Abstract
BackgroundMetabolic 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.MethodsFactor 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.ResultsEight 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.ConclusionsEight 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.
Highlights
Metabolic syndrome (MetS) is a public health challenge because of its high prevalence and association with the risk of cardiovascular disease (CVD) [1,2] and type 2 diabetes [3,4]
body mass index (BMI), diastolic BP (DBP), serum uric acid (SUA), TG, white blood cell (WBC) count, ALT, gamma-glutamyl transpeptidase (GGT), CREA, Hb, HCT and prevalence of NAFLD were higher in males than in females, while systolic blood pressure (SBP), fasting blood-glucose (FBG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were higher in females than in males
MetS was commonly defined by the presence of obesity, diabetes, hypertension, and dyslipidemia, and these factors were involved in the mechanisms of insulin resistance, inflammation, and atherosclerosis [7]
Summary
Metabolic syndrome (MetS) is a public health challenge because of its high prevalence and association with the risk of cardiovascular disease (CVD) [1,2] and type 2 diabetes [3,4]. According to the criteria recommended by the Diabetes Branch of Chinese Medical Association [5], MetS encompasses a cluster of metabolically related CVD risk factors: being overweight or obese, high blood pressure, dyslipidemia, and hyperglycemia. MetS, defined by either Chinese or international criteria, is defined using factors including obesity, diabetes, hypertension, and dyslipidemia Because these factors are involved in the mechanisms of insulin resistance, and the process of inflammation and atherosclerosis, this complex relationship has been suggested in study as the metabolic network of MetS [7]. We aimed to select several cardiovascular risk biomarkers involved in the above metabolic network using robust bio-statistical modeling technique to develop a MetS related synthetic predictor (SP) for classifying subjects with or without CVD, and to predict high risk of CVD using data from a large-scale routine health checkup sample among urban Chinese residents. 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
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