Predictive Value of Non-Traditional Lipid Parameters NHHR and the TyG Index for Rapid Kidney Function Decline in Diabetic Patients: A Longitudinal Cohort Study Based on CHARLS

Department of Nephrology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518033, China

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Abstract

Objective: The objective of this study was to investigate the predictive value of the non-high-density lipoprotein cholesterol (nonHDL-C)/high-density lipoprotein cholesterol (HDL-C) ratio (NHHR) and the triglyceride–glucose (TyG) index for rapid kidney function decline (RKFD) in Chinese patients with diabetes. Methods: This study used data from the China Health and Retirement Longitudinal Study (CHARLS) 2011–2015. A total of 1051 patients with diabetes were analyzed with rapid kidney function decline (RKFD) as the primary endpoint. Associations between NHHR and the TyG index with renal outcomes were examined using logistic regression models. Restricted cubic splines and subgroup analyses were performed to investigate potential linear relationships and consistency across population strata. Receiver operating characteristic (ROC) curves were used to assess the predictive performance of NHHR and the TyG index. Results: The study included 1051 participants. During a median follow-up of four years, 189 individuals (17.98%) developed RKFD. After full adjustment for confounding factors, both NHHR and the TyG index showed significant positive correlations with RKFD risk. Restricted cubic spline (RCS) analysis revealed linear relationships between both metrics and RKFD risk. ROC analysis demonstrated that adding the NHHR to the baseline model significantly improved predictive ability, whereas adding the TyG index did not result in a statistically significant improvement. Subgroup analyses indicated that the predictive role of the NHHR was more pronounced in women and non-smokers. Conclusion: In patients with diabetes, elevated levels of NHHR and the TyG index represent independent risk factors for RKFD, with NHHR exhibiting a more stable incremental predictive value.