Urine steroid metabolomics as a diagnostic tool in primary aldosteronism.
Aldosterone-producing adenoma
Bilateral primary aldosteronism
Hybrid steroids
KCNJ5
Primary aldosteronism
Urine steroid metabolome
Journal
The Journal of steroid biochemistry and molecular biology
ISSN: 1879-1220
Titre abrégé: J Steroid Biochem Mol Biol
Pays: England
ID NLM: 9015483
Informations de publication
Date de publication:
03 2024
03 2024
Historique:
received:
12
09
2023
revised:
03
11
2023
accepted:
13
12
2023
medline:
5
2
2024
pubmed:
18
12
2023
entrez:
17
12
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 38104729
pii: S0960-0760(23)00201-7
doi: 10.1016/j.jsbmb.2023.106445
pii:
doi:
Substances chimiques
Steroids
0
Aldosterone
4964P6T9RB
G Protein-Coupled Inwardly-Rectifying Potassium Channels
0
KCNJ5 protein, human
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
106445Subventions
Organisme : Medical Research Council
ID : MC_UP_1605/15
Pays : United Kingdom
Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare no competing interests in relation to this work.