Journal of Diabetes (Nov 2022)

2型糖尿病患者的长期空腹血糖变异性与癌症风险:上海地区一项基于人群的回顾性队列研究‐‐空腹血糖变异性对癌症的作用

  • Xiao‐rui Cui,
  • Jun Li,
  • Ya‐ting Yang,
  • Jing‐yi Wu,
  • Hui‐lin Xu,
  • Yong‐fu Yu,
  • Guo‐you Qin

DOI
https://doi.org/10.1111/1753-0407.13329
Journal volume & issue
Vol. 14, no. 11
pp. 727 – 738

Abstract

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Abstract Backgrounds Fasting blood glucose (FBG) variability may make an impact on adverse events in patients with diabetes mellitus. However, the association between long‐term changes in FBG and cancer remains unclear. We aimed to investigate this association in a large‐scale longitudinal study. Methods Data were collected from 46 761 patients with type 2 diabetes mellitus aged 20–80 years who participated in the Diabetes Standardized Management Program in Shanghai, China. We adopted four indicators, including standard deviation (SD), coefficient of variation (CV), variation independent of the mean (VIM), and average real variability (ARV) to describe FBG variability. Adjusted multivariable Cox regression analyses and restricted cubic splines were used to investigate the association between long‐term FBG variability and cancer risk. We also determined the interactive effect of FBG variability with hypertension and FBG‐mean with hypertension on cancer risk, respectively. Results In this study, we confirmed 2218 cancer cases (51.1% male) over a median follow‐up of 2.86 years. In the multivariable‐adjusted models, participants in the highest quartile of FBG variability had an increased risk of cancer compared with those in the lowest quartile. The nonlinear association was found when using FBG‐VIM, FBG‐ARV, and FBG‐SD in restricted cubic spline plots. There was a significant interaction effect of FBG variability with hypertension on cancer, whereas the effect of FBG‐mean with hypertension did not attain significance. Conclusions Our retrospective cohort study demonstrated a positive association between the long‐term changes in FBG and cancer risk in patients with type 2 diabetes mellitus. FBG variability may independently predict cancer incidence.

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