Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0-3 and the development of a machine learning prediction model: a nationwide prospective cohort study

Abstract

Background: The American Heart Association recently introduced the concept of cardiovascular-kidney-metabolic (CKM) syndrome, highlighting the increasing importance of the complex interplay between metabolic, renal, and cardiovascular diseases (CVD). While substantial evidence supports a correlation between the estimated glucose disposal rate (eGDR) and CVD events, its predictive value compared with other insulin resistance (IR) indices, such as triglyceride-glucose (TyG) index, TyG-waist circumference, TyG-body mass index, TyG-waist-to-height ratio, triglyceride-to-high density lipoprotein cholesterol ratio, and the metabolic score for insulin resistance, remains unclear.

Methods: This prospective cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS). The individuals were categorized into four subgroups based on the quartiles of eGDR. The associations between eGDR and incident CVD were evaluated using multivariate logistic regression analyses and restricted cubic spline. Seven machine learning models were utilized to assess the predictive value of the eGDR index for CVD events. To assess the model's performance, we applied receiver operating characteristic (ROC) and precision-recall (PR) curves, calibration curves, and decision curve analysis.

Results: A total of 4,950 participants (mean age: 73.46 ± 9.93 years), including 50.4% females, were enrolled in the study. During follow-up between 2011 and 2018, 697 (14.1%) participants developed CVD, including 486 (9.8%) with heart disease and 263 (5.3%) with stroke. The eGDR index outperformed six other IR indices in predicting CVD events, demonstrating a significant and linear relationship with all outcomes. Each 1-unit increase in eGDR was associated with a 14%, 14%, and 19% lower risk of CVD, heart disease, and stroke, respectively, in the fully adjusted model. The incorporation of the eGDR index into predictive models significantly improved prediction performance for CVD events, with the area under the ROC and PR curves equal to or exceeding 0.90 in both the training and testing sets.

Conclusions: The eGDR index outperforms six other IR indices in predicting CVD, heart disease, and stroke in individuals with CKM syndrome stages 0-3. Its incorporation into predictive models enhances risk stratification and may aid in the early identification of high-risk individuals in this population. Further studies are needed to validate these findings in external cohorts.

Keywords: Cardiovascular disease; Cardiovascular-kidney-metabolic syndrome; Estimated glucose disposal rate; Insulin resistance.

Fig. 1

Flowchart of the study

Fig. 2

Predictive value of seven IR surrogate indices for cardiovascular diseases in individuals with cardiovascular-kidney-metabolic syndrome stages 0–3. CVD, cardiovascular disease; IR, insulin resistance; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value; eGDR, estimated glucose disposal rate; TyG, triglyceride–glucose; TyG-WC, TyG-waist circumference; TyG-BMI, TyG-body mass index; TyG-WHtR, TyG-waist-to-height ratio; TG/HDL-C, triglyceride-to-high density lipoprotein cholesterol ratio; METS-IR, metabolic score for insulin resistance

Fig. 3

Restricted cubic spline curves for CVD, heart disease, and stroke according to the eGDR in the A, B,and C Model 1, D, E,and F Model 2, and G, H,and I Model 3, respectively. Model 1 was unadjusted; Model 2 was adjusted for age, gender, education level, marital status, smoking status, and alcohol consumption status; and Model 3 adjusted age, BMI, WC, hypertension, diabetes, and alcohol consumption status. CVD, cardiovascular disease; eGDR, estimated glucose disposal rate; BMI, body mass index; WC, waist circumference; OR, odds ratio

Fig. 4

Subgroup analysis of the association between estimated glucose disposal rate and A CVD, B heart disease, and C stroke. CVD, cardiovascular disease; OR, odds ratio; BMI, body mass index

Fig. 5

Feature selection based on the LASSO algorithm. A Selection of the tuning parameter (λ) in the LASSO model via 10-fold cross-validation based on minimum criteria. The optimal λ value of 0.008. B The LASSO coefficient profiles of clinical features. C The coefficients of LASSO regression analysis. LASSO, least absolute shrinkage and selection operator

Fig. 6

ROC and PR curves of the modified ML model, which incorporated the estimated glucose disposal rate, were plotted for predicting CVD, heart disease, and stroke in both the training and testing sets. A–F ROC curves of the modified ML model for predicting CVD, heart disease, and stroke in both the training and testing sets. G–L PR curves of the modified ML model for predicting CVD, heart disease, and stroke in both the training and testing sets. ROC, receiver operating characteristic; PR, precision-recall; ML, machine learning; CVD, cardiovascular disease; AUC, area under the curve