ArticleEuropean journal of medical research2025
A nomogram for assessing the risk of MAFLD based on the albumin-to-gamma-glutamyl transferase ratio.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
aimThe prevalence of metabolic-associated fatty liver disease (MAFLD) is rapidly increasing, posing a pressing challenge to global health. The purpose of this study is to establish a predictive model based on the immunoinflammatory marker, the albumin-gamma-glutamyl transferase ratio (AGTR), to facilitate the early recognition and risk assessment of MAFLD.
methodsThis study used data from 2331 participants in the NHANES database from 2017 to 2018. LASSO and logistic regression analyses were employed to identify risk factors and to establish a nomogram prediction model for MAFLD. External validation was conducted using data from the NHANES 2021-2023. Sensitivity analysis was employed to investigate the independent predictive value of AGTR for MAFLD.
resultsThe results indicated that among 67 variables, BMI, waist-to-hip ratio, waist circumference, AGTR and the triglyceride-glucose index (TyG) were all independent influencing factors for MAFLD. These risk factors were used to create a nomogram prediction risk model, which had AUCs for the training set, internal validation and external validation sets of 0.847 (95% CI 0.830-0.866), 0.834 (95% CI 0.806-0.863) and 0.851 (95% CI 0.834-0.868), respectively. The Hosmer-Lemeshow test p values were all greater than 0.05. Calibration and DCA indicate that the predictive model possesses good consistency and clinical validity. The sensitivity analysis revealed that AGTR remained an independent predictor following adjustment for demographic and lifestyle confounders.
conclusionsAs an independent immune-inflammatory predictor of MAFLD, early monitoring of AGTR may be crucial for predicting the emergence of MAFLD. Meanwhile, the nomogram model established in this study can identify high-risk patients for MAFLD.
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