Cohort study evaluation of New Chinese Diabetes Risk Score: A new non-invasive indicator for predicting metabolic syndrome

  • Yifei Feng
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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  • Xingjin Yang
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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  • Yang Li
    Affiliations
    Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
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  • Minghui Han
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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  • Ranran Qie
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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  • Shengbing Huang
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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  • Xiaoyan Wu
    Affiliations
    Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
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  • Yanyan Zhang
    Affiliations
    Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
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  • Yuying Wu
    Affiliations
    Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
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  • Dechen Liu
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China

    Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
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  • Fulan Hu
    Affiliations
    Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
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  • Ming Zhang
    Affiliations
    Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
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  • Yongli Yang
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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  • Xuezhong Shi
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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  • Jie Lu
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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  • Sun Liang
    Affiliations
    Department of Social Medicine and Health Service Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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  • Dongsheng Hu
    Correspondence
    Corresponding authors at: Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 100 Kexue Avenue, Zhengzhou, Henan 450001, People’s Republic of China.
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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  • Yang Zhao
    Correspondence
    Corresponding authors at: Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 100 Kexue Avenue, Zhengzhou, Henan 450001, People’s Republic of China.
    Affiliations
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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      Highlights

      • New Chinese Diabetes Risk Score (NCDRS) is low cost and non-invasive.
      • Study based on a prospective cohort study, which may provide convincing findings.
      • Our results showed NCDRS was positively associated with risk of MetS.
      • Results showed high power of NCDRS in predicting MetS in three diagnostic criteria.

      Abstract

      Objectives

      To investigate the association of the baseline New Chinese Diabetes Risk Score (NCDRS) with metabolic syndrome (MetS) risk and to evaluate the power of the baseline NCDRS to predict MetS based on the rural Chinese cohort study.

      Methods

      Study participants were classified by baseline quartiles of NCDRS by gender. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for risk of MetS according to different diagnostic criteria. The receiver operating characteristic curve (ROC) and area under the ROC curve (AUC) were used to evaluate the power of the baseline NCDRS for predicting MetS according to different diagnostic criteria.

      Results

      We included 7,133 participants, and 1,651 MetS cases were identified after 6 years follow-up. After adjusting for multivariable confounding factors and with NCDRS quartile 1 as the reference, with quartile 4, the risk of MetS was increased for all participants, men and women: ORs (95% CIs) 4.03 (3.23−5.02), 3.59 (2.56−5.05) and 5.71 (4.23−7.70), respectively. Similar results were found on sensitivity analysis. The baseline NCDRS was a good predictor of MetS for all participants, men and women with MetS defined according to the diagnostic criteria of the Chinese Joint Committee on the Development of Guidelines for the Prevention and Treatment of Dyslipidemia in Adults (JCDCG).

      Conclusions

      Our study, based on the cohort study, found that the baseline NCDRS was positively associated with risk of MetS. Furthermore, our study might provide suggestions for developing a useful and inexpensive tool for predicting MetS in the Chinese population.

      Keywords

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