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.106445-106445, Article 106445 |
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Zusammenfassung: | 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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ISSN: | 0960-0760 1879-1220 |
DOI: | 10.1016/j.jsbmb.2023.106445 |