Urine steroid metabolomics as a diagnostic tool in primary aldosteronism

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...

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Veröffentlicht in:The Journal of steroid biochemistry and molecular biology 2024-03, Vol.237, p.106445, Article 106445
Hauptverfasser: Prete, Alessandro, Lang, Katharina, Pavlov, David, Rhayem, Yara, Sitch, Alice J., Franke, Anna S., Gilligan, Lorna C., Shackleton, Cedric H.L., Hahner, Stefanie, Quinkler, Marcus, Dekkers, Tanja, Deinum, Jaap, Reincke, Martin, Beuschlein, Felix, Biehl, Michael, Arlt, Wiebke
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container_start_page 106445
container_title The Journal of steroid biochemistry and molecular biology
container_volume 237
creator Prete, Alessandro
Lang, Katharina
Pavlov, David
Rhayem, Yara
Sitch, Alice J.
Franke, Anna S.
Gilligan, Lorna C.
Shackleton, Cedric H.L.
Hahner, Stefanie
Quinkler, Marcus
Dekkers, Tanja
Deinum, Jaap
Reincke, Martin
Beuschlein, Felix
Biehl, Michael
Arlt, Wiebke
description 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. •We measured 34 steroid metabolites in 24-hour urine samples of patients with primary aldosteronism and healthy controls.•Machine learning applied to the urinary steroid metabolome was highly accurate in identifying primary aldosteronism cases.•Aldosterone-producing adenomas harbouring mutations of the KCNJ5 gene had specific urine steroid fingerprints.•Urine steroid metabolome analysis is a non-invasive candidate test for diagnosing and subtyping primary aldosteronism.
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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. •We measured 34 steroid metabolites in 24-hour urine samples of patients with primary aldosteronism and healthy controls.•Machine learning applied to the urinary steroid metabolome was highly accurate in identifying primary aldosteronism cases.•Aldosterone-producing adenomas harbouring mutations of the KCNJ5 gene had specific urine steroid fingerprints.•Urine steroid metabolome analysis is a non-invasive candidate test for diagnosing and subtyping primary aldosteronism.</description><identifier>ISSN: 0960-0760</identifier><identifier>ISSN: 1879-1220</identifier><identifier>EISSN: 1879-1220</identifier><identifier>DOI: 10.1016/j.jsbmb.2023.106445</identifier><identifier>PMID: 38104729</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Adenoma - diagnosis ; Adrenal Cortex Neoplasms - genetics ; Adrenocortical Adenoma - genetics ; Adult ; Aldosterone - metabolism ; Aldosterone-producing adenoma ; Bilateral primary aldosteronism ; G Protein-Coupled Inwardly-Rectifying Potassium Channels - genetics ; G Protein-Coupled Inwardly-Rectifying Potassium Channels - metabolism ; Humans ; Hybrid steroids ; Hyperaldosteronism - diagnosis ; Hyperaldosteronism - genetics ; Hyperaldosteronism - metabolism ; KCNJ5 ; Mass Spectrometry ; Mutation ; Primary aldosteronism ; Steroids ; Urine steroid metabolome</subject><ispartof>The Journal of steroid biochemistry and molecular biology, 2024-03, Vol.237, p.106445, Article 106445</ispartof><rights>2023 The Authors</rights><rights>Copyright © 2023 The Authors. 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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. 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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. •We measured 34 steroid metabolites in 24-hour urine samples of patients with primary aldosteronism and healthy controls.•Machine learning applied to the urinary steroid metabolome was highly accurate in identifying primary aldosteronism cases.•Aldosterone-producing adenomas harbouring mutations of the KCNJ5 gene had specific urine steroid fingerprints.•Urine steroid metabolome analysis is a non-invasive candidate test for diagnosing and subtyping primary aldosteronism.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>38104729</pmid><doi>10.1016/j.jsbmb.2023.106445</doi><oa>free_for_read</oa></addata></record>
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subjects Adenoma - diagnosis
Adrenal Cortex Neoplasms - genetics
Adrenocortical Adenoma - genetics
Adult
Aldosterone - metabolism
Aldosterone-producing adenoma
Bilateral primary aldosteronism
G Protein-Coupled Inwardly-Rectifying Potassium Channels - genetics
G Protein-Coupled Inwardly-Rectifying Potassium Channels - metabolism
Humans
Hybrid steroids
Hyperaldosteronism - diagnosis
Hyperaldosteronism - genetics
Hyperaldosteronism - metabolism
KCNJ5
Mass Spectrometry
Mutation
Primary aldosteronism
Steroids
Urine steroid metabolome
title Urine steroid metabolomics as a diagnostic tool in primary aldosteronism
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