Shi Chao, Cheng Yin, Ma Ling, Wu Lanqiqi, Shi Hongjuan, Liu Yining, Ma Jinyu, Tong Huitian
People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, China.
Ningxia Institute of Clinical Medicine, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region, China.
Front Endocrinol (Lausanne). 2025 Jun 11;16:1587354. doi: 10.3389/fendo.2025.1587354. eCollection 2025.
This study aimed to develop and validate models for identifying individuals at high risk for metabolic syndrome (MetS) and pre-MetS using easily collectible indices.
A cross-sectional analysis was conducted using data from the Ningxia Cardiovascular Disorders Survey (NCDS) in China, collected between January 2020 and December 2021. The study population comprised 10,520 participants with complete demographic, anthropometric, and laboratory data. The diagnostic models for MetS were developed using five easily collectible indicators. The performance of the models was compared with that of Lipid Accumulation Product (LAP), Triglyceride-Glucose (TyG) Index, and Waist-to-Height Ratio (WHtR). These same models were subsequently applied to pre-MetS detection as a secondary analysis. Area under the receiver operating characteristic curve (AUC), Hosmer and Lemeshow test, bootstrap method, Brier score and Decision Curve Analysis were employed to evaluate the performance of the models.
Model 1 comprised factors such as WC, SBP, DBP and gender. In contrast, Model 2 included all the variables from Model 1 while additionally incorporating FPG. In the training set, the AUC for Model 1 and Model 2 were 0.914 and 0.924, respectively. The AUC for Model 1 and Model 2 in identifying the presence of pre-MetS and MetS conditions were 0.883 and 0.902, respectively. In the external validation set, the AUC for Model 1 and Model 2 in identifying the presence of MetS were 0.929 and 0.934, respectively. For detecting pre-MetS and MetS conditions, the AUC for Model 1 and Model 2 were 0.885 and 0.902, respectively. Compared to TyG, LAP, and WHtR, model 1 and 2 exhibited a superior ability to identify MetS as well as pre-MetS and MetS conditions in both the training and validation sets.
Our models offered an easy, accurate and efficient tool for identifying MetS and pre-MetS, which might be used in large-scale population screening or self-health management at home.
本研究旨在开发并验证使用易于收集的指标来识别代谢综合征（MetS）和代谢综合征前期个体的高危模型。
利用2020年1月至2021年12月期间在中国进行的宁夏心血管疾病调查（NCDS）的数据进行横断面分析。研究人群包括10520名具有完整人口统计学、人体测量学和实验室数据的参与者。使用五个易于收集的指标开发了MetS的诊断模型。将这些模型的性能与脂质蓄积产物（LAP）、甘油三酯-葡萄糖（TyG）指数和腰高比（WHtR）的性能进行比较。随后，将这些相同的模型应用于代谢综合征前期检测作为二次分析。采用受试者工作特征曲线下面积（AUC）、Hosmer和Lemeshow检验、自助法、Brier评分和决策曲线分析来评估模型的性能。
模型1包括腰围（WC）、收缩压（SBP）、舒张压（DBP）和性别等因素。相比之下，模型2包含模型1的所有变量，同时还纳入了空腹血糖（FPG）。在训练集中，模型1和模型2的AUC分别为0.914和0.924。模型1和模型2在识别代谢综合征前期和MetS情况时的AUC分别为0.883和0.902。在外部验证集中，模型1和模型2在识别MetS存在时的AUC分别为0.929和0.934。对于检测代谢综合征前期和MetS情况，模型1和模型2的AUC分别为0.885和0.902。与TyG、LAP和WHtR相比，模型1和模型2在训练集和验证集中识别MetS以及代谢综合征前期和MetS情况的能力更强。
我们的模型为识别MetS和代谢综合征前期提供了一种简便、准确且高效的工具，可用于大规模人群筛查或家庭自我健康管理。
