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[Average blood glucose construction: a multi-level Bayes model approach and application].

Jinzhuo Ge1, Xuenan Peng2, Chunhua Zhao1

  • 1School of Public Health, Soochow University, Suzhou 215123, China.

Wei Sheng Yan Jiu = Journal of Hygiene Research
|October 12, 2019
PubMed
Summary

The complete Bayesian method accurately estimates average blood glucose. This method found a link between higher average blood glucose levels and an increased risk of fatal stroke in adults.

Keywords:
cerebral apoplexy morbidityconstruction methodsmulti-level Bayes modelthe average blood glucose

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Statistics

Background:

  • Accurate estimation of average blood glucose is crucial for understanding metabolic health.
  • Existing methods may have limitations in precision and application.
  • Multi-level Bayes models offer a sophisticated approach to statistical modeling.

Purpose of the Study:

  • To develop and evaluate a novel average blood glucose construction method using a multi-level Bayes model.
  • To assess the accuracy of the proposed method through simulation.
  • To investigate the association between estimated average blood glucose and fatal stroke risk in a cohort.

Main Methods:

  • Simulated data were generated using a multi-level Bayes model to compare three average blood glucose construction methods.
  • A cohort of 12,321 participants (aged >45 years, without stroke) in Suzhou was followed from 2011-2018.
  • Cox regression analysis was employed to determine the effect of mean blood glucose on fatal stroke incidence.

Main Results:

  • The complete Bayesian method demonstrated high accuracy in estimating average blood glucose (correlation coefficient r=0.898).
  • Simulations showed the complete Bayesian method's estimation error was 0.278, with an average blood glucose estimation of 0.527 mmol/L.
  • A significant association was observed between elevated mean blood glucose and increased risk of fatal stroke (P<0.05).

Conclusions:

  • The complete Bayesian multi-level latent variable model provides an accurate method for estimating average blood glucose.
  • Higher average blood glucose levels are associated with a significantly increased risk of fatal stroke.
  • This modeling approach has potential applications in epidemiological studies and clinical risk assessment.