2024 年第 3 期 第 19 卷

代谢健康状态和肥胖对代谢相关脂肪性肝病患者发生肝纤维化风险的影响研究

The influence of metabolic health and obesity on the risk of liver fibrosis progression in metabolic associated fatty liver disease

作者：刘云霄1牛丽娜1王转国2余虹1孙微1郭燕2郭峰2王晓忠2

英文作者：Liu Yunxiao1 Niu Lina1 Wang Zhuanguo2 Yu Hong1 Sun Wei1 Guo Yan2 Guo Feng2 Wang Xiaozhong2

单位：1新疆医科大学第四临床医学院，乌鲁木齐830000；2新疆医科大学附属中医医院肝病科，乌鲁木齐830000

英文单位：1Fourth Clinical Medical College of Xinjiang Medical University Urumqi 830000 China; 2Department of Hepatology Traditional Chinese Medical Hospital Affiliated to Xinjiang Medical University Urumqi 830000 China

关键词：代谢相关脂肪性肝病；代谢健康；肥胖；肝纤维化

英文关键词：Metabolicassociatedfattyliverdisease;Metabolichealth;Obesity;Liverfibrosis

• 摘要：

• 目的 探讨代谢健康状态和肥胖对代谢相关脂肪性肝病（MAFLD）患者发生肝纤维化风险的影响。方法 选取2020年1月至2022年12月在新疆医科大学附属中医医院住院确诊为MAFLD患者347例，按代谢是否健康和体重指数是否≥28 kg/m2，分为代谢健康非肥胖组（77例）、代谢不健康非肥胖组（65例）、代谢健康肥胖组（91例）、代谢不健康肥胖组（114例）。比较4组患者临床资料和无创肝纤维化指标及发生肝纤维化的差异，单因素及多因素Logistic回归方法分析MAFLD患者发生进展性肝纤维化的危险因素。构建Logistic回归模型，通过绘制受试者工作特征(ROC)曲线评估模型对MAFLD患者发生进展性肝纤维化的预测价值。结果 代谢不健康的非肥胖和肥胖MAFLD患者发生中重度肝纤维化（≥F2分期）和进展性肝纤维化的比例均明显高于代谢健康者。多因素Logistic回归分析结果显示，体重指数、丙氨酸转氨酶（ALT）、受控衰减参数（CAP）、天冬氨酸转氨酶与血小板计数比值指数(APRI)是MAFLD患者发生进展性肝纤维化的独立危险因素（均P＜0.05）。将上述危险因素纳入Logistic回归分析，建立回归分析模型：logit（P）=-11.67+体重指数×0.20+ALT×0.02+CAP×0.01+APRI×2.73。按照预测概率logit（P）绘制预测MAFLD患者发生进展性肝纤维化的ROC曲线，当logit(P)＞0.478时,曲线下面积为0.782,敏感度为63.3%,特异度为84.5%。结论 体重指数、ALT、CAP、APRI是MAFLD患者发生进展性肝纤维化的危险因素，提示减轻体质量、保肝降酶、控制代谢因素有利于延缓MAFLD患者肝纤维化的发展。

• Objective To investigate the effects of metabolic health and obesity on the risk associated with liver fibrosis in metabolic associated fatty liver disease (MAFLD). Methods From January 2020 to December 2022, 347 patients diagnosed with MAFLD in the Traditional Chinese Medical Hospital Affiliated to Xinjiang Medical University were enrolled. Patients were classified into the metabolic healthy non-obese group (77 cases), the metabolic unhealthy non-obese group (65 cases), the metabolic healthy obese group (91 cases) and the metabolic unhealthy obese group (114 cases), according to whether they had metabolic health or their body mass index (BMI) was ≥28 kg/m2.The differences in clinical data, non-invasive liver fibrosis indicators and the degree of liver fibrosis were compared among the four groups. Univariable and multivariable Logistic regression analyses were performed to evaluate the risk factors for significant liver fibrosis in MAFLD. The Logistic regression model would be evaluated by the receiver operating characteristic (ROC) curve analysis in order to predict the progressive liver fibrosis in MAFLD. Results When non-obese or obese, the proportion of moderate to severe liver fibrosis (≥ F2 stage) and progressive liver fibrosis in patients with unhealthy metabolism were significantly higher than those in metabolically healthy people. Multivariable analysis showed that body mass index, alanine aminotransferase (ALT), controlled attenuation parameter (CAP) and aspartate transaminase to platelet count ratio index (APRI) were independent risk factors for liver fibrosis in MAFLD (all P＜0.05). The above risk factors were included in Logistic regression analysis. The regression analysis model was as follows: logit(P)=-11.67+BMI×0.20+ALT×0.02+CAP×0.01+APRI×2.73. The ROC curve for predicting advanced liver fibrosis occurrence in MAFLD was drawn by predictive probabilities logit(P). When logit(P)＞0.478, the area under the curve was 0.782, the diagnostic sensitivity was 63.3%, and the specificity was 84.5%. Conclusion Body mass index, ALT, CAP, and APRI are risk factors for progressive liver fibrosis in MAFLD patients, indicating that reducing body mass, protecting liver and lowering enzymes, and controlling metabolic factors are beneficial for delaying the development of liver fibrosis in MAFLD patients.