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Associations of Fasting Blood Glucose with Influencing Factors in Northeast China: A Quantile Regression Analysis

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Listed:
  • Xin Guo

    (Epidemiology and Statistics, School of Public Health, Jilin University, Changchun 130021, China)

  • Li Shen

    (Epidemiology and Statistics, School of Public Health, Jilin University, Changchun 130021, China)

  • Jing Dou

    (Epidemiology and Statistics, School of Public Health, Jilin University, Changchun 130021, China)

  • Yaogai Lv

    (Epidemiology and Statistics, School of Public Health, Jilin University, Changchun 130021, China)

  • Anning Zhang

    (Epidemiology and Statistics, School of Public Health, Jilin University, Changchun 130021, China)

  • Fanchao Shi

    (Epidemiology and Statistics, School of Public Health, Jilin University, Changchun 130021, China)

  • Zhiqiang Xue

    (Epidemiology and Statistics, School of Public Health, Jilin University, Changchun 130021, China)

  • Yaqin Yu

    (Epidemiology and Statistics, School of Public Health, Jilin University, Changchun 130021, China)

  • Lina Jin

    (Epidemiology and Statistics, School of Public Health, Jilin University, Changchun 130021, China)

  • Yan Yao

    (Epidemiology and Statistics, School of Public Health, Jilin University, Changchun 130021, China)

Abstract

Background : Diabetes mellitus (DM) has become a major public health problem in China. Although a number of researchers have investigated DM risk factors, little is known about the associations between values of fasting blood glucose (FBG) and influencing factors. This study aims to explore these associations by the quantile regression (QR) model. Methods : A cross-sectional survey based on a sample of 23,050 adults aged 18 to 79 years was conducted in Jilin in 2012, and some subjects were excluded due to missing values with respect to necessary variables or having glycemic control, in accordance with the purposes of this study. Finally, in total 14,698 people were included in this study. QR was performed to identify the factors influencing the level of FBG in different quantiles of FBG. Results: The distribution of FBG status was different between males and females ( χ 2 = 175.30, p < 0.001). The QR model provided more detailed views on the associations of FBG with different factors and revealed apparent quantile-related patterns separately for different factors. Body mass index (BMI) was positively associated with the low and middle quantiles of FBG. Waist circumference (WC) had a positive association with the high quantiles of FBG. Conclusions : FBG had a positive association with BMI in normal FBG, and a positive association with WC in high FBG. Diet and alcohol intake were associated with FBG in normal FBG. FBG was more likely to be elevated in the elderly, female workers, and people with family history of DM.

Suggested Citation

  • Xin Guo & Li Shen & Jing Dou & Yaogai Lv & Anning Zhang & Fanchao Shi & Zhiqiang Xue & Yaqin Yu & Lina Jin & Yan Yao, 2017. "Associations of Fasting Blood Glucose with Influencing Factors in Northeast China: A Quantile Regression Analysis," IJERPH, MDPI, vol. 14(11), pages 1-11, November.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:11:p:1368-:d:118298
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Minghui Yin & Balekouzou Augustin & Chang Shu & Tingting Qin & Ping Yin, 2016. "Probit Models to Investigate Prevalence of Total Diagnosed and Undiagnosed Diabetes among Aged 45 Years or Older Adults in China," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-13, October.
    3. Steven E. Kahn & Rebecca L. Hull & Kristina M. Utzschneider, 2006. "Mechanisms linking obesity to insulin resistance and type 2 diabetes," Nature, Nature, vol. 444(7121), pages 840-846, December.
    4. Rui Wang & Peng Zhang & Xin Lv & Lingling Jiang & Chunshi Gao & Yuanyuan Song & Yaqin Yu & Bo Li, 2016. "Situation of Diabetes and Related Disease Surveillance in Rural Areas of Jilin Province, Northeast China," IJERPH, MDPI, vol. 13(6), pages 1-10, May.
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