Background: Establishing endocrine diagnoses including pituitary Cushings remains challenging with delayed or misdiagnosis being very costly for both patient and the health care system. A key issue is that standard diagnostic tests are typically single time point, single analyte samples, which do not consider dynamic variation intrinsic to endocrine systems. Having previously demonstrated that free tissue hormones correlate strongly with plasma concentrations, we present a novel diagnostic approach that utilises ambulatory free adrenal steroid metabolodynamics, without the need for blood sampling.
Methods: Clinical diagnosis of pituitary Cushings disease was established using conventional means (e.g. DST, urine collection, salivary cortisol, inferior petrosal sinus sampling). Healthy volunteers were recruited for comparison (age 1868, no current or recent glucocorticoid use). All participants underwent 24-h microdialysis sampling using 20 kDa linear sampling catheters inserted in abdominal subcutaneous tissue. Microdialysate samples were generated every 20 min using our novel U-RHYTHM fraction collector. Measurement of tissue free hormones concentrations in each sample was achieved using LCMS/MS. Patients with Cushings disease were sampled prior to primary pituitary surgery, with diagnosis confirmed by histological presence of corticotroph adenoma.
Results: Data is presented from patients with Cushings disease (n=6) and healthy volunteers (n=47). Multiple hormones showed dynamic variation over the 24-h sampling period. An algorithm-based model was developed that considered multiple features in the data set including AUC, timing, rhythmicity, interactions and nadir concentrations. The model successfully discriminated all Cushings disease profiles from healthy normal variation.
Conclusions: We describe a novel method of blood-free ambulatory sampling that successfully discriminates abnormal adrenal metabolodynamics in pituitary Cushings disease from healthy normal variation. The technique has considerable potential for both diagnosis and monitoring of endocrine conditions. Validation in a larger cohort of both patients and volunteers is now being conducted.