SFEBES2025 Poster Presentations Late Breaking (68 abstracts)
Urine steroid metabolomics and timed urine steroid profiling for the diagnosis and differential diagnosis of cushing’s syndrome
Alessandro Prete 1,2,3,4 , Lida Abdi 5,6 , Joshua T. Bain 1,3 , Onnicha Suntornlohanakul 1,3,7 , Elisa Deflorenne 8,9 , Anne Blanchard 10 , Manuela Nestola 11 , Christina Pamporaki 12 , Irene Tizianel 13,14 , Elisabeth Nowak 15 , Jérôme Bertherat 16 , Dimitra A. Vassiliadi 17 , Irina Bancos 18 , M. Conall Dennedy 19 , Darko Kastelan 20,21 , Martin Fassnacht 22,23 , Paolo Mulatero 24 , Urszula Ambroziak 25 , Antoine Tabarin 26 , Marcus Quinkler 27 , Soraya Puglisi 28 , Jaap Deinum 29 , Grethe Æ. Ueland 30 , Livia Lenzini 31 , Felix Beuschlein 32,15 , Martin Reincke 15 , Filippo Ceccato 13,14 , Graeme Eisenhofer 33,12 , Salvatore M. Corsello 11,34,35 , Laurence Amar 36,9 , Maria-Christina Zennaro 37 , Niki Karavitaki 1,3,4 , Angela E. Taylor 1,3 & Wiebke Arlt 5,6
1Department of Metabolism and Systems Science, School of Medical Sciences, College of Medicine and Health, University of Birmingham, Birmingham, United Kingdom. 2National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, United Kingdom. 3Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, United Kingdom. 4Department of Endocrinology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. 5Medical Research Council Laboratory of Medical Sciences, London, United Kingdom. 6Institute of Clinical Sciences, Imperial College London, London, United Kingdom. 7Endocrinology and Metabolism Unit, Division of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand. 8Université Paris Cité, PARCC, INSERM, F-7500, Paris, France. 9Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Unité Hypertension artérielle, Paris, France. 10Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Centre d’Investigations Cliniques 9201, Paris, France. 11Unit of Endocrinology, Università Cattolica del Sacro Cuore, Rome, Italy. 12Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany. 13Department of Medicine DIMED, University of Padova, Padua, Italy. 14Endocrine Disease Unit, University-Hospital of Padova, Padua, Italy. 15Department of Medicine IV, LMU University Hospital, LMU, Munich, Germany. 16Department of Endocrinology, French Reference Center for Rare Adrenal Disorders, Hôpital Cochin, Université Paris Cité, Institut Cochin, Inserm U1016, CNRS UMR8104, F-75014, Paris, France. 17Department of Endocrinology, Diabetes and Metabolism, Evangelismos Hospital, Athens, Greece. 18Division of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, MN, USA. 19The Discipline of Pharmacology and Therapeutics, School of Medicine, National University of Ireland, Galway, Ireland. 20Department of Endocrinology, University Hospital Zagreb, Zagreb, Croatia. 21School of Medicine, University of Zagreb, Zagreb, Croatia. 22Department of Internal Medicine, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany. 23Central Laboratory, University Hospital Würzburg, Würzburg, Germany. 24Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, Turin, Italy. 25Department of Internal Medicine and Endocrinology, Medical University of Warsaw, Poland, Banacha 1 a, 02-097, Warsaw, Poland. 26Service d’Endocrinologie, Centre Hospitalier Universitaire, Hopital du Haut Leveque, Pessac, France. 27Endocrinology in Charlottenburg, Berlin, Germany. 28Department of Clinical and Biological Sciences, San Luigi Hospital, University of Turin, Turin, Italy. 29Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands. 30Department of Medicine, Haukeland University Hospital, 5021, Bergen, Norway. 31Internal & Emergency Medicine- ESH Specialized Hypertension Center, Department of Medicine-DIMED, University of Padua, Padua, Italy. 32Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, UniversitätsSpital Zürich (USZ) und Universität Zürich (UZH), Zurich, Switzerland. 33Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany. 34Department of Translational Medicine and Surgery, Università Cattolica del Sacro Cuore, Rome, Italy. 35Unicamillus-Saint Camillus International University of Health Sciences, Rome, Italy. 36Université Paris Cité, PARCC, INSERM, F-75006, Paris, France. 37Université Paris Cité, PARCC, INSERM, F-75006 Paris, France; Service de Génétique, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, F-75015, Paris, France
Introduction: Twenty-four-hour urinary free cortisol is used to diagnose Cushing’s syndrome (CS); amongst its limitations is failure to differentiate ACTH-dependent CS (AD-CS) from ACTH-independent CS (AI-CS). We tested the performance of urine steroid metabolomics (USM), the computational analysis of 24-hour urine steroid metabolome data by machine learning, for the diagnosis and differential diagnosis of CS. Given the physiological diurnal rhythm of cortisol secretion and its loss in CS, we also hypothesised that the difference in glucocorticoid excretion between CS and controls should be higher during nighttime, thereby facilitating more sensitive detection of CS.
Methods: 264 subjects completed a 24-hour urine collection (40 AD-CS, 103 AI-CS due to adrenocortical adenoma or hyperplasia, 121 healthy subjects). A subset of 52 subjects (13 CS, 39 healthy) provided a nighttime urine collection and a daytime urine collection. Mass spectrometry-based multi-steroid profiling was used to quantify the urinary excretion of 27 steroid metabolites. Data were analysed by generalised matrix learning vector quantisation, a prototype-based supervised machine learning approach.
Results: Twenty-four-hour USM demonstrated very high accuracy in differentiating CS from healthy subjects (area under the receiver-operating characteristics curve [AUC-ROC] 0.99), reflected by higher urinary excretion of glucocorticoid and glucocorticoid precursor metabolites in CS. USM yielded high accuracy in differentiating AD-CS from AI-CS (AUC-ROC 0.88), with androgen metabolites being the most discriminatory. Timed steroid excretion in healthy subjects reflected the diurnal pattern of adrenal steroid secretion, with lower nighttime than daytime excretion of glucocorticoid metabolites. Nighttime glucocorticoid metabolite excretion (AUC-ROC 0.97) performed better than daytime (AUC-ROC 0.85) and 24-hour excretion (AUC-ROC 0.92) in separating CS cases from healthy subjects.
Conclusions: USM is a non-invasive, one-step candidate test for the accurate diagnosis and differential diagnosis of CS. Timed nighttime urine collection leverages cortisol circadian rhythmicity and improves the diagnostic accuracy of the current reference standard 24-hour collection.
Volume 109
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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