Predicting Treatment Outcome in Congenital Adrenal Hyperplasia Using Urine Steroidomics and Machine Learning.

Abstract

Objective: Treatment monitoring of individuals with congenital adrenal hyperplasia (CAH) remains unsatisfactory. Comprehensive 24h urine steroid profiling provides detailed insight into adrenal steroid pathways. We investigated whether 24h urine steroid profiling can predict treatment control in children and adolescents with CAH using machine learning (ML).

Design: Prospective observational cohort study.

Methods: This study included children with 21-hydroxylase deficiency. On 24h urines of 2 consecutive visits 40 steroids were measured by gas chromatography-mass spectrometry. Treatment outcome was clinically classified as undertreated, optimally treated or overtreated. We used sparse partial least-squares discriminant analysis (sPLS-DA) to investigate prediction of treatment outcome. We computed area under the ROC-curve (AUC) of two sPLS-DA models: (1) using only 24h urine metabolites, (2) adding clinical variables.

Results: We included 112 visits (68 optimal, 44 undertreatment) from 59 patients: 27 (46%) girls, 46 (78%) classic CAH, 19 (32%) prepubertal. Mean age at first visit was 11.9 ± 4.0 years and mean BMI SDS 0.6 ± 1.1. SPLS-DA using 24h urine metabolites showed clear clustering of optimally treated patients on two components, while undertreated patients were more heterogenous (AUC 0.88). The model selected pregnanetriol and 17α-hydroxypregnanolone contributing to excluding optimal treatment and 5 metabolites contributing to excluding undertreatment: 17β-estradiol, cortisone, tetrahydroaldosterone, androstenetriol, and etiocholanolone. Addition of clinical variables marginally improved classification (AUC 0.90).

Conclusions: Using ML on 24h urine steroid profiling predicted treatment outcome in children with CAH, even in the absence of clinical data, suggesting that routine comprehensive 24h urine steroid profiling could improve treatment monitoring in CAH.