Characteristics of preoperative steroid profiles and glucose metabolism in patients with primary aldosteronism developing adrenal insufficiency after adrenalectomy

Wang, Xiao; Heinrich, Daniel A.; Kunz, Sonja L.; Heger, Nina; Sturm, Lisa; Uhl, Olaf; Beuschlein, Felix; Reincke, Martin und Bidlingmaier, Martin (2021): Characteristics of preoperative steroid profiles and glucose metabolism in patients with primary aldosteronism developing adrenal insufficiency after adrenalectomy. In: Scientific Reports, Bd. 11, Nr. 1, 11181

Abstract

Treatment of choice in patients with unilateral aldosterone producing adenoma (APA) is adrenalectomy. Following surgery, most patients retain normal adrenal function, while some develop adrenal insufficiency (AI). To facilitate early detection and treatment of AI, we aimed to identify variables measured pre-operatively that are associated with post-operative AI. Variables obtained from 66 patients before and after surgery included anthropometrical data, clinical chemistry, endocrine work-up. LC-MS/MS steroid hormone profiles from tests before surgery (ACTH-stimulation, saline infusion, dexamethasone suppression) were obtained. Based on 78 variables, machine-learning methods were used in model fitting for classification and regression to predict ACTH-stimulated cortisol after surgery. Among the 78 variables, insulin concentration during pre-operative oral glucose tolerance test (OGTT) correlated positively, and dexamethasone suppressed glucocorticoids correlated negatively with ACTH-stimulated cortisol after surgery. Inclusion of LC-MS/MS measurements allowed construction of better models associated with the occurrence of AI in the training data, but did not allow reliable prediction in cross-validation. Our results suggest that glucocorticoid co-secretion (low insulin during pre-operative OGTT and insufficient suppression of glucocorticoids following dexamethasone) are correlated with the development of post-operative AI. Addition of steroid profiles improved the accuracy of prediction, but cross validation revealed lack of reliability in the prediction of AI.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 2045-2322
• Sprache: Englisch