Cargando…
Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs
BACKGROUND: This study involved the development of a predictive 5-year morbidity nomogram for cardiovascular diseases (CVD) in Xinjiang Kazakhs based on cytokine levels. METHODS: The nomogram was based on a baseline survey of the town of Nalati in the Kazakh Autonomous Prefecture of Xinjiang from 20...
Autores principales: | , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6525937/ https://www.ncbi.nlm.nih.gov/pubmed/31191115 http://dx.doi.org/10.1155/2019/4756295 |
_version_ | 1783419799188013056 |
---|---|
author | Mao, Lei Zhang, Xianghui Hu, Yunhua Wang, Xinping Song, Yanpeng He, Jia Yang, Wenwen Ma, Jiaolong Yan, Yizhong Mu, Lati Zhang, Jingyu Wang, Kui Guo, Heng Ma, Rulin Guo, Shuxia |
author_facet | Mao, Lei Zhang, Xianghui Hu, Yunhua Wang, Xinping Song, Yanpeng He, Jia Yang, Wenwen Ma, Jiaolong Yan, Yizhong Mu, Lati Zhang, Jingyu Wang, Kui Guo, Heng Ma, Rulin Guo, Shuxia |
author_sort | Mao, Lei |
collection | PubMed |
description | BACKGROUND: This study involved the development of a predictive 5-year morbidity nomogram for cardiovascular diseases (CVD) in Xinjiang Kazakhs based on cytokine levels. METHODS: The nomogram was based on a baseline survey of the town of Nalati in the Kazakh Autonomous Prefecture of Xinjiang from 2009 to 2013. By 2016, we had monitored 1508 people for a median time of 5.17 years and identified CVD events in the study population by collecting case information from local hospitals. The study population was divided into the training (n = 1005) and validation cohorts (n = 503) in a 2 : 1 ratio. The area under the receiver operating characteristic curve (AUC) was used to verify the predictive accuracy of the nomogram. The result was assessed in a validation cohort. RESULTS: At the end of the study, the incidence of CVD in Xinjiang Kazakhs was found to be 11.28%. We developed a new nomogram to predict the 5-year incidence of CVD based on age, interleukin-6 (IL-6), and adiponectin (APN) levels, diastolic blood pressure, and dyslipidemia. The AUC for the predictive accuracy of the nomogram was 0.836 (95% confidence interval: 0.802–0.869), which was higher than that for IL-6 and APN. These results were supported by validation studies. CONCLUSIONS: The nomogram model can more directly assess the risk of CVD in Kazakhs and can be used for CVD risk assessment. |
format | Online Article Text |
id | pubmed-6525937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-65259372019-06-12 Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs Mao, Lei Zhang, Xianghui Hu, Yunhua Wang, Xinping Song, Yanpeng He, Jia Yang, Wenwen Ma, Jiaolong Yan, Yizhong Mu, Lati Zhang, Jingyu Wang, Kui Guo, Heng Ma, Rulin Guo, Shuxia Mediators Inflamm Research Article BACKGROUND: This study involved the development of a predictive 5-year morbidity nomogram for cardiovascular diseases (CVD) in Xinjiang Kazakhs based on cytokine levels. METHODS: The nomogram was based on a baseline survey of the town of Nalati in the Kazakh Autonomous Prefecture of Xinjiang from 2009 to 2013. By 2016, we had monitored 1508 people for a median time of 5.17 years and identified CVD events in the study population by collecting case information from local hospitals. The study population was divided into the training (n = 1005) and validation cohorts (n = 503) in a 2 : 1 ratio. The area under the receiver operating characteristic curve (AUC) was used to verify the predictive accuracy of the nomogram. The result was assessed in a validation cohort. RESULTS: At the end of the study, the incidence of CVD in Xinjiang Kazakhs was found to be 11.28%. We developed a new nomogram to predict the 5-year incidence of CVD based on age, interleukin-6 (IL-6), and adiponectin (APN) levels, diastolic blood pressure, and dyslipidemia. The AUC for the predictive accuracy of the nomogram was 0.836 (95% confidence interval: 0.802–0.869), which was higher than that for IL-6 and APN. These results were supported by validation studies. CONCLUSIONS: The nomogram model can more directly assess the risk of CVD in Kazakhs and can be used for CVD risk assessment. Hindawi 2019-05-05 /pmc/articles/PMC6525937/ /pubmed/31191115 http://dx.doi.org/10.1155/2019/4756295 Text en Copyright © 2019 Lei Mao et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Mao, Lei Zhang, Xianghui Hu, Yunhua Wang, Xinping Song, Yanpeng He, Jia Yang, Wenwen Ma, Jiaolong Yan, Yizhong Mu, Lati Zhang, Jingyu Wang, Kui Guo, Heng Ma, Rulin Guo, Shuxia Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs |
title | Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs |
title_full | Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs |
title_fullStr | Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs |
title_full_unstemmed | Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs |
title_short | Nomogram Based on Cytokines for Cardiovascular Diseases in Xinjiang Kazakhs |
title_sort | nomogram based on cytokines for cardiovascular diseases in xinjiang kazakhs |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6525937/ https://www.ncbi.nlm.nih.gov/pubmed/31191115 http://dx.doi.org/10.1155/2019/4756295 |
work_keys_str_mv | AT maolei nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT zhangxianghui nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT huyunhua nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT wangxinping nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT songyanpeng nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT hejia nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT yangwenwen nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT majiaolong nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT yanyizhong nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT mulati nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT zhangjingyu nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT wangkui nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT guoheng nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT marulin nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs AT guoshuxia nomogrambasedoncytokinesforcardiovasculardiseasesinxinjiangkazakhs |