Cargando…
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...
Autores principales: | , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1782433693178003456 |
---|---|
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. |
format | Online Article Text |
id | pubmed-4879553 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT wanganxin riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT chenguojuan riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT suzhaoping riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT liuxiaoxue riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT liuxiangtong riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT lihaibin riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT luoyanxia riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT taolixin riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT guojin riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT liulong riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT chenshuohua riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT wushouling riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy AT guoxiuhua riskscoresforpredictingincidenceoftype2diabetesinthechinesepopulationthekailuanprospectivestudy |