Identification of Cardiovascular Risk Components in Urban Chinese with Metabolic Syndrome and Application to Coronary Heart Disease Prediction: A Longitudinal Study

<div><p>Background</p><p>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.</p> <p>Methods</p><p>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.</p> <p>Results</p><p>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 <i>SP</i> 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 <i>SP</i> was a powerful predictor for CHD. The <i>SP</i>-based 5-year CHD risk matrix provided as convenient tool for CHD risk appraisal.</p> <p>Conclusions</p><p>Eight factors were extracted from sixteen biomarkers in subjects with MetS and the <i>SP</i> adds to new insights into studies of prediction of CHD risk using data from routine health check-up.</p>