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 |
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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. |
doi_str_mv | 10.1016/j.jsbmb.2023.106445 |
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•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. Published by Elsevier Ltd.. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c354t-c3e5d99e6557247e7f88bff3bacb709d27082b3400c6cd0a273a73d7b65b9f6e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0960076023002017$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38104729$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Prete, Alessandro</creatorcontrib><creatorcontrib>Lang, Katharina</creatorcontrib><creatorcontrib>Pavlov, David</creatorcontrib><creatorcontrib>Rhayem, Yara</creatorcontrib><creatorcontrib>Sitch, Alice J.</creatorcontrib><creatorcontrib>Franke, Anna S.</creatorcontrib><creatorcontrib>Gilligan, Lorna C.</creatorcontrib><creatorcontrib>Shackleton, Cedric H.L.</creatorcontrib><creatorcontrib>Hahner, Stefanie</creatorcontrib><creatorcontrib>Quinkler, Marcus</creatorcontrib><creatorcontrib>Dekkers, Tanja</creatorcontrib><creatorcontrib>Deinum, Jaap</creatorcontrib><creatorcontrib>Reincke, Martin</creatorcontrib><creatorcontrib>Beuschlein, Felix</creatorcontrib><creatorcontrib>Biehl, Michael</creatorcontrib><creatorcontrib>Arlt, Wiebke</creatorcontrib><title>Urine steroid metabolomics as a diagnostic tool in primary aldosteronism</title><title>The Journal of steroid biochemistry and molecular biology</title><addtitle>J Steroid Biochem Mol Biol</addtitle><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.</description><subject>Adenoma - diagnosis</subject><subject>Adrenal Cortex Neoplasms - genetics</subject><subject>Adrenocortical Adenoma - genetics</subject><subject>Adult</subject><subject>Aldosterone - metabolism</subject><subject>Aldosterone-producing adenoma</subject><subject>Bilateral primary aldosteronism</subject><subject>G Protein-Coupled Inwardly-Rectifying Potassium Channels - genetics</subject><subject>G Protein-Coupled Inwardly-Rectifying Potassium Channels - metabolism</subject><subject>Humans</subject><subject>Hybrid steroids</subject><subject>Hyperaldosteronism - diagnosis</subject><subject>Hyperaldosteronism - genetics</subject><subject>Hyperaldosteronism - metabolism</subject><subject>KCNJ5</subject><subject>Mass Spectrometry</subject><subject>Mutation</subject><subject>Primary aldosteronism</subject><subject>Steroids</subject><subject>Urine steroid metabolome</subject><issn>0960-0760</issn><issn>1879-1220</issn><issn>1879-1220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1LxDAQhoMo7rr6CwTp0UvXSdI27cGDiLrCghf3HPIxlSxtsyZdwX9v9kOPwjADw_vOx0PINYU5BVrdrefrqHs9Z8B46lRFUZ6QKa1Fk1PG4JRMoakgB1HBhFzEuAYAzqk4JxNeUygEa6ZksQpuwCyOGLyzWY-j0r7zvTMxUyky69TH4OPoTDZ632VuyDbB9Sp8Z6qzfm8cXOwvyVmruohXxzojq-en98dFvnx7eX18WOaGl8WYMpa2abAqS8EKgaKta922XCujBTSWCaiZ5gWAqYwFxQRXgluhq1I3bYV8Rm4PczfBf24xjrJ30WDXqQH9NkrWpCeTi9Mk5QepCT7GgK08Xi4pyB1CuZZ7hHKHUB4QJtfNccFW92j_PL_MkuD-IMD05pfDIKNxOBi0LqAZpfXu3wU_SGODVg</recordid><startdate>202403</startdate><enddate>202403</enddate><creator>Prete, Alessandro</creator><creator>Lang, Katharina</creator><creator>Pavlov, David</creator><creator>Rhayem, Yara</creator><creator>Sitch, Alice J.</creator><creator>Franke, Anna S.</creator><creator>Gilligan, Lorna C.</creator><creator>Shackleton, Cedric H.L.</creator><creator>Hahner, Stefanie</creator><creator>Quinkler, Marcus</creator><creator>Dekkers, Tanja</creator><creator>Deinum, Jaap</creator><creator>Reincke, Martin</creator><creator>Beuschlein, Felix</creator><creator>Biehl, Michael</creator><creator>Arlt, Wiebke</creator><general>Elsevier Ltd</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>202403</creationdate><title>Urine steroid metabolomics as a diagnostic tool in primary aldosteronism</title><author>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</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c354t-c3e5d99e6557247e7f88bff3bacb709d27082b3400c6cd0a273a73d7b65b9f6e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adenoma - diagnosis</topic><topic>Adrenal Cortex Neoplasms - genetics</topic><topic>Adrenocortical Adenoma - genetics</topic><topic>Adult</topic><topic>Aldosterone - metabolism</topic><topic>Aldosterone-producing adenoma</topic><topic>Bilateral primary aldosteronism</topic><topic>G Protein-Coupled Inwardly-Rectifying Potassium Channels - genetics</topic><topic>G Protein-Coupled Inwardly-Rectifying Potassium Channels - metabolism</topic><topic>Humans</topic><topic>Hybrid steroids</topic><topic>Hyperaldosteronism - diagnosis</topic><topic>Hyperaldosteronism - genetics</topic><topic>Hyperaldosteronism - metabolism</topic><topic>KCNJ5</topic><topic>Mass Spectrometry</topic><topic>Mutation</topic><topic>Primary aldosteronism</topic><topic>Steroids</topic><topic>Urine steroid metabolome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Prete, Alessandro</creatorcontrib><creatorcontrib>Lang, Katharina</creatorcontrib><creatorcontrib>Pavlov, David</creatorcontrib><creatorcontrib>Rhayem, Yara</creatorcontrib><creatorcontrib>Sitch, Alice J.</creatorcontrib><creatorcontrib>Franke, Anna S.</creatorcontrib><creatorcontrib>Gilligan, Lorna C.</creatorcontrib><creatorcontrib>Shackleton, Cedric H.L.</creatorcontrib><creatorcontrib>Hahner, Stefanie</creatorcontrib><creatorcontrib>Quinkler, Marcus</creatorcontrib><creatorcontrib>Dekkers, Tanja</creatorcontrib><creatorcontrib>Deinum, Jaap</creatorcontrib><creatorcontrib>Reincke, Martin</creatorcontrib><creatorcontrib>Beuschlein, Felix</creatorcontrib><creatorcontrib>Biehl, Michael</creatorcontrib><creatorcontrib>Arlt, Wiebke</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>The Journal of steroid biochemistry and molecular biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Prete, Alessandro</au><au>Lang, Katharina</au><au>Pavlov, David</au><au>Rhayem, Yara</au><au>Sitch, Alice J.</au><au>Franke, Anna S.</au><au>Gilligan, Lorna C.</au><au>Shackleton, Cedric H.L.</au><au>Hahner, Stefanie</au><au>Quinkler, Marcus</au><au>Dekkers, Tanja</au><au>Deinum, Jaap</au><au>Reincke, Martin</au><au>Beuschlein, Felix</au><au>Biehl, Michael</au><au>Arlt, Wiebke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Urine steroid metabolomics as a diagnostic tool in primary aldosteronism</atitle><jtitle>The Journal of steroid biochemistry and molecular biology</jtitle><addtitle>J Steroid Biochem Mol Biol</addtitle><date>2024-03</date><risdate>2024</risdate><volume>237</volume><spage>106445</spage><pages>106445-</pages><artnum>106445</artnum><issn>0960-0760</issn><issn>1879-1220</issn><eissn>1879-1220</eissn><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.
•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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