Zhang Wenchao, Chen Qicai, Yuan Zhongshang, Liu Jing, Du Zhaohui, Tang Fang, Jia Hongying, Xue Fuzhong, Zhang Chengqi
Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, 250012, China.
Shengli Qilfield Central Hospital, Dongying, 257034, China.
BMC Public Health. 2015 Jan 31;15:64. doi: 10.1186/s12889-015-1424-z.
Many MetS related biomarkers had been discovered, which provided the possibility for building the MetS prediction model. In this paper we aimed to develop a novel routine biomarker-based risk prediction model for MetS in urban Han Chinese population.
Exploring Factor analysis (EFA) was firstly conducted in MetS positive 13,345 males and 3,212 females respectively for extracting synthetic latent predictors (SLPs) from 11 routine biomarkers. Then, depending on the cohort with 5 years follow-up in 1,565 subjects (male 1,020 and female 545), a Cox model for predicting 5 years MetS was built by using SLPs as predictor; Area under the ROC curves (AUC) with 10 fold cross validation was used to evaluate its power. Absolute risk (AR) and relative absolute risk (RAR) were calculated to develop a risk matrix for visualization of risk assessment.
Six SLPs were extracted by EFA from 11 routine health check-up biomarkers. Each of them reflected the specific pathogenesis of MetS, with inflammatory factor (IF) contributed by WBC & LC & NGC, erythrocyte parameter factor (EPF) by Hb & HCT, blood pressure factor (BPF) by SBP & DBP, lipid metabolism factor (LMF) by TG & HDL-C, obesity condition factor (OCF) by BMI, and glucose metabolism factor (GMF) by FBG with the total contribution of 81.55% and 79.65% for males and females respectively. The proposed metabolic syndrome synthetic predictor (MSP) based predict model demonstrated good performance for predicting 5 years MetS with the AUC of 0.802 (95% CI 0.776-0.826) in males and 0.902 (95% CI 0.874-0.925) in females respectively, even after 10 fold cross validation, AUC was still enough high with 0.796 (95% CI 0.770-0.821) in males and 0.897 (95% CI 0.868-0.921) in females. More importantly, the MSP based risk matrix with a series of risk warning index provided a feasible and practical tool for visualization of risk assessment in the prediction of MetS.
MetS could be explained by six SLPs in Chinese urban Han population. The proposed MSP based predict model demonstrated good performance for predicting 5 years MetS, and the MetS-based matrix provided a feasible and practical tool.
已发现许多与代谢综合征（MetS）相关的生物标志物，这为构建MetS预测模型提供了可能。在本文中，我们旨在为中国城市汉族人群开发一种基于常规生物标志物的新型MetS风险预测模型。
首先分别对13345名MetS阳性男性和3212名MetS阳性女性进行探索性因子分析（EFA），以从11种常规生物标志物中提取综合潜在预测因子（SLP）。然后，根据对1565名受试者（男性1020名，女性545名）进行的5年随访队列，以SLP作为预测因子构建预测5年MetS的Cox模型；使用10倍交叉验证的ROC曲线下面积（AUC）评估其效能。计算绝对风险（AR）和相对绝对风险（RAR）以建立风险矩阵，用于风险评估的可视化。
通过EFA从11种常规健康检查生物标志物中提取了6个SLP。它们各自反映了MetS的特定发病机制，其中炎症因子（IF）由白细胞、淋巴细胞和中性粒细胞贡献，红细胞参数因子（EPF）由血红蛋白和红细胞压积贡献，血压因子（BPF）由收缩压和舒张压贡献，脂质代谢因子（LMF）由甘油三酯和高密度脂蛋白胆固醇贡献，肥胖状况因子（OCF）由体重指数贡献，葡萄糖代谢因子（GMF）由空腹血糖贡献，男性和女性的总贡献率分别为81.55%和79.65%。所提出的基于代谢综合征综合预测因子（MSP）的预测模型在预测5年MetS方面表现良好，男性的AUC为0.802（95%CI 0.776 - 0.826），女性为0.902（95%CI 0.874 - 0.925）；即使经过10倍交叉验证，男性的AUC仍足够高，为0.796（95%CI 0.770 - 0.821），女性为0.897（95%CI 0.868 - 0.921）。更重要的是，基于MSP的风险矩阵以及一系列风险预警指标为MetS预测中的风险评估可视化提供了一种可行且实用的工具。
在中国城市汉族人群中，MetS可以由6个SLP来解释。所提出的基于MSP 的预测模型在预测5年MetS方面表现良好，并提供了一种可行且实用的工具。
