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ea0090oc11.2 | Oral Communications 11: Late Breaking | ECE2023

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

Melson Eka , Rocha Thais P. , Veen Roland J. , Abdi Lida , Mcdonnell Tara , Tandl Veronika , Hawley James M. , Wittemans Laura B.L. , Anthony Amarah V. , Gilligan Lorna C. , Shaheen Fozia , Kempegowda Punith , Gillett Caroline D.T , Cussen Leanne , Missbrenner Cornelia , Lajeunesse-Trempe Fannie , Gleeson Helena , Aled Rees D. , Robinson Lynne , Jayasena Channa , Randeva Harpal S. , Dimitriadis Georgios K. , Gomes Larissa , Sitch Alice J. , Vradi Eleni , Taylor Angela E. , O'Reilly Michael W. , Obermayer-Pietsch Barbara , Biehl Michael , Arlt Wiebke

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 androge...

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ea0094oc4.1 | Reproductive Endocrinology | SFEBES2023

Unsupervised steroid metabolome cluster analysis to dissect androgen excess and metabolic dysfunction in 488 women with polycystic ovary syndrome – results from the prospective DAISy-PCOS study

Melson Eka , Rocha Thais P. , Veen Roland J. , Abdi Lida , McDonnell Tara , Tandl Veronika , Hawley James M. , Wittemans Laura B. L. , Anthony Amarah V. , Gilligan Lorna C. , Shaheen Fozia , Kempegowda Punith , Gillett Caroline D.T. , Cussen Leanne , Missbrenner Cornelia , Lajeunesse-Trempe Fannie , Gleeson Helena , Rees Aled , Robinson Lynne , Jayasena Channa , Randeva Harpal S. , Dimitriadis Georgios K. , Gomes Larissa G. , J. Sitch Alice , Vradi Eleni , Obermayer-Pietsch Barbara , O'Reilly Michael W. , Taylor Angela E. , Biehl Michael , Arlt Wiebke

Introduction: Polycystic ovary syndrome (PCOS) affects 10% of women and is associated with a 2-3fold risk of type 2 diabetes (T2D), hypertension, fatty liver and cardiovascular disease. Androgen excess has been implicated as a major contributor to metabolic risk in PCOS. We aimed to identify PCOS sub-types with distinct androgen profiles and compare their cardiometabolic risk.Methods: We cross-sectionally studied 488 tre...

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ea0094oc5.1 | Adrenal and Cardiovascular | SFEBES2023

Urine steroid metabolomics as a diagnostic tool in endocrine hypertension

Prete Alessandro , Abdi Lida , Suntornlohanakul Onnicha , Lang Katharina , Riancho Julien , Lazkani Aida , Larsen Casper K. , Gimenez-Roqueplo Anne-Paule , Pecori Alessio , Tetti Martina , Monticone Silvia , Muller Lisa M. , Adolf Christian , Timmers Henri J.L.M. , Hampson Stephanie , Eisenhofer Graeme , Ceccato Filippo , Beuschlein Felix , Kabat Marek , Bertherat Jerome , Dennedy Conall , Davies Eleanor , Deinum Jaap , Reincke Martin , Paolo Rossi Gian , Mulatero Paolo , Amar Laurence , Zennaro Maria-Christina , Sitch Alice J. , Tino Peter , Biehl Michael , Taylor Angela E. , Arlt Wiebke

Background: Hypertension affects more than 30% of the adult population worldwide and is a major cardiovascular risk factor. Identifying secondary causes of hypertension is key to offering targeted treatment and mitigating adverse health outcomes. We tested the performance of urine steroid metabolomics (USM), the computational analysis of 24-hour urine steroid metabolome data by machine learning, for diagnosing endocrine hypertension.<str...

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ea0086oc4.4 | Adrenal and Cardiovascular | SFEBES2022

Steroid and global metabolome in benign adrenal tumours with mild autonomous cortisol secretion: analysis by mass spectrometry and machine learning to understand metabolic risk

Prete Alessandro , Abdi Lida , Canducci Marco , Taylor Angela E. , Bancos Irina , Gilligan Lorna C. , Jenkinson Carl , Albors-Zumel Ariadna , van den Brandhof Elina , Zhang Yuanqing , Chortis Vasileios , Tsagarakis Stylianos , Lang Katharina , Macech Magdalena , Delivanis Danae A. , Pupovac Ivana D. , Reimondo Giuseppe , Marina Ljiljana V. , Deutschbein Timo , Balomenaki Maria , O'Reilly Michael W. , Bednarczuk Tomasz , Zhang Catherine D. , Dusek Tina , Diamantopoulos Aristidis , Asia Miriam , Kondracka Agnieszka , Li Dingfeng , Masjkur Jimmy R. , Quinkler Marcus , Ueland Grethe AE. , Conall Dennedy M. , Beuschlein Felix , Tabarin Antoine , Fassnacht Martin , Ivovic Miomira , Terzolo Massimo , Kastelan Darko , Young Jr William F. , Manolopoulos Konstantinos M. , Ambroziak Urszula , Vassiliadi Dimitra A. , Sitch Alice J. , Tino Peter , Biehl Michael , Dunn Warwick B. , Arlt Wiebke

Background: Benign adrenal tumours are found in 3-10% of adults and can be non-functioning (NFAT) or associated with adrenal hormone excess. Analysing 1305 prospectively recruited patients with benign adrenal tumours, we recently demonstrated that 45% had mild autonomous cortisol secretion (MACS), i.e. biochemical cortisol excess without signs of Cushings syndrome (CS). MACS increases the prevalence and severity of hypertension and type 2 diabetes (Ann Int Med. 2022 Doi:...

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Endocrine 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

Unsupervised steroid metabolome cluster analysis to dissect androgen excess and metabolic dysfunction in 488 women with polycystic ovary syndrome – results from the prospective DAISy-PCOS study

Urine steroid metabolomics as a diagnostic tool in endocrine hypertension

Steroid and global metabolome in benign adrenal tumours with mild autonomous cortisol secretion: analysis by mass spectrometry and machine learning to understand metabolic risk

BiosciAbstracts