Endocrine Abstracts

Gas chromatography mass spectrometry (GC-MS) is the gold standard for urinary steroid profiling. Our established GC-MS method quantifies mineralocorticoids, glucocorticoids, and androgens. GC-MS assays require chemical derivatisation and long run times, rendering them unsuitable for high-throughput analysis. Our aim was to develop and validate a high-throughput urinary steroid profiling method using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-LC-MS/MS). Chromatography and mass spectrometry parameters were optimised for 29 urinary steroids. Steroids were extracted from 200 μL of urine after the isotopically labelled internal standards were added. Samples underwent hydrolysis to remove sulfate and glucuronide conjugates. Unconjugated steroids were extracted using a C18 96-well plate solid-phase extraction cartridge. Steroid separation was achieved using a Waters HSS T3 column (1.8 μm 1.2 x 50 mm) with a water and methanol (0.1 % formic acid) gradient maintained at a 0.6 ml/min flow rate, on a Waters Acquity UPLC system coupled to a Waters TQ-XS mass spectrometer. The method was validated, determining the linear calibration range, assay imprecision, accuracy, reproducibility, recovery, and matrix effects. Furthermore, quantification was compared to GC-MS. Steroids were quantified across the large concentration ranges observed in the urine metabolome (0.5-3000 ng/ml). Lower limits of quantification ranged from 0.5 to 10 ng/ml. Accuracy, measured as percentage bias was < ±15%, and imprecision, measured as percentage variance was <15% for all steroids measured at three concentrations (30, 200, and 700 ng/ml). Mean recovery was 89% (range 61-131%) with acceptable matrix effects. The total run time was 22 minutes. A comparison of GC-MS and UPLC-MS/MS revealed similar quantitation for all steroids. We have developed a powerful tool for the comprehensive profiling of 29 urinary steroids using UPLC-MS/MS. Compared to the GC-MS method, this assay reduced sample preparation and run time whilst maintaining the resolution, allowing for greater sample throughput.