Abstract
Introduction: Preoperative diagnostic workup of adrenal tumors is based on imaging and hormone analyses, but charged with uncertainties. Steroid profiling by liquid chromatography tandem mass spectrometry (LC-MS/MS) in 24-h urine has shown potential to discriminate benign and malignant adrenal tumors. Our aim was to develop and validate a specific and accurate LC-MS/MS method for the quantification of deconjugated urinary marker steroids, to evaluate their pre-analytical stability and to apply the method to clinical samples of patients with adrenal tumors. Methods: A method for the quantification of 11 deconjugated steroids (5-pregnenetriol, dehydroepiandrosterone, cortisone, cortisol, alpha-cortolone, tetrahydro-11-deoxycortisol, etiocholanolone, pregnenolone, pregnanetriol, pregnanediol, and 5-pregnenediol) in human urine was developed and validated based on international guidelines. Steroids were enzymatically deconjugated and extracted by solid phase extraction before LC-MS/MS quantification in positive electrospray ionization mode. Results: Excellent linearity with R-2 > 0.99 and intra- and inter-day precisions of < 10.1 % were found. Relative matrix effects were between 96.4 % and 101.6 % and relative recovery was between 98.2 % and 115.0 %. Sufficient pre-freeze stability for all steroids in urine was found at 20-25 degrees C for seven days and at 4-6 degrees C for up to 28 days. Samples were stable during long-term storage at -20 degrees C and -80 degrees C for 6 months. Conclusions: A sensitive and robust LC-MS/MS method for the quantification of 11 urinary steroids was developed and validated according to international guidelines. Pre-analytical matrix stability was evaluated and the suitability of the method for the analysis of clinical samples and prospective validation studies was shown.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Medizin |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
ISSN: | 2667-145X |
Sprache: | Englisch |
Dokumenten ID: | 112310 |
Datum der Veröffentlichung auf Open Access LMU: | 02. Apr. 2024, 07:35 |
Letzte Änderungen: | 02. Apr. 2024, 07:35 |