BMJ Open (Jan 2024)

Translating the potential of the urine steroid metabolome to stage NAFLD (TrUSt-NAFLD): study protocol for a multicentre, prospective validation study

  • David Sheridan,
  • Jeremy F Cobbold,
  • Jeremy W Tomlinson,
  • William Alazawi,
  • Richard Parker,
  • Alice J Sitch,
  • Fredrik Karpe,
  • David Harman,
  • Hamish Miller,
  • Márta Korbonits,
  • Philip N Newsome,
  • Guruprasad Padur Aithal,
  • Pinelopi Manousou,
  • Wiebke Arlt,
  • Matthew Neville,
  • Michael Biehl

DOI
https://doi.org/10.1136/bmjopen-2023-074918
Journal volume & issue
Vol. 14, no. 1

Abstract

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Introduction Non-alcoholic fatty liver disease (NAFLD) affects approximately one in four individuals and its prevalence continues to rise. The advanced stages of NAFLD with significant liver fibrosis are associated with adverse morbidity and mortality outcomes. Currently, liver biopsy remains the ‘gold-standard’ approach to stage NAFLD severity. Although generally well tolerated, liver biopsies are associated with significant complications, are resource intensive, costly, and sample only a very small area of the liver as well as requiring day case admission to a secondary care setting. As a result, there is a significant unmet need to develop non-invasive biomarkers that can accurately stage NAFLD and limit the need for liver biopsy. The aim of this study is to validate the use of the urine steroid metabolome as a strategy to stage NAFLD severity and to compare its performance against other non-invasive NAFLD biomarkers.Methods and analysis The TrUSt-NAFLD study is a multicentre prospective test validation study aiming to recruit 310 patients with biopsy-proven and staged NAFLD across eight centres within the UK. 150 appropriately matched control patients without liver disease will be recruited through the Oxford Biobank. Blood and urine samples, alongside clinical data, will be collected from all participants. Urine samples will be analysed by liquid chromatography-tandem mass spectroscopy to quantify a panel of predefined steroid metabolites. A machine learning-based classifier, for example, Generalized Matrix Relevance Learning Vector Quantization that was trained on retrospective samples, will be applied to the prospective steroid metabolite data to determine its ability to identify those patients with advanced, as opposed to mild-moderate, liver fibrosis as a consequence of NAFLD.Ethics and dissemination Research ethical approval was granted by West Midlands, Black Country Research Ethics Committee (REC reference: 21/WM/0177). A substantial amendment (TrUSt-NAFLD-SA1) was approved on 26 November 2021.Trial registration number ISRCTN19370855.