Principal component analysis (PCA) is a mathematical model which simplifies data into new, combined variables. Optimal treatment of pediatric congenital adrenal hyperplasia (CAH) remains a challenge and requires evaluation of all biochemical and clinical markers. The aim of this study was to introduce PCA methodology as a tool to optimize management in a cohort of pediatric and adolescent patients with CAH by including adrenal steroid measurements and clinical parameters.
This retrospective, longitudinal cohort of 33 children and adolescents with CAH due to 21-hydroxylase deficiency included 406 follow-up observations. PCAs were applied to serum hormone concentrations and compared to treatment efficacy evaluated by clinical parameters.
We provide and describe the first PCA models with hormone parameters denoted in sex- and age-adjusted standard deviation (SD) scores to comprehensibly describe the combined ‘endocrine profiles’ of patients with classical and non-classical CAH, respectively. Endocrine profile scores were predictive markers of treatment efficacy for classical (AUC=92%; accuracy 95%; p=1.8e-06) and non-classical CAH (AUC=80%; accuracy 91%; p=0.004). A combined PCA demonstrated clustering of patients with classical and non-classical CAH by serum 17-hydroxyprogesterone (17-OHP) and dehydroepiandrosterone-sulphate (DHEAS) concentrations.
As an example of the possibilities of PCA, endocrine profiles were successfully able to distinguish between patients with CAH according to treatment efficacy and to elucidate biochemical differences between classical and non-classical CAH.