Machine Learning Reveals the Contribution of Lipoproteins to Liver Triglyceride Content and Inflammation

Abstract

Context: Metabolic dysfunction-associated steatotic liver disease (MASLD) is currently the most common chronic liver disease worldwide and is strongly associated with metabolic comorbidities, including dyslipidemia.

Objective: Herein, we aim to estimate the prevalence of MASLD and metabolic dysfunction-associated steatohepatitis (MASH) in Europeans with isolated hypercholesterolemia and isolated hypertriglyceridemia in the UK Biobank and to estimate the independent contribution of lipoproteins to liver triglyceride content.

Methods: We selected 218 732 Europeans from the UK Biobank without chronic viral hepatitis and other causes of liver disease, of whom 14 937 with liver magnetic resonance imaging data available. Next, to examine the relationships between traits in predicting liver triglyceride content, we compared the predictive performance of several machine learning methods and selected the best performing algorithms based on the minimum cross-validated mean squared error (MSE).

Results: There was an approximately 3-fold and 4-fold enrichment of MASLD and MASH in individuals with isolated hypertriglyceridemia (P = 1.23 × 10-41 and P = 1.29 × 10-10, respectively), whereas individuals with isolated hypercholesterolemia had a marginal higher rate of MASLD and no difference in MASH rate compared with the control group (P = .019 and P = .97, respectively). Among machine learning methods, the feed-forward neural network had the best cross-validation MSE on the validation set. Circulating triglycerides, after body mass index, were the second strongest independent predictor of liver proton density fat fraction with the largest absolute mean Shapley additive explanation value.

Conclusion: Isolated hypertriglyceridemia is the second strongest, after obesity, independent predictor of MASLD/MASH. Individuals with hypertriglyceridemia, but not with hypercholesterolemia, should be screened for liver disease.

Keywords: MASLD; MRI-PDFF; NAFLD; NASH; artificial intelligence; neural network.