Authors
Jialong Wang, Shengqi Zheng, Xingzhou Guo, Zeyuan Song, Sishuai Mao, Yifan Li
Published in
Medical science monitor : international medical journal of experimental and clinical research. Volume 32. Pages e952525. Sep 04, 2026. Epub Sep 04, 2026.
Abstract
BACKGROUND Insulin resistance is associated with kidney stone risk, and estimated glucose disposal rate (eGDR) is a validated surrogate marker of insulin resistance. This study examined the association between eGDR and kidney stone prevalence. MATERIAL AND METHODS Data were derived from a cross-sectional dataset collected at the Health Management Center, Affiliated Hospital of Yangzhou University (HMC-AHYU). Participants were stratified into 4 groups according to eGDR quartile. Logistic regression models were used to estimate the association between eGDR and kidney stone prevalence. Nonlinear relationships were explored via restricted cubic splines and threshold-effect analyses. Subgroup analyses were conducted to examine potential effect modification, and receiver operating characteristic curves were used to compare discriminative performances among metrics. RESULTS Overall, 23 527 individuals from HMC-AHYU were included. When stratified by quartile, lower eGDR values were associated with increased odds of kidney stones. After multivariable adjustment, each 1-unit increase in eGDR was associated with a 16% reduction in the odds of kidney stones (odds ratio [OR]=0.84, 95% confidence interval [CI]: 0.81-0.87). Compared with the lowest quartile (Q1), the highest quartile (Q4) exhibited 48% lower odds (OR=0.52, 95% CI: 0.41-0.65), with a significant dose-response relationship observed across quartiles. Threshold-effect analyses identified eGDR inflection points at 9.02 and 10.87. eGDR demonstrated the strongest discriminative ability for kidney stones (area under the curve=0.692, 95% CI: 0.679-0.705). Validation using 1000 bootstrap resamples confirmed model stability. CONCLUSIONS Lower eGDR levels were associated with increased kidney stone prevalence. eGDR may serve as a biomarker for high kidney stone risk.
PMID:
42696503
Bibliographic data and abstract were imported from PubMed on 05 Sep 2026.
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