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Identification of cardiovascular risk components in urban Chinese with metabolic syndrome and application to coronary heart disease prediction: a longitudinal study
Journal article   Open access

Identification of cardiovascular risk components in urban Chinese with metabolic syndrome and application to coronary heart disease prediction: a longitudinal study

Zhenxin Zhu, Yanxun Liu, Chengqi Zhang, Zhongshang Yuan, Qian Zhang, Fang Tang, Haiyan Lin, Yongyuan Zhang, Longjian Liu and Fuzhong Xue
PloS one, v 8(12), pp e84204-e84204
2013
DOI: https://doi.org/10.1371/journal.pone.0084204
PMID: 24358344
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1371/journal.pone.0084204View
Published, Version of Record (VoR) Open

Abstract

Cardiovascular Diseases - etiology Prevalence Humans Middle Aged Coronary Disease - etiology Coronary Disease - epidemiology Male Risk China - epidemiology Urban Population Metabolic Syndrome - complications Sex Factors Biomarkers Cardiovascular Diseases - epidemiology Adult Female ROC Curve Aged Longitudinal Studies Population Surveillance
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. 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. 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. 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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3 Record Views
10 citations in Web of Science
10 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Multidisciplinary Sciences
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