Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: Insights from Bayesian-network modelling
BackgroundAn 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 ove...
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| Format: | Article |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1063882/full |
| _version_ | 1797900584635858944 |
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| author | T. Waddell T. Waddell A. Namburete P. Duckworth A. Fichera A. Telford H. Thomaides-Brears D. J. Cuthbertson D. J. Cuthbertson M. Brady |
| author_facet | T. Waddell T. Waddell A. Namburete P. Duckworth A. Fichera A. Telford H. Thomaides-Brears D. J. Cuthbertson D. J. Cuthbertson M. Brady |
| author_sort | T. Waddell |
| collection | DOAJ |
| description | BackgroundAn 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.MethodsWe 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.ResultsIn 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%.ConclusionUsing 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. |
| first_indexed | 2024-04-10T08:48:15Z |
| format | Article |
| id | doaj.art-b4428c44b52b4f7ebe345276cd0b5650 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-10T08:48:15Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-b4428c44b52b4f7ebe345276cd0b56502023-02-22T05:27:52ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-02-011410.3389/fendo.2023.10638821063882Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: Insights from Bayesian-network modellingT. Waddell0T. Waddell1A. Namburete2P. Duckworth3A. Fichera4A. Telford5H. Thomaides-Brears6D. J. Cuthbertson7D. J. Cuthbertson8M. Brady9Department of Engineering Science, The University of Oxford, Oxford, United KingdomPerspectum Ltd, Oxford, United KingdomDepartment of Computer Science, The University of Oxford, Oxford, United KingdomOxford Robotics Institute, The University of Oxford, Oxford, United KingdomPerspectum Ltd, Oxford, United KingdomPerspectum Ltd, Oxford, United KingdomPerspectum Ltd, Oxford, United KingdomDepartment of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United KingdomLiverpool University Hospitals NHS Foundation Trust, Liverpool, United KingdomPerspectum Ltd, Oxford, United KingdomBackgroundAn 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.MethodsWe 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.ResultsIn 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%.ConclusionUsing 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.https://www.frontiersin.org/articles/10.3389/fendo.2023.1063882/fullEctopic fat depositionmagnetic-resonance imagingtype-2 diabetesnon-alcoholic steatohepatitisbody composition |
| spellingShingle | T. Waddell T. Waddell A. Namburete P. Duckworth A. Fichera A. Telford H. Thomaides-Brears D. J. Cuthbertson D. J. Cuthbertson M. Brady Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: Insights from Bayesian-network modelling Frontiers in Endocrinology Ectopic fat deposition magnetic-resonance imaging type-2 diabetes non-alcoholic steatohepatitis body composition |
| title | Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: Insights from Bayesian-network modelling |
| title_full | Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: Insights from Bayesian-network modelling |
| title_fullStr | Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: Insights from Bayesian-network modelling |
| title_full_unstemmed | Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: Insights from Bayesian-network modelling |
| title_short | Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: Insights from Bayesian-network modelling |
| title_sort | poor glycaemic control and ectopic fat deposition mediates the increased risk of non alcoholic steatohepatitis in high risk populations with type 2 diabetes insights from bayesian network modelling |
| topic | Ectopic fat deposition magnetic-resonance imaging type-2 diabetes non-alcoholic steatohepatitis body composition |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1063882/full |
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