ECE2024 Rapid Communications Rapid Communications 11: Adrenal and Cardiovascular Endocrinology | Part II (7 abstracts)
Urine steroid metabolomics to diagnose endocrine hypertension: results from the ENS@T-HT project
Alessandro Prete 1,2 , Lida Abdi 3 , Onnicha Suntornlohanakul 1,4 , Katharina Lang 1 , Roland Veen 3,5 , Marco Canducci 6 , Julien Riancho 7,8 , Aida Lazkani 9 , Casper K. Larsen 7 , Anne-Paule Gimenez-Roqueplo 7,10 , Alessio Pecori 11 , Martina Tetti 11 , Silvia Monticone 11 , Lisa M. Müller 12 , Christian Adolf 12 , Henri JLM Timmers 13 , Stephanie Hampson 14 , Graeme Eisenhofer 15 , Filippo Ceccato 16 , Felix Beuschlein 12,17 , Marek Kabat 18 , Jerome Bertherat 19 , M. Conall Dennedy 20 , Eleanor Davies 14 , Jaap Deinum 13 , Martin Reincke 12 , Gian Paolo Rossi 21 , Paolo Mulatero 11 , Laurence Amar 7,8 , Maria-Christina Zennaro 22 , Alice J. Sitch 2,23 , Peter Tino 6 , Michael Biehl 5,24 , Angela E. Taylor 1 & Wiebke Arlt 1,3,25
1Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; 2NIHR Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; 3Medical Research Council Laboratory of Medical Sciences, London, UK; 4Endocrinology and Metabolism Unit, Division of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Thailand; 5Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, the Netherlands; 6School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom; 7Université Paris Cité, PARCC, INSERM, F-75006 Paris, France; 8Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Unité Hypertension artérielle, Paris, France; 9Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Centre d’Investigations Cliniques 9201, Paris, France; 10Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Génétique, F-75015 Paris, France; 11Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, Italy; 12Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, LMU München, Munich, Germany; 13Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; 14School of Cardiovascular and Metabolic Health, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow G12 8TA, UK; 15Department of Medicine III, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; 16UOC Endocrinologia, Dipartimento di Medicina DIMED, Azienda Ospedaliera-Università di Padova, Padua, Italy; 17Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, UniversitätsSpital Zürich (USZ) und Universität Zürich (UZH), Zurich, Switzerland; 18Department of Hypertension, National Institute of Cardiology, Warsaw, Poland; 19Department of Endocrinology, French Reference Center for Rare Adrenal Disorders, Hôpital Cochin, Université Paris Cité, Institut Cochin, Inserm U1016, CNRS UMR8104, F-75014, Paris, France; 20The Discipline of Pharmacology and Therapeutics, School of Medicine, National University of Ireland 33 Galway, Ireland; 21Internal & Emergency Medicine- ESH Specialized Hypertension Center, Department of Medicine-DIMED, University of Padova, Padua, Italy; 22Université Paris Cité, PARCC, INSERM, F-75006 Paris, France; Service de Génétique, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, F-75015 Paris, France; 23Institute of Applied Health Research, University of Birmingham, Birmingham B15 2TT, United Kingdom; 24Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK; 25Institute of Clinical Sciences, Imperial College London, London, UK
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 treatments 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 forms of hypertension.
Methods: 1400 hypertensive adults with and without endocrine causes were recruited through the ENS@T-HT EU-funded Horizon 2020 research and innovation project; of these, 351 and 1049 were collected retrospectively and prospectively, respectively. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based multi-steroid profiling was used to quantify the excretion of 27 steroid metabolites in 24-hour urine samples. The prospective cohort underwent standardised measurement of the aldosterone-renin-ratio (ARR) to screen for primary aldosteronism (PA). Data were analysed by generalised matrix learning vector quantisation, a prototype-based algorithm of supervised machine learning, using the retrospective cohort (RC) for training and the prospective cohort (PC) for validation.
Results: We included 610 patients with PA (110 RC, 500 PC), 126 with phaeochromocytoma-paraganglioma (PPGL; 82 RC, 44 PC), 83 with Cushing’s syndrome (CS; 48 RC, 35 PC), and 581 with primary hypertension (PHT; 111 RC, 470 PC). USM demonstrated high accuracy (area under the receiver-operating characteristics curve [AUC-ROC] 0.93) in identifying CS cases, which showed higher urinary excretion of glucocorticoid and glucocorticoid precursor metabolites. USM yielded moderate accuracy (AUC-ROC 0.73) in differentiating PHT from PA. However, the performance improved considerably (AUC-ROC 0.87) when comparing PA cases to low-renin PHT (n=235), with the major aldosterone metabolite – 3α,5β-tetrahydroaldosterone – being the most discriminatory. Within the prospective cohort, USM had similar accuracy to the ARR in differentiating PHT from PA (AUC-ROC 0.87 and 0.88, respectively). The performance improved when combining USM results with renin alone (AUC-ROC 0.90) or the ARR (AUC-ROC 0.93). Expectedly, USM could not reliably differentiate PHT from PPGL (AUC-ROC 0.57).
Conclusions: Urine steroid metabolomics is a non-invasive candidate test for accurately diagnosing hypertension secondary to cortisol and aldosterone excess and can improve diagnosis and delivery of appropriate treatment in affected individuals.
Article tools
My recent searches
My recently viewed abstracts
Authors
Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
© Bioscientifica 2024 |
Privacy policy |
Cookie settings
BiosciAbstracts
Bioscientifica Abstracts is the gateway to a series of products that provide a permanent, citable record of abstracts for biomedical and life science conferences.
Find out more