基于生物信息学技术构建代谢功能障碍相关脂肪性肝病风险预测模型的研究进展
Research Progress on Constructing Risk Prediction Models for Metabolic Associated Steatotic Liver Disease Based on Bioinformatics Technology
摘要: 代谢功能障碍相关脂肪性肝病(Metabolic Associated Steatotic Liver Disease, MASLD)是全球发病率最高的慢性肝脏疾病之一，其特征是肝脏脂肪过度积累，与胰岛素抵抗相关，并定义为肝细胞中脂肪变性>5%的组织学存在。MASLD疾病谱广泛，早期阶段往往没有明显症状，导致很多患者错过了最佳干预时机。因此，通过有效手段早期识别高危人群并进行风险分层，对于阻止或延缓疾病进展具有重要意义。随着生物信息学技术的飞速发展，基于多组学数据的MASLD风险预测模型研究取得了显著进展，本综述总结了风险预测模型最新研究进展及其特点。
Abstract: Metabolic Associated Steatotic Liver Disease (MASLD) is one of the most prevalent chronic liver diseases in the world. MASLD is characterized by excessive accumulation of fat in the liver and is associated with insulin resistance. And defined as the histological presence of steatosis >5% in hepatocytes. MASLD has a wide spectrum of diseases, and there are often no obvious symptoms in the early stage, resulting in many patients missing the best opportunity for intervention. Therefore, early identification and risk stratification of high-risk groups by effective means is of great significance for preventing or delaying disease progression. With the rapid development of bioinformatics technology, significant progress has been made in the research of MASLD risk prediction models based on multi-omics data. This review summarizes the latest research progress and characteristics of risk prediction models.
文章引用：卢琛, 钟东升. 基于生物信息学技术构建代谢功能障碍相关脂肪性肝病风险预测模型的研究进展[J]. 临床医学进展, 2026, 16(9): 529-533. https://doi.org/10.12677/acm.2026.1693076

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