代谢综合征的影响因素分析及列线图模型构建：横断面研究

Prevalence and associated factors of metabolic syndrome in adults and construction of nomogram model：a cross-sectional analysis

摘要

目的 探讨赣南地区人群代谢综合征（Metabolic syndrome，MetS）的患病情况及其相关影响因素，并构建代谢综合征的患病风险列线图预测模型。 方法 选取2022年赣南地区35岁以上常住居民作为调查对象，通过多阶段分层随机抽样获取样本人群。收集参与者的人口统计学特征、病史、血液生化数据和体格检查等数据。依据国际糖尿病联合会（International Diabetes Federation，IDF）的标准对患者进行MetS诊断。本研究以单因素分析，对MetS患病风险因素进行筛选，用多变量Logistic回归分析探讨风险因素与MetS患病的相关系数。随后，将数据分为训练集和验证集，并以Nomogram来建立MetS患病的预测模型。训练集用于Nomogram模型的构建和初步验证，验证集用于预测模型的内部验证。根据受试者工作特征曲线（Receiver operating characteristic curve，ROC）、校准曲线和决策曲线评估Nomogram预测模型的效能。 结果 本次研究共纳入赣州常住居民1 581人，MetS患病率为27.39%（95%CI：25.19%～29.59%），年龄标准化患病率为27.81%。Logistic回归分析结果表明年龄、居住地、职业、高脂血症史、高尿酸血症史、臀围、糖化血红蛋白（Glycated hemoglobin A1c，HbA1c）、静息心率（Resting heart rate，RHR）和体重指数（Body mass index，BMI）等9个因素是赣南地区MetS患病的独立影响因素。参与者被随机划分为训练集（n=1 107，70%）和验证集（n=474，30%）。模型初步验证曲线下面积（Area under curve，AUC）为0.844，内部验证AUC为0.825，说明模型有较高的区分度，校准曲线说明模型有较高的校准度。 结论 赣南地区中老年人MetS患病率较高，以年龄、居住地、职业、高脂血症病史、高尿酸血症病史、臀围、HbA1c、RHR、BMI等因素构建预测模型有较好的预测效能，可用于中老年人MetS患病预测。

Abstract

Objective This study aimed to investigate the prevalence and determinants of metabolic syndrome （MetS） among the population in Gannan （Southern Jiangxi）， and to develop a nomogram for MetS prediction. Methods In 2022， a multi-stage stratified random sampling method was used to select permanent residents aged 35 years and above in southern Jiangxi as study participants. MetS was defined according to the International Diabetes Federation （IDF） criteria. Participants' demographics， medical history， blood biochemistry data， and anthropometric variables were collected to screen for significant variables for the prediction model of MetS. Multivariable logistic regression was employed to explore the factors associated with MetS. Subsequently， the data were divided into a training set and a validation set， and a nomogram was developed to create the predictive model for MetS. The training set was utilized for nomogram model construction and preliminary validation， while the validation set was used for internal validation. The performance of the nomogram was assessed based on receiver operating characteristic curve （ROC）， calibration curves， and decision curve analysis （DCA）. Results A total of 1 581 participants were enrolled in the study， revealing a prevalence of MetS of 27.39% （95%CI： 25.19%-29.59%）. The age-standardized prevalence was calculated to be 27.81%. Nine variables were identified as influencing factors for MetS： age， residence， occupation， history of hyperlipidemia， history of hyperuricemia， hip circumference， glycated hemoglobin A1c （HbA1c）， resting heart rate （RHR）， and body mass index （BMI）. The participants were randomly divided into a training set （n=1 107， 70%） and a validation set （n=474， 30%）. The nomogram was validated through preliminary validation area under curve （AUC： 0.844） and internal validation （AUC： 0.825）. Calibration plots demonstrated good agreement in the training sets. Conclusion The prevalence of MetS is notably high in Ganzhou，Jiangxi. The nomogram， which is based on age， residence， occupation， history of hyperlipidemia， history of hyperuricemia， hip circumference，HbA1c，RHR and BMI variables，exhibits strong predictive efficacy and can be utilized to assess the risk of MetS in middle-aged and elderly populations.

关键词

赣南地区 / 代谢综合征 / 流行现状 / 影响因素 / 列线图预测模型

Key words

Gannan / Metabolic syndrome / Prevalence / Influencing factors / Nomogram