Ma Yanting, Li Yongyuan, Zhang Zhanfeng, Du Guomei, Huang Ting, Zhao Zhi Zhong, Liu Shou, Dang Zhancui
Department of Public Health, Medical College, Qinghai University, Xining, Qinghai, People's Republic of China.
Disease Control department, Huangzhong District health Bureau, Xining, Qinghai, People's Republic of China.
Diabetes Metab Syndr Obes. 2024 May 17;17:2041-2052. doi: 10.2147/DMSO.S445650. eCollection 2024.
The prevalence of metabolic syndrome (MetS) is increasing worldwide, and early prediction of MetS risk is highly beneficial for health outcomes. This study aimed to develop and validate a nomogram to predict MetS risk in Qinghai Province, China, and it provides a methodological reference for MetS prevention and control in Qinghai Province, China.
A total of 3073 participants living between 1900 and 3710 meters above sea level in Qinghai Province participated in this study between March 2014 and March 2016. We omitted 12 subjects who were missing diagnostic component data for MetS, ultimately resulting in 3061 research subjects, 70% of the subjects were assigned randomly to the training set, and the remaining subjects were assigned to the validation set. The least absolute shrinkage and selection operator (LASSO) regression analysis method was used for variable selection via running cyclic coordinate descent with 10-fold cross-validation. Multivariable logistic regression was then performed to develop a predictive model and nomogram. The receiver operating characteristic (ROC) curves was used for model evaluation, and calibration plot and decision curve analysis (DCA) were used for model validation.
Of 24 variables studied, 6 risk predictors were identified by LASSO regression analysis: hyperlipidaemia, hyperglycemia, abdominal obesity, systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI). A prediction model including these 6 risk factors was constructed and displayed good predictability with an area under the ROC curve of 0.914 for the training set and 0.930 for the validation set. DCA revealed that if the threshold probability of MetS is less than 82%, the application of this nomogram is more beneficial than both the treat-all or treat-none strategies.
The nomogram developed in our study demonstrated strong discriminative power and clinical applicability, making it a valuable reference for meets prevention and control in the plateau areas of Qinghai Province.
代谢综合征（MetS）在全球范围内的患病率正在上升，早期预测MetS风险对健康结局非常有益。本研究旨在开发并验证一种预测中国青海省MetS风险的列线图，为中国青海省MetS的预防和控制提供方法学参考。
2014年3月至2016年3月期间，共有3073名居住在青海省海拔1900至3710米之间的参与者参加了本研究。我们排除了12名缺失MetS诊断成分数据的受试者，最终得到3061名研究对象，其中70%的受试者被随机分配到训练集，其余受试者被分配到验证集。采用最小绝对收缩和选择算子（LASSO）回归分析方法，通过运行循环坐标下降法和10倍交叉验证进行变量选择。然后进行多变量逻辑回归以建立预测模型和列线图。采用受试者工作特征（ROC）曲线进行模型评估，采用校准图和决策曲线分析（DCA）进行模型验证。
在研究的24个变量中，LASSO回归分析确定了6个风险预测因子：高脂血症、高血糖、腹型肥胖、收缩压（SBP）、舒张压（DBP）和体重指数（BMI）。构建了一个包含这6个风险因素的预测模型，训练集的ROC曲线下面积为0.914，验证集为0.930，显示出良好的预测能力。DCA显示，如果MetS的阈值概率小于82%，应用该列线图比全治疗或不治疗策略更有益。
我们研究中开发的列线图具有很强的鉴别力和临床适用性，使其成为青海省高原地区MetS预防和控制的有价值参考。
