ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2024
Institutional Nomogram for Estimating Risk of Metabolic Associated Fatty Liver Disease (MAFLD).
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Novel inflammatory metabolic parameters as predictors of metabolic dysfunction-associated fatty liver disease in people living with HIV receiving antiretroviral therapy: a retrospective cohort study.Frontiers in public health · 2026Article
- Establishment of a Risk Prediction Model for Metabolic Dysfunction-Associated Steatotic Liver Disease in People Living with HIV in China.Infection and drug resistance · 2026Article
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Authors and funding
11 authors.
Funding
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Abstract
Background: Metabolic Associated Fatty Liver Disease (MAFLD) poses a significant threat to human health, as it can result in hepatic fibrosis and potentially progress to cirrhosis, in addition to causing a range of extrahepatic complications. The early detection of MAFLD is crucial, particularly during the initial stages when the condition may be amenable to reversal and the body composition could be vital importance. Methods: Data from participants at the Jiangsu Province Hospital of Traditional Chinese Medicine, covering the period from January 1 to December 31, 2022, were collected and subsequently randomized into training and validation cohorts. Independent risk factors for MAFLD were identified using statistical methodologies in conjunction with clinical relevance, and these factors were ultimately utilized to develop the nomogram. Results: In the training cohort, there were 356 cases of MAFLD out of a total of 513 patients, representing 71.2%, while in the validation cohort, 161 cases of MAFLD were identified out of 220 patients, accounting for 73.2%. In terms of statistical outcomes and clinical relevance, we identified a total of 12 closely related or significant variables. To enhance our understanding of the critical role of body composition parameters in predicting the incidence of MAFLD, we developed two distinct nomograms, one of which included body composition data. Notably, the nomogram that incorporated body composition demonstrated superior predictive performance, as evidenced by a well-fitted calibration curve and a C-index of 0.893 (with a range of 0.8625 to 0.9242). Furthermore, the decision curve analysis indicated that utilizing the nomogram that included body composition would yield greater benefits. Conclusion: The nomogram serves as an effective tool for predicting MAFLD. Its utility in early risk identification of MAFLD is of significant importance, as it facilitates timely intervention and treatment for patients affected by this condition.
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