Cargando…
Identification of Cardiovascular Risk Components in Urban Chinese with Metabolic Syndrome and Application to Coronary Heart Disease Prediction: A Longitudinal Study
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...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866125/ https://www.ncbi.nlm.nih.gov/pubmed/24358344 http://dx.doi.org/10.1371/journal.pone.0084204 |
_version_ | 1782296114110660608 |
---|---|
author | Zhu, Zhenxin Liu, Yanxun Zhang, Chengqi Yuan, Zhongshang Zhang, Qian Tang, Fang Lin, Haiyan Zhang, Yongyuan Liu, Longjian Xue, Fuzhong |
author_facet | Zhu, Zhenxin Liu, Yanxun Zhang, Chengqi Yuan, Zhongshang Zhang, Qian Tang, Fang Lin, Haiyan Zhang, Yongyuan Liu, Longjian Xue, Fuzhong |
author_sort | Zhu, Zhenxin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3866125 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38661252013-12-19 Identification of Cardiovascular Risk Components in Urban Chinese with Metabolic Syndrome and Application to Coronary Heart Disease Prediction: A Longitudinal Study Zhu, Zhenxin Liu, Yanxun Zhang, Chengqi Yuan, Zhongshang Zhang, Qian Tang, Fang Lin, Haiyan Zhang, Yongyuan Liu, Longjian Xue, Fuzhong PLoS One Research Article 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. Public Library of Science 2013-12-17 /pmc/articles/PMC3866125/ /pubmed/24358344 http://dx.doi.org/10.1371/journal.pone.0084204 Text en © 2013 Zhu et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Zhu, Zhenxin Liu, Yanxun Zhang, Chengqi Yuan, Zhongshang Zhang, Qian Tang, Fang Lin, Haiyan Zhang, Yongyuan Liu, Longjian Xue, Fuzhong Identification of Cardiovascular Risk Components in Urban Chinese with Metabolic Syndrome and Application to Coronary Heart Disease Prediction: A Longitudinal Study |
title | Identification of Cardiovascular Risk Components in Urban Chinese with Metabolic Syndrome and Application to Coronary Heart Disease Prediction: A Longitudinal Study |
title_full | Identification of Cardiovascular Risk Components in Urban Chinese with Metabolic Syndrome and Application to Coronary Heart Disease Prediction: A Longitudinal Study |
title_fullStr | Identification of Cardiovascular Risk Components in Urban Chinese with Metabolic Syndrome and Application to Coronary Heart Disease Prediction: A Longitudinal Study |
title_full_unstemmed | Identification of Cardiovascular Risk Components in Urban Chinese with Metabolic Syndrome and Application to Coronary Heart Disease Prediction: A Longitudinal Study |
title_short | Identification of Cardiovascular Risk Components in Urban Chinese with Metabolic Syndrome and Application to Coronary Heart Disease Prediction: A Longitudinal Study |
title_sort | identification of cardiovascular risk components in urban chinese with metabolic syndrome and application to coronary heart disease prediction: a longitudinal study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866125/ https://www.ncbi.nlm.nih.gov/pubmed/24358344 http://dx.doi.org/10.1371/journal.pone.0084204 |
work_keys_str_mv | AT zhuzhenxin identificationofcardiovascularriskcomponentsinurbanchinesewithmetabolicsyndromeandapplicationtocoronaryheartdiseasepredictionalongitudinalstudy AT liuyanxun identificationofcardiovascularriskcomponentsinurbanchinesewithmetabolicsyndromeandapplicationtocoronaryheartdiseasepredictionalongitudinalstudy AT zhangchengqi identificationofcardiovascularriskcomponentsinurbanchinesewithmetabolicsyndromeandapplicationtocoronaryheartdiseasepredictionalongitudinalstudy AT yuanzhongshang identificationofcardiovascularriskcomponentsinurbanchinesewithmetabolicsyndromeandapplicationtocoronaryheartdiseasepredictionalongitudinalstudy AT zhangqian identificationofcardiovascularriskcomponentsinurbanchinesewithmetabolicsyndromeandapplicationtocoronaryheartdiseasepredictionalongitudinalstudy AT tangfang identificationofcardiovascularriskcomponentsinurbanchinesewithmetabolicsyndromeandapplicationtocoronaryheartdiseasepredictionalongitudinalstudy AT linhaiyan identificationofcardiovascularriskcomponentsinurbanchinesewithmetabolicsyndromeandapplicationtocoronaryheartdiseasepredictionalongitudinalstudy AT zhangyongyuan identificationofcardiovascularriskcomponentsinurbanchinesewithmetabolicsyndromeandapplicationtocoronaryheartdiseasepredictionalongitudinalstudy AT liulongjian identificationofcardiovascularriskcomponentsinurbanchinesewithmetabolicsyndromeandapplicationtocoronaryheartdiseasepredictionalongitudinalstudy AT xuefuzhong identificationofcardiovascularriskcomponentsinurbanchinesewithmetabolicsyndromeandapplicationtocoronaryheartdiseasepredictionalongitudinalstudy |