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. 2024 Aug;16(8):e13595.
doi: 10.1111/1753-0407.13595.

Association between estimated glucose disposal rate control level and stroke incidence in middle-aged and elderly adults

Jiangnan Yao  1 , Feng Zhou  2 , Lingzhi Ruan  3 , Yiling Liang  1 , Qianrong Zheng  1 , Jiaxin Shao  1 , Fuman Cai  1 , Jianghua Zhou  4 , Hao Zhou  4
Affiliations

Association between estimated glucose disposal rate control level and stroke incidence in middle-aged and elderly adults

Jiangnan Yao et al. J Diabetes. 2024 Aug.

Abstract

Background: To estimate glucose disposal rate (eGDR) as a newly validated surrogate marker of insulin resistance. Few studies have explored the association between changes in eGDR levels and stroke incidence. This study aims to explore the effect of the level of eGDR control on stroke and events.

Methods: Data were obtained from the China Longitudinal Study on Health and Retirement (CHARLS). The eGDR control level was classified using K-means cluster analysis. Logistic regression analysis was used to explore the association between different eGDR control levels and incident stroke. Restrictive cubic spline regression was used to test the potential nonlinear association between cumulative eGDR and stroke incidence.

Results: Of the 4790 participants, 304 (6.3%) had a stroke within 3 years. The odds ratio (OR) was 2.34 (95% confidence interval [CI], 1.42-3.86) for the poorly controlled class 4 and 2.56 (95% CI, 1.53-4.30) for the worst controlled class 5 compared with class 1 with the best controlled eGDR. The OR for well-controlled class 2 was 1.28 (95% CI, 0.79-2.05), and the OR for moderately controlled class 3 was 1.95 (95% CI, 1.14-3.32). In restrictive cubic spline regression analysis, eGDR changes are linearly correlated with stroke occurrence. Weighted quartile and regression analysis identified waist circumference and hypertension as key variables of eGDR for predicting incident stroke.

Conclusions: Poorly controlled eGDR level is associated with an increased risk of stroke in middle-aged and elderly people. Monitoring changes in eGDR may help identify individuals at high risk of stroke early.

Keywords: CHARLS; estimated glucose disposal rate; stroke.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
The eGDR clustering by K‐means clustering.
FIGURE 2
FIGURE 2
Flowchart of the study population.
FIGURE 3
FIGURE 3
Cubic model of the association between different classes and cumulative eGDR.
FIGURE 4
FIGURE 4
Estimated weights assigned to eGDR with the WQS model. Weights in a negative direction were obtained when the effect parameter of the WQS model was constrained to the negative direction with 1000 repeated holdout validations for eGDR in 2012 (A) and 2015 (B).
See this image and copyright information in PMC

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