title: A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, 250012, China. 790130852@qq.com.
authors: Wenchao
authors: W
authors: Zhang
authors: Shengli Qilfield Central Hospital, Dongying, 257034, China. cqc136@163.com.
authors: Qicai
authors: Q
authors: Chen
authors: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, 250012, China. yuanzhongshang@sdu.edu.cn.
authors: Zhongshang
authors: Z
authors: Yuan
authors: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, 250012, China. 1273146539@qq.com.
authors: Jing
authors: J
authors: Liu
authors: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, 250012, China. duzhh1226@hotmail.com.
authors: Zhaohui
authors: Z
authors: Du
authors: Health Management Center, Shandong Provincial QianFoShan Hospital, Jinan, 250014, China. iamtangfang@126.com.
authors: Fang
authors: F
authors: Tang
authors: The Second Hospital of Shandong University, Jinan, 250033, China. Jiahongying@sdu.edu.cn.
authors: Hongying
authors: H
authors: Jia
authors: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, 250012, China. xuefzh@sdu.edu.cn.
authors: Fuzhong
authors: F
authors: Xue
authors: Health Management Center, Shandong Provincial QianFoShan Hospital, Jinan, 250014, China. chengqizhangsd@126.com.
authors: Chengqi
authors: C
authors: Zhang
mesh_terms: D044466:Asian People; D015415:Biomarkers; D001794:Blood Pressure; D015992:Body Mass Index; D002681:China / Q000453:epidemiology; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D018836:Inflammation Mediators; D050356:Lipid Metabolism; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood* / Q000453:epidemiology / Q000208:ethnology*; D008875:Middle Aged; D018570:Risk Assessment; D014505:Urban Population
chemical_list: D015415:Biomarkers; D018836:Inflammation Mediators
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