1Department of Engineering Science, The University of Oxford, Oxford, United Kingdom.
2Perspectum Ltd, Oxford, United Kingdom.
3Department of Computer Science, The University of Oxford, Oxford, United Kingdom.
4Oxford Robotics Institute, The University of Oxford, Oxford, United Kingdom.
5Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
6Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom.
Background: An estimated 55.5% and 37.3% of people globally with type 2 diabetes (T2D) will have concomitant non-alcoholic fatty liver disease (NAFLD) and the more severe fibroinflammatory stage, non-alcoholic steatohepatitis (NASH). NAFLD and NASH prevalence is projected to increase exponentially over the next 20 years. Bayesian Networks (BNs) offer a powerful tool for modelling uncertainty and visualising complex systems to provide important mechanistic insight.
Methods: We applied BN modelling and probabilistic reasoning to explore the probability of NASH in two extensively phenotyped clinical cohorts: 1) 211 participants with T2D pooled from the MODIFY study & UK Biobank (UKBB) online resource; and 2) 135 participants without T2D from the UKBB. MRI-derived measures of visceral (VAT), subcutaneous (SAT), skeletal muscle (SMI), liver fat (MRI-PDFF), liver fibroinflammatory change (liver cT1) and pancreatic fat (MRI-PDFF) were combined with plasma biomarkers for network construction. NASH was defined according to liver PDFF >5.6% and liver cT1 >800ms. Conditional probability queries were performed to estimate the probability of NASH after fixing the value of specific network variables.
Results: In the T2D cohort we observed a stepwise increase in the probability of NASH with each obesity classification (normal weight: 13%, overweight: 23%, obese: 36%, severe obesity: 62%). In the T2D and non-T2D cohorts, elevated (vs. normal) VAT conferred a 20% and 1% increase in the probability of NASH, respectively, while elevated SAT caused a 7% increase in NASH risk within the T2D cohort only. In those with T2D, reducing HbA1c from the 'high' to 'low' value reduced the probability of NASH by 22%.
Conclusion: Using BNs and probabilistic reasoning to study the probability of NASH, we highlighted the relative contribution of obesity, ectopic fat (VAT and liver) and glycaemic status to increased NASH risk, namely in people with T2D. Such modelling can provide insights into the efficacy and magnitude of public health and pharmacological interventions to reduce the societal burden of NASH.