基于饮食及合并疾病的基层高尿酸血症预测模型构建  

Construction of a risk prediction model of hyperuricemia for community-dwelling residents

作　　者：谢聪 张锦秀[2] 茹晋丽[2] Xie Cong;Zhang Jinxiu;Ru Jinli(The Second Clinical Medical College of Shanxi Medical University,Taiyuan 030000,China;Department of General Practice,Second Hospital of Shanxi Medical University,Taiyuan 030000,China)

机构地区：[1]山西医科大学第二临床医学院,太原030000 [2]山西医科大学第二医院全科医学科,太原030000

出　　处：《中华全科医师杂志》2025年第3期308-314,共7页Chinese Journal of General Practitioners

摘　　要：目的根据饮食及合并疾病构建基层高尿酸血症(HUA)患病风险的预测模型。方法该研究为横断面研究。2020年3—11月以分层抽样的方式选取山西省太原市南寨社区总人口的10%,根据纳入、排除标准入选1967名居民作为研究对象,选取其中1555名(80%)居民为训练集,其余412名(20%)居民为验证集构建预测模型。两组数据均根据是否有HUA(血尿酸>420 mmol/L)分为HUA组和非HUA组,通过logistic回归分析普通人群HUA的关联因素,构建风险预测模型,采用Hosmer-Lemeshow拟合优度检验,绘制受试者工作特征(ROC)曲线,评价预测模型的预测效能。结果训练集1555名居民中,有285例HUA患者,总检出率为18.3%。男性检出率约为29.8%(220/739),远高于女性的8.0%(65/816)(χ2=123.17,P<0.05)。两组间比较,HUA组患者腹围值和体重指数(BMI)均高于非HUA组,HUA组吸烟、饮酒、熬夜以及合并高血压、血脂异常的比例更高,喝茶、咖啡、牛奶、食用水果频率及饮料量与非HUA组比较,差异均有统计学意义(P<0.05)。多因素logistic回归结果显示高BMI、咖啡、血脂异常(OR=1.132、1.337、1.479)与HUA呈正向关联,女性(OR=0.213)与HUA呈负向关联。再次纳入全部因素构建logistic回归模型显示,性别、BMI、咖啡、饮料量、钠盐克数、睡眠质量、血脂异常(OR=0.213、1.113、1.353、0.788、1.320、0.788、1.651)为HUA预测模型的重要组成部分。将常量、上述因素及回归系数代入logistic回归方程公式可得Logit(P)=-1.530-1.547×性别+0.107×BMI+0.303×咖啡饮用频次-0.238×钠盐克数+0.278×饮料量-0.238×睡眠情况+0.502×血脂异常。绘制ROC曲线,可得曲线下面积为0.750;Hosmer-Lemeshow拟合优度检验:P=0.632,P>0.05,拟合满意;将公式带入验证集进行内部验证,得到曲线下面积为0.745。结论HUA的发生受多重因素影响,以居民饮食生活习惯及合并疾病所构建的预测模型可用于生活习惯相似的基层、社区等�Objective To construct a risk prediction model of hyperuricemia(HUA)for community-dwelling residents.Methods This cross-sectional study was conducted from March to November 2020.A total of 1967 residents in the Nanzhai community of Taiyuan city were selected by stratified sampling method as study subjects,among whom 1555(80%)subjects served as the training set and the remaining 412(20%)as the validation set.Blood uric acid was measured in all subjects and level>420 mmol/L was defined as HUA.The risk factors of HUA were determined with multivariate logistic regression analysis,and a risk prediction model was constructed.The Hosmer-Lemeshow goodness-of-fit test and the receiver operating characteristic(ROC)curve were used to evaluate the predictive performance of the model.Results Among the 1555 residents in the training set,HUA was detected in 285 cases(18.3%).The detection rate in men was significantly higher than that in women[29.8%(220/739)vs.8.0%(65/816),χ2=123.17,P<0.05].Compared to non-HUA group,the waist circumference and BMI,and the proportion of smoking,drinking,staying up late,and prevalence of hypertension and dyslipidemia were significant higher in HUA group.There was significant difference in the frequency of drinking tea,coffee,milk,and eating fruits,as well as the amount of beverages consumed between the two groups(P<0.05).Multivariate logistic regression analysis showed that high BMI(OR=1.132,95%CI:1.070-1.197,P<0.001),coffee consumption(OR=1.337,95%CI:1.027-1.742,P=0.032),and dyslipidemia(OR=1.479,95%CI:1.049-2.086,P=0.025)were risk factors of HUA,while female sex(OR=0.213,95%CI:0.146-0.390,P<0.001)was protective factor of HUA.When all factors were included in the logistic regression model,gender,BMI,coffee consumption,beverage intake,sodium salt intake,sleep quality,and dyslipidemia(OR=0.213,1.113,1.353,0.788,1.320,0.788,1.651)were important components of the HUA prediction model.By substituting the constant,these factors,and the regression coefficients into the logistic regression equation,the

关 键 词：高尿酸血症 饮食习惯 合并疾病 预测模型 

分 类 号：R58[医药卫生—内分泌]