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BMC Public Health
Published in: BMC Public Health 1/2015

Open Access 01-12-2015 | Research article

A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population

Authors: Wenchao Zhang, Qicai Chen, Zhongshang Yuan, Jing Liu, Zhaohui Du, Fang Tang, Hongying Jia, Fuzhong Xue, Chengqi Zhang

Published in: BMC Public Health | Issue 1/2015

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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.
Appendix
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Metadata
Title
A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population
Authors
Wenchao Zhang
Qicai Chen
Zhongshang Yuan
Jing Liu
Zhaohui Du
Fang Tang
Hongying Jia
Fuzhong Xue
Chengqi Zhang
Publication date
01-12-2015
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2015
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-015-1424-z

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