ArticleThe Journal of clinical endocrinology and metabolism2024
Machine Learning Reveals the Contribution of Lipoproteins to Liver Triglyceride Content and Inflammation.
Article in The Journal of clinical endocrinology and metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Applying Artificial Intelligence to Childhood Obesity: T2DM and MASLD Risk Predictive Models.Diagnostics (Basel, Switzerland) · 2026Review
- Small dense LDL: An underestimated driver of atherosclerosis (Review).Molecular medicine reports · 2025Review
- Pediatric Hypertriglyceridemia: Lipoprotein Metabolism, Etiology, and Management.Current pediatrics reports · 2025Article
- Clinical staging to guide management of metabolic disorders and their sequelae: a European Atherosclerosis Society consensus statement.European heart journal · 2025Article
- Precision medicine and nucleotide-based therapeutics to treat steatotic liver disease.Clinical and molecular hepatology · 2025Review
- Machine Learning Reveals the Contribution of Lipoproteins to Liver Triglyceride Content and Inflammation.The Journal of clinical endocrinology and metabolism · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
contextMetabolic dysfunction-associated steatotic liver disease (MASLD) is currently the most common chronic liver disease worldwide and is strongly associated with metabolic comorbidities, including dyslipidemia.
objectiveHerein, 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.
methodsWe 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).
resultsThere 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.
conclusionIsolated 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.