[Precise clinical classification of metabolic associated fatty liver disease]

Metabolic associated fatty liver disease (MAFLD) is the most common chronic liver disease worldwide, and its significant clinical and pathophysiological heterogeneity poses a serious challenge to traditional "one-size-fits-all" diagnostic and therapeutic approaches. Therefore, constructing a precise clinical classification system to identify patient subgroups with varying risks and treatment responses has become a core frontier in this field. This article systematically summarizes the main current MAFLD classification methods, covering content from simple classification based on clinical phenotypes (e.g., body mass index) to non-invasive risk stratification via serology and imaging markers, and further extending to deep molecular subtyping driven by cutting-edge multi-omics technologies and artificial intelligence. In addition, it analyzes and points out the limitations of single-dimensional classification approaches. The future development trend is to construct a multi-dimensional, dynamic assessment system that integrates clinical, non-invasive biomarkers and deep molecular features, providing a comprehensive framework for laying a scientific foundation for elucidating the origins of disease heterogeneity, achieving personalized risk assessment, precision-targeted interventions, and innovative clinical trial designs, ultimately propelling MAFLD into the era of precision medicine.