SFEBES2023 Oral Communications Adrenal and Cardiovascular (6 abstracts)
Urine steroid metabolomics as a diagnostic tool in endocrine hypertension
Alessandro Prete 1,2 , Lida Abdi 1,3 , Onnicha Suntornlohanakul 1,4 , Katharina Lang 1 , Julien Riancho 5,6 , Aida Lazkani 7 , Casper K. Larsen 5 , Anne-Paule Gimenez-Roqueplo 5,8 , Alessio Pecori 9 , Martina Tetti 9 , Silvia Monticone 9 , Lisa M. Müller 10 , Christian Adolf 10 , Henri J.L.M. Timmers 11 , Stephanie Hampson 12 , Graeme Eisenhofer 13,14 , Filippo Ceccato 15 , Felix Beuschlein 16,10 , Marek Kabat 17 , Jerome Bertherat 18 , Conall Dennedy 19 , Eleanor Davies 12 , Jaap Deinum 11 , Martin Reincke 10 , Gian Paolo Rossi 20 , Paolo Mulatero 9 , Laurence Amar 5,6 , Maria-Christina Zennaro 8 , Alice J. Sitch 21,2 , Peter Tino 22 , Michael Biehl 23,24 , Angela E. Taylor 1 & Wiebke Arlt 1,3
1Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom. 2NIHR Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. 3Medical Research Council London Institute of Medical Sciences, London, United Kingdom. 4Endocrinology and Metabolism Unit, Division of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Hat Yai, Thailand. 5Université Paris Cité, PARCC, INSERM, F-75006, Paris, France. 6Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Unité Hypertension artérielle, Paris, France. 7Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Centre d’Investigations Cliniques 9201, Paris, France. 8Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Génétique, Paris, France. 9Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, Turin, Italy. 10Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU München, Munich, Germany. 11Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands. 12School of Cardiovascular and Metabolic Health, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom. 13Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Carl Gustav Carus, Dresden, Germany. 14Department of Medicine III, University Hospital Carl Gustav Carus, Dresden, Germany. 15UOC Endocrinologia, Dipartimento di Medicina DIMED, Azienda Ospedaliera-Università di Padova, Padua, Italy. 16Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, UniversitätsSpital Zürich (USZ) und Universität Zürich (UZH), Zurich, Switzerland. 17Department of Hypertension, National Institute of Cardiology, Warsaw, Poland. 18Department of Endocrinology, French Reference Center for Rare Adrenal Disorders, Hôpital Cochin, Université Paris Cité, Institut Cochin, Inserm U1016, CNRS UMR8104, F-75014, Paris, France. 19The Discipline of Pharmacology and Therapeutics, School of Medicine, National University of Ireland, Galway, Ireland. 20Internal & Emergency Medicine- ESH Specialized Hypertension Center, Department of Medicine-DIMED, University of Padua, Padua, Italy. 21Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom. 22School of Computer Science, University of Birmingham, Birmingham, United Kingdom. 23Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands. 24Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
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.
Methods: Mass spectrometry-based multi-steroid profiling was used to quantify the excretion of 27 steroid metabolites in 24-hour urine samples from 1400 hypertensive adults with and without endocrine causes (351 retrospectively and 1049 prospectively collected). Data were analysed by generalised matrix learning vector quantisation, a prototype-based algorithm of supervised machine learning, using the retrospective cohort for training and the prospective for validation.
Results: We included 610 patients with primary aldosteronism (PA; 110 retrospective, 500 prospective), 126 with phaeochromocytoma-paraganglioma (PPGL; 82 retrospective, 44 prospective), 83 with Cushing’s syndrome (CS; 48 retrospective, 35 prospective), and 581 with primary hypertension (PHT; 111 retrospective, 470 prospective). Of the prospective patients with PHT, 188 had low renin levels (low-renin PHT). USM demonstrated high accuracy in identifying CS cases (area under the receiver-operating characteristics curve [AUC-ROC] 0.93), which showed higher urinary excretion of glucocorticoid and glucocorticoid precursor metabolites. USM yielded moderate accuracy in differentiating PHT from PA (AUC-ROC 0.73); however, the performance improved considerably when comparing PA cases to low-renin PHT (AUC-ROC 0.86), with the major aldosterone metabolite – 3α,5β-tetrahydroaldosterone – being the most discriminatory. USM could not reliably differentiate PHT from PPGL (AUC-ROC 0.57).
Conclusions: Urine steroid metabolomics is a non-invasive candidate test for the accurate diagnosis of hypertension secondary to cortisol and aldosterone excess, and can improve diagnosis and delivery of appropriate treatment in affected individuals.
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