| Title: | Combining Insulin Resistance and Renal Function to Predict Cardiovascular Events: A Multicenter Cohort Study |
| Journal: | BIO Integration |
| Published: | 15 Jun 2026 |
| DOI: | https://doi.org/10.15212/bioi-2026-0017 |
Publication 20637
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Abstract
Background: Insulin resistance (IR) and chronic kidney disease (CKD) are independent risk factors for cardiovascular disease (CVD). The triglyceride-glucose (TyG) index is recognized as a convenient marker for IR, while the estimated glomerular filtration rate based on both creatinine and cystatin C (eGFRcr-cys) is commonly used to evaluate kidney function. Therefore, the TyG:eGFRcr-cys ratio is introduced with the aim of obtaining a more effective predictor for CVD risk. Method: This prospective cohort analysis included 261,865 UK Biobank participants with available information. Cox proportional hazards models were used to evaluate the associations of TyG, eGFRcr-cys, and the TyG:eGFRcr-cys ratio with incident CVD. Restricted cubic splines for model fitting with three knots placed at the 10 th , 50 th , 90 th percentiles were used to determine the non-linear relationship between the TyG:eGFRcr-cys ratio and CVD. Furthermore, subgroup and sensitivity analyses were performed to illustrate the disparate associations across diverse groups and to reinforce the conclusions drawn, respectively. An independent single-center clinical cohort was assembled from the First Affiliated Hospital of Zhengzhou University for external validation by enrolling consecutive patients between 2018 and 2020 with baseline TyG index and eGFRcr-cys measurements and up to 4 years of follow-up. External validation with major adverse cardiovascular events was further performed as the outcome and observed consistent dose-response associations and robust risk stratification for the TyG:eGFRcr-cys ratio. Result: Following extensive covariate adjustment, the hazard ratios [HRs] (95% confidence intervals [CIs]) for total CVD across increasing quartiles of the TyG:eGFRcr-cys ratio, with the lowest quartile (Q1) as the reference, were 1.15 (1.10-1.21) for Q2 versus Q1, 1.17 (1.12-1.23) for Q3 versus Q1, and 1.24 (1.18-1.30) for Q4 versus Q1, respectively, for the TyG:eGFRcr-cys ratio. In addition, each standard deviation increase in the TyG:eGFRcr-cys ratio was associated with a higher risk of CVD, coronary heart disease, and stroke, corresponding to estimated increases of 249% [HR, 3.49; 95% CI, 2.94-4.15], 250% [HR, 3.50; 95% CI, 2.92-4.20], and 165% [HR, 2.65; 95% CI, 1.80-3.90], respectively, in the fully adjusted models. The non-linear relationship between the TyG:eGFRcr-cys ratio and CVD ( P -value for the overall model < 0.001 and P -value for the non-linear model < 0.001) indicated that the CVD risk increased as the TyG:eGFRcr-cys ratio increased. Subgroup analysis results suggested that these associations were more pronounced in White individuals and females. Conclusion: As the TyG:eGFRcr-cys ratio increased, the risk of CVD also increased with a higher likelihood in females and White individuals. </p>
10 Authors
- Ge Zhang
- Tianshu Gu
- Kaisaierjiang Kadier
- Zhijie Zhao
- Qian Guo
- Shanshan Cai
- Wenming He
- Fangkun Yang
- Bo Yang
- Chaoyang Yu
1 Application
| Application ID | Title |
|---|---|
| 102158 | Causal gene mapping for complex traits by leveraging genomics and proteomics data |
Enabling scientific discoveries that improve human health