Open AccessIdentification of Cardiovascular Risk Components in Urban Chinese with Metabolic Syndrome and Application to Coronary Heart Disease Prediction: A Longitudinal Study
Author(s) -
Zhenxin Zhu,
Yanxun Liu,
Chengqi Zhang,
Zhongshang Yuan,
Qian Zhang,
Fang Tang,
Haiyan Lin,
Yongyuan Zhang,
Longjian Liu,
Fuzhong Xue
Publication year - 2013
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0084204
Subject(s) - metabolic syndrome , medicine , logistic regression , cohort , disease , risk factor , diabetes mellitus , obesity , cohort study , framingham risk score , cardiology , endocrinology
Background Metabolic syndrome (MetS) is proposed as a predictor for cardiovascular disease (CVD). It involves the mechanisms of insulin resistance, obesity, inflammation process of atherosclerosis, and their complex relationship in the metabolic network. Therefore, more cardiovascular risk-related biomarkers within this network should be considered as components of MetS in order to improve the prediction of CVD. Methods Factor analysis was performed in 5311 (4574 males and 737 females) Han Chinese subjects with MetS to extract CVD-related factors with specific clinical significance from 16 biomarkers tested in routine health check-up. Logistic regression model, based on an extreme case-control design with 445 coronary heart disease (CHD) patients and 890 controls, was performed to evaluate the extracted factors used to identify CHD. Then, Cox model, based on a cohort design with 1923 subjects followed up for 5 years, was conducted to validate their predictive effects. Finally, a synthetic predictor (SP) was created by weighting each factor with their risks for CHD to develop a risk matrix to predicting CHD. Results Eight factors were obtained from both males and females with a similar pattern. The AUC to classify CHD under the extreme case-control suggested that SP might serve as a useful tool in identifying CHD with 0.994 (95%CI 0.984-0.998) for males and 0.998 (95%CI 0.982-1.000) for females respectively. In the cohort study, the AUC to predict CHD was 0.871 (95%CI 0.851-0.889) for males and 0.899 (95%CI 0.873-0.921) for females, highlighting that SP was a powerful predictor for CHD. The SP -based 5-year CHD risk matrix provided as convenient tool for CHD risk appraisal. Conclusions Eight factors were extracted from sixteen biomarkers in subjects with MetS and the SP adds to new insights into studies of prediction of CHD risk using data from routine health check-up.