Steroid metabolomics: a rapid computational approach for accurate differentiation of inborn disorders of steroidogenesis

2019

Background: Measurement of steroid metabolite excretion in urine by gas chromatography-mass spectrometry (GC-MS) provides a comprehensive profile of an individual’s adrenal and gonadal steroid production. It has long been acknowledged as a useful tool for diagnosis of inborn disorders of steroidogenesis leading to congenital adrenal hyperplasia and disorders of sex development. Ratios of steroid metabolites can be employed as surrogates for enzymatic activities of distinct steroidogenic enzymes and can also be applied to single random urine samples, making this a more feasible approach for use with paediatric patients. However, widespread use in the acute setting for diagnosis of these disorders is hampered by the considerable expertise required for interpretation. Here we developed a novel steroid metabolomics approach for the detection and differential diagnosis of inborn steroidogenic disorders, combining mass spectrometry-based steroid profiling with machine learning-based data analysis, suitable for automation and interpretation without specialist expertise. Methods: We performed multi-steroid profiling by GC-MS, quantifying 34 steroid metabolites, in urine samples from 829 healthy controls and 178 untreated patients with inborn steroidogenic disorders. This cohort included patients with inborn deficiencies in the following enzymes: CYP21A2 (n=26), CYP11B1 (n=12), CYP17A1 (n=30), POR (n=37), HSD3B2 (n=22), and SRD5A2 (n=51). We assessed the diagnostic performance of conventional biochemical assessment employing 15 steroid precursor-to-product ratios, each historically established as indicative of a distinct steroidogenesis disorder. We compared this to the performance of our novel steroid metabolomics approach, which involved analysis of the GC-MS multi-steroid profiles by a custom-designed approach, Angle Learning Vector Quantization (ALVQ), which classifies samples by comparing similarity of their steroid metabolome to representative steroid metabolome prototypes for each enzyme deficiency. Results: The conventional biochemical steroid ratio approach demonstrated acceptable sensitivity and specificity. However, the automated steroid metabolomics approach (ALVQ) performed significantly superior to this, particularly with regards to specificity. For differentiating patients from healthy controls, sensitivity and specificity of ALVQ were 97% and 98%, respectively. For differentiation of each pathogenic enzymatic defect, ALVQ performed superiorly, with sensitivity and specificity ranging between 95 and 100%. Conclusion: We present a novel steroid metabolomics approach, able to automatically detect and differentiate six different inborn disorders of steroidogenesis, with improved performance when compared to reference standard metabolite ratios. Steroid metabolomics can expedite and standardise interpretation of complex urinary steroid metabolome data, making this technique more accessible to clinicians, and has excellent potential for implementation in routine clinical practice.

Link

@inproceedings{baranowski2019EA,
title = {Steroid metabolomics: a rapid computational approach for accurate differentiation of inborn disorders of steroidogenesis},
author = {Baranowski, Elizabeth and Ghosh, Sreejita and Shackleton, Cedric HL and Taylor, Angela E and Hughes, Beverley A and    Lorna C Gilligan and Agustini Utari and Sultana MH Faradz and Antonius E van Herwaarden and Hedi L Claahsen-van der Grinten and     Biehl, Michael and Guran, Tulay and Bunte, Kerstin Tino, Peter and Arlt, Wiebke},
booktitle = {Endocrine Abstracts},
volume = {63},
year = {2019},
month = {5},
day = {1},
doi = {10.1530/endoabs.63.OC10.1},
publisher = {bioscientifica},
abstract = {   Background: Measurement of steroid metabolite excretion in urine by gas chromatography-mass spectrometry (GC-MS) provides a comprehensive profile of an individual’s adrenal and gonadal steroid production. It has long been acknowledged as a useful tool for diagnosis of inborn disorders of steroidogenesis leading to congenital adrenal hyperplasia and disorders of sex development. Ratios of steroid metabolites can be employed as surrogates for enzymatic activities of distinct steroidogenic enzymes and can also be applied to single random urine samples, making this a more feasible approach for use with paediatric patients. However, widespread use in the acute setting for diagnosis of these disorders is hampered by the considerable expertise required for interpretation. Here we developed a novel steroid metabolomics approach for the detection and differential diagnosis of inborn steroidogenic disorders, combining mass spectrometry-based steroid profiling with machine learning-based data analysis, suitable for automation and interpretation without specialist expertise.    Methods: We performed multi-steroid profiling by GC-MS, quantifying 34 steroid metabolites, in urine samples from 829 healthy controls and 178 untreated patients with inborn steroidogenic disorders. This cohort included patients with inborn deficiencies in the following enzymes: CYP21A2 (n=26), CYP11B1 (n=12), CYP17A1 (n=30), POR (n=37), HSD3B2 (n=22), and SRD5A2 (n=51). We assessed the diagnostic performance of conventional biochemical assessment employing 15 steroid precursor-to-product ratios, each historically established as indicative of a distinct steroidogenesis disorder. We compared this to the performance of our novel steroid metabolomics approach, which involved analysis of the GC-MS multi-steroid profiles by a custom-designed approach, Angle Learning Vector Quantization (ALVQ), which classifies samples by comparing similarity of their steroid metabolome to representative steroid metabolome prototypes for each enzyme deficiency.    Results: The conventional biochemical steroid ratio approach demonstrated acceptable sensitivity and specificity. However, the automated steroid metabolomics approach (ALVQ) performed significantly superior to this, particularly with regards to specificity. For differentiating patients from healthy controls, sensitivity and specificity of ALVQ were 97% and 98%, respectively. For differentiation of each pathogenic enzymatic defect, ALVQ performed superiorly, with sensitivity and specificity ranging between 95 and 100%.    Conclusion: We present a novel steroid metabolomics approach, able to automatically detect and differentiate six different inborn disorders of steroidogenesis, with improved performance when compared to reference standard metabolite ratios. Steroid metabolomics can expedite and standardise interpretation of complex urinary steroid metabolome data, making this technique more accessible to clinicians, and has excellent potential for implementation in routine clinical practice.  },
language = {English},
issn = {1479-6848},
}