Urine steroid metabolomics as a diagnostic tool in primary aldosteronism
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Author
Prete, AlessandroLang, 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
Publication date
2023-12-15Subject
Biochemistry
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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.Citation
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 Mar;237:106445. doi: 10.1016/j.jsbmb.2023.106445. Epub 2023 Dec 15.Type
ArticleAdditional Links
https://www.sciencedirect.com/journal/the-journal-of-steroid-biochemistry-and-molecular-biologyPMID
38104729Publisher
Elsevierae974a485f413a2113503eed53cd6c53
10.1016/j.jsbmb.2023.106445