Association of remnant cholesterol with insulin resistance and type 2 diabetes: mediation analyses from NHANES 1999–2020 - Yuying Li; Qiao Zeng; Danping Peng; Pingsheng Hu; Jiahua Luo; Keyang Zheng; Yuzhe Yin; Rite Si; Jingyi Xiao; Shaofen Li; Jinxiang Fu; Jinping Liu; et al. (Lipids in Health and Disease)
Abstract Background Previous studies have established a correlation between elevated levels of remnant cholesterol (RC) and the occurrence of type 2 diabetes mellitus (T2D) as well as insulin resistance (IR); however, the precise nature of these associations remains incompletely elucidated. This study aimed to evaluate the relationships between RC and IR, as well as RC and T2D, and to determine the extent to which IR mediated the relationship between RC and T2D. Methods This was an observational study that utilized cross-sectional methods to examine the general population in the National Health and Nutrition Examination Survey (NHANES) 1999–2020. The participants were divided into 4 groups according to the RC quartiles. The outcome was the prevalence of IR and T2D. Survey-weighted binary logistic regression analysis was used to analyze the associations, and the restricted cubic spline (RCS) curve was used to further analyze the nonlinear relationship. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance, and the areas under the curves (AUC) of RC, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) were compared using the DeLong test. The mediating effect of IR on the relationship between RC and T2D was evaluated through mediation analysis. Results A total of 23,755 participants (46.02 ± 18.48 years, 48.8% male) were included in our study. Higher RC levels were significantly associated with increased prevalence of both IR and T2D. After adjusting for potential confounders, logistic regression analysis showed that higher RC quartiles were associated with the increased prevalence of IR [Quartile 4 vs. Quartile 1: odds ratio (OR) (95% confidence interval, CI): 1.65 (1.41–1.94), p < 0.001] and T2D [Quartile 4 vs. Quartile 1: OR (95% CI): 1.24 (1.03–1.50), p = 0.024]. RCS analysis revealed two distinct nonlinear relationships: one between RC levels and the prevalence of IR (nonlinear p < 0.001), and another between RC levels and the prevalence of T2D (nonlinear p < 0.001). ROC curve analysis demonstrated that RC had the highest discriminative ability, significantly outperforming LDL-C, HDL-C, and TG in predicting both IR and T2D risk (all P < 0.001 by DeLong test). Mediation analysis revealed that IR significantly mediated the relationship between RC and T2D, with approximately 54.1% of the effect of RC on T2D being indirect through IR. Conclusions Higher RC level was associated with increased prevalence of IR and T2D. IR mediated 54.1% of the association between RC and T2D, suggesting that managing IR could be crucial in reducing the risk of T2D in individuals with elevated RC levels.