Association of estimated glucose disposal rate with incident cardiovascular disease under different metabolic and circadian rhythm states: findings from a national population-based prospective cohort study

Abstract

Abstract Background Recent studies have shown that both metabolic syndrome and circadian rhythm syndrome are firmly associated with the occurrence of cardiovascular disease (CVD), with insulin resistance playing a significant role. The estimated glucose disposal rate (eGDR) is considered to be a reliable surrogate marker for insulin resistance. However, the relationship between eGDR and CVD under different metabolic and circadian rhythm states has not been thoroughly studied, and large-scale prospective cohort studies are needed to clarify this relationship. Methods This study is based on the China Health and Retirement Longitudinal Study (CHARLS), recruiting individuals aged 45 and above with complete eGDR data. The eGDR was calculated by the formula: eGDR(mg/kg/min) = 21.158 − (0.09 × WC) − (3.407 × hypertension) − (0.551 × HbA1c) [WC (cm), hypertension (yes = 1/no = 0), and HbA1c (%)] (Zabala et al. in Cardiovasc Diabetol 20(1):202; 2021).Participants were divided into four subgroups based on the quartiles (Q) of eGDR.The cumulative incidence rates and hazard ratios (HR) with 95% confidence intervals (CI) were calculated, with the lowest eGDR quartile (representing the highest degree of insulin resistance) as the reference. Participants were further divided into subgroups based on the diagnosis of Metabolic syndrome (MetS) or circadian syndrome (CircS) to explore the relationship between eGDR and CVD under different metabolic and circadian rhythm conditions. The dose–response relationship between eGDR and CVD incidence was investigated using a restricted cubic spline (RCS) based on a Cox regression model. Receiver operating characteristic (ROC) curves were generated to assess the predictive value of eGDR for CVD incidence. A clinical decision curve analysis (DCA) was also conducted to assess the clinical utility of the basic model. Results 6507 participants were included, with a median age of 58 years [52 years, 64 years], and 55% were female. Over a median follow-up duration of 87 months, 679 first-episode CVD events were recorded, including heart disease and stroke. The RCS curves demonstrated a significant dose–response relationship between eGDR and the incidence of first-presentation CVD in different metabolic and circadian rhythm subgroups (all P-values 0.05). eGDR exhibited a significant linear relationship with all outcomes (non-linearity P < 0.05). The Kaplan–Meier cumulative incidence curves showed that as eGDR levels increased, the cumulative incidence rates of first CVD, heart disease, and stroke gradually decreased from Q1 to Q4 groups. Significant differences were observed across all metabolic and circadian rhythm subgroups (log-rank test P < 0.001). Through the Cox proportional hazards model, we confirmed a significant association between baseline eGDR levels and first-onset CVD, heart disease, and stroke. Subgroup analyses indicated that the predictive ability of eGDR for CVD risk varied across different Body mass index (BMI) (P for interaction = 0.025) and age (P for interaction = 0.045) subgroups. Mediation analysis revealed that CircS partially mediated this association. Furthermore, time-dependent ROC curves demonstrated the potential of eGDR as a predictor of CVD risk, revealing possible differences in the model's application across different cardiovascular conditions. Conclusion eGDR is an independent predictor of CVD risk, with lower eGDR levels being closely associated with a higher risk of CVD (including heart disease and stroke). In populations with MetS or CircS, the association between lower eGDR levels and increased risk is more pronounced.

Keywords

• Insulin resistance
• Cardiovascular disease
• Estimated glucose disposal rate
• Metabolic syndrome
• Circadian rhythm syndrome