Establishment and Analysis of Risk Prediction Model for Metabolic Dysfunction-Associated Fatty Liver Disease in Physical Examination Population

  Objective   To analyze the risk factors of metabolic dysfunction-associated fatty liver disease (MAFLD) in the physical examination population, to establish a risk prediction model for the occurrence of MAFLD, and to provide management strategies for the prevention and occurrence of the disease.

  Methods   A total of 14664 people who underwent physical examination at the Physical Examination Center, West China Hospital, Sichuan University between January 2018 and December 2021 were selected as research subjects. The subjects were divided into a MAFLD group (n=4013) and a non-MAFLD group (n=10651) according to whether they had MAFLD. The differences in biochemical indices, for example, glycolipid metabolism levels, were compared and logistic regression was conducted to analyze the risk factors for MAFLD, thereby establishing a nomogram prediction model. The prediction effect of the model was validated and evaluated with the consistency index (C-index) and the calibration curve.

  Results  Among the 14664 subjects who underwent physical examination, 4013 were MAFLD patients, presenting an overall prevalence of 27.37%, with significantly higher prevalence in men than that in women (38.99% vs. 10.06%, P<0.001). Compared with those of the non-MAFLD group, the levels of glucose (GLU), total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transpeptidase (GGT) and uric acid (UA) were increased (P<0.05), while the high density lipoprotein cholesterol (HDL-C) level was decreased (P<0.05) in the MAFLD group. The results of logistic regression analysis showed that male sex, age, body mass index, GLU, TG and hypertension were all independent risk factors of MAFLD, while HDL-C was a protective factor of MAFLD. The risk factors were used to establish a nomogram risk prediction model and the C-index and calibration curve showed that the nomogram model produced good predictive performance. The receiver operating characteristic (ROC) curve showed that the nomogram model had good predictive value for the risk of MAFLD.

  Conclusion   We found a relatively high prevalence of MAFLD in the physical examination population, and the nomogram model established with routine physical examination screening can provide indications for the clinical screening and analysis of high-risk patients, which has an early warning effect on the high-risk population.