| Title: | Association between triglyceride-glucose index combined with a body shape index and atherosclerotic cardiovascular disease risk varies by glycemic status: insights from the UK Biobank |
| Journal: | Cardiovascular Diabetology |
| Published: | 13 Dec 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41390803/ |
| DOI: | https://doi.org/10.1186/s12933-025-03021-y |
Publication 17182
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
ObjectiveThe triglyceride-glucose (TyG) index, a measure of insulin resistance, has been confirmed to be associated with adverse clinical outcomes. A new composite indicator, the TyG-A body type index (TyG-ABSI), was developed by integrating the TyG index and the A body type index. This study aimed to thoroughly investigate the association between the TyG-ABSI and the risk of atherosclerotic cardiovascular disease (ASCVD) among individuals with normoglycemia, dysglycemia, and diabetes.MethodsParticipants from the UK Biobank were included and categorized into 3 groups: normoglycemia, dysglycemia (prediabetes), and diabetes. The primary study outcome was the incidence of all-cause ASCVD. Cox regression and restricted cubic spline (RCS) analyses were performed to evaluate the linear and nonlinear associations between the TyG-ABSI and ASCVD. Furthermore, time-dependent receiver operating characteristic (ROC) curves were constructed to evaluate the discriminative performance of the TyG-ABSI and other indices. Additional analyses, including Kaplan-Meier survival curves, subgroup analyses, and sensitivity analyses, were conducted to assess robustness. A mediation analysis was performed to identify potential biomarkers.ResultsDuring an average follow-up of 14.8 years, a total of 29,680 ASCVD events were documented. The results indicated that elevated TyG-ABSI values were positively associated with the risk of ASCVD in the population with normoglycemia (nonlinear, P for nonlinear < 0.001) and diabetes (linear, P for nonlinear = 0.96) (HR = 1.07, 95% CI: 1.05-1.09; HR = 1.10, 95% CI: 1.06-1.14, respectively). No significant associations were detected in the dysglycemia group. Compared with the TyG index and other TyG-derived metrics, the TyG-ABSI demonstrated superior predictive performance (e.g., 10-year AUC = 0.634 in the normoglycemia group). Subgroup analyses and sensitivity analyses confirmed the robustness of our findings. Moreover, the results of the mediation analysis demonstrated that white blood cell (WBC) count (mediation proportions: 3.74-9.73%) and C-reactive protein (CRP) level (mediation proportions: 9.96-26.89%) significantly mediated the association between the TyG-ABSI and ASCVD.ConclusionsIn the normoglycemia subgroup, the association between the TyG-ABSI and ASCVD was nonlinear. Moreover, the TyG-ABSI was linearly associated with an increased risk of ASCVD in the diabetes subgroup but not in the dysglycemia subgroup. The predictive value of the TyG-ABSI across different glycemic statuses provides new evidence for medical practice. Furthermore, the TyG-ABSI may serve as a useful tool for identifying high-risk individuals within the seemingly low-risk normoglycemic population and for further risk identification among diabetic patients.Graphical abstract</p>
24 Keywords
- Adult
- Aged
- Atherosclerosis
- Biological Specimen Banks
- Biomarkers
- Blood Glucose
- Body Mass Index
- Diabetes Mellitus
- Female
- Heart Disease Risk Factors
- Humans
- Incidence
- Insulin Resistance
- Male
- Middle Aged
- Prediabetic State
- Predictive Value of Tests
- Prognosis
- Risk Assessment
- Risk Factors
- Time Factors
- Triglycerides
- UK Biobank
- United Kingdom
8 Authors
- Xinyi Shao
- Zhaofu Tan
- Lifu Sun
- Yidian Fu
- Aijun Chen
- Jin Chen
- Genlong Bai
- Jingbo Zhang
1 Application
| Application ID | Title |
|---|---|
| 747364 | Identification and Prediction of Risk Factors for Immune-Mediated Inflammatory Diseases, with a Focus on Skin Disorders: An Integrated Epidemiological, Genetic, and Environmental Approach |
Enabling scientific discoveries that improve human health