Comparison of TyG and Newly TyG Related Indicators for Chronic Kidney Diseases Estimation in a Chinese Population

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Background: Obesity and insulin resistance (IR) are positively associated with chronic kidney disease (CKD). Previous studies have identified triglyceride-glucose index (TyG) as a valuable surrogate of insulin resistance. Recently, new indicators combining TyG and simple anthropometric indices have emerged, The objective of this study was to assess the diagnostic accuracy of TyG and newly TyG related indicators in detecting CKD and explore which indices were superior in associating with CKD in Chinese population. Methods: Correlation test, logistic regression analysis, and receiver operating characteristic (ROC) analyses were used to evaluate the optimal cut-off and value of TyG, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), TyG-waist to height ratio (TyG-WHtR) for predicting CKD. Results: TyG-WHtR, TyG-WC, and TyG-BMI correlated with several risk factors for CKD. After adjusting for confounders, TyG-WHtR and TyG-WC remained significantly associated with CKD, while TyG-BMI did not. The highest quartiles of TyG-WHtR and TyG-WC had 1.95- and 1.91-fold increased risk of CKD than the lowest quartiles (P<0.05). TyG-WHtR had the largest AUC (0.687) for CKD detection, followed by TyG-WC (0.669), TyG (0.652), and TyG-BMI (0.648). A united model that involved TyG-WHtR and other risk variables had higher predictive performance (AUC=0.791) than a single TyG related indicator. However, TyG had the highest OR (2.713, 95% CI, 1.446-5.090) for reduced eGFR in the fully adjusted model. A united model that involved TyG and WHtR separately had stronger predictive ability (AUC: 0.794) than the model that involved TyG-WHtR individually (AUC:0.791). Conclusion: This study found that TyG-WHtR had a better diagnostic value in the diagnosis of CKD, compared to other TyG related indicators, but none of the TyG related indicators showed a stronger association with CKD than TyG. Further research and more refined algorithms are needed to verify these new indicators.

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