Association between insulin resistance and multiple chronic diseases: a cross-sectional study from CHARLS.

BACKGROUND

Chronic disease is a global public health problem. This study aimed to explore the association between insulin resistance (IR)-related indices and various chronic diseases, and to evaluate the predictive capacity of IR-related indices for these diseases.

METHODS

The data used in this study came from CHARLS. Binary logistic regression analysis and RCS were used to analyze the relationship between IR-related indices, including TyG, TyG-BMI, TyG-WHtR, METS-IR and eGDR, with nine chronic diseases. Subgroup analysis was performed to test the stability of the results. Finally, the predictive power of IR-related indices for chronic diseases was tested by ROC curve.

RESULTS

A total of 8,177 participants were included in this study. The study found that elevated prevalence of multiple chronic diseases is positively associated with increases in TyG, TyG-BMI, TyG-WHtR, and METS-IR, and negatively associated with eGDR. ROC analysis revealed that IR-related indices had the best accuracy in predicting dyslipidemia compared to other diseases, with TyG being the best predictor.

CONCLUSIONS

IR-related indices were positively associated with the prevalence of multiple chronic diseases. The burden of chronic diseases can be reduced by improving IR in middle-aged and older people.