ArticleLipids in health and disease2023
Development and validation of a nomogram for predicting metabolic-associated fatty liver disease in the Chinese physical examination population.
Article in Lipids in health and disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Association of glycolipid metabolism 6 factors index with cardiovascular and metabolic diseases, and its predictive value in disease progression.BMC endocrine disorders · 2026Article
- Article
- 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
- Analysis of risk factors associated with metabolic-associated fatty liver disease from Suzhong Region, China: a cross-sectional study.BMJ open · 2025Article
- A nomogram incorporating clinical and laboratory indicators for predicting metabolic dysfunction-associated fatty liver disease in newly diagnosed type 2 diabetes patients.Journal of diabetes investigation · 2025Article
- Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.Scientific reports · 2025Article
- Potential alternative and novel biomarkers for paediatric MAFLD: exploratory evidence from a Chinese cohort.BMC gastroenterology · 2025Article
- Development and validation of a nomogram model for predicting the risk of MAFLD in the young population.Scientific reports · 2024Article
- Nomogram for predicting the risk of nonalcoholic fatty liver disease in older adults in Qingdao, China: A cross-sectional study.Asia Pacific journal of clinical nutrition · 2024Article
- Non-alcoholic fatty liver disease risk prediction model and health management strategies for older Chinese adults: a cross-sectional study.Lipids in health and disease · 2023Article
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6 authors.
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Abstract
aimWe aim to develop and validate a nomogram including readily available clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in the Chinese physical examination population.
methodsThe annual physical examination data of Chinese adults from 2016 to 2020 were retrospectively analyzed. We extracted the clinical data of 138 664 subjects and randomized participants to the development and validation groups (7:3). Significant predictors associated with MAFLD were identified by using univariate and random forest analyses, and a nomogram was constructed to predict the risk of MAFLD based on a Lasso logistic model. Receiver operating characteristic curve analysis, calibration curves, and decision curve analysis were used to verify the discrimination, calibration, and clinical practicability of the nomogram, respectively.
resultsTen variables were selected to establish the nomogram for predicting MAFLD risk: sex, age, waist circumference (WC), uric acid (UA), body mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting plasma glucose (FPG), triglycerides (TG), and alanine aminotransferase (ALT). The nomogram built on the nonoverfitting multivariable model showed good prediction of discrimination (AUC 0.914, 95% CI: 0.911-0.917), calibration, and clinical utility.
conclusionsThis nomogram can be used as a quick screening tool to assess MAFLD risk and identify individuals at high risk of MAFLD, thus contributing to the improved management of MAFLD.
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