Steroid metabolomics for accurate and rapid diagnosis of inborn steroidogenic disorders

2017

Background Urinary steroid metabolite profiling is an accurate reflection of adrenal and gonadal steroid output and metabolism in peripheral target cells of steroid action. Measurement of steroid metabolite excretion by gas chromatography-massspectrometry (GC–MS) is considered reference standard for biochemical diagnosis of steroidogenic disorders. However, performance of GC–MS analysis and interpretation of the resulting data requires significant expertise and age- and sex-specific reference ranges. Here we developed novel computational approaches for rapid interpretation of GC–MS data for diagnosis of inborn steroidogenic disorders Methods We analysed the urinary steroid metabolome by GC–MS in 829 healthy controls(302 neonates and infants, 167 children and 360 adults) and 118 untreated patients with genetically confirmed inborn disorders (21-hydroxylase deficiency,17-hydroxylase deficiency, POR deficiency, 11b-hydroxylase deficiency, 3b-HSD2 deficiency, 17b-HSD3 deficiency, 5a-reductase type 2 deficiency, cytochrome b5 deficiency). We calculated age-related normative values for established metabolite ratios representing distinct enzymatic functions. We developed a novel interpretable machine learning technique, Angle Learning Vector Quantisation (ALVQ), which looks at all possible metabolite ratios, computationally reduces these to the most relevant for discrimination, and differentiates disease states by comparison to a representative prototype. The method runs independent of sex and age information, units of measurement and method of urine collection. Results Conventional biochemical ratios had 100% sensitivity but only very poor specificity. By contrast, ALVQ predicted 'affected urine' vs 'healthy urine' with 100% sensitivity and 97% specificity. For our three most prevalent conditions(PORD, SRD5A2 and CYP21A2), the specific condition was identified correctly in 96% of cases. Conclusion We developed a novel Steroid Metabolomics approach to automatically diagnose inborn steroidogenic disorders with very high sensitivity and specificity, superior to current methods, and with high potential for implementation in routine clinical care.

Link

@inproceedings{baranowski2017steroid,
title = {Steroid metabolomics for accurate and rapid diagnosis of inborn steroidogenic disorders},
author = {Baranowski, Elizabeth and Bunte, Kerstin and Shackleton, Cedric HL and Taylor, Angela E and Hughes, Beverley A and Biehl, Michael and Tino, Peter and Guran, Tulay and Arlt, Wiebke},
booktitle = {Endocrine Abstracts},
number = {9},
year = {2017},
doi = {10.1530/endoabs.49.OC1.3},
publisher = {bioscientifica},
abstract = {   Background   Urinary  steroid  metabolite  profiling  is  an  accurate  reflection  of  adrenal  and gonadal steroid output and metabolism in peripheral target cells of steroid action.   Measurement  of  steroid  metabolite  excretion  by  gas  chromatography-massspectrometry (GC–MS) is considered reference standard for biochemical diagnosis of steroidogenic disorders.    However, performance of GC–MS analysis and interpretation of the resulting data requires significant expertise and age- and sex-specific reference ranges.    Here we developed novel computational approaches  for  rapid  interpretation  of  GC–MS  data  for  diagnosis  of  inborn steroidogenic disorders   Methods   We analysed the urinary steroid metabolome by GC–MS in 829 healthy controls(302 neonates and infants, 167 children and 360 adults) and 118 untreated patients with    genetically confirmed inborn disorders (21-hydroxylase deficiency,17-hydroxylase deficiency, POR deficiency, 11b-hydroxylase deficiency, 3b-HSD2 deficiency, 17b-HSD3 deficiency, 5a-reductase type 2   deficiency, cytochrome b5 deficiency).    We calculated age-related normative values for established metabolite ratios representing distinct enzymatic functions.   We developed a novel interpretable machine learning technique, Angle Learning Vector Quantisation (ALVQ), which looks at all possible metabolite ratios,   computationally reduces these to the most relevant for discrimination, and differentiates disease states by comparison to a representative prototype.    The method runs independent of sex and age information, units of measurement and method of urine collection.    Results Conventional biochemical ratios had 100% sensitivity but only very poor specificity. By contrast, ALVQ predicted 'affected urine' vs 'healthy urine' with 100% sensitivity and 97% specificity.   For our three most prevalent conditions(PORD, SRD5A2 and CYP21A2), the specific condition was identified correctly in 96% of cases.    Conclusion    We developed a novel Steroid Metabolomics approach to automatically diagnose inborn steroidogenic disorders with very high sensitivity and specificity, superior to current methods,     and with high potential for implementation in routine clinical care.  },
}