Urinary GC-MS steroid metabotyping in treated children with congenital adrenal hyperplasia

Background: Treatment of children with classic congenital adrenal hyperplasia (CAH) is a difficult balance between hypercortisolism and hyperandrogenism. Biochemical monitoring of treatment is not well defined. Objective: Cluster analysis of the urinary steroid metabolome obtained by targeted gas chromatography-mass spectrometry (GC-MS) for treatment monitoring of children with CAH. Methods: We evaluated 24-h urinary steroid metabolome analyses of 109 prepubertal children aged 7.0 +/- 1.6 years with classic CAH due to 21-hydroxylase deficiency treated with hydrocortisone and fludrocortisone. 24-h urinary steroid metabolite excretions were transformed into CAH-specific z-scores. Subjects were divided into groups (metabotypes) by k-means clustering algorithm. Urinary steroid metabolome and clinical data of patients of each metabotype were analyzed. Results: Four unique metabotypes were generated. Metabotype 1 (N = 21 (19%)) revealed adequate metabolic control with low cortisol metabolites (mean: -0.57z) and suppressed androgen and 17 alpha-hydroxyprogesterone (17OHP) metabolites ( -0.79z). Metabotype 2 (N = 23 (21%)) showed overtreatment consisting of a constellation of elevated urinary cortisol metabolites (0.62z) and low metabolites of androgens and 17OHP (-0.75z). Metabotype 3 (N = 32 (29%)) demonstrated undertreated patients with low cortisol metabolites (-0.69z) and elevated metabolites of androgens and 17OHP (0.50z). Metabotype 4 (N = 33 (30%)) presented patients with treatment failure reflected by unsuppressed androgen- and 17OHP metabolites (0.71z) despite elevated urinary cortisol metabolites (039z). Conclusion: Metabotyping, which means grouping metabolically similar individuals, helps to monitor treatment of children with CAH using GC-MS urinary steroid metabolome analysis. This method allows classification in adequately-, over-, or undertreated children as well as identification of patients with treatment failure. (C) 2020 Elsevier Inc. All rights reserved.