Establishing of a risk prediction model for metabolic dysfunction-associated steatotic liver disease: a retrospective cohort study.

OBJECTIVES

Over 30% of people worldwide suffer from metabolic dysfunction-associated steatotic liver disease (MASLD), a significant global health issue. Identifying and preventing high-risk individuals for MASLD early is crucial. The purpose of our study is to investigate the factors related to the development of MASLD and develop a risk prediction model for its occurrence.

METHODS

The study included 5107 subjects, divided into training and validation groups in a 7:3 ratio using a random number table method. Collinearity diagnosis and Cox regression were used to identify factors associated with MASLD incidence, and a risk prediction model was created. The model's accuracy, reliability, and clinical applicability were assessed.

RESULTS

Our study indicated that male, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG), serum uric acid to creatinine ratio (SUA/Cr) and white blood cell (WBC) were associated with MASLD incidence. The elements were determined to be crucial for creating a risk prediction model. The model showed strong discriminative potential with a C-index of 0.783 and the time-dependent AUCs of 0.781, 0.789, 0.814 and 0.796 for 1-4 years in the training group, and a C-index of 0.788 and the time-dependent AUCs of 0.798, 0.782, 0.787 and 0.825 for 1-4 years in validation. Calibration curves confirmed the model's accuracy, and decision curve analysis (DCA) validated its clinical utility.

CONCLUSIONS

The model may provide clinical physicians with a reliable method for identifying high-risk populations for MASLD and serve as a guide for developing prediction models for other diseases.