Obesity-Related Insulin Resistance Indices and CKD Risk in Patients with Diabetes and Coronary Heart Disease: A Multicenter Cohort Analysis.

BACKGROUND

Chronic kidney disease (CKD) is primarily caused by diabetes, a condition frequently complicated by obesity and insulin resistance (IR)-factors that also promote the development of diabetes and coronary heart disease (CHD). Consequently, whether the obesity-related IR index independently increases CKD risk requires further exploration.

METHODS

A total of 7741 patients with coexisting diabetes and CHD from three centers were included in this study. Multivariable-adjusted Cox regression analyses were performed to investigate the associations between four obesity-related IR indices-weight-adjusted-waist index (WWI), triglyceride glucose-body mass index (TyG-BMI), TyG combined with waist circumference (TyG-WC), and metabolic score for visceral fat (METS-VF)-and the risk of CKD in patients with concomitant diabetes and CHD. Restricted cubic splines (RCS) were plotted to further explore potential threshold effects. The predictive ability and incremental predictive value of these indices were evaluated using receiver operating characteristic (ROC) curves, time-dependent ROC curves, decision curve analysis (DCA), and the C-index.

RESULTS

Obesity-related IR indices were closely associated with an increased risk of CKD in patients with diabetes and CHD, with evidence of threshold effects. Notably, the risk became particularly pronounced when WWI, TyG-BMI, TyG-WC, and METS-VF exceeded 11.2 cm/√kg, 245 kg/m2, 530 cm, and 6.2 mg/dL*kg/m2, respectively. Furthermore, ROC analysis, time-dependent ROC curves, DCA, and the C-index consistently demonstrated that METS-VF possessed the greatest predictive capacity and the highest incremental predictive value among the four indices.

CONCLUSION

Obesity-related IR indices are significantly associated with an increased risk of CKD in patients with diabetes and CHD. Notably, METS-VF, which reflects visceral obesity and IR, demonstrated the strongest predictive capacity among the indices. These findings may have important implications for CKD risk assessment and clinical intervention in this high-risk population.