中青年久坐人群代谢相关脂肪性肝病风险预测模型的构建与验证

作者：李瑞玲1  葛珊珊2  胡晓瑾2  李书慧1  杨采铮1

单位：1.山西医科大学 护理学院 山西 太原 030000 2. 山西医科大学第一医院 健康管理中心 山西 太原 030000

关键词：代谢相关脂肪性肝病 久坐 中青年 危险因素 列线图

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出版年,卷(期):页码：2025,17(4):21-30

摘要：

摘要：目的 构建中青年久坐人群代谢相关脂肪性肝病（metabolic associated fatty liver disease，MAFLD）的风险预测模型。方法 以2024年4月1日至2024年8月31日于山西医科 大学第一医院进行健康体检的893例久坐中青年为研究对象，收集研究对象的腰围、臀 围、体重指数（body mass index，BMI）、空腹血糖、总胆固醇（total cholesterol，TC）、 甘油三酯（triglyceride，TG）、高密度脂蛋白胆固醇（high-density lipoprotein cholesterol， HDL-C）、低密度脂蛋白胆固醇（low-density lipoprotein cholesterol，LDL-C），血尿素 氮、肌酐、血尿酸、丙氨酸氨基转移酶（alanine aminotransferase，ALT）、γ-谷氨酰转 移酶（γ-glutamyl transferase，GGT）、天冬氨酸氨基转移酶（aspartate aminotransferase， AST）、总胆红素（total bilirubin，TBil）、红细胞、白细胞、血小板、血红蛋白、血 清甲状腺素（thyroxine，T4）、三碘甲状腺原氨酸（triiodothyronine，T3）、血清促甲状 腺素（thyroid-stimulating hormone，TSH）、久坐时间、心理压力、睡眠质量等指标。 采用随机数字法按7∶3将其分为训练集（625例）和验证集（268例）。根据患者是否 患MAFLD将其分为MAFLD组和非MAFLD组，比较两组患者上述指标的差异。采用 Logistic回归分析训练集中MAFLD的危险因素，采用R studio 4.3.3构建列线图模型， 采用受试者工作特征（receiver operator characteristic，ROC）曲线、校准曲线及临床 决策曲线评估列线图的预测价值和临床实用性。结果 中青年久坐人群MAFLD患病 率为45.0%（402/893）。训练集和验证集患者一般资料差异均无统计学意义（P均＞ 0.05）。多因素Logistic回归分析表明，久坐时间长 [≥ 8 h ～ ≤ 10 h（OR = 1.940， 95%CI：1.027～3.665，P = 0.041）；＞ 10 h（OR = 5.274，95%CI：2.783～9.996， P ＜ 0.001）]、高水平心理压力（OR = 2.430，95%CI：1.183～4.992，P = 0.016）、 BMI（OR = 1.568，95%CI：1.372～1.793，P ＜ 0.001）、空腹血糖（OR = 1.407， 95%CI：1.023～1.936，P = 0.036）、TG（OR = 1.279，95%CI：1.076～1.520，P = 0.005）是中青年久坐人群MAFLD的独立危险因素，HDL-C是保护因素（OR = 0.251， 95%CI：0.070～0.898，P = 0.034）。根据以上6个预测因子构建列线图模型，ROC 曲线分析表明训练集的ROC曲线下面积为0.913（95%CI：0.890～0.935），敏感度为 0.911，特异度为0.768，验证集的ROC曲线下面积为0.931（95%CI：0.897～0.959）， 敏感度为0.872，特异度为0.964。临床决策曲线表明当阈值概率为0.01～0.94时，使用 列线图预测模型预测中青年久坐人群MAFLD的发生风险更有益。结论 本研究构建的列 线图模型可个体化预测中青年久坐人群MAFLD的发生风险，具有较高的预测准确性。

Abstract: Objective To construct a risk prediction model for metabolic associated fatty liver disease (MAFLD) in middle-aged and young sedentary population. Methods A total of 893 sedentary middle-aged and young adults who underwent physical examination in the First Hospital of Shanxi Medical University from April 1st, 2024 to August 31st, 2024 were conveniently selected as the research objects. Indicators such as waist circumference, hip circumference, body mass index (BMI), fasting blood glucose, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen, creatinine, blood uric acid, alanine aminotransferase (ALT), γ-glutamyl transferase (GGT), aspartate aminotransferase (AST), total bilirubin (TBil), red blood cells, white blood cells, platelets, hemoglobin, serum thyroxine (T4), triiodothyronine (T3), thyroid-stimulating hormone (TSH), sedentary time, psychological stress and sleep quality were collected. The participants were divided into a training set (625 cases) and a validation set (268 cases) at a ratio of 7∶3 using a random number method. According to whether the patients had MAFLD, they were further divided into a MAFLD group and a non-MAFLD group. The differences in the above-mentioned indicators between the two groups were compared. Logistic regression analysis was used to identify the risk factors for MAFLD in the training set. A nomogram model was constructed using R Studio 4.3.3. The predictive value and clinical utility of the nomogram were evaluated by receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve. Results The prevalence of MAFLD in middle-aged and young sedentary population was 45.0% (402/893). There was no significant difference in clinical indexes between the training set and the verification set (all P ＞ 0.05). Multivariate Logistic regression analysis showed that long sedentary time [≥ 8 h ～ ≤ 10 h (OR = 1.940, 95%CI: 1.027～3.665, P = 0.041); ＞ 10 h (OR = 5.274, 95%CI: 2.783～9.996, P ＜ 0.001)], high psychological stress (OR = 2.430, 95%CI: 1.183～4.992, P = 0.016), BMI (OR = 1.568, 95%CI: 1.372～1.793, P ＜ 0.001), fasting blood glucose (OR = 1.407, 95%CI: 1.023～1.936, P = 0.036) and TG (OR = 1.279, 95%CI: 1.076～1.520, P = 0.005) were independent risk factors for MAFLD in middle-aged and young sedentary population, and HDL-C was a protective factor (OR = 0.251, 95%CI: 0.070～0.898, P = 0.034). The nomogram model was constructed based on the above six predictors. ROC curve showed that the area under the ROC curve of the training set was 0.913 (95%CI: 0.890～0.935), with a sensitivity of 0.911 and a specificity of 0.768. The area under the ROC curve of the validation set was 0.931 (95%CI: 0.897～0.959), with a sensitivity of 0.872 and a specificity of 0.964. Clinical decision curve analysis indicated that using the nomogram prediction model to predict the risk of MAFLD in young and middle aged sedentary populations was more beneficial when the threshold probability was between 0.01 and 0.94. Conclusion The nomogram model constructed in this study can individually predict the risk of MAFLD in young and middle-aged sedentary populations and has high predictive accuracy.

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