Association of estimated glucose disposal rate with chronic kidney disease: comparative analysis against traditional insulin resistance indices.

Background: Chronic kidney disease (CKD) is a widespread condition, marked by significant morbidity and mortality rates, particularly in individuals with comorbidities such as diabetes and hypertension. While insulin resistance (IR) has been linked to CKD, the traditional methods used to measure IR have inherent limitations. This necessitates the exploration of alternative indicators that can more accurately reflect the relationship between IR and CKD. Methods: This study employed a cross-sectional design, utilizing data extracted from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2013 to 2018. The study sample comprised 7423 participants. Comprehensive demographic, anthropometric, and laboratory data were collected and analyzed. The estimated glucose disposal rate (eGDR), along with established measures of insulin resistance such as HOMA-IR, QUICKI, and the TyG, TyG-BMI, and TyG-WC indices were computed. The relationships between these indices and CKD indicators, specifically the eGFR and UACR, were assessed using a combination of linear and logistic regression models. Additionally, the performance of these indices was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Elevated levels of the eGDR were significantly correlated with improved kidney function and a reduced prevalence of chronic kidney disease (CKD) and albuminuria. The correlation coefficients (R²) demonstrated that eGDR had a stronger association with the estimated glomerular filtration rate (eGFR) at R²=0.1379 and with the urinary albumin-to-creatinine ratio (UACR) at R²=0.0816, compared to the traditional measures of insulin resistance. eGDR also declined progressively across worsening CKD stages (p for trend< 0.001), highlighting a dose-response relationship. Logistic regression analysis further revealed that higher eGDR levels were associated with a decreased risk of developing CKD and proteinuria. Additionally, the ROC curve analysis indicated that eGDR exhibited the highest predictive accuracy for CKD, with an area under the curve (AUC) of 0.75, and for proteinuria, with an area under the curve (AUC) of 0.68. Conclusion: The eGDR has emerged as a reliable and practical marker of insulin resistance associated with CKD indicators, demonstrating stronger associations with eGFR and UACR compared to traditional measures like HOMA-IR, QUICKI, TyG, TyG-BMI and TyG-WC. The simplicity of calculating eGDR enhances its utility as a valuable tool for the early detection and management of CKD, potentially improving clinical outcomes.