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Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study

Few risk scores have been specifically developed to identify individuals at high risk of type 2 diabetes in China. In the present study, we aimed to develop such risk scores, based on simple clinical variables. We studied a population-based cohort of 73,987 adults, aged 18 years and over. After 5.35...

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Autores principales: Wang, Anxin, Chen, Guojuan, Su, Zhaoping, Liu, Xiaoxue, Liu, Xiangtong, Li, Haibin, Luo, Yanxia, Tao, Lixin, Guo, Jin, Liu, Long, Chen, Shuohua, Wu, Shouling, Guo, Xiuhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4879553/
https://www.ncbi.nlm.nih.gov/pubmed/27221651
http://dx.doi.org/10.1038/srep26548
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author Wang, Anxin
Chen, Guojuan
Su, Zhaoping
Liu, Xiaoxue
Liu, Xiangtong
Li, Haibin
Luo, Yanxia
Tao, Lixin
Guo, Jin
Liu, Long
Chen, Shuohua
Wu, Shouling
Guo, Xiuhua
author_facet Wang, Anxin
Chen, Guojuan
Su, Zhaoping
Liu, Xiaoxue
Liu, Xiangtong
Li, Haibin
Luo, Yanxia
Tao, Lixin
Guo, Jin
Liu, Long
Chen, Shuohua
Wu, Shouling
Guo, Xiuhua
author_sort Wang, Anxin
collection PubMed
description Few risk scores have been specifically developed to identify individuals at high risk of type 2 diabetes in China. In the present study, we aimed to develop such risk scores, based on simple clinical variables. We studied a population-based cohort of 73,987 adults, aged 18 years and over. After 5.35 ± 1.59 years of follow-up, 4,726 participants (9.58%) in the exploration cohort developed type 2 diabetes and 2,327 participants (9.44%) in the validation cohort developed type 2 diabetes. Age, gender, body mass index, family history of diabetes, education, blood pressure, and resting heart rate were selected to form the concise score with an area under the receiver operating characteristic curve (AUC) of 0.67. The variables in the concise score combined with fasting plasma glucose (FPG), and triglyceride (TG) or use of lipid-lowering drugs constituted the accurate score with an AUC value of 0.77. The utility of the two scores was confirmed in the validation cohort with AUCs of 0.66 and 0.77, respectively. In summary, the concise score, based on non-laboratory variables, could be used to identify individuals at high risk of developing diabetes within Chinese population; the accurate score, which also uses FPG and TG data, is better at identifying such individuals.
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spelling pubmed-48795532016-06-07 Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study Wang, Anxin Chen, Guojuan Su, Zhaoping Liu, Xiaoxue Liu, Xiangtong Li, Haibin Luo, Yanxia Tao, Lixin Guo, Jin Liu, Long Chen, Shuohua Wu, Shouling Guo, Xiuhua Sci Rep Article Few risk scores have been specifically developed to identify individuals at high risk of type 2 diabetes in China. In the present study, we aimed to develop such risk scores, based on simple clinical variables. We studied a population-based cohort of 73,987 adults, aged 18 years and over. After 5.35 ± 1.59 years of follow-up, 4,726 participants (9.58%) in the exploration cohort developed type 2 diabetes and 2,327 participants (9.44%) in the validation cohort developed type 2 diabetes. Age, gender, body mass index, family history of diabetes, education, blood pressure, and resting heart rate were selected to form the concise score with an area under the receiver operating characteristic curve (AUC) of 0.67. The variables in the concise score combined with fasting plasma glucose (FPG), and triglyceride (TG) or use of lipid-lowering drugs constituted the accurate score with an AUC value of 0.77. The utility of the two scores was confirmed in the validation cohort with AUCs of 0.66 and 0.77, respectively. In summary, the concise score, based on non-laboratory variables, could be used to identify individuals at high risk of developing diabetes within Chinese population; the accurate score, which also uses FPG and TG data, is better at identifying such individuals. Nature Publishing Group 2016-05-25 /pmc/articles/PMC4879553/ /pubmed/27221651 http://dx.doi.org/10.1038/srep26548 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Wang, Anxin
Chen, Guojuan
Su, Zhaoping
Liu, Xiaoxue
Liu, Xiangtong
Li, Haibin
Luo, Yanxia
Tao, Lixin
Guo, Jin
Liu, Long
Chen, Shuohua
Wu, Shouling
Guo, Xiuhua
Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study
title Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study
title_full Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study
title_fullStr Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study
title_full_unstemmed Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study
title_short Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study
title_sort risk scores for predicting incidence of type 2 diabetes in the chinese population: the kailuan prospective study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4879553/
https://www.ncbi.nlm.nih.gov/pubmed/27221651
http://dx.doi.org/10.1038/srep26548
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