ECE2017 Oral Communications Receptors & Signalling (5 abstracts)
Accurate staging of non-alcoholic fatty liver disease through analysis of the urinary steroid metabolome
Ahmad Moolla 1 , Amin Amin 2 , Beverly Hughes 2 , Wiebke Arlt 2 , Zaki Hassan-Smith 2 , Lorna Gilligan 2 , Matt Armstrong 3 , Philip Newsome 3 , Tahir Shah 3 , Luc Van Gaal 4 , An Verrijken 4 , Sven Francque 4 , Jane Grove 5 , Neil Guha 5 , Guruprasad Aithal 5 , Ellie Barnes 6 , Michael Biehl 7 & Jeremy Tomlinson 1
1Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK; 2Centre for Endocrinology, Diabetes & Metabolism, University of Birmingham, Birmingham, UK; 3Centre for Liver Research, University of Birmingham, Birmingham, UK; 4University of Antwerp, Antwerp, Belgium; 5NIHR Nottingham Digestive Diseases Biomedical Research Unit, Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, UK; 6Translational Gastroenterology Unit, University of Oxford, Oxford, UK; 7University of Groningen, Groningen, The Netherlands.
Introduction: Non-alcoholic fatty liver disease (NAFLD) is associated with dysregulated glucocorticoid metabolism. Advanced stages of NAFLD are associated with adverse outcome and current strategies to stage disease severity are still reliant upon liver biopsy. We have previously described changes to enzymatic pathways that regulate cortisol bioavailability; 11β-hydroxysteroid dehydrogenase type-1 (11β-HSD1) regenerates cortisol from inactive cortisone, and A-ring reductases, 5α- and 5β-reductase (5αR/5βR), inactivate cortisol to tetrahydrocortisol metabolites (THF/5αTHF). Changes to these pathways are apparent across the NAFLD spectrum (simple steatosis, steatohepatitis (NASH), fibrosis and cirrhosis). Here we have further validated these observations in a large cohort of patients with NAFLD and compared these to healthy controls and to patients with alcoholic cirrhosis.
Methods: Using gas chromatography / mass spectrometry, we analysed steroid metabolites in spot urine samples (corrected for creatinine) in patients with biopsy proven NASH (n=65), NAFLD cirrhosis (n=51), alcoholic cirrhosis (n=48) and in healthy controls (n=58). Additionally, we used machine learning-based analysis to investigate changes across 32 steroid metabolites.
Results: Cortisol regeneration (11β-HSD1 activity: THF+5αTHF/THE ratio), was significantly increased in NAFLD cirrhosis (P=0.0004), and alcoholic cirrhosis (P<0.0001), compared to controls. Cortisol inactivation (A-ring reductase activity: THF/5αTHF ratio) was significantly reduced in NAFLD cirrhosis compared to controls (P=0.0004), but not between controls and alcoholic cirrhosis (P>0.99) nor NASH (P=0.332). Machine learning-based analysis by generalised matrix-learning vector quantisation (GMLVQ) achieved excellent separation of control and NASH groups (AUC-ROC=0.87). Furthermore, there was near perfect separation of controls from NAFLD cirrhosis (AUC-ROC=0.99) and controls from alcoholic cirrhosis (AUC-ROC=0.98).
Conclusion: This data is consistent with our previous findings that identified differentially regulated steroid metabolic pathways across the spectrum of NAFLD. Furthermore, unbiased GMLVQ analysis of the urinary steroid metabolome appears robust in differentiating healthy controls from cirrhosis and warrants further exploration as a novel non-invasive biomarker tool to assess the severity of NAFLD.
Volume 49
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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