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Prete A, Lang K, Pavlov D, Rhayem Y, Sitch AJ, Franke AS, Gilligan LC, Shackleton CHL, Hahner S, Quinkler M, Dekkers T, Deinum J, Reincke M, Beuschlein F, Biehl M, Arlt W. Urine steroid metabolomics as a diagnostic tool in primary aldosteronism. J Steroid Biochem Mol Biol 2024; 237:106445. [PMID: 38104729 DOI: 10.1016/j.jsbmb.2023.106445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/03/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Primary aldosteronism (PA) causes 5-10% of hypertension cases, but only a minority of patients are currently diagnosed and treated because of a complex, stepwise, and partly invasive workup. We tested the performance of urine steroid metabolomics, the computational analysis of 24-hour urine steroid metabolome data by machine learning, for the identification and subtyping of PA. Mass spectrometry-based multi-steroid profiling was used to quantify the excretion of 34 steroid metabolites in 24-hour urine samples from 158 adults with PA (88 with unilateral PA [UPA] due to aldosterone-producing adenomas [APAs]; 70 with bilateral PA [BPA]) and 65 sex- and age-matched healthy controls. All APAs were resected and underwent targeted gene sequencing to detect somatic mutations associated with UPA. Patients with PA had increased urinary metabolite excretion of mineralocorticoids, glucocorticoids, and glucocorticoid precursors. Urine steroid metabolomics identified patients with PA with high accuracy, both when applied to all 34 or only the three most discriminative steroid metabolites (average areas under the receiver-operating characteristics curve [AUCs-ROC] 0.95-0.97). Whilst machine learning was suboptimal in differentiating UPA from BPA (average AUCs-ROC 0.65-0.73), it readily identified APA cases harbouring somatic KCNJ5 mutations (average AUCs-ROC 0.79-85). These patients showed a distinctly increased urine excretion of the hybrid steroid 18-hydroxycortisol and its metabolite 18-oxo-tetrahydrocortisol, the latter identified by machine learning as by far the most discriminative steroid. In conclusion, urine steroid metabolomics is a non-invasive candidate test for the accurate identification of PA cases and KCNJ5-mutated APAs.
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Affiliation(s)
- Alessandro Prete
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK; Department of Endocrinology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
| | - Katharina Lang
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK; Department of Endocrinology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - David Pavlov
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, the Netherlands
| | - Yara Rhayem
- Medizinische Klinik and Poliklinik IV, Klinikum der Universität, Ludwig-Maximilians-Universität München, Munich, Germany; Service de Biologie Clinique, Hôpital Foch, Suresnes, France
| | - Alice J Sitch
- NIHR Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Anna S Franke
- Medizinische Klinik and Poliklinik IV, Klinikum der Universität, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Lorna C Gilligan
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Cedric H L Shackleton
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; UCSF Benioff Children's Hospital Oakland Research Institute, Oakland, CA, USA
| | - Stefanie Hahner
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany
| | | | - Tanja Dekkers
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jaap Deinum
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Martin Reincke
- Medizinische Klinik and Poliklinik IV, Klinikum der Universität, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Felix Beuschlein
- Medizinische Klinik and Poliklinik IV, Klinikum der Universität, Ludwig-Maximilians-Universität München, Munich, Germany; Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitäts-Spital Zürich (USZ) und Universität Zürich (UZH), Zurich, Switzerland
| | - Michael Biehl
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, the Netherlands; Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK
| | - Wiebke Arlt
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK; Department of Endocrinology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Medical Research Council Laboratory of Medical Sciences, London, UK; Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, UK
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