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Updated: May 30, 2025
Author Spotlight: Establishing MASLD Cell Models for Investigating Disease Mechanisms and the Lipid-Lowering Effects of Koumiss
Published on: July 19, 2024
Establishing of a risk prediction model for metabolic dysfunction-associated steatotic liver disease: a retrospective
Nan Li1, Chenbing Liu1, Zhangfan Lu1
1Health Management Center, the First Affiliated Hospital of Zhejiang University School of Medicine, No.79 Qingchun Road, Shangcheng District, Hangzhou, Zhejiang, China.
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.
