Background: Limited studies have explored the relationship between surrogate markers of insulin resistance (IR) with both cardiovascular disease (CVD) risk and CVD mortality in individuals with metabolic dysfunction-associated fatty liver disease (MASLD). This study aimed to assess the associations of various IR surrogates with CVD risk and mortality and to identify effective predictors of cardiovascular outcomes in this population. Design: This study constituted a population-based cross-sectional investigation, utilizing data derived from six cycles of the National Health and Nutrition Examination Survey (NHANES) spanning from 2005 to 2016. The data were linked to the NHANES public-use linked mortality files up to December 31, 2019, to facilitate follow-up on CVD mortality. Following predefined exclusion criteria, participants diagnosed with MASLD were identified and incorporated into the study. The research assessed the associations between surrogate markers of IR and both the prevalence of CVD and CVD mortality. Methods: The primary outcome of this study was the incidence of CVD in individuals with MASLD, and the secondary outcome was CVD mortality. Weighted multivariate logistic regression was utilized to examine the relationship between surrogate markers of IR with total CVD and other subtypes. Weighted multivariate Cox regression and Kaplan–Meier analysis were used to examine the relationship between surrogate markers of IR and CVD mortality. Restricted cubic spline (RCS) was used to explore potential nonlinear relationships. Receiver operating characteristics (ROC) and calibration curves were plotted to evaluate the discriminability and accuracy of IR surrogate markers in predicting CVD risk. Furthermore, mediation analysis was conducted to determine whether surrogate markers of liver fibrosis play a mediating role in the association of surrogate markers of IR with the risk of total CVD and other subtypes. Results: This study suggests that in the fully adjusted model, estimated glucose disposal rate (eGDR) was significantly negatively correlated with total CVD, congestive heart failure (CHF), heart attack (HA), and CVD mortality. Metabolic score for IR and triglyceride-glucose-body mass index were significantly positively correlated with CVD mortality, total CVD, and CHF. eGDR was best correlated with total CVD (odds ratio (OR) = 0.40, 95% confidence interval (CI): 0.25, 0.63), CHF (OR = 0.15, 95% CI: 0.06, 0.38), and HA (OR = 0.40, 95% CI: 0.22, 0.71). Weighted multivariate Cox regression indicated that for each unit increase in eGDR, the risk of CVD mortality decreased by 27%. RCS analysis showed that all surrogate markers of IR were linearly related to CVD and CVD mortality ( P -nonlinear relationship >0.05). In addition, the ROC curve showed that eGDR had a more robust diagnostic efficacy than other IR markers, and eGDR had a higher accuracy in predicting long-term CVD mortality. Adding eGDR to the base model improved the C statistic, net reclassification improvement, and composite discrimination improvement for most outcome variables. In mediation analysis, Metabolic Dysfunction Associated Fibrosis 5 mediated part of the association between eGDR and total CVD, coronary heart disease, HA, and stroke. Conclusion: In conclusion, eGDR outperformed other IR surrogates in predicting both CVD risk and CVD mortality in MASLD. Incorporating eGDR into prediction models may enhance risk stratification and facilitate early identification of high-risk individuals in this population. Further studies are warranted to validate these findings in external cohorts.
Yang et al. (Sun,) studied this question.