代谢指标联合人体测量指标对代谢相关脂肪性肝病及其中高危程度的预测价值及列线图模型构建OA
Value of metabolic markers combined with anthropometric indicators in predicting and risk stratification of metabolic dysfunction-associated fatty liver disease and establishment of a nomogram model
目的 构建基于代谢与人体测量指标的代谢相关脂肪性肝病(MAFLD)临床预测模型,为MAFLD筛查和干预提供更有效的工具.方法 回顾性纳入2024年1月1日—2025年1月1日于兰州大学第一医院体检中心进行腹部彩超检查的2 824例体检人群为研究对象,将其按7∶3的比例随机分为训练集(n=1 976)和验证集(n=848).收集研究对象的临床资料、血清学指标及腹部超声结果,计算甘油三酯-葡萄糖(TyG)指数、甘油三酯/高密度脂蛋白胆固醇比值(TG/HDL-C)及多种人体测量指标.符合正态或近似正态分布的计量资料2组间比较采用独立样本t检验,偏态分布的计量资料2组间比较采用Mann-Whitney U检验,计数资料组间比较采用χ2检验或Fisher精确检验.采用多因素Logistic回归分析分别筛选MAFLD及中高危MAFLD的独立相关因素,根据MAFLD的独立影响因素构建5种风险预测模型.绘制受试者操作特征曲线评估模型效能,并计算曲线下面积(AUC).采用校准曲线评估模型的预测准确性,采用决策曲线分析评估模型的临床实用价值,并与传统模型的性能进行比较.结果 训练集1 976例体检者中937例(47.42%)为MAFLD,中高危MAFLD有423例(21.41%);验证集848例体检者中MAFLD为 406例(47.88%).多因素Logistic回归分析显示,男性[比值比(OR)=0.23,95%置信区间(CI):0.13～0.39]、腰围(OR=1.11,95%CI:1.06～1.17)、丙氨酸氨基转移酶(ALT)>40 U/L(OR=2.24,95%CI:1.44～3.51)、高密度脂蛋白胆固醇(OR=0.07,95%CI:0.04～0.15)、TyG指数(OR=8.27,95%CI:5.09～13.44)、TG/HDL-C(OR=0.84,95%CI:0.71～0.99)、身体形态指数(ABSI)(OR=0.45,95%CI:0.39～0.52)及体圆指数(BRI)(OR=2.31,95%CI:1.50～3.55)为MAFLD的独立影响因素(P值均<0.05).而中高危MAFLD的独立影响因素则包括男性(OR=0.17,95%CI:0.10～0.31)、年龄(OR=1.09,95%CI:1.07～1.11)、血红蛋白(OR=0.98,95%CI:0.97～0.98)、血小板计数(OR=0.81,95%CI:0.70～0.93)、空腹血糖(OR=0.80,95%CI:0.71～0.89)、甘油三酯(OR=0.14,95%CI:0.07～0.29)、TG/HDL-C(OR=0.78,95%CI:0.67～0.91)、TyG指数(OR=5.26,95%CI:3.32～8.33)、腰围(OR=2.50,95%CI:1.72～3.61)、ABSI(OR=0.58,95%CI:0.51～0.66)及BRI指数(OR=0.01,95%CI:0.00～0.21)(P值均<0.05).在构建的5种预测模型中,模型5(包含性别、ALT升高、HDL-C、TyG指数、TG/HDL-C、腰围和ABSI)表现最优,其在训练集中的AUC为0.917(95%CI:0.905～0.929),验证集中AUC为0.911(95%CI:0.892～0.930).校准曲线显示模型5具有良好的预测准确性,决策曲线分析证实其具有临床实用价值.结论 基于代谢指标联合人体测量指标构建的MAFLD预测模型具有良好的判别能力,可用于MAFLD患病风险的评估.此外,本研究发现腰围、TyG指数、TG/HDL-C、ABSI及BRI等指标与中高危MAFLD独立相关,但其对肝纤维化进展的预测价值尚需进一步验证.
Objective To develop a novel clinical predictive model for metabolic dysfunction-associated fatty liver disease(MAFLD)based on metabolic markers and anthropometric indicators,and to provide a more effective tool for the early screening and intervention of MAFLD.Methods A retrospective analysis was performed for 2 824 individuals who underwent abdominal color Doppler ultrasound at Health Examination Center of The First Hospital of Lanzhou University from January 1,2024 to January 1,2025,and at a ratio of 7∶3,they were randomly divided into training set with 1 976 patients and validation set with 848 patients.Clinical data,serological markers,and abdominal ultrasound results were collected from all patients,and triglyceride-glucose(TyG)index,triglyceride-to-high-density lipoprotein cholesterol(TG/HDL-C)ratio,and anthropometric indicators were calculated.The independent samples t-test was used for comparison of normally distributed or approximately normally distributed continuous data between two groups,and the Mann-Whitney U test was used for comparison of continuous data with skewed distribution between two groups;the chi-square test or the Fisher's exact test was used for comparison of categorical data between groups.The multivariate logistic regression analysis was used to identify independent predictive factors for MAFLD and intermediate-to high-risk MAFLD.Five risk prediction models were established for MAFLD based on the independent influencing factors,and a nomogram was plotted.The receiver operating characteristic(ROC)curve was plotted to assess model performance,and the area under the ROC curve(AUC)was calculated.The calibration curve was used to evaluate the predictive accuracy of the models,and decision curve analysis was used to assess the clinical practicability of the models.These models were then compared with traditional models.Results Among the 1 976 individuals in the training set,937(47.42%)were diagnosed with MAFLD,and 423(21.41%)were diagnosed with intermediate-to high-risk MAFLD;among the 848 individuals in the validation set,406(47.88%)were diagnosed with MAFLD.The multivariate logistic regression analysis showed that male sex(odds ratio[OR]=0.23,95%confidence interval[CI]:0.13-0.39,P<0.05),waist circumference(OR=1.11,95%CI:1.06-1.17,P<0.05),alanine aminotransferase(ALT)>40 U/L(OR=2.24,95%CI:1.44-3.51,P<0.05),high-density lipoprotein cholesterol(HDL-C)(OR=0.07,95%CI:0.04-0.15,P<0.05),TyG index(OR=8.27,95%CI:5.09-13.44,P<0.05),TG/HDL-C ratio(OR=0.84,95%CI:0.71-0.99,P<0.05),A Body Shape Index(ABSI)(OR=0.45,95%CI:0.39-0.52,P<0.05),and body roundness index(BRI)(OR=2.31,95%CI:1.50-3.55,P<0.05)were independent influencing factors for MAFLD,and male sex(OR=0.17,95%CI:0.10-0.31,P<0.05),age(OR=1.09,95%CI:1.07-1.11,P<0.05),hemoglobin(OR=0.98,95%CI:0.97-0.98,P<0.05),platelet count(OR=0.81,95%CI:0.70-0.93,P<0.05),fasting blood glucose(OR=0.80,95%CI:0.71-0.89,P<0.05),triglycerides(OR=0.14,95%CI:0.07-0.29,P<0.05),TG/HDL-C ratio(OR=0.78,95%CI:0.67-0.91,P<0.05),TyG index(OR=5.26,95%CI:3.32-8.33),waist circumference(OR=2.50,95%CI:1.72-3.61,P<0.05),ABSI(OR=0.58,95%CI:0.51-0.66,P<0.05),and BRI(OR=0.01,95%CI:0.00-0.21,P<0.05)were independent influencing factors for intermediate-to high-risk MAFLD.Among the five models established,model 5(incorporating sex,ALT elevation,HDL-C,TyG index,TG/HDL-C ratio,waist circumference,and ABSI)had the best performance,with an AUC of 0.917(95%CI:0.905-0.929)in the training set and 0.911(95%CI:0.892-0.930)in the validation set.The calibration curve showed that model 5 had good predictive accuracy,and the decision curve analysis confirmed its clinical practicability.Conclusion The predictive model for MAFLD constructed based on metabolic markers and anthropometric indicators has good discriminatory ability and can be used to assess the risk of MAFLD.In addition,this study shows that waist circumference,TyG index,TG/HDL-C ratio,ABSI,and BRI are independently associated with intermediate-to high-risk MAFLD,but further studies are needed to confirm their value in predicting liver fibrosis progression.
赵思瑞;李哲宇;何文强;李俊峰;张立婷
兰州大学第一临床医学院,兰州 730000兰州大学第一临床医学院,兰州 730000兰州大学第一医院肝病科/传染病研究室,兰州 730000兰州大学第一临床医学院,兰州 730000||兰州大学第一医院肝病科/传染病研究室,兰州 730000兰州大学第一临床医学院,兰州 730000||兰州大学第一医院肝病科/传染病研究室,兰州 730000||兰州大学第一医院门静脉高压研究所,兰州 730000
代谢相关脂肪性肝病危险因素Logistic模型
Metabolic Dysfunction-Associated Fatty Liver DiseaseRisk FactorsLogistic Models
《临床肝胆病杂志》 2026 (5)
1056-1066,11
国家自然科学基金(82360132)甘肃省重点研发计划(22YF7FA085)甘肃省联合科研基金(23JRRA1489)甘肃省中医药课题重点项目(GZKZ-2022-7)甘肃省重点人才项目(甘组通字[2024]4号)兰州大学第一医院院内基金(ldyyyn2020-02,ldyyyn2020-14)北京肝胆相照公益基金会人工肝专项基金(iGandanF-1082024-RGG130) National Natural Science Foundation of China(82360132)Gansu Provincial Key R&D Program(22YF7FA085)Gansu Provincial Joint Research Fund(23JRRA1489)Gansu Provincial Key Project on Traditional Chinese Medicine(GZKZ-2022-7)Gansu Provincial Key Talent Program(Gan Zu Tong Zi[2024]No.4)Lanzhou University The First Hospital Internal Fund(ldyyyn2020-02,ldyyyn2020-14)Beijing iGandan Foundation Artificial Liver Special Fund(iGandanF-1082024-RGG130)
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