SFEBES2018 Oral Communications Obesity & diabetes (6 abstracts)
Staging of non-alcoholic fatty liver disease through LC-MS/MS analysis of the urinary steroid metabolome
Ahmad Moolla 1 , Angela Taylor 2 , Lorna Gilligan 2 , Jasper De Boer 3 , Amin Amin 2 , David Pavlov 3 , Beverly Hughes 2 , Zaki Hassan-Smith 2 , Matt Armstrong 4 , Philip Newsome 4 , Tahir Shah 4 , Luc Van Gaal 5 , An Verrijken 5 , Sven Francque 5 , Jane Grove 6 , Neil Guha 6 , Guruprasad Aithal 6 , Ellie Barnes 7 , Wiebke Arlt 2 , Michael Biehl 3 & Jeremy Tomlinson 1
1Oxford Centre for Diabetes, Endocrinology and Metabolism, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; 2Centre for Endocrinology, Diabetes & Metabolism, University of Birmingham, Birmingham, UK; 3University of Groningen, Groningen, Netherlands; 4Centre for Liver Research, University of Birmingham, Birmingham, UK; 5University of Antwerp, Antwerp, Belgium; 6NIHR Nottingham Digestive Diseases Biomedical Research Unit, Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, UK; 7Translational Gastroenterology Unit, University of Oxford, Oxford, UK.
Introduction: The development of accurate non-invasive markers to diagnose and stage non-alcoholic fatty liver disease (NAFLD) is of high importance to reduce the need for an invasive liver biopsy. These markers help to stratify patients at highest risk of hepatic and cardio-metabolic complications and allow tracking of disease progression and treatment response. We have previously described alterations in glucocorticoid metabolism that are differentially regulated across the NAFLD spectrum (simple steatosis, steatohepatitis (NASH), fibrosis, cirrhosis) using gas chromatography-mass spectrometry (GC-MS) coupled with machine-learning analysis. However, GC-MS is time-consuming and labour intensive, which can limit its utility. Here we compare GC-MS derived data with high-throughput liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis in patients with NAFLD.
Methods: GC-MS and LC-MS/MS were used to analyse 26 distinct steroid metabolites in spot urine samples (corrected for creatinine) from 71 patients with biopsy-proven NAFLD, including 55 with NAFLD cirrhosis. Machine learning-based analysis (generalised matrix-learning vector quantisation, GMLVQ) was used to determine NAFLD stage and diagnostic performance of GC-MS and LC-MS/MS, respectively.
Results: GMLVQ analysis achieved excellent separation of early from advanced NAFLD fibrosis. Performance was almost identical using GC-MS (AUC-ROC=0.87) and LC-MS/MS (AUC-ROC=0.86), respectively. Significantly, this performance was superior to published, validated non-invasive markers (Fib-4 and NAFLD Fibrosis scores, AUC-ROC=0.80 for both). Additionally, there was very good separation of non-cirrhotic compared to cirrhotic patients (GC-MS AUC-ROC=0.82; LC-MS/MS AUC-ROC=0.77).
Conclusion: Unbiased GMLVQ analysis of the urinary steroid metabolome appears to be a robust non-invasive risk stratification tool in patients with NAFLD and is potentially superior to existing established non-invasive markers. We show also that LC-MS/MS analysis, a more cost- and time-efficient methodology, performs similarly to established GC-MS profiling. With further development and validation, this LC-MS/MS platform has potential to be adopted into large-scale clinical practice to enhance patient care.
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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