Development and validation of a nomogram for predicting metabolic-associated fatty liver disease in the Chinese physical examination population

Abstract

Aim We aim to develop and validate a nomogram including readily available clinical and laboratory indicators to predict the risk of MAFLD in the Chinese physical examination population.Methods The 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, and the 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.Results Ten variables were selected to establish the nomogram for predicting MAFLD risk: sex, age, waist circumference, uric acid, BMI, WHR, SBP, FPG, TG, and ALT. The nomogram built on the non-overfitting multivariable model showed good prediction of discrimination (AUC 0.914, 95% CI: 0.911–0.917), calibration, and clinical utility.Conclusions This 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.