of Endocrinology and Diabetology, Department of Internal Medicine, Graz,; 6Wythenshawe Hospital, Manchester University NHS Foundation Trust, Department of Clinical Biochemistry, Manchester,; 7University of Oxford, Big Data Institute, Oxford,; 8University of Birmingham, Institute of Applied Health Research, Birmingham,; 9National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, United Kingdom; 10King’s College Hospital NHS Foundation Trust, Department of Endocrinology, London, United Kingdom; 11Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Department of Endocrinology, Birmingham, United Kingdom; 12Cardiff University, Neuroscience and Mental Health Research Institute, School of Medicine , Cardiff, United Kingdom; 13Birmingham Women’s Hospital, Birmingham Women’s and Children’s Hospital NHS Foundation Trust, Birmingham; 14Imperial College London, Department of Metabolism, Digestion and Reproduction, London, United Kingdom; 15University of Warwick, Warwick Medical School, Coventry, United Kingdom; 16King’s College London, Obesity, Type 2 Diabetes and Immunometabolism Research Group, Faculty of Cardiovascular and Metabolic Medicine & Sciences, School of Life Course Sciences, London, United Kingdom; 17Bayer AG, Berlin, Germany; 18University of Groningen, Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Gronigen, Netherlands; 19Imperial College London, Institute of Clinical Sciences, Faculty of Medicine, London, United Kingdom; 20Medical Research Council London Institute of Medical Sciences (MRC LMS), London, United Kingdom
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/m2] 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,120 min insulin and HOMA-IR, and lower ISI than GAE and MAE clusters (all P<0.01). The AAE cluster also had a 2-3fold higher prevalence of impaired glucose tolerance and newly diagnosed type 2 diabetes.
Conclusion: Unsupervised cluster analysis revealed three PCOS subtypes with distinct androgen excess profiles. 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 major drivers of metabolic risk in PCOS and provide proof-of-principle for an androgen-based stratification tool that could guide future preventative and therapeutic strategies in women with PCOS.
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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