Steroid Metabolomics: A Powerful Technique for Differentiating Inborn Disorders of Steroidogenesis

Background: The urinary steroid metabolome is considered the fingerprint of adrenal gland function. Novel methods using mass spectrometry profiling have seen the advent of a new era for metabolomics with powerful implications for both diagnostics and discovery. Its interpretation is difficult and performed by few specialists with the expertise to do so. This makes it a relatively inaccessible tool for the majority of Clinical Endocrinologists. Objective: To create an automatous method for accurate diagnosis and differentiation of inborn steroidogenic disorders using 34 distinct measured urinary steroid metabolites, that is accurate, reproducible and suitable for high-throughput use. Methods: Using GC/MS, 829 healthy control urines were analysed (302 neonates and infant, 149 children, 18 adolescents, 326 adults, 34 unknown age) and baseline urine from 118 newly diagnosed with inborn steroidogenic disorders (P450 oxidoreductase deficiency, 21 hydroxylase deficiency, 5a reductase deficiency, 17bHSD3 deficiency, 17 hydroxylase deficiency, 3bHSD2 deficiency, 11b hydroxylase type 1 and type 2 deficiency and cyt B5 deficiency). We custom-designed an interpretable machine learning technique, Angle Learning Vector Quantisation, designed to distinguish these conditions using the urinary steroid metabolome. We looked at all possible steroid ratios and by means of ANOVA reduced this to 165 most informative steroid ratios. Using these, the method is able to computationally determine a reduced list of the most relevant ratios to differentiate specific disorders. The method runs independent of sex and age information, method of urine collection (spot, nappy, 24 h collection), and compensates for missing measurements. Results: Our machine learning method was able to predict an affected urine vs a healthy urine with a sensitivity of 100% and specificity of 97%. For our three most prevalent conditions (PORD, SRD5A2 and CYP21A2), the method correctly identified the specific condition in 96% of cases. Where it incorrectly identified the condition, it was mistaken for a biologically very similar one. Conclusion: We have developed a novel machine learning which is highly sensitive and specific. With further validation, it has potential for application in routine clinical practice.