Comparative Analysis of Six Surrogate Insulin Resistance Indices for Comparison of the Predictive Value of Insulin Resistance Substitution Index for Cardiovascular Disease Risk in Adult Patients with Cardiovascular-kidney-metabolic Syndrome 0-3 Stage
10.11714/jsysu.med.YX20260005
- VernacularTitle:胰岛素抵抗替代指数对心血管-肾脏-代谢综合征0-3期成人心血管疾病风险预测价值比较
- Author:
Qiannan ZHAO
1
;
Yutao WANG
2
;
Huimin ZHANG
2
;
Wei WAN
3
;
Ling WANG
2
;
Zixuan ZHANG
2
;
Lizhen LAN
2
Author Information
1. Academy of Medical Sciences， Shanxi Medical University， Taiyuan 030001， China
2. First Clinical Medical College of Shanxi Medical University， Shanxi Medical University， Taiyuan 030001， China
3. School of Public Health， Shanxi Medical University， Taiyuan 030001， China
- Publication Type:Journal Article
- Keywords:
cardiovascular-kidney-metabolic syndrome;
insulin resistance;
triglyceride-glucose;
cardiovascular disease;
risk prediction;
cohort study
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2026;47(3):539-550
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo evaluate the predictive value of six surrogate indices of insulin resistance （IR） for cardiovascular disease （CVD） risk in adults with cardiovascular-kidney-metabolic syndrome （CKM） stages 0-3. MethodsRelated data of 6 813 participants from the China health and retirement longitudinal study （CHARLS） were analyzed. Cox proportional hazards models and restricted cubic spline （RCS） analyses were performed to evaluate the associations between six insulin resistance surrogate indices （TyG， TyG-BMI， TyG-WC， TyG-WHtR， TyG-CVAI， CVAI） and CVD incidence. Predictive performance of 6 indices was comprehensively compared from 3 dimensionalities， using receiver operating characteristic （ROC） curves for discriminative ability， net reclassification improvement （NRI） for incremental predictive value， and decision curve analysis （DCA） for clinical utility. ResultsAll six IR surrogate indices significantly predicted CVD in the CKM population. RCS analysis revealed linear associations between each index and CVD incidence. ROC analysis showed that CVAI had the highest AUC （0.628，95%CI： 0.611-0.646）， significantly outperforming the single TyG index （P=0.001 6）. NRI analysis indicated significant reclassification improvement for the event group with CVAI （event NRI=0.042 8）. DCA demonstrated that CVAI achieved the highest net benefit across most thresholds. Subgroup analysis confirmed stronger associations between CVAI and CVD risk in CKM stages 2-3. ConclusionsThese six IR surrogate indices serve as valuable tools for predicting CVD incidence in individuals with CKM stages 0-3. Among them， CVAI exhibits optimal predictive value for future CVD risk in adults with CKM stages 1-3.
