Risk of insulin resistance and metabolic syndrome in women with hyperandrogenemia: A comparison between pcos phenotypes and beyond

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in premeno-pausal women, with a wide spectrum of possible phenotypes, symptoms and sequelae according to the current clinical definition. However, there are women who do not fulfill at least two out of the three commonly used “Rotterdam criteria” and their risk of developing type 2 diabetes or obesity later in life is not defined. Therefore, we addressed this important gap by conducting a retrospective analysis based on 750 women with and without PCOS. We compared four different PCOS pheno-types according to the Rotterdam criteria with women who exhibit only one Rotterdam criterion and with healthy controls. Hormone and metabolic differences were assessed by analysis of vari-ance (ANOVA) as well as logistic regression analysis. We found that hyperandrogenic women have per se a higher risk of developing insulin resistance compared to phenotypes without hyperandro-genism and healthy controls. In addition, hyperandrogenemia is associated with developing insulin resistance also in women with no other Rotterdam criterion. Our study encourages further diagnostic and therapeutic approaches for PCOS phenotypes in order to account for varying risks of developing metabolic diseases. Finally, women with hyperandrogenism as the only symptom should also be screened for insulin resistance to avoid later metabolic risks

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

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