Urine steroid metabolomics as a diagnostic tool in primary aldosteronism

Alessandro Prete; Katharina Lang; David Pavlov; Yara Rhayem; Alice J Sitch; Anna S Franke; Lorna C Gilligan; Cedric H L Shackleton; Stefanie Hahner; Marcus Quinkler; Tanja Dekkers; Jaap Deinum; Martin Reincke; Felix Beuschlein; Michael Biehl; Wiebke Arlt

doi:10.1016/j.jsbmb.2023.106445

Urine steroid metabolomics as a diagnostic tool in primary aldosteronism

Alessandro Prete^*, Katharina Lang, David Pavlov, Yara Rhayem, Alice J Sitch, Anna S Franke, Lorna C Gilligan, Cedric H L Shackleton, Stefanie Hahner, Marcus Quinkler, Tanja Dekkers, Jaap Deinum, Martin Reincke, Felix Beuschlein, Michael Biehl, Wiebke Arlt

^*Corresponding author for this work

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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.

Original language	English
Article number	106445
Journal	The Journal of Steroid Biochemistry and Molecular Biology
Volume	237
Early online date	15 Dec 2023
DOIs	https://doi.org/10.1016/j.jsbmb.2023.106445
Publication status	Published - 1 Mar 2024

Bibliographical note

Source of funding
This work has been supported by the Medical Research Council UK (Clinical Research Training Fellowship MR/R002339/1 to K.L.; Strategic Biomarker Grant G0801473 to W.A.), Diabetes UK (Sir George Alberti Research Training Fellowship 18/0005782, to A.P.), the Friedrich Baur Stiftung (Grant Number 46/16 to Y.R.), the Else Kröner-Fresenius Stiftung (German Conn’s Registry-Else-Kröner Hyperaldosteronism Registry; 2013_A182, to M.R.), the Deutsche Forschungsgemeinschaft (RE 752/20–1, to M.R., and BE2177/13–1, to F.B.), the Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to F.B.), the Netherlands Organisation for Health Research and Development (E&K 171002102, to J.D.), the European Research Council (Grant number 694913 [PAPA] to M.R.), the European Commission Horizon 2020 Program under grant agreement 633983 (ENSAT-HT, to W.A., M.R., J.D., and F.B.), and the European Commission Horizon 2022 Program under grant agreement 101095407 (HT-ADVANCE, to A.P., J.D., and F.B.). A.P. and A.J.S. receive support from the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham (Grant Reference Number NIHR203326). The funders of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care UK.

Copyright © 2023 The Authors.

Keywords

Urine steroid metabolome
Primary aldosteronism
Aldosterone-producing adenoma
Bilateral primary aldosteronism
Hybrid steroids
KCNJ5

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HT-ADVANCE: Improving treatment efficacy in hypertension by biomarker-guided personalised decision support
Taylor, A. & Prete, A.
UKRI Horizon Europe Underwriting â€“ Innovate UK
1/03/23 → 28/02/29
Project: Research
Exploring the metabolic impact of mild autonomous cortisol excess (MACE), an underestimated cause of diabetes
Manolopoulos, K., Prete, A. & Arlt, W.
Diabetes Uk
1/12/18 → 31/12/22
Project: Research
Selective CYP11B1 inhibtors as a novel treatment option for endogenous cortisol excess
Lavery, G. & Arlt, W.
Medical Research Council
4/01/18 → 3/09/22
Project: Research Councils
H2020_COLLAB_ENSAT-HT
Arlt, W.
European Commission
1/05/15 → 31/10/21
Project: EU
Steroid Profiling as a Biomarker Tool in the Diagnosis and Monitoring of Adrenal Tumours
Arlt, W. & Stewart, P.
Medical Research Council
2/03/09 → 29/02/12
Project: Research Councils

Cite this

Prete, A., Lang, K., Pavlov, D., Rhayem, Y., Sitch, A. J., Franke, A. S., Gilligan, L. C., Shackleton, C. H. L., Hahner, S., Quinkler, M., Dekkers, T., Deinum, J., Reincke, M., Beuschlein, F., Biehl, M., & Arlt, W. (2024). Urine steroid metabolomics as a diagnostic tool in primary aldosteronism. The Journal of Steroid Biochemistry and Molecular Biology, 237, Article 106445. https://doi.org/10.1016/j.jsbmb.2023.106445

@article{b6b7cb4b00bf4925a8800ca7c4f2434e,

title = "Urine steroid metabolomics as a diagnostic tool in primary aldosteronism",

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.",

keywords = "Urine steroid metabolome, Primary aldosteronism, Aldosterone-producing adenoma, Bilateral primary aldosteronism, Hybrid steroids, KCNJ5",

author = "Alessandro Prete and Katharina Lang and David Pavlov and Yara Rhayem and Sitch, {Alice J} and Franke, {Anna S} and Gilligan, {Lorna C} and Shackleton, {Cedric H L} and Stefanie Hahner and Marcus Quinkler and Tanja Dekkers and Jaap Deinum and Martin Reincke and Felix Beuschlein and Michael Biehl and Wiebke Arlt",

note = "Source of funding This work has been supported by the Medical Research Council UK (Clinical Research Training Fellowship MR/R002339/1 to K.L.; Strategic Biomarker Grant G0801473 to W.A.), Diabetes UK (Sir George Alberti Research Training Fellowship 18/0005782, to A.P.), the Friedrich Baur Stiftung (Grant Number 46/16 to Y.R.), the Else Kr{\"o}ner-Fresenius Stiftung (German Conn{\textquoteright}s Registry-Else-Kr{\"o}ner Hyperaldosteronism Registry; 2013_A182, to M.R.), the Deutsche Forschungsgemeinschaft (RE 752/20–1, to M.R., and BE2177/13–1, to F.B.), the Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to F.B.), the Netherlands Organisation for Health Research and Development (E&K 171002102, to J.D.), the European Research Council (Grant number 694913 [PAPA] to M.R.), the European Commission Horizon 2020 Program under grant agreement 633983 (ENSAT-HT, to W.A., M.R., J.D., and F.B.), and the European Commission Horizon 2022 Program under grant agreement 101095407 (HT-ADVANCE, to A.P., J.D., and F.B.). A.P. and A.J.S. receive support from the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham (Grant Reference Number NIHR203326). The funders of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care UK. Copyright {\textcopyright} 2023 The Authors.",

year = "2024",

month = mar,

day = "1",

doi = "10.1016/j.jsbmb.2023.106445",

language = "English",

volume = "237",

journal = "The Journal of Steroid Biochemistry and Molecular Biology",

issn = "0960-0760",

publisher = "Elsevier",

}

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 2024, 'Urine steroid metabolomics as a diagnostic tool in primary aldosteronism', The Journal of Steroid Biochemistry and Molecular Biology, vol. 237, 106445. https://doi.org/10.1016/j.jsbmb.2023.106445

TY - JOUR

T1 - Urine steroid metabolomics as a diagnostic tool in primary aldosteronism

AU - Prete, Alessandro

AU - Lang, Katharina

AU - Pavlov, David

AU - Rhayem, Yara

AU - Sitch, Alice J

AU - Franke, Anna S

AU - Gilligan, Lorna C

AU - Shackleton, Cedric H L

AU - Hahner, Stefanie

AU - Quinkler, Marcus

AU - Dekkers, Tanja

AU - Deinum, Jaap

AU - Reincke, Martin

AU - Beuschlein, Felix

AU - Biehl, Michael

AU - Arlt, Wiebke

N1 - Source of funding This work has been supported by the Medical Research Council UK (Clinical Research Training Fellowship MR/R002339/1 to K.L.; Strategic Biomarker Grant G0801473 to W.A.), Diabetes UK (Sir George Alberti Research Training Fellowship 18/0005782, to A.P.), the Friedrich Baur Stiftung (Grant Number 46/16 to Y.R.), the Else Kröner-Fresenius Stiftung (German Conn’s Registry-Else-Kröner Hyperaldosteronism Registry; 2013_A182, to M.R.), the Deutsche Forschungsgemeinschaft (RE 752/20–1, to M.R., and BE2177/13–1, to F.B.), the Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to F.B.), the Netherlands Organisation for Health Research and Development (E&K 171002102, to J.D.), the European Research Council (Grant number 694913 [PAPA] to M.R.), the European Commission Horizon 2020 Program under grant agreement 633983 (ENSAT-HT, to W.A., M.R., J.D., and F.B.), and the European Commission Horizon 2022 Program under grant agreement 101095407 (HT-ADVANCE, to A.P., J.D., and F.B.). A.P. and A.J.S. receive support from the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham (Grant Reference Number NIHR203326). The funders of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care UK. Copyright © 2023 The Authors.

PY - 2024/3/1

Y1 - 2024/3/1

N2 - 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.

AB - 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.

KW - Urine steroid metabolome

KW - Primary aldosteronism

KW - Aldosterone-producing adenoma

KW - Bilateral primary aldosteronism

KW - Hybrid steroids

KW - KCNJ5

U2 - 10.1016/j.jsbmb.2023.106445

DO - 10.1016/j.jsbmb.2023.106445

M3 - Article

C2 - 38104729

SN - 0960-0760

VL - 237

JO - The Journal of Steroid Biochemistry and Molecular Biology

JF - The Journal of Steroid Biochemistry and Molecular Biology

M1 - 106445

ER -

Urine steroid metabolomics as a diagnostic tool in primary aldosteronism

Abstract

Bibliographical note

Keywords

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Projects

HT-ADVANCE: Improving treatment efficacy in hypertension by biomarker-guided personalised decision support

Exploring the metabolic impact of mild autonomous cortisol excess (MACE), an underestimated cause of diabetes

Selective CYP11B1 inhibtors as a novel treatment option for endogenous cortisol excess

H2020_COLLAB_ENSAT-HT

Steroid Profiling as a Biomarker Tool in the Diagnosis and Monitoring of Adrenal Tumours

Cite this