实用肝脏病杂志 ›› 2024, Vol. 27 ›› Issue (2): 193-197.doi: 10.3969/j.issn.1672-5069.2024.02.009

• 非酒精性脂肪性肝病 • 上一篇    下一篇

2型糖尿病合并非酒精性脂肪性肝病患者肝纤维化检出率及预测因素分析*

李敏, 张丽, 曾艾, 刘世裕   

  1. 844500 新疆维吾尔自治区喀什市 新疆英吉沙县人民医院内分泌科(李敏);新疆医科大学第五附属医院内分泌科(张丽);第六附属医院内分泌科(李敏);超声室(曾艾);检验科(刘世裕)
  • 收稿日期:2023-02-23 出版日期:2024-02-10 发布日期:2024-03-08
  • 作者简介:李敏,女,45岁,医学硕士,副主任医师。E-mail: jny1859905@163.com
  • 基金资助:
    *新疆维吾尔自治区自然科学基金自助项目(编号:2020D01A128)

Liver fibrosis in patients with diabetes mellitus type 2 and non-alcoholic fatty liver diseases

Li Min, Zhang Li, Zeng Ai, et al.   

  1. Department of Endocrinology, People's Hospital, Yingjisha County, Kashgar 844500, Xinjiang Uygur Autonomous Region, China
  • Received:2023-02-23 Online:2024-02-10 Published:2024-03-08

摘要: 目的 探讨2型糖尿病(T2DM)患者非酒精性脂肪性肝病(NAFLD)及其肝纤维化发生率和影响因素。方法 2020年4月~2022年4月我院收治的482例T2DM患者, 使用超声检查诊断NAFLD, 检测杨氏模量值诊断肝纤维化。应用Lasso回归模型和有序多分类Logistic回归分析法筛查和分析T2DM患者罹患NAFLD严重程度的风险因素, 绘制受试者工作特征(ROC)曲线评估风险因素预测NAFLD患者发生肝纤维化的效能。结果 在482例T2DM患者中, 诊断NAFLD者276例(57.3%), 后者包括肝纤维化者58例(12.0%); T2DM/NAFLD/肝纤维化组年龄、周围神经病发生率、体质指数和杨氏模量值分别为(46.0±4.2)岁、82.8%、(28.3±3.2)kg/m2和(10.8±2.2)kPa, 均显著大于T2DM/NAFLD组【分别为(45.6±4.9)岁、45.4%、(27.9±3.1)kg/m2和(5.3±1.0)kPa, P<0.05】或T2DM组【分别为(42.5±5.8)岁、44.2%、(25.0±2.7)kg/m2和(4.8±0.6)kPa, P<0.05】;T2DM/NAFLD/肝纤维化组血清甘油三酯、尿酸、HOMA-IR、甘油三酯/血糖/体质指数、血清TNF-α和IL-6水平分别为(2.6±0.8)mmol/L、(355.2±24.6)μmol/L、(2.6±0.4)、(270.8±18.5)、(8.2±2.3)pg/ml和(13.6±2.4)pg/ml, 显著大于T2DM/NAFLD组【分别为(2.2±0.4)mmol/L、(334.6±31.0)μmol/L、(2.6±0.4)、(219.1±40.4)、(5.5±1.4)pg/ml和(9.7±2.1)pg/ml, P<0.05】或T2DM 【分别为(2.1±0.5)mmol/L、(328.7±36.8)μmol/L、(2.3±0.7)、(207.9±39.1)、(5.3±1.1)pg/ml和(6.7±1.1)pg/ml, P<0.05】;LASSO回归和多因素Logistic回归分析结果显示, TyG-BMI(OR=1.012, P=0.000)、IL-6(OR=2.782, P=0.000)、TNF-α(OR=1.008, P=0.026)、UA(OR=1.530, P=0.000)和糖尿病周围神经病(OR=1.855, P=0.010)为T2DM患者罹患NAFLD严重程度的独立危险因素;ROC曲线分析结果显示, TyG-BMI、IL-6、TNF-α和UA预测患者发生肝纤维化的曲线下面积(AUC)分别为0.897、0.928、0.840和0.762。 结论 T2DM合并NAFLD患者可能会进展至肝纤维化, 了解一些预测因子并进行有效的干预可能能延缓病情发展。

关键词: 非酒精性脂肪性肝病, 肝纤维化, 2型糖尿病, Lasso回归模型, 诊断

Abstract: Objective The aim of this study was to investigate the risk factors for liver fibrosis (LF) in patients with diabetes mellitus type 2 (T2DM) and non-alcoholic fatty liver diseases(NAFLD). Methods 482 patients with T2DM were recruited in our hospital between April 2020 and April 2022, and the NAFLD was diagnosed based on ultrasonography and the LF was determined by the Young's modulus. The Lasso regression model and ordinal multicategory Logistic regression were applied to reveal the risk factors for LF, and the receiver operating characteristic curves(ROC) was drawn to assess the predictive efficacy of risk factors for LF in patients with NAFLD. Results Out of the 482 patients with T2DM in our series, the concomitant NAFLD was found in 276 cases(57.3%), including LF in 58 cases(12.0%) in the latter; the age, incidence of peripheral neuropathy (PN), body mass index and Young's modulus in patients with T2DM/NAFLD/LF were (46.0±4.2)yr, 82.8%, (28.3±3.2)kg/m2 and (10.8±2.2)kPa, all significantly higher than in patients with T2DM/NAFLD or in patients with T2DM; blood TG, UA, HOMA-IR, ratio of triglyceride glucose/body mass index (TyG-BMI), serum TNF-α and IL-6 level in patients with T2DM/NAFLD/LF were(2.6±0.8)mmol/L, (355.2±24.6)μmol/L, (2.6±0.4), (270.8±18.5), (8.2±2.3)pg/ml and (13.6±2.4)pg/ml, all significantly higher than in patients with T2DM/NAFLD or in patients with T2DM; the LASSO and multivariate Logistic regression analysis showed that the ratio of TyG-BMI(OR=1.012, P=0.000), IL-6(OR=2.782, P=0.000), TNF-α(OR=1.008, P=0.026), UA(OR=1.530, P=0.000) and PN (OR=1.855, P=0.010) were the independent risk factors for the severe NAFLD in patients with T2DM; the ROC analysis demonstrated that the AUCs were 0.897, 0.928, 0.840 and 0.762, when the ratio of TyG-BMI, IL-6, TNF-α and UA were applied to predict LF in patients with T2DM/NAFLD. Conclusions The LF could deteriorate the disease in patients with T2DM/NAFLD, and the clinicians should take the risk factors into consideration and the early intervention might improve the prognosis.

Key words: Non-alcoholic fatty liver diseases, Type 2 diabetes mellitus, Liver fibrosis, Lasso regression model, Diagnosis