Relationship between nontraditional lipid parameters and the risk of type 2 diabetes in individuals recovered from dyslipidemia: a cohort study - Wang, Yuxian; Chen, Zekai; He, Zhen; Qin, Hailun; Wu, Kuangyi; Zheng, Huancong; Fu, Peng; Cai, Zefeng; Wu, Weiqiang; Lan, Yulong; Wu, Dan; Wu, Shouling; et al. (Frontiers in Endocrinology, 2025)
Aims/IntroductionLipid profiles are essential for assessing type 2 diabetes (T2D) risk, but individuals who recover from dyslipidemia are often overlooked, leaving their residual risk unclear. This study aimed to evaluate T2D risk in individuals with varying lipid change patterns and investigate the associations between traditional and novel lipid parameters with T2D risk among those recovered from dyslipidemia.Materials and MethodsIn this prospective cohort study of the Kailuan Study, 39,283 non-diabetic participants were followed to evaluate T2D risk across lipid change patterns using the Cox proportional hazards models. A subset of 3,850 individuals recovered from dyslipidemia was analyzed to examine the associations between both traditional and novel lipid parameters and T2D risk using Cox models and restricted cubic splines. Predictive performance was assessed using the C-index.ResultsDuring follow-up, 5,223 participants developed T2D. Individuals recovered from dyslipidemia had a significantly higher T2D risk (hazard ratio [HR], 1.37; 95% CI, 1.25-1.51) compared to those with persistent normal lipid levels. In this group, high-density lipoprotein cholesterol (HDL-C) was inversely associated with T2D risk, while triglyceride (TG), lipoprotein combine index (LCI), atherogenic index of plasma (AIP), non-HDL-C, Castelli’s index-I, Castelli’s index-II and triglyceride-glucose index (TyG) were positively associated. AIP and TyG outperformed other parameters in predictive ability.ConclusionsIndividuals recovered from dyslipidemia remain at an elevated risk for T2D. Novel lipid parameters, particularly AIP and TyG, demonstrate superior predictive performance in this group, providing valuable insights for risk stratification and targeted prevention strategies.