ArticleScientific reports2025
Development and validation of a scoring system to predict MASLD patients with significant hepatic fibrosis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Overcoming the barriers in the screening, diagnosis, and follow-up of patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH).Reviews in endocrine & metabolic disorders · 2026Review
- Refining MASLD Phenotypes: Clinical, Metabolic, and Elastographic Differences Between Adipose Tissue Dysfunction and Obesity-Driven Disease.Nutrients · 2025Observational
- Crosstalk Between Metabolic Dysfunction-Associated Steatotic Liver Disease and Atrial Fibrillation: Shared Mechanism, Diagnostic Integration, and Management Implications.Life (Basel, Switzerland) · 2025Review
- Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature.World journal of gastroenterology · 2025Article
- Impact of Short-Term Liraglutide Therapy on Non-Invasive Markers of Liver Fibrosis in Patients with MASLD.Metabolites · 2025Article
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Authors and funding
12 authors.
Funding
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Abstract
To address the need for a simple model to predict ≥ F2 fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, a study utilized data from 791 biopsy-proven MASLD patients from the NASH Clinical Research Network and Jinan University First Affiliated Hospital. The data were divided into training and internal testing sets through randomized stratified sampling. A multivariable logistic regression model using key categorical variables was developed to identify ≥ F2 fibrosis. External validation was performed using data from the FLINT trial and multiple centers in China. The DA-GAG score, incorporating diabetes, age, GGT, aspartate aminotransferase/ platelet ratio, and globulin/ total protein ratio, demonstrated superior performance in distinguishing ≥ F2 fibrosis with an area under the receiver operating characteristic curve of 0.79 in training and over 0.80 in testing datasets. The DA-GAG score efficiently identifies MASLD patients with ≥ F2 fibrosis, significantly reducing the medical burden.
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